<%BANNER%>

Medical Cost Offset Effects in Pulmonary and Cardiac Patients with Depression or Anxiety

xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E20101210_AAAABW INGEST_TIME 2010-12-10T08:49:46Z PACKAGE UFE0014363_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES
FILE SIZE 104461 DFID F20101210_AAAOLL ORIGIN DEPOSITOR PATH lee_a_Page_22.jp2 GLOBAL false PRESERVATION BIT MESSAGE_DIGEST ALGORITHM MD5
f3d21d8bdb2714af25e6dca3394380da
SHA-1
841e0d57bff738d3f1963032a2fe20fa0428156e
69614 F20101210_AAAOGO lee_a_Page_38.jpg
7bba2f1091ee5a129bce0d97a7ffc20d
88f9cc9b94a86a8ae338f7eb898c3944fed01161
464 F20101210_AAAOQI lee_a_Page_17.txt
b3bc25076d6d30a6da09777d6f1edf9e
d1c6cd82df321599e5fa6c49b249ccc0ff4547e3
93167 F20101210_AAAOLM lee_a_Page_24.jp2
4f9a53dd7a4990f827627d6bbd788271
ce2ab48d3e1bdf3eeed9c364e78fa147e36c278b
2548 F20101210_AAAOGP lee_a_Page_04thm.jpg
a0d1471ecab84d86e49033f7441f5e1d
8f80cc8885b34f08e28a36ad828a7b0f01ee56d6
1829 F20101210_AAAOQJ lee_a_Page_18.txt
a468bc8f314aa021f76840197006424d
189d57bfcd073fe0f9002f48f096bce05e51feb3
111056 F20101210_AAAOLN lee_a_Page_25.jp2
ca27fd23d438b6c5018ea013284a0838
e77d93f99ff1519c8dc70d038af8d2574f45882e
2216 F20101210_AAAOGQ lee_a_Page_51.txt
d57a807dd2763d2a9fd18b6436c8cf69
6d546d319ce81a333dc8685b46c25a26949b6171
1750 F20101210_AAAOQK lee_a_Page_19.txt
7d8ac87852d7302b7c0ae9ff17ea53f1
1ed37335c21023005f423574954dba2480b44e89
110970 F20101210_AAAOLO lee_a_Page_26.jp2
a560e4a5df86230990b1dd6248bec658
f79dbdf9837e466e0d26f28252f44148f3d34e05
1053954 F20101210_AAAOGR lee_a_Page_39.tif
6545ba8ee6c03fccd5b1b1cac7e6d706
6f48f3c4c4aa2db327d1429d0eaaf97f304ffc0d
2026 F20101210_AAAOQL lee_a_Page_20.txt
2653305d8f58f01507dc6d791f291ea9
f8e9f64a7c1fbb799c504dbeea0834d1a1fbc0dd
102404 F20101210_AAAOLP lee_a_Page_27.jp2
b5482148de484cdcda693dec26288b38
7c520d26f92fb9636c280b8e4427eedba134646d
2999 F20101210_AAAOGS lee_a_Page_37.txt
32ee7ba673b8f46d35e4d4003c1b0c03
27de95e1fddeec1f4aaf17b1ebe2c0dbe43be7fa
1956 F20101210_AAAOQM lee_a_Page_21.txt
66d6cc2dac4a14209726beafd9000484
89168d99ccfa30e8965eb92e6e29d173ed059768
106288 F20101210_AAAOLQ lee_a_Page_29.jp2
15284f1bfc9907c4b896c7e582bf47a8
fcab266a4de8e9dbe837dbf57649aa3869729fea
97196 F20101210_AAAOGT lee_a_Page_19.jp2
be0b20f0386531ad78f1e835d7ea1905
df8826e83a2eb47cd909caed0297b5f8073bd75b
1926 F20101210_AAAOQN lee_a_Page_22.txt
30a8f1869879f5d30f452d15f0409bce
b07a970043a105be003d9a763f86a383919183aa
104282 F20101210_AAAOLR lee_a_Page_30.jp2
3d5dd5a75d0bc31c8e141b4cfb671081
f0d566a0a32286e34e1fa6823efab557f79b3225
17945 F20101210_AAAOGU lee_a_Page_44.QC.jpg
cf66ad36d286bebbac3413335f9162cd
7c26e21052def7362ec4ff89b8d0302b8035f5b5
2018 F20101210_AAAOQO lee_a_Page_23.txt
f4c4c9de07ebcdd6521ac0cc9c43f160
85c1cd080366025a3f8735198d9ce5a44a9dfd91
111378 F20101210_AAAOLS lee_a_Page_32.jp2
a73028cb3c9a821dc5956b6e9524dfcf
44d7f5c5f5bf4104a3ad3024161344ad26e89afe
6318 F20101210_AAAOGV lee_a_Page_22thm.jpg
2f34bc8f73104b4f52f630cd58270db5
54048210b0f03ded88634e2871a3591c92b30a48
2010 F20101210_AAAOQP lee_a_Page_26.txt
ad6a9578b7c67a3a90b62daf8f3e14d4
13db67fe27b090ac9ca40219b67853bb3fc6d4b8
54005 F20101210_AAAOLT lee_a_Page_36.jp2
8ac109dbf970447465fc874d45764639
f0706441b018a9a252dcc70c6573ebb2b2f4019f
110134 F20101210_AAAOGW lee_a_Page_31.jp2
f6d59110485f61f4e575dedcaa3d0c4f
d919d54402dfdb2c7f34a99e9ebf0233737e3903
1984 F20101210_AAAOQQ lee_a_Page_28.txt
e2d4d265d21f562245ad2c73494f98c3
836b12403c6e17760b23b67cbd00d718f0899074
67618 F20101210_AAAOLU lee_a_Page_38.jp2
f9af9afab8be0c5437fe8417d9e976e8
891ff8517860e59ddfd49d3ff6b7feb85166d87d
109562 F20101210_AAAOGX lee_a_Page_13.jp2
86225f5a11aec9b183543feeea63017a
4a375f3eef559a7698f72b446a61605c485bfadf
1943 F20101210_AAAOQR lee_a_Page_29.txt
e261bc22b71d4ddef2cd4a15300e7486
d917ac12c5348155d514c577e006e4a2554ed05b
63373 F20101210_AAAOLV lee_a_Page_39.jp2
8cf5010a20923d695af1973d9983fd50
65cd07777f041b0b408032f37e56b5c772f04bbb
70893 F20101210_AAAOGY lee_a_Page_12.jpg
37322f592090dc144a46c70d2aaacd89
79cfb9cd6b64fc12283866cbca339c97d3108ad6
1906 F20101210_AAAOQS lee_a_Page_30.txt
6535885c95d13f7f872a05c3f3a5425d
4be7510bb20e63ea5a05de1e4af3169d55e53fee
63528 F20101210_AAAOLW lee_a_Page_40.jp2
d1a9e15ea0e51677f28ae6348ac10fff
8028a2b79b5d9bbe54793edecb56d5dc8107af00
24383 F20101210_AAAOGZ lee_a_Page_20.QC.jpg
2efbf7a0e3570021cebf2b137bdd7428
aff681a246a608d89227a91a7485f012e4e17edd
2024 F20101210_AAAOQT lee_a_Page_32.txt
df40e218b7c3cd475af0832d517cdaae
96eb15aa4e839ee54f35eccfe1777168ba666f42
63675 F20101210_AAAOLX lee_a_Page_41.jp2
f1d6a487aa445c1d71e8617802e6bf6a
84d44a5aa204865159ff1becafdcd42c7c7791cc
1971 F20101210_AAAOQU lee_a_Page_33.txt
77835176492dc502a4baa6d200494034
4c91c57ba382f906602ad87d503fc472d308eedf
62599 F20101210_AAAOLY lee_a_Page_42.jp2
23ad3348098ad7815e85e3fa4db02906
48ff57c4be7e8a5b53a409bca71db202db31bf24
1928 F20101210_AAAOQV lee_a_Page_35.txt
99a785b02c23134c86ee2dd2f6e9b2a2
2e7ce751daea96d913579493c96664fc7a00e213
53411 F20101210_AAAOLZ lee_a_Page_43.jp2
711f5a571c3ccd8549c7aa482beda28f
faad6888a3045c4eb5c0416d909391175bfdcdc7
948 F20101210_AAAOQW lee_a_Page_36.txt
6cc62ab743d577b598e6919fd26cb490
147ac78a502089e62de494aeabc5dc6b2fbe864e
109309 F20101210_AAAOJA lee_a_Page_49.jp2
48435ad020caabc6a7106f126c9539f2
c2889b9e32e80609f651a4c1b1c61160f0bf1086
2605 F20101210_AAAOQX lee_a_Page_38.txt
3a00f2e97f4adbf9b05dbdef76ac7e23
b1f0253b1d3661accea600cc0718d1cba33993c0
F20101210_AAAOJB lee_a_Page_01.tif
70d3b0ef6333958a96c1b88efbb93a2a
506389b94817a0c8c31cf0c86e8f7120d5ab6165
F20101210_AAAOOA lee_a_Page_50.tif
704fd8cb3a34cc3b925ef4e2035a4e23
06c00274948ac5b4b45a93993a8ef8a2f19e6f0d
1743 F20101210_AAAOQY lee_a_Page_39.txt
e9811cc3a425f7f674534aeb40ce0a57
9476e4e47e4be4f09bfa62a0c712cc9c4658b762
14100 F20101210_AAAOJC lee_a_Page_03.jpg
63c58570738e5f499b5f288b0daad1b0
9f0fc3cc6ae7796a65cee34e81370b1aadf68e63
F20101210_AAAOOB lee_a_Page_51.tif
e21f01ae31dde1f387a8fd8dada4b234
5ed9b0dc7eeed4ab29dfe77f0d7facccebde4553
1888 F20101210_AAAOQZ lee_a_Page_40.txt
900e44456537ac9b0c4b97968aecbf2f
8732b4c1a91605315b6384d944e3aa153322a4b4
469 F20101210_AAAOJD lee_a_Page_09.txt
69d6135a51c9201a29b1519d7f2e27f6
4a937ff026884248ec9f574aa7284f01ddbb5a62
25271604 F20101210_AAAOOC lee_a_Page_52.tif
f5f6afa3a914b22c6134e2b7b3889b3e
11a6dc5771a2f05647972eea788b1ae6ef120e3e
11720 F20101210_AAAOJE lee_a_Page_09.pro
02169a748e9bb32913d9241f2eaa5556
14ec40461c042c6187488102e46b836c10c60a94
22177 F20101210_AAAOTA lee_a_Page_30.QC.jpg
41b08e1040147a7942d78fb20ed056b6
2ff18277b9f203007c77172dc992230d474be297
F20101210_AAAOOD lee_a_Page_53.tif
de4a900df0ca5bc65ff185c532ff8866
74fec52fb5f79cc95a344b76e79924b897d2043a
22934 F20101210_AAAOJF lee_a_Page_14.QC.jpg
d47e62eba5c84a3db37a2ef2d96465f1
df901e76dc0a54db3ca893d72522fabf9f1415df
20065 F20101210_AAAOJG lee_a_Page_24.QC.jpg
a10d2070e0122e5affc46def121b3770
2a7e50d4fdff6ff9be4f8723a05d44d5e4dc9993
22971 F20101210_AAAOTB lee_a_Page_31.QC.jpg
e91137c0ace4546fb5be396bc86a8466
1f060a91929138aec7889e80d61900a4cecced36
F20101210_AAAOOE lee_a_Page_54.tif
9ec8daae3f2b9a1db794b2f29885fecc
9021cd1326d4eb82890f6406e9493e71fba759b7
64068 F20101210_AAAOJH UFE0014363_00001.mets FULL
c4127fcd05c0b8f1fc56b71078c06a4c
b201f749f2b6cdc1432a0b1b63f4064b3572ddb0
6544 F20101210_AAAOTC lee_a_Page_31thm.jpg
61da8395432a81410e50a0db65e04d95
4831b7322931a30aa271aa0aca66a906965704b0
8008 F20101210_AAAOOF lee_a_Page_01.pro
4d935533471cc389f9509cbce892b557
ed630a52114fd96aa3af65cd8ffe9562da748c19
23835 F20101210_AAAOTD lee_a_Page_32.QC.jpg
170cff1738a3d290e0651a23bf3689f7
3e4c045df452978d61e918db9c21e53db45d4a4e
3860 F20101210_AAAOOG lee_a_Page_03.pro
5a96f184da41798ed93a1097643dba26
a5d78a2fa48f6710f38f8785c435b6708e5b3bab
6482 F20101210_AAAOTE lee_a_Page_32thm.jpg
1a4df3b333fe1023c22f8594eb8001d8
ae02ef14d5004eb111154da452b32e88539ec9de
11434 F20101210_AAAOOH lee_a_Page_04.pro
f8fcdfcafa0998bd2bba38606e7573c9
22736903503f90b975ef06be1d4b0a4cb5d9a5bc
22933 F20101210_AAAOJK lee_a_Page_01.jpg
a6da045c64f34e015f767890098f984e
bd4e3d1528491b86812cdbed896f92e90352dba3
23369 F20101210_AAAOTF lee_a_Page_33.QC.jpg
b2c16de24c2a8bc39feae9e7ccf4b0f6
67f87d8ceef665163f6511f0fbd89978ce5a9082
77247 F20101210_AAAOOI lee_a_Page_05.pro
b8d0af3d74667e5101b5c6033286de96
e882f009c193a063707027c4e163066fb33a9dab
23837 F20101210_AAAOJL lee_a_Page_04.jpg
52aeb7addee2b86024dbd58edc0a99b5
a0e4c2632cd83e180d0d4728e75b0b01b2697955
6531 F20101210_AAAOTG lee_a_Page_33thm.jpg
be29208a348d2982bd09083c42dff5b5
7291232c14629af7a11a3792a7e8c0b7a8d14887
6920 F20101210_AAAOOJ lee_a_Page_06.pro
d65a94c5d08d702bc598377c8e15c3ea
272735afd0c01932a41351f1cc5cdd63c29a1c7d
67064 F20101210_AAAOJM lee_a_Page_05.jpg
edf5c15285843ab276e46ba9ce47cd20
54fdc7972752f2317b72f154c984472667940a08
21836 F20101210_AAAOTH lee_a_Page_34.QC.jpg
b7df038c539ce7eddb1b0ee03e53b59f
ef69794961b46eacf27afc4f494e57ab776f0b8f
31856 F20101210_AAAOOK lee_a_Page_07.pro
1d9fa4e7c5fe3245fa7ea22d8ba9d392
f11c5a8ca271318c2f408a374edaf6e4cbdccebd
13057 F20101210_AAAOJN lee_a_Page_06.jpg
9d8c72340d0350a67a3493efca6b54fd
e934b68cdaf171c7d8f3321c770e3ba5bd129477
6177 F20101210_AAAOTI lee_a_Page_34thm.jpg
e923ecb605931a99e82541168d18db62
cf9cdbba8a073ca40aa1341fb80133b05080b620
45197 F20101210_AAAOOL lee_a_Page_10.pro
4ffea815f2485ecb0f1c9e2eb2b116db
0817365e48199eb255faff376b026efb3d3c3967
52884 F20101210_AAAOJO lee_a_Page_07.jpg
60fd2d7b4fa2f916f9166d057c92b85c
dbb0a98d283664437816383c494b292dd828eb07
49667 F20101210_AAAOOM lee_a_Page_12.pro
c6b92f8b01a4c0ca0a9ac35d31e7593c
92bb9af9fb9639bf049dacca4c007ae240e5c656
22550 F20101210_AAAOJP lee_a_Page_09.jpg
e82ef1a498ac9ee041867512e8cd0ce3
fdb87c3ace023f843ed0641f1cb8e3c2b734a4de
6304 F20101210_AAAOTJ lee_a_Page_35thm.jpg
0f05f28bb886de354b3560b9d10a7229
a8ef429b8dd109c6809b4fbcb55b38e25e3fca5a
50518 F20101210_AAAOON lee_a_Page_13.pro
5701e0c9babcfb832c719c5513214f3b
d29003066ad54a08131e399749fb46a6fefd1a4f
71082 F20101210_AAAOJQ lee_a_Page_11.jpg
afb5224b94881465bc30fa006c641bba
27cf13c4a25566bee5b515bb94ccc4a328c32b7a
12805 F20101210_AAAOTK lee_a_Page_36.QC.jpg
733bfc88e5c6258c63d12d0605d98603
b3f0e73d11d128770d292c289db4757af3a242ed
50115 F20101210_AAAOOO lee_a_Page_14.pro
92c0aafe5a9dcad68802e135f6dae2f5
84a95421815407830104a740d828f928d526246d
72243 F20101210_AAAOJR lee_a_Page_13.jpg
d894dfad265591609e722e8e7e447a62
c6a48980cfaf019c0cc81fb46726fecd4252abdb
3737 F20101210_AAAOTL lee_a_Page_36thm.jpg
36fb56f74bc52befe9b051c530489bcd
660f0f085a64576eb8096fbc4576de85e64c4f7c
50432 F20101210_AAAOOP lee_a_Page_15.pro
121f2787bb112dda8dc1b26229a10611
9a86b34ce858e14752598ca8f1872cb8cebf3df2
70932 F20101210_AAAOJS lee_a_Page_14.jpg
6dc4309c631e8a5dac548578e83aba19
7bcb6df0644387ba33482c526a4ab8ad40ba8915
18361 F20101210_AAAOTM lee_a_Page_37.QC.jpg
d76e55ba1e700a14ca4dffac21db9e5e
435532021609e875167e2b9403b0adae379b91e2
45007 F20101210_AAAOOQ lee_a_Page_16.pro
72fc48abde883990c36283b562ad2713
14d346ab81f28d90a1513d9475594a249a3bd124
70565 F20101210_AAAOJT lee_a_Page_15.jpg
e3d42abc9347cac08abf07a92938ef52
06c24af67582caeca86e2609f8d3ef063bad2721
5012 F20101210_AAAOTN lee_a_Page_37thm.jpg
4f1e526aca07c0b7c5aa44282ae729df
2b9bae6095b945f7096e7f2cf89519ef60a1e280
10571 F20101210_AAAOOR lee_a_Page_17.pro
fa2beb9e7e272f1b1a048f63d3416ce0
d845a1e32be77ec136e657b42e9266e1d50ba6bf
65307 F20101210_AAAOJU lee_a_Page_16.jpg
c4934573e72f5699d7fadccbcdadf1f9
777548165166c6744c84a347121643008e5737ae
5540 F20101210_AAAOTO lee_a_Page_38thm.jpg
b7136aca3024f4a0f5df27b23d81c184
f482e432bb999a7053ac199d6b7f27900ced64cb
43687 F20101210_AAAOOS lee_a_Page_18.pro
da042a47706d1ee4be18f93c742a61ef
85c4d1a361b62372e7aa2d681103980fd8731d3e
65303 F20101210_AAAOJV lee_a_Page_18.jpg
f3ee8ee87969f0b0c60170ca0136fc67
a9c238d51384fa08b746984f241d6ba4cd918887
17420 F20101210_AAAOTP lee_a_Page_40.QC.jpg
7e808319fb206b518f8a1fb894d4564d
df86cbf1461fe3559886d88d0867f1ff8afc3717
43175 F20101210_AAAOOT lee_a_Page_19.pro
5e726cd2a05571fcb7b426d84eade0eb
9446973b33189833cec529f7fc268669b71860b8
73722 F20101210_AAAOJW lee_a_Page_20.jpg
34108e3f6589f5d07cb939307ac20a3d
93ad01150ec1b29935fd3ff8e22109c1fb3efbba
4920 F20101210_AAAOTQ lee_a_Page_40thm.jpg
26fa6e6802103284027a64630e110183
65e618a9b9f181fbfe2b1c6f854a81763d298d73
49818 F20101210_AAAOOU lee_a_Page_21.pro
dd3b9ed2071d74ab3a8c43b9231e1f56
e9f8e1bdf3281abbb1470a3c56edb2f651d9bbaf
72794 F20101210_AAAOJX lee_a_Page_25.jpg
89ba6d4b78dff2c9bf2d60f68e2c6728
79423bbdcce3c3ba38e39e14c65cf1400bac27d6
4877 F20101210_AAAOTR lee_a_Page_41thm.jpg
89400ea248bc46e56bc21e1ff5fb130c
deffcdbdf6cd236461a127a6d701e0ff4cfe9196
48749 F20101210_AAAOOV lee_a_Page_22.pro
97fc465f0c59c004577714140d12084b
fb22b240f057d22e4e3e43d68acdc8ab8d92a1db
67037 F20101210_AAAOJY lee_a_Page_27.jpg
771afcefe474a73c4b6fa9a9e3598bc8
bf54b1df1f2f5803e4d81c49afafa95260358af3
4958 F20101210_AAAOTS lee_a_Page_42thm.jpg
5df94f3e25d2fb64431a4535aec5b630
5fb86af5cf7cd463793376556f9de4b2c4bf4a7b
50172 F20101210_AAAOOW lee_a_Page_23.pro
c79d8ea3db7a4d7192e7901a7bcaa05f
bf5f31b34f1b025613817ab13c1d227add8612af
69303 F20101210_AAAOHA lee_a_Page_35.jpg
2f68c317fae2fa1a89223b3c89164e97
a122f7d0ba04c5107898baeef50149d22408bc90
70771 F20101210_AAAOJZ lee_a_Page_28.jpg
22dd029a1ba882a062726351ec94e2b5
015573ede14dac356676a338808e64c6dd94ed37
16259 F20101210_AAAOTT lee_a_Page_43.QC.jpg
640db2326004e7633cd78b1497ec6642
de9042719253f57d9d614087657f2b74ee5f3ce1
42131 F20101210_AAAOOX lee_a_Page_24.pro
37b20d55e974949ac0ae15a7a0b89458
fc930af02a92a2bb5e285adc223b048ead750d4b
110131 F20101210_AAAOHB lee_a_Page_23.jp2
cc08cd632093ef26060b4eafba8c4baf
3e8f19b2a07ad0fab41b21f2a4b0cced67d6d862
4926 F20101210_AAAOTU lee_a_Page_43thm.jpg
113b362daf3e5d00a6af180c19718fae
ddab7c90b27f63802507590e750a6f9f7650f3a8
50991 F20101210_AAAOOY lee_a_Page_25.pro
6603b62339d5cfcce73a7eb66c33f7b4
7d679a98f5cad9d5115659bf8b784ee272735607
17522 F20101210_AAAOHC lee_a_Page_39.QC.jpg
ff7bc280dfd57578f9fb8680916649c0
ec64a3ae8b2879a9ec8c32f2f6833f18e8501fa4
15643 F20101210_AAAOTV lee_a_Page_45.QC.jpg
ece45dfa4493eed44105fca0ebff1923
56e627c0706790244ed2bcc999d763cfa878efd1
55083 F20101210_AAAOMA lee_a_Page_44.jp2
9cb0aaef57998b4e767a4c4a14006dc4
7e82f9a2a1d86f721eb1bc0bd08c8fac6283c35f
46623 F20101210_AAAOOZ lee_a_Page_27.pro
3cb6e00035329835411cf4c6d8239905
0f4ba887c3b33d6444688f6a29a145f2a8752167
39639 F20101210_AAAOHD lee_a_Page_08.pro
a4f7d37a727a9ff9519713f588acc21a
e8f6ec61a77fc01e3bcf42910b68c0ef620b4495
4804 F20101210_AAAOTW lee_a_Page_45thm.jpg
0c647ce664a23f2512cd0a6500625edd
caa2db7d19f5f55a793e5c627743ee7f21bde762
52388 F20101210_AAAOMB lee_a_Page_45.jp2
a41be95a1524af5773d6feaf28bdab5f
c9c83d995afa4d87fa7e69644c64ef5a47cc502f
64685 F20101210_AAAOHE lee_a_Page_10.jpg
52ec8918fbe4ff4e0331f56e29601f6f
9c9ecf86ba89477cf6f334df5b4048131da250b9
17930 F20101210_AAAOTX lee_a_Page_46.QC.jpg
c5c59ef7afabbbd7173a86b6d1a9b0a2
61114130d73dc7cbab43b01c4171ad3bd93f8f39
110722 F20101210_AAAOHF lee_a_Page_21.jp2
e508cce502b26d80d2bcd35b38b7df88
3a9c9c5a3f286a36e9839507ace8c20809951454
1828 F20101210_AAAORA lee_a_Page_41.txt
b61facb068b350def427e7d1625ecea2
dd4d38e7688fa0d24b74f8ba5c19da1f4730aa10
5283 F20101210_AAAOTY lee_a_Page_46thm.jpg
d547e350630f6afafe3ba31ff9f3b67d
8dd6d317438edd824ec1825c77e43d7e92240a9b
56230 F20101210_AAAOMC lee_a_Page_46.jp2
fb3326a40d8707464417fa9196574b09
8f6248060e480ee4d81e6f12e3046dbf0d9d2d43
108754 F20101210_AAAOHG lee_a_Page_15.jp2
73cf8a3eda9fc5e0b760b89c331c7747
424aba9bd39f9208e81322b38710d1b9252991c6
1708 F20101210_AAAORB lee_a_Page_42.txt
430dc4ae1a1790a944f4296d3127ca58
025065c250624583ff0e4e0787ceeea4b09eed4c
21070 F20101210_AAAOTZ lee_a_Page_47.QC.jpg
f5a9ed2adf4f197090b17421abfd8a07
b5d08aa0c936e59b4fa6427c3f9e345a6b3339dd
99425 F20101210_AAAOMD lee_a_Page_47.jp2
96b38b0b155cba3e12f75565c9040bbf
6f209e2f1f45394c89c31df8f5e7e9df4b8eb9ab
60631 F20101210_AAAOHH lee_a_Page_08.jpg
f14eaa6725165b4c5aa289e9d68716c3
d92d21e8c132e41448a09939c4f9fd1602c82420
2003 F20101210_AAAORC lee_a_Page_43.txt
953996dd46e96bfbe7709a464731222f
896f155fa1cdb7d88decdb6b0e69d293b011d77c
111091 F20101210_AAAOME lee_a_Page_48.jp2
0bcbd3c5e0d1afc691456437e18d748d
a10856a14bbb6a408b2c8e82e29c939d54f63bf0
1873 F20101210_AAAOHI lee_a_Page_34.txt
a8b88ab36706c2015d51925b76e0d556
eca4305f231824a2a012efd01edbbde8b6f0c8e9
2203 F20101210_AAAORD lee_a_Page_44.txt
0753c74fd76ce24097c64e05816e975b
b0aef891ec1d34ea9c34b3e2fa7d6e3b94271407
116336 F20101210_AAAOMF lee_a_Page_51.jp2
89ab3088af5fe5fd71985799e64663dc
c6315c3f5fec22a65bfa31aaa0dea8e4a09e9adf
63517 F20101210_AAAOHJ lee_a_Page_19.jpg
82826d1ea72c118c5041437e34e47b21
e829960ab87e7a746efffa5e587f763fc2fceb5a
2006 F20101210_AAAORE lee_a_Page_45.txt
c3dd23ceb826d61d040641da0e14a995
7ae46e4c320ab29b940ea2a841fdf68413943007
1051982 F20101210_AAAOMG lee_a_Page_52.jp2
7f4eb14ad734edac1899644265a93407
d05598424fc03f99c23fc78c53319fc069249688
1993 F20101210_AAAOHK lee_a_Page_15.txt
100294b3aaa2000fbbf072ed10e6b9d6
47e7046e684ac6deb106dff4162795bf78b63d3b
2209 F20101210_AAAORF lee_a_Page_46.txt
9f3738310779fd11a1b5dfd675984fc2
367142e499365d3e8bd552bb7b4b8e265d140381
31358 F20101210_AAAOMH lee_a_Page_53.jp2
a38184391b653cd9533f888f53e0049c
f091b591fd9b1b3ff85cc2d7669c9b614a7f6164
109269 F20101210_AAAOHL lee_a_Page_28.jp2
54fc1911090e4af68fec3e98c22630b1
a1c6f981a5fb7764648f08e87a1aaa598d55f522
2051 F20101210_AAAORG lee_a_Page_48.txt
46012142ce0724f6dddf3732f96a63ad
6876514d8975af29694cf6535825dafc7fc67d94
25836 F20101210_AAAOMI lee_a_Page_54.jp2
b4f9add313cc89711cb18c9bb8482738
65cfbd0f074b1d4aefa916fa53f1a625f19c3c3e
108319 F20101210_AAAOHM lee_a_Page_33.jp2
5e0806c06a5b9d695921c4035aa6bccb
c7d9622efc3e1d1b027535d385a91c50b1238403
F20101210_AAAOMJ lee_a_Page_02.tif
b14c8028757ec3df75c433c2ae0d45a2
be51c1d53feb1a34c9409954a70785637b4c04f4
16647 F20101210_AAAOHN lee_a_Page_50.QC.jpg
7d259b50c9b90c4937ec43d3d752870c
7d987f22869174a09141aafb6c539544c060102c
2030 F20101210_AAAORH lee_a_Page_49.txt
b0229894d8972d53a54042c3d1297a7c
a3f282b8b26507da9029f04306b550f0069033f2
F20101210_AAAOMK lee_a_Page_03.tif
67b87871fa75e8cb48601cd4da6efb61
39fa2c82a58e00dcc49a21be80262ff503f6d00b
48346 F20101210_AAAOHO lee_a_Page_45.jpg
aa5e4ca2ee66d782767e196f9ff0e666
4e11d2d19f62a2d0de1a2d0e40ebd2c7829725da
1349 F20101210_AAAORI lee_a_Page_50.txt
3dd404d7d9eafdee4cce742c42a87636
316fc707dc7ece83eb2ad5b392e22eb81999e487
F20101210_AAAOML lee_a_Page_04.tif
b1e88ab305ce4f952370ce2272926bf3
194f157926e1d172ed0964f01cac55e3b1521e96
23436 F20101210_AAAOHP lee_a_Page_13.QC.jpg
b6f22d8b3ef4ee40a9dacc6563b2e318
143f6a0db17bfa34987edab700af3387b0c08e36
2693 F20101210_AAAORJ lee_a_Page_52.txt
09fdc9f88b0e89a85cf1e8f52a47dc9b
2b803ef805b37fc43a2bf44d7003d1772dfe07fc
F20101210_AAAOMM lee_a_Page_05.tif
c7001f152b11b75cf049b8d7654f0c0a
a4105cc1059bd7f879bd06ef10edc0d01c17fb48
573 F20101210_AAAORK lee_a_Page_53.txt
f4fc68164c8c50b6e14d02706a28987f
7e747ff4a38d0d7bd1aeac9cdd98723a5975f5c3
F20101210_AAAOMN lee_a_Page_06.tif
1cb2ecf041d1ba9864442d1290536213
834b734e64e8b10c090460ba2b7546c6b4ec206d
72127 F20101210_AAAOHQ lee_a_Page_48.jpg
efee6f1922fa957576dcce9c4c5a54be
0ff416e19b848b0f52bf4b11bdb862c7d2d31c3b
208297 F20101210_AAAORL lee_a.pdf
f2b29fbb8ea00d7ca9399aee2051c788
5adbf03b7c634d5ca8804c7ddede566bbdf12bd4
F20101210_AAAOMO lee_a_Page_07.tif
3713a6ef584352ac2d9e29f03a785569
ab6d3cc195a77381db0d37fd3ccf61b8890bf061
6294 F20101210_AAAOHR lee_a_Page_15thm.jpg
5bcc0ef644a5928e0513fdd655e327dc
14051a7ad974a593e4111db463ebba083f155a0a
82361 F20101210_AAAORM UFE0014363_00001.xml
27160fc7e103911c811839f9bcbabe78
9671f2fe55fbc0ed6bc9ae8d47d1d22db50e6f3c
F20101210_AAAOMP lee_a_Page_08.tif
ad857b88e8480d4211176f792098d9e5
59f9bc9895734454f4b3e7f340c04c74256247e3
61744 F20101210_AAAOHS lee_a_Page_24.jpg
761b2105064c612a927f75ab1fb608dd
bbb7b0c235e8c3b97e0226c5c6867f19e750dcbb
7320 F20101210_AAAORN lee_a_Page_01.QC.jpg
ab6e2acc89b9daa0501cdf196152f0ea
855efdb1ff34f4635cb4f8e8c5869ea2dfc578ce
F20101210_AAAOMQ lee_a_Page_09.tif
ecfdeba364624b1a68fa693bb6d282bc
3456aa989c8a58d8f3d96d1e4e1dd72e93d3cf53
6529 F20101210_AAAOHT lee_a_Page_30thm.jpg
bfea7cff211d910e3c8f4c2562db558f
e9c0692a5d15223c7169a66e7774ca09771e6741
3278 F20101210_AAAORO lee_a_Page_02.QC.jpg
cfee596137f994d0bebef63412e8ce88
5f375cd9aaccaa791b5a7f2400fa8ad22d21642f
F20101210_AAAOMR lee_a_Page_10.tif
1aa36949dcdafd7c784ed2e13806b029
e0ab888287ec9c2d192ba4b8e58e5ed50761be8e
109 F20101210_AAAOHU lee_a_Page_02.txt
a60042f85c857bd5807bac6fa6843d3b
361851c82ea0348f621658c574e1cc8b7b21d9de
4078 F20101210_AAAORP lee_a_Page_03.QC.jpg
8273e10d7148c6c79fc8da87117dd530
2b8db3539252e5d698b5773b948a0f6532d1d934
F20101210_AAAOMS lee_a_Page_11.tif
45e9c7512f6bed24e4a18d5f8cc93689
c5da012809943f387174c8cbe3115373bd8b33b8
21631 F20101210_AAAOHV lee_a_Page_16.QC.jpg
0a45c29c0cc3d03b001c6820198d1422
3580f117f81c602c7dcb80ff826626f269bed09b
1684 F20101210_AAAORQ lee_a_Page_03thm.jpg
e229db28bc038c9f306ed58e885b4861
40aa2201dcf8f3dac4e67ac782960833c19429d3
F20101210_AAAOMT lee_a_Page_12.tif
183ddeca7c8ad7fcc6b24f0cf1fc3aca
0979f498eef7c7a84d29293adff72e35251a6ffd
17511 F20101210_AAAOHW lee_a_Page_42.QC.jpg
2cf020090dceddb02e0a6d7edfec51c7
5e79817823d7dd3b610b78bcf60dad1ff1205153
7774 F20101210_AAAORR lee_a_Page_04.QC.jpg
3c039cb80f7369e2bf83aea324decbb2
f4180b9041760678faf6baf33ec0f3797d6c3601
F20101210_AAAOMU lee_a_Page_13.tif
aea160dd8cc111be8354cc294e402ad3
02fa6c8042eda0d7c5e70857e66c13e880ad5f20
5812 F20101210_AAAOHX lee_a_Page_18thm.jpg
06e4f839baf60c478fcefd7b0613cc32
276d6bf4fd2af397b0f188ab7c201b492d5fa4d6
17270 F20101210_AAAORS lee_a_Page_05.QC.jpg
427779b85327a14b0677c2d6945414c4
742ceb44625553b12521b918e6397cef456e206c
F20101210_AAAOMV lee_a_Page_15.tif
3a1b4e4f381cd62791abd0a44e523530
99d10d85616758f4ecc46ac51203452002ccb3d8
3923 F20101210_AAAOHY lee_a_Page_06.QC.jpg
0a319c2b36aac52ea4238ed147bcb127
36b08f5a1b13d2267b3628ea00899a60eb1f7691
4534 F20101210_AAAORT lee_a_Page_05thm.jpg
d75b0f895e3243925f84fccc4fc308f5
5a703d6bd4b3235f8be21375dcac309594e5efb0
F20101210_AAAOMW lee_a_Page_16.tif
c81df702536b2e47ceda56d0411458a8
650f48bc9c7e8735b24f0b1ecfa559e6c62eb54d
22987 F20101210_AAAOHZ lee_a_Page_35.QC.jpg
61622ec4502d738478546b29521f5159
be765858908d8914eaa7e9e15a15cbbfedc213a0
1555 F20101210_AAAORU lee_a_Page_06thm.jpg
568a7d1fb2dd66914026e8d432e8eb58
3bc8febb4cc30d4fda8ae1cc03b91b9fd9833626
F20101210_AAAOMX lee_a_Page_18.tif
b3b6e73021c7d5622f72c7b4f1423b47
6c59be2b005d8d4d059af039cac40bb92f1a8f17
16082 F20101210_AAAORV lee_a_Page_07.QC.jpg
8475651379428a89f5b57b0572039ece
e60897f1c43812e4122fe935a22717b4d8dde2b4
F20101210_AAAOMY lee_a_Page_19.tif
ad1d487cfab3ff595a42cc70ed77735a
a64742176498e83552d03176b525af87fab14baa
4404 F20101210_AAAORW lee_a_Page_07thm.jpg
78719f0d1de1d2464755b0b59ff76554
ba44d6a77f2c761ac0c4c2e72c2ee5c59f022a46
70343 F20101210_AAAOKA lee_a_Page_29.jpg
7692e256b029c6b662982a037e7bd8b2
cc027fbb8ab2618a5329744ce7219986348788fc
F20101210_AAAOMZ lee_a_Page_20.tif
afed9a02b07927058a77d481f02d6115
6bab48856396c698400c86fef05ddfa0d9ee8162
19097 F20101210_AAAORX lee_a_Page_08.QC.jpg
1d59ea41a4b0c0fede8fc04548185127
9bf44115351f1a2a6a05553fa2d06317555cd9ae
67791 F20101210_AAAOKB lee_a_Page_30.jpg
75a94fdad48e90742f9329d220f19a36
285104aaec50f25baf6670743fc5d7daf7c921b5
5334 F20101210_AAAORY lee_a_Page_08thm.jpg
abd2aa8e72d366e8cdcc1df5aeba930a
c76833e044e229b690f635f71eb0a808c1e66f17
71764 F20101210_AAAOKC lee_a_Page_31.jpg
caa247905a09c5e8776ada96d21b2f46
d1d24a547a0b06b573662bf275cbfbacd77b5812
50195 F20101210_AAAOPA lee_a_Page_28.pro
076118abd886a466a482c52f35f9b24d
23f7121775ea8548534cc9e5814d07f011f8946c
72711 F20101210_AAAOKD lee_a_Page_32.jpg
446d797795941fc322a6807a652f1fc2
26f966c50cafe4c5df7034c8c7f05c59490d4323
49051 F20101210_AAAOPB lee_a_Page_29.pro
058a6fc714f43586126ed62196266b87
8280d507d2e690e9df0724dfb1771b834f8156bb
7347 F20101210_AAAORZ lee_a_Page_09.QC.jpg
4ec115f2e72b3d80eb8fb77fe8f9533b
7f7ff2d426e960383e673935bc68bb68b180065a
72378 F20101210_AAAOKE lee_a_Page_33.jpg
e1626150139c0d509dbb2d48d97a6436
d4e442fad74fa4c57ae091bb6a108294a995a74e
50355 F20101210_AAAOPC lee_a_Page_31.pro
31ac515821b7639d94101faf518acffd
3f70719ccd73796ba8015763dac4141a5b047fb5
67045 F20101210_AAAOKF lee_a_Page_34.jpg
3f5ca8b216bef071f62aa72f35209f81
a2ef102b865f881419d8c42f3855957773ef59fd
5976 F20101210_AAAOUA lee_a_Page_47thm.jpg
e9aad76d71ca82b8b6a8505abe8bff48
6ba933edb2fd953c4e5a562fbb55f7b9a01ab6de
49799 F20101210_AAAOPD lee_a_Page_33.pro
16e5f0a92b1c24ada59de956dc3e5fcc
177d5dfffd02026f5f4362a1452a431d8e15ea9d
39258 F20101210_AAAOKG lee_a_Page_36.jpg
0accf68e1e7391c5067e986e4885542a
be75932bdf24ec7720875176f89d295bc1e486b9
23209 F20101210_AAAOUB lee_a_Page_49.QC.jpg
71e0e71ef6a808688884056381ed97fa
c0eb79bcf452d6f166956ce432d2290749e9c0aa
47290 F20101210_AAAOPE lee_a_Page_34.pro
d93989922bd5f1a2647b6cd4ad8b6ed8
422e43d4c4955e22001219f8808c08366d883238
56807 F20101210_AAAOKH lee_a_Page_37.jpg
da0b52b14d7be0d2c26a44b08adda07c
5291b51600fe9e7ba9e4085d5655e744dce604f6
6339 F20101210_AAAOUC lee_a_Page_49thm.jpg
5aebf1f480a8c6b711996d4513115d87
1a930acbc60f9e8c674825125850fb2580fca026
55561 F20101210_AAAOKI lee_a_Page_39.jpg
66b7af6ce12bb9f0b7e51d0d0d5b8798
2a4f5d30dd85bacaa1bc961d54937b319f85adfc
4833 F20101210_AAAOUD lee_a_Page_50thm.jpg
fad73dea1841f7817d54127d695ea853
3f15be2941fd9eff9afe2a9adf1cbf0807006e02
23704 F20101210_AAAOPF lee_a_Page_36.pro
b829f2038c63022ea83296252a9b652e
660b5b18d0066015210b859868c2eb8505de854d
55663 F20101210_AAAOKJ lee_a_Page_40.jpg
3ba0cfcc972438feffcac0a57bde34e1
a6fc79787c341c55519f214f589b3032f6cfe836
22089 F20101210_AAAOUE lee_a_Page_51.QC.jpg
1c54eec72992c5763ccc6f528e321210
25584e99d823a9c073686a80971b3c8c367e102d
32630 F20101210_AAAOPG lee_a_Page_37.pro
1b6a3796b55183fbac92baa4be2e0e05
28bd2bf0f89f2ea2e1cacf4d28f9a95b58cc4c14
55694 F20101210_AAAOKK lee_a_Page_41.jpg
7bdb7b824135ec7154812a4fd0dcd4b7
d2e8e0cb2798535f88a55ae1816ed868d112f300
6094 F20101210_AAAOUF lee_a_Page_51thm.jpg
8d49dcc9d55ad2faf3cf7cc9a198daa4
debb460ab36e6eb011f932f00defcd95f818fd50
33603 F20101210_AAAOPH lee_a_Page_38.pro
87cce9dbad69cd3255acb1d310157e54
8d5ea55d97689ff8d8fd5f5a4a7e56416e26807e
55464 F20101210_AAAOKL lee_a_Page_42.jpg
8eb82fac74c97bb0bfa4da710ef297f6
0db9fc3b8c16e7a913a4bb6412f8c1adcb63b72b
29085 F20101210_AAAOUG lee_a_Page_52.QC.jpg
4cd77e98e29a876349d2fe55c4b591da
2f1fd3f7a48654be8becaeef1cadcedbc447f8d6
33681 F20101210_AAAOPI lee_a_Page_39.pro
c2d5658b8dcc1ac53235bd2d9be996c9
f471110cf539ac2af6818e06a02cf96c2726a957
7445 F20101210_AAAOUH lee_a_Page_52thm.jpg
316415dc2a0dd298ad9909a86837fc31
bf8a28aa246b2c2664186525eb5159c6ac7ae303
35110 F20101210_AAAOPJ lee_a_Page_40.pro
9132b219792c83c294cad27afc18021c
45bc7be5bfa6e98d525c402c0fe30c4b850777a4
49055 F20101210_AAAOKM lee_a_Page_43.jpg
57b73f882296f62cd9fb1ea9c3979666
d3f9511d4a855d72cfd35d614ee27ca71ab558f7
7862 F20101210_AAAOUI lee_a_Page_53.QC.jpg
56f1bc0e6c83a0aa7321b79dfbda85af
9450696364e27a83a36b7f55e515719c09222da2
34546 F20101210_AAAOPK lee_a_Page_41.pro
2dcc2e0704bcf8c1813121c6a7be631b
3f632da596c4911a97fa6d014397152661e4af9d
52568 F20101210_AAAOKN lee_a_Page_44.jpg
0ac66b28d264169622193a6904ff0554
4673f9aa0e6e6eea5961ada965d12efdd28d8d2c
2493 F20101210_AAAOUJ lee_a_Page_53thm.jpg
b3f516f9d880caf98274c7394cecdfb6
176b64fd772c2faaa8672897b84d2ac000aecdd5
33099 F20101210_AAAOPL lee_a_Page_42.pro
73687e247bf5812fd4df5294943925a8
2032a1e0aa5f3899398f8ec95bcab1bbd6f90dce
53048 F20101210_AAAOKO lee_a_Page_46.jpg
1f30ebf4f728aeddafdc452232b3f5fa
f823fdfe0df63aac4d53a42d0632e016731a99e9
34355 F20101210_AAAOPM lee_a_Page_43.pro
735b184e2acdc3d7312c5ca790fd6697
905ec5915aafaa8313faf65881728ff7d95a74eb
65224 F20101210_AAAOKP lee_a_Page_47.jpg
bebbfe205b82e1042568292efde365f6
94fe6a2a6be26ae075862b4c33ba601678a6442d
7208 F20101210_AAAOUK lee_a_Page_54.QC.jpg
993bf33bf5d52841629aa9bf67db12be
a573d48bf7a995ab66a0b05aa75f91e4c4500f26
23633 F20101210_AAAOFT lee_a_Page_26.QC.jpg
3a2e64939c90f277bf28ce105984aab2
8b47807057eb8ee7dacc0c708209514bc360ba92
38300 F20101210_AAAOPN lee_a_Page_44.pro
4389337724532b4090b28dac71b074e0
b487789398b02fb80f84994de6fcb695af7a9f16
71092 F20101210_AAAOKQ lee_a_Page_49.jpg
24f66c0d8268d38380dd10f777b76f31
70f0de2efaf0c1256423428336e0dc8ebea23c5d
2429 F20101210_AAAOUL lee_a_Page_54thm.jpg
8514e7d01865588d2e1339be539b89da
c89224f56efc3da9edd3649e9c74e644477e7044
71790 F20101210_AAAOFU lee_a_Page_23.jpg
09b72811710ac94acc1d945c129c99dc
a7440da22231656f4d36d1f54e72b0184e418d02
33723 F20101210_AAAOPO lee_a_Page_45.pro
a4000bf71e9c2f733538d5fcaf9059f3
56c8767b4630d0f1128379e085f2fb2311cf619d
50560 F20101210_AAAOKR lee_a_Page_50.jpg
c968816d3a617b442841c1963700b34c
9e40d8e030260b015b02a3f6ac333e49387c5986
6340 F20101210_AAAOFV lee_a_Page_48thm.jpg
2688d3efa2c7538dfcdf40bdf1d94f05
e4826eb8c17bc66a97e6ae86d06da65f99db05c1
37937 F20101210_AAAOPP lee_a_Page_46.pro
88f9ab1ecff8956e2d45b76edb0f0daa
7f037469ed75452903f3e638c847cb3c98e55b82
77161 F20101210_AAAOKS lee_a_Page_51.jpg
f444cc295e7fee3550eee021afc220b0
0ba67b67e7ecc2e365ceb4af3ba7018c72c5272a
49037 F20101210_AAAOFW lee_a_Page_11.pro
602a28ab3e255f794a122ab4c4c4536f
0b9f44d7fd3abb00507969175b8698347eaa53ca
45550 F20101210_AAAOPQ lee_a_Page_47.pro
a160c6c21921ded9f25dac6740122a88
282ff1d9178459ddf84d05c49e143d62ec9638cb
107513 F20101210_AAAOKT lee_a_Page_52.jpg
2fdbb1bbd0b19183c782314e41ccb4f3
6a950c1af435b9dc28f7dd6c4d29cda886f0445f
23568 F20101210_AAAOFX lee_a_Page_48.QC.jpg
2617c0633f5fc829deb33a890c639e53
d8be12ef8c47680e5cb1cef6717a08cc606efa00
51128 F20101210_AAAOPR lee_a_Page_48.pro
b123fcd5c4c62c97a3232da097bcc817
fd66175fc0c0f71d9efe008c489c1ab49c059d64
26309 F20101210_AAAOKU lee_a_Page_53.jpg
b49a1a2183438e8da7537bd82a924e42
1f28973ceecc3ae304613a92b054d3c6c10607d4
17509 F20101210_AAAOFY lee_a_Page_41.QC.jpg
dfb577c3f2dc6897c7166bed5aefdd38
56256f6c49277c6a9cfd8576573a8a0a36b50d13
50771 F20101210_AAAOPS lee_a_Page_49.pro
156489b4ba09cd4cb3f5fccdf1e42f34
4cb55c7b8a2cf86f30709fb75f4ba703e0ee625f
22049 F20101210_AAAOKV lee_a_Page_54.jpg
55b36a6d8f673aafc2a497b2cab195a7
552c67ee19cdcc2cd12068804e6fe35d0c2b557a
51275 F20101210_AAAOFZ lee_a_Page_32.pro
775408ecdab1cd4e0b90687492e40791
e55e9bc8a6d2a0f72624b808623fd5599a4215b9
54467 F20101210_AAAOPT lee_a_Page_51.pro
c845049d100722c2b1e2446cbf796fa2
3be5b555bf82d65a16f3bd772f5f6e1fc9437db6
23764 F20101210_AAAOKW lee_a_Page_01.jp2
6ff4bce997fb3abf2b3f178e1e21a3bc
e3a298e32bf79d056eddf7f1aff3a5e0c12a8047
12928 F20101210_AAAOPU lee_a_Page_53.pro
fb142ddbce50cabe4001b24f6ef6d49b
582b21a15437b0823b5d796828feb0e1ea110431
5464 F20101210_AAAOKX lee_a_Page_02.jp2
282c1513d37b17d160cfabd8058d9ce0
d332bdf85a50e168bba102cdb30884a4eb7a07d4
9900 F20101210_AAAOPV lee_a_Page_54.pro
8a9df15c3b699717452f37ac86a42e62
f45fb2eef6db3d6691b886070b583fbd0c55a5f2
11160 F20101210_AAAOKY lee_a_Page_03.jp2
ed60e4c293bddd673ab34c0d27c41c43
1279b7b5d5b9204b07a713f9cd846112ebe086c4
468 F20101210_AAAOPW lee_a_Page_01.txt
15945a8aab823916c259bb6636dfca24
7f2899a41ce6cf0bea471c20e7226afce5d5425c
51265 F20101210_AAAOIA lee_a_Page_20.pro
bbff97da4e703b31e95e49e6b4a6b8cf
9e13ca7e1eb10ba28e51119e27d583c715db725a
28048 F20101210_AAAOKZ lee_a_Page_04.jp2
62be1ea1a3ba94a7bf841ba44392dd5b
06b59e96b547c5e1c473b3c215334d5200ae1398
216 F20101210_AAAOPX lee_a_Page_03.txt
e5a0f7887fc29e342e230d6c12cd7971
5429c5c6725ef31cbee77cec8fc68d47933ebe5f
28570 F20101210_AAAOIB lee_a_Page_09.jp2
c80c770db540a6d06cf99b5fe72617b6
0ec9f4395533dff7d060a46bc4b27bae314b76d0
F20101210_AAAONA lee_a_Page_21.tif
25e7a238a83ddbb4e714fe48650ce65f
8b2162c51a3107d62c8226ff07258f719ac1b4e5
503 F20101210_AAAOPY lee_a_Page_04.txt
978ba0db9ed4494a7a7a1af7f08866db
a9fde3153d90e9c412a638f2fe9bf2d6f12a6fa1
1996 F20101210_AAAOIC lee_a_Page_31.txt
27d5c390d001bb224fe8ca37b05e1748
3f6ca8c88790f7999e872fb9bbb869114d6577c5
F20101210_AAAONB lee_a_Page_22.tif
d138208344f07e4c660da7fc49b283bb
0bd38a56a2dcae53a234fa300bcd043fae177fef
3214 F20101210_AAAOPZ lee_a_Page_05.txt
c832f41f3e766f469b792117c46bad2f
d67a78b794a8b6a81f3119c3e52d4c3743164d73
64011 F20101210_AAAOID lee_a_Page_37.jp2
dc6f3ede168945747cebd0ed5559ea29
2518d898199798f475a10576ab8c8a33e64cae36
F20101210_AAAONC lee_a_Page_23.tif
9324061acf40284f317e46e2c6fe64fa
bd5b74aec233832d1ff7ecece04eb75088c94b27
51003 F20101210_AAAOIE lee_a_Page_26.pro
6a886e7f3a4acdac3be17c80f33ab479
05f325efa8041f5fc90e9e98a16f146d705da2a7
443 F20101210_AAAOIF lee_a_Page_54.txt
8725c38d117770fe5904b8838df33e36
aad53ff647e3f8f8f7768852528aa487c7585eb0
2438 F20101210_AAAOSA lee_a_Page_09thm.jpg
e9957e0e0fbe5f1c335dd127315f0968
f6358fc81201545cb32a5af1dea25d8c093e8c12
F20101210_AAAOND lee_a_Page_25.tif
f4ce138191c8b700e38c016071921d0f
73ea7cf154e17375ef479c697768074bc68dfacd
21764 F20101210_AAAOIG lee_a_Page_38.QC.jpg
c7526471c45a71487df26cac616794e3
c1bae9599eca2aee6919c3006d958c19f493ca63
21134 F20101210_AAAOSB lee_a_Page_10.QC.jpg
08ca55a674f3c8aa40b10c9ad38cf4e2
884a709647f2e3be1a39ad3a9198cfabc603dbd3
F20101210_AAAONE lee_a_Page_26.tif
b02cd9f94add4c26a49ad8917a5919d8
7b3aacfddcf60e6326aa458e8e0a95912d5ab2d4
66259 F20101210_AAAOIH lee_a_Page_52.pro
ce358194579dabbf4d6981df972c2f54
cae4ceac747d341dc2dd6684a70451bc6a2ac705
6056 F20101210_AAAOSC lee_a_Page_10thm.jpg
461251580f1b236c8da51cb0501dd04f
963d0e37246814148dbfd8a1c37b4362e788dc8b
F20101210_AAAONF lee_a_Page_27.tif
2ef010ae6a17456483754b7b4c6ab7b9
67caddeab1d848d6cf003971cffcb194f3684570
72777 F20101210_AAAOII lee_a_Page_26.jpg
f28b92e53699b2e65c0e21697b38f74a
4dcbb3a3d3fb78641bef78f6dd04deec6a2b141f
22874 F20101210_AAAOSD lee_a_Page_11.QC.jpg
3716138b41a28bafbe0858283e2bab28
7513edb05fdcfe8f7a9fb1e9b4d5cdc29323affa
F20101210_AAAONG lee_a_Page_28.tif
23a47b525c206597ea7408b6577fc502
1f16b70e22983af2ac075c2783629f262490598b
F20101210_AAAOIJ lee_a_Page_40.tif
a96ff4486181d2aea24dbf9c5e591a4c
942ae06204a183aa8991418d1a0ea711d7f4f729
6580 F20101210_AAAOSE lee_a_Page_11thm.jpg
a6536043fb5d0e100e2e0a5ef5f88bdd
995f9606403db19de3f40ed5718c96e1db423ac5
F20101210_AAAONH lee_a_Page_29.tif
3ad0edabfcc4af5845560d41e54cccc8
00266d55b2190e225f5be0b8783ba1086ee68243
104003 F20101210_AAAOIK lee_a_Page_34.jp2
2955a42722055cbc901dbb47e4b709b1
3539cb8f32ff63bea45c22fd05f23193c65f1e7a
23057 F20101210_AAAOSF lee_a_Page_12.QC.jpg
0bd26a32765e199a5b0fc6cf55c877f8
111289dad490dab32283fabf1dd6b47da116e205
5234 F20101210_AAAOIL lee_a_Page_44thm.jpg
195c84d8bccd917fce7030e12b4b14a5
c6d66d15442ba7d996713b070b4f1b4739f70304
6293 F20101210_AAAOSG lee_a_Page_14thm.jpg
2b9ac7c5748df4c7bbb63825fa74ca77
c1a2da64a76136c583eab271101cbbfdc5459f9b
F20101210_AAAONI lee_a_Page_30.tif
76e5f3789c5dd9c4196250e18e6a9095
7435562373b8c223615e592359ded4303aa27734
20850 F20101210_AAAOIM lee_a_Page_18.QC.jpg
4ffc6cb23d92ed2d5a4e8d2aa0adb82c
834d5bb3e2377b9f65af8904fd46f22f433864bf
23226 F20101210_AAAOSH lee_a_Page_15.QC.jpg
94bd9a586c0af0d65885dc6185217b70
826a5bd1b15d9bdec8432e4f465ab9475beb089f
F20101210_AAAONJ lee_a_Page_31.tif
0d72f04ea39b516b344f0991bf8e5d40
492904a4abdf20f7af43c846b6872864979dfc73
6361 F20101210_AAAOIN lee_a_Page_21thm.jpg
5afcd856d9b3caf2ba0d75d26212e4a0
512bb6d6885cab49cc951aae593e3e07fb242191
F20101210_AAAONK lee_a_Page_32.tif
600e1add9d9fab94aa53dc79cf22f924
266e1c8cf7a12fd322c0d634ae30c06eddb2e94b
106733 F20101210_AAAOIO lee_a_Page_35.jp2
c86523cb9e93009353fa2094ec63c244
c0f874b5dee7ba1ff764b93a7ecfd0c8872c630c
6098 F20101210_AAAOSI lee_a_Page_16thm.jpg
e85fe19f2a3d41329e51604a999650c6
007d0430dce436cdd73ad223c06b45e8c9a7cc41
F20101210_AAAONL lee_a_Page_33.tif
4875c3d2a9bd4dc0415d85729fd0b1c8
73a020da54a8470bef1ece06f63614215140c9f0
1930 F20101210_AAAOIP lee_a_Page_11.txt
faa83e5bb359e29640cec52d5e456c4a
cd752f9f60bbdc707ebc216d99f38541e4a57ce4
6865 F20101210_AAAOSJ lee_a_Page_17.QC.jpg
590d0a492a5d75f30e999606263741e7
645d35d700b458bd4f607365567abbf16498f858
F20101210_AAAONM lee_a_Page_34.tif
946df9ee8815e068b8cd484e298ca919
c184ef6c91992005c96b80bc969440b7c1284899
10232 F20101210_AAAOIQ lee_a_Page_02.jpg
5b7dfe09b7b5c2cc730b6b45a5a625cb
712a55992b660ec24981d3bf89d4466ff8285d00
2303 F20101210_AAAOSK lee_a_Page_17thm.jpg
90516cedf195936c6a69df1b08766d29
81e0d10408f54e968b7f4a0eac492e43143718ee
F20101210_AAAONN lee_a_Page_35.tif
8f3c72054f69449d53163fcd308fe5d6
fa8ac6592d42c9ccc1f596399d2595cb32075be7
33596 F20101210_AAAOIR lee_a_Page_50.pro
ed3283251d94fc437bf576670b9f74a7
41a0a820b4ceeb6323eb96ff77599741a2c3e01f
20358 F20101210_AAAOSL lee_a_Page_19.QC.jpg
8a1c19bcae03dcdd0ce041043c441cad
6ccd654d269f294e95fad049f50584a498a22ef9
F20101210_AAAONO lee_a_Page_36.tif
16db2dd56b84625b59ad197c1cf2ddb9
45f93bbe4d450d0c6964bab2a9b1d45834afdabc
72583 F20101210_AAAOIS lee_a_Page_21.jpg
265ad5d247531ca20f5e0b6e0cc7f47e
a1db0309185682d45f012843c057576773e79997
6008 F20101210_AAAOSM lee_a_Page_19thm.jpg
02ae476ed48ecef3d53632d5d3222223
96e8a49fc236894d886356c141d4e9767b3133bd
F20101210_AAAONP lee_a_Page_37.tif
03496155f27e64babe0507b1c8060c29
abdacc24e8c0a1c60237a46266106ebf661b0890
1119 F20101210_AAAOIT lee_a_Page_02.pro
673675174265f5a3bc934e3c339c2211
c8621e00545c8e24c85aab6cc4a59808bcb60a6a
6756 F20101210_AAAOSN lee_a_Page_20thm.jpg
5611b91513f2906dd657263c241f759b
8d33577c6fc0149d20f9b692ddc0e2f3c0f16a48
F20101210_AAAONQ lee_a_Page_38.tif
ba65a195b2fe934958d6e079bcd4b3f4
2f4c907cf48eeff87a9035fad5e7864769335aa2
6636 F20101210_AAAOIU lee_a_Page_25thm.jpg
c871fda56531f950f3d945031e14abd4
93a5f07933f176a6d1390feab21a1ac485b41478
23987 F20101210_AAAOSO lee_a_Page_21.QC.jpg
08cd811c52c7083a10f1bf986759ba48
d6aaf69ea4d5b27bb7dcf47f23ac1eed3a5c24ba
F20101210_AAAONR lee_a_Page_41.tif
d5c8f967e4994293040277025cfac63a
3b10841430c15afc6a3324c19927a40247c09a3f
2014 F20101210_AAAOIV lee_a_Page_25.txt
43bddb5781a08fbe4aad026887eac1f1
b1ef5f1a3563a2cf4bb1f73c77526ed62667d9d9
22327 F20101210_AAAOSP lee_a_Page_22.QC.jpg
e7bf5ac954f148487845aee30389b696
cf63459ddba282aea6964e7af9ab6f3f2f918a86
F20101210_AAAONS lee_a_Page_42.tif
c9280125c779d4b362f4615bdd77fa00
ca60611838ae6cf60867a906bd7afe8f2b1c170f
20914 F20101210_AAAOIW lee_a_Page_17.jpg
c5ca823331caf38ba0299859b8179e79
23e70ba557cdf0de65e80da3bf947a943fdf2643
6407 F20101210_AAAOSQ lee_a_Page_23thm.jpg
e0d4a88ddf51aa756d005c0bc2b357a8
4573de242fae215abe9d7845019a73dc7f5f262c
F20101210_AAAONT lee_a_Page_43.tif
c8e57738eee9bcc4d5f521b0cc2356e5
782c1248436d4e9d9428a730a75ad55b1872d2cf
47400 F20101210_AAAOIX lee_a_Page_30.pro
f2989c02ef96448a6200a9eeebdf1f1c
fec68bfba81ee4c3535d9ee560fe8b861543de67
5667 F20101210_AAAOSR lee_a_Page_24thm.jpg
71292708fe6b174dc07624b1abcc0957
b32dd42115b499e781541da517ca49a23e91f356
F20101210_AAAONU lee_a_Page_44.tif
1f79efc3fb307a67b006be94346d0a63
8f034b0fab61f7462e17472dec8f05cb094fe5b6
69192 F20101210_AAAOIY lee_a_Page_22.jpg
6646dd539d7eb72ed7442896df317095
2e5d0d1de3fda5441985a9659a841be9d1ed83ae
23681 F20101210_AAAOSS lee_a_Page_25.QC.jpg
0f69d1d2451c7c3b5949c40d72701e5c
eeb6d3e11c0ac108843a33a4acf106751bf476ac
F20101210_AAAONV lee_a_Page_45.tif
d3944ed39acc2380269eec9e64d7411a
3ab2566383ac136b649e1ba9a9a7fd5dac8c8fcf
4793 F20101210_AAAOIZ lee_a_Page_39thm.jpg
edded723597a3968d1b26ba7c889de04
1caad864428b35117d4529f410635fd821eb2771
6548 F20101210_AAAOST lee_a_Page_26thm.jpg
36a618614f182277a18856b61832bf76
98fee23fa180ecc40fc91e4b88d8aebee0f10b95
F20101210_AAAONW lee_a_Page_46.tif
5b4224588994d137a80f57b0ce4b8a25
b58146065b7e789c6421e713e4943b6676e1de68
F20101210_AAAOGA lee_a_Page_17.tif
d2ff2a99a8fe34fb1a6bcb994408fce0
a28970934fc816e684a359436c36af54bd8ea7ea
22162 F20101210_AAAOSU lee_a_Page_27.QC.jpg
e89b22294688be25f55c19a232f51cb2
046c22810193f5dc82bffc2570f4438a54f0b6e1
F20101210_AAAONX lee_a_Page_47.tif
ca1d282cb3a0975c8e909d1d24dca548
47c3627ba33a99a267d08ef38a867c42c5ac89a3
F20101210_AAAOGB lee_a_Page_14.tif
1fdc4d3e55d09fc32d79a05f5b3e10a7
43694db9d59db51e3ba7627c3c1aaa839a79619c
F20101210_AAAOSV lee_a_Page_27thm.jpg
646c894c25cd68ab41a09aba4b8c7e58
80f4c0fe1207bf6332cca7e1200075962f078f84
1051985 F20101210_AAAOLA lee_a_Page_05.jp2
d2334f92245c2928e8855d6bedd3f4b7
36294befdcc759521dfd87323da1569c5acfdf62
F20101210_AAAONY lee_a_Page_48.tif
71f56cc80bf68de97d4e7d5ce5c3a43e
b83e82fabd80bb0414cbd92b6943c81fa6530880
1369 F20101210_AAAOGC lee_a_Page_02thm.jpg
b969cf0bca8052481be3cf0eb47dc5b2
1b2aa634f84d6025fa513a3c506819e68c210269
22959 F20101210_AAAOSW lee_a_Page_28.QC.jpg
c78a68c948d4cfa9e2479dd80de740cf
727bbf4530765766035bf88c35d5d39a420d05ea
F20101210_AAAONZ lee_a_Page_49.tif
55d8b0becd647403ddf152852491904c
6e546172f068112d486c07cdb6666cf0bf87c8f1
1874 F20101210_AAAOGD lee_a_Page_47.txt
b86aa7744c9c76c9e9455791b7a6735d
58a1ca0108cc97db87cdc74ee3542cef9b113529
6467 F20101210_AAAOSX lee_a_Page_28thm.jpg
aabcdb3bdb2bbcb7fa9085b148322453
907a387a004cc571357b39042353cfee39943580
153450 F20101210_AAAOLB lee_a_Page_06.jp2
3d49040217ca6b80a13f4af4f1980888
9030dceae09dcc825224aff76eea1f2127aab5dd
23088 F20101210_AAAOGE lee_a_Page_23.QC.jpg
2da0dd63d8010d9f5c9309b7d0910740
7b9449b5f6c6b04731a3745ac2400009adf6bc23
276 F20101210_AAAOQA lee_a_Page_06.txt
55c691a1b78e0b806ebd001aa1383421
20ddd4d6dc6b7f43c8a82cbb929f58c0069bc92a
22586 F20101210_AAAOSY lee_a_Page_29.QC.jpg
b08f1b73b821e9448e0013968daa2848
570ff21428b9f15367418ff2ca65fd62a7e3b704
1051967 F20101210_AAAOLC lee_a_Page_07.jp2
c65af740d9a5c424b9eb30e1d88d47ea
dce5f34dafc3f83f118fdc4a56954674fa3b381b
1855 F20101210_AAAOGF lee_a_Page_27.txt
6d903d411f20954ea4372c660d6ddba8
8c868183d9e1c0abc650b6d45e0f033955460992
1311 F20101210_AAAOQB lee_a_Page_07.txt
00b775737bc40d432cbae41368330ce0
f04fb45562f4cec8b564fea861c85e4919e85777
6389 F20101210_AAAOSZ lee_a_Page_29thm.jpg
49f917a5601967a99b41d3aa0ed0e8f9
b910e626b8a1414da135f982575dc30f6cc47688
97966 F20101210_AAAOLD lee_a_Page_10.jp2
b511a259a3ba745d49d1e770d42e8831
a1c2b2255f22da3d0f3c2c2be3a08489be325a28
6443 F20101210_AAAOGG lee_a_Page_12thm.jpg
b0dcaa2d0f7eb753578092ab61c02fd7
8785eec7fd05fba5f296f421974cc04304d7ea54
1749 F20101210_AAAOQC lee_a_Page_08.txt
288fd37ec44bb2262a4ff9cba21abf95
52c057609bd83445160fb67669a2a7349f4a7ac5
107783 F20101210_AAAOLE lee_a_Page_11.jp2
39b3f1e08fb3f4645bd8eee6fe297119
9b6110513d50bc34f2e6d86974ba27449150639c
88125 F20101210_AAAOGH lee_a_Page_08.jp2
cccf23f8106176e9e66d6387cbcfad7d
8a587d0b88c357b37bd528d6bc756ab9f6b512df
1863 F20101210_AAAOQD lee_a_Page_10.txt
0769b14a28b6d7812b7589fa9dda7c1e
f2a786106a1968a2ccfb3b2f3f1beab4aec40b24
108663 F20101210_AAAOLF lee_a_Page_12.jp2
0d5fbb96bf1b75406331fb69e0de0b8a
9b6620c6d19d813d7f68fdd28222675ae6f8935d
76172 F20101210_AAAOGI lee_a_Page_50.jp2
89af5faca8fdcaeb5d221851e543e49c
3c39303e35d7669e3769e164ab744637250ec2fe
1962 F20101210_AAAOQE lee_a_Page_12.txt
cbc5a850a97c39149d2fbbf8816aca82
1e9ae47b9bc7749ca515a1d0289b0628e513723b
108358 F20101210_AAAOLG lee_a_Page_14.jp2
d5a4638cfa9c61f4a810e8773e36825e
ae9a6b246c55ae71577ccc611e5fd74b0b9d8e4f
F20101210_AAAOGJ lee_a_Page_24.tif
af23bb58e53b5d6074acf7c144eebe2d
b8704dbe22c9c3b8a098e860a1bdc250c2d07c18
98578 F20101210_AAAOLH lee_a_Page_16.jp2
57cdb186183bc1d9ea7f33157fcbb054
251e3609b7f42ed3b927ad2afdae6b4011ac1a19
6547 F20101210_AAAOGK lee_a_Page_13thm.jpg
ebaf07818467877dfa046d2ddf6299fd
fad5f29eb2a47f84e8aca5e3bb177751e3166fac
1997 F20101210_AAAOQF lee_a_Page_13.txt
13fd1dc5633b355f346b882036812e17
8a392aed47c58f3e5026b5c6a0c579816c03ebe0
25437 F20101210_AAAOLI lee_a_Page_17.jp2
90d9610014b8e499cc1a8ce24d430a0e
566845e567389eec268cda89325ea931a23cbea4
48787 F20101210_AAAOGL lee_a_Page_35.pro
fd3aad6503777f47bcbe9ac489a8923a
a7a94910d4d9aa2da2438fe98ef3d2c8a99f77fd
96666 F20101210_AAAOLJ lee_a_Page_18.jp2
9ac535ea55dd769c68a19f69de129a5b
c2b105c0ad243e4ec3bff35218b1d4952871b1d3
1781 F20101210_AAAOGM lee_a_Page_24.txt
bcd5aeb9c42a82f72c62e1e430ad025f
25178c99dca2d87c2ca5a06b5b7d6c1c8ca6fad4
1979 F20101210_AAAOQG lee_a_Page_14.txt
65f5345432228d4177e9c0b9e28cd277
4a0c6a11bbaa112d248822075fb74df64da6efcb
113303 F20101210_AAAOLK lee_a_Page_20.jp2
ee44423e301a0f623d3b0bbbcdd22001
2cd3e54432df395a31f6a4a6e6f678b7752525c9
2422 F20101210_AAAOGN lee_a_Page_01thm.jpg
aef4606a7e978a010257b05d0da7c4d8
55b7b856b9538b815cfa9740266c1da014e4dfb6
1821 F20101210_AAAOQH lee_a_Page_16.txt
f9e79dee3f6ad220b8f7d583510406d6
a739ce43823f9ef95a386ce4ec574f1c1de510b1



PAGE 1

MEDICAL COST OFFSET EFFECTS IN PULMONARY AND CARDIAC PATIENTS WITH DEPRESSION OR ANXIETY By ANDREA M. LEE A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006

PAGE 2

Copyright 2006 by Andrea M. Lee

PAGE 3

This document is dedicated to my parents, J ack and Ellen Lee, and to my grandparents, Harvey Lim, Lan Chan Lim, Sonny Lee, and Laura Lee.

PAGE 4

iv ACKNOWLEDGMENTS I would first like to thank my mentors, R obert G. Frank and Jeffrey S. Harman, for their support and guidance on this masters thes is. They have been a tremendous help throughout the process. I would also like to thank my parents, Jack and Ellen Lee, for their unwavering support and firm belief in my abilities. Their support enables my successes and gives me the strength to continue on this academic journey.

PAGE 5

v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii ABSTRACT.....................................................................................................................viii CHAPTER 1 INTRODUCTION........................................................................................................1 2 DATA AND METHODS.............................................................................................9 Data Source...................................................................................................................9 Variables.....................................................................................................................10 Dependent Variables...........................................................................................10 Independent Variables.........................................................................................11 Control Variables.................................................................................................12 Statistical Analyses.....................................................................................................14 3 RESULTS...................................................................................................................15 Pulmonary Conditions................................................................................................15 Comorbidity and Expenditures............................................................................15 Depression Treatment and Expenditures.............................................................16 Depression Treatment and H ealth Care Utilization.............................................17 Anxiety Treatment and Expenditures..................................................................18 Anxiety Treatment and Health Care Utilization..................................................19 Cardiac Conditions.....................................................................................................21 Comorbidity and Expenditures............................................................................21 Depression Treatment and Expenditures.............................................................22 Depression Treatment and H ealth Care Utilization.............................................23 Anxiety Treatment and Expenditures..................................................................24 Anxiety Treatment and Health Care Utilization..................................................25 4 DISCUSSION.............................................................................................................38 Limitations..................................................................................................................39 Implications................................................................................................................40

PAGE 6

vi LIST OF REFERENCES...................................................................................................42 BIOGRAPHICAL SKETCH.............................................................................................45

PAGE 7

vii LIST OF TABLES Table page 3-1. Clinical Classification C odes and Diagnostic Categories..........................................28 3-2. Antidepressant and Antianxiety Medication Names.................................................29 3-3. Descriptive Statistics of Pulm onary Respondents (Comorbidity)..............................30 3-4. Descriptive Statistics of Pu lmonary Respondents (Treatment)..................................31 3-5. Descriptive Statistics of Respondents with Cardiac Conditi ons (Comorbidity)........32 3-6. Descriptive Statistics of Pulmon ary Condition Respondents (Treatment).................33 3-7. Statistical Results of Pulmonary C ondition Respondents (Total Expenditures)........34 3-8. Statistical Results of Pulmonary C ondition Respondents (Medical Expenditures)...34 3-9. Statistical Results of Pulmonary Cond ition Respondents (Health Care Utilization).35 3-10. Statistical Results of Cardiac Condition Respondents (Total Expenditures)...........36 3-11. Statistical Results of Cardiac Cond ition Respondents (Medical Expenditures).......36 3-12. Statistical Results of Cardiac R ecipients (Health Care Utilization).........................37

PAGE 8

viii Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science MEDICAL COST OFFSET EFFECTS IN PULMONARY AND CARDIAC PATIENTS WITH DEPRESSION OR ANXIETY By Andrea M. Lee May 2006 Chair: Robert G. Frank Major Department: Clini cal and Health Psychology An intervention that reduces or prevents usual costs to the health care system is called a medical cost offset or the cost offset effect. This study examined a sample of pulmonary patients and cardiac patients to determ ine if a cost offset effect was apparent. Three research questions were examined in this study: (1) whether depression or anxiety in pulmonary or heart patients increased heal th care expenditures, (2) whether depression or anxiety treatment decreased health care expenditures, and (3) whether depression or anxiety treatment decreased the number of emergency room visits, inpatient days, outpatient visits, or office-base d provider visits. Data were obtained from the Medical Expenditure Panel Survey (MEPS), a nationa lly representative survey of the US noninstitutionalized, civilian popul ation. The results of the study revealed that in pulmonary patients, the presence of depression increas ed expenditures, whereas the presence of anxiety decreased expenditures. Furthermor e, depressed pulmonary patients showed a decrease in expenditures and this effect was not explained by a decrease in the number of

PAGE 9

ix outpatient hospital visits, inpatient hospita l nights, office-based provider visits, or emergency room visits. Anxious pulmonary patients who received mental health treatment showed an increase in expenditu res; however, there was a reduction in outpatient hospital visits in this sample. The results suggest that the medical cost offset effect is not a constant phenomenon and appears to vary across psychological and medical conditions.

PAGE 10

1 CHAPTER 1 INTRODUCTION The health care system in the United States is in a state of fiscal crisis. Total health care spending is on the rise each year and there is no evidence that this trend will subside. Between 1987 and 2000, health care spe nding among the noninstitutionalized US population increased by about $199 billion (about 3 percent pe r year) (Thorpe, Florence, & Joski, 2004). Mental health treatment ha s been cited as a solution to reducing rising costs in the health care system (Friedman et al., 1995). The cost offset effect occurs when an intervention reduces or prevents us ual costs to the health care system. There are many reasons for the rise in hea lth care expenditures, one of which is the rise in the number of individuals with chro nic diseases. With the aging of the population, the rise in chronic diseases has seen a dr amatic rise in recent years (World Health Organization [WHO], 2006). The majority of this change was attributed to spending for cardiac disease, psychological conditions, pulmona ry disorders, cancer, and trauma. In a report by the Agency for Healthcare Research and Quality (AHRQ), the most expensive type of chronic condition in 1997 and 2002 was cardiac conditions and the greatest increase in health care expenditures occurred for pulmonary conditions and psychological conditions (Olin & Rhoades, 2005). The utilization pattern of patients with chronic medical diseases is complicated when patients have comorbid psychological conditions. Due to th e ongoing nature of chronic diseases, patients who have one or mo re chronic diseases tend to be high utilizers of the health care system and thereby expensive to the system. Although cardiac

PAGE 11

2 conditions, pulmonary conditions, and psychologi cal conditions have been identified as expensive chronic conditions in the US hea lth care system, when cardiac conditions and pulmonary conditions are comorbid with ps ychological conditions, expenditures tend to be greater than the cost of each condition alone Primary care patients with psychological conditions tend to utilize the health care sy stem more often than patients without comorbid psychological conditions. In studies of primary care patien ts, medical costs of patients with depressive symptoms or major depression were higher than patients without depression (Katon, 2003). For example, patients with congestive heart failure who also present with depression have medical cost s 26 to 29 percent highe r than those with congestive heart failure only. The prevalence of psychological conditions comorbid with cardiac conditions or pulmonary conditions is high. In particular, depression and a nxiety are more frequent in these medical populations. A study determin ing the associations between anxiety disorders and physical illness found that both ma les and females with an anxiety disorder have higher rates of ca rdiac disorders and pulmonary illn esses compared to individuals without anxiety disorders (Har ter, Conway, & Merikangas, 2003). In a pilot study on individuals with conge nital heart disease, 27.3 percent met the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnostic crit eria for depressive episode and 9.1 percent met the DSM-IV criteria for generali zed anxiety disorder (GAD) (Bromberg et al., 2003). Depression comorbid with the respiratory condition, chronic obstructive pulmonary disorder (COPD) is estima ted to be up to four times more frequent than in COPD alone (van Ede et al., 1999, as cited in Kunik et al., 2005). In a study

PAGE 12

3 screening COPD patients for depression or anxiety, 80 percent screened positive for depression, anxiety, or both (Kunik et al., 2005). Despite the prevalence of depression and anxiety in medical populations, there is evidence that the diagnosis and treatment of patients with depression or anxiety is lacking. Epidemiological studies generally s how that those with mental health problems underutilize mental health care (C ollins et al., 2004). Only 25 percent to 40 percent of severely mentally ill patients access specialty mental hea lth care and only 15.3 percent receive adequate treatment (as cited in Collin s et al., 2004). For those with anxiety and mood disorders, the average delay for seeki ng professional treatment was 8 years. Given the current trend toward inadequate detection and treatment of psychological conditions, as well as the prevalence and hi gh expenditures of patients with comorbid psychological conditions and medical conditio ns, the question becomes whether treating the mental health problems of medical patien ts would reduce expenditures in the health care system. When an intervention reduces or prevents usual cost s to the health care system, this is called a medical cost offset or the cost offset effect (Carlson & Bultz, 2004). Numerous studies have attempted to ascertain a medical cost-offset effect of mental health care in primary care patients with psyc hological conditions. On e of the first offset studies was conducted by Folle tte and Cummings (1968). The medical records of 152 randomly selected adults who sought psychologi cal services were examined. Data on their health services utili zation were collected one year prior to the beginning of psychological treatment, as well as five years following treatment. Comparing the data to a group matched for age, sex, socioeconomic status, and medical ut ilization rates who

PAGE 13

4 had not received psychological treatment, it was found that this comparison group had higher health care utilization rates over time, in addition to a reduction in health care utilization for the group receiv ing psychological treatment. Following the Follette and Cummings (1968) study, a series of cost-offset studies were conducted. In 1984, two me ta-analyses of the literature were published (Mumford et al., 1984). One analysis was conducted on Blue Cross Blue Shield Federal Employee Plan claims from 1974 to 1978, and the othe r analysis was conduc ted on 58 published studies. The basic conclusion from these meta -analyses was that 85 pe rcent of the studies found a cost-offset effect, mainly observed in the reduction of inpatient days. In another meta-analysis of 91 studies from 1967 to 1997, 90 percent of the studies reported a reduction in medical utilization follo wing mental health interventions (Chiles, Lambert, & Hatch, 1999). Twenty-eight arti cles reported dollar savings and 31 percent reported savings after taking into account the cost of mental health treatment. Overall, a savings of about 20 to 30 percent was reporte d across the articles. The effect was most evident for behavioral medicine a nd psychoeducational interventions. Despite the evidence supporting the cost o ffset effect, several studies provide evidence against the effect. The Medical Outcomes St udy involved 22,000 outpatients who were screened for several chronic me dical conditions and these patients were followed over time (Wells et al., 1996). On e focus of the study was on the comparisons between patients who received appropriate me ntal health treatment and those who had not. The study produced no evidence of reduced inpatient or outpatient services. Instead, cost-shifting occurred, in which the care received simply shifted from the patients general medical provider to a mental health provider.

PAGE 14

5 The Fort Bragg Evaluation Project involve d data collection of children and their families over seven occasions to evaluate the effectiveness of comprehensive mental health services to childre n and adolescents (Bickman, 1996). Compared to children receiving care under traditional insurance, ment al health expenditures were much higher for children who received comprehe nsive care and this rise in cost was not offset by cost savings elsewhere. In two related studies looking at the medical utilization of patients with and without psychological symptoms and the possible reduc tion in medical utilization in patients referred to a psychiatry clinic, it was found that depressed and anxious patients who saw a mental health provider had significantly more medical visits, emergency room visits, and medical outpatient visits than patients with depression or anxi ety who had not seen mental health providers (Carbone et al., 2000). There were no signi ficant differences in medical costs between patients seeing mental health providers and those who had not. However, both studies did not control for il lness severity or comorbid medical or psychiatric conditions. The patients in bot h studies had a relatively young median age and one of the two studies had a small sample size. These factors may have made medical cost-offset effects more difficult to demonstrate. In a 2-year longitudinal study comparing adults who had majo r depression who had remitted, improved but not remitted, or remain ed depressed, it was found that recovery from depression was associated with increases in the probability of paid employment and reductions in days missed from work due to illness (Simon et al., 2000). In terms of health care costs, there were no significant differences in cost among groups in year one. However, cost savings for patients with bett er outcomes in year two showed marginal

PAGE 15

6 significance, suggesting the longterm nature of medical cost-offset. It should also be noted that the study sample was derived from HMO clinics, which has implications for cost offset effects. That is, to the extent that managed care restrict ions reduce length of treatment, dramatic cost offsets would also be reduced (Otto, 1999). There are several limitations to cost offset studies and this study was an attempt to address these limitations. First, when studies compare the costs of treated and untreated patients, there may be a selection bias in which samples are not comparable (Sturm, 2001). That is, patients who received treatmen t may have different characteristics than patients who did not receive treatment. If there is limited case-mix information in the data, the selection bias is particularly pronounc ed. This is particularly problematic with administrative datasets. In this study, the use of a large comprehensive dataset allowed for greater control of potential confounding va riables, such as illness severity and comorbid medical conditions. Second, cost offsets have traditionally been referred to as a general phenomenon applying to all medical populations Past cost offset research has not yet teased apart which medical populations benefit from psyc hological interventions This study is a preliminary effort to identify specific cost o ffset effects in partic ular populations. The populations of interest were pulmonary and cardiac patients who had comorbid depression or anxiety. The dataset was a na tionally representative sample, which allowed for greater generalizability for the populations in question. Rapid changes in healthcare financing a nd spending patterns necessitates frequent review of offset effects refelcting curr ent pricing in pharmaco logical and medical treatments (Hunsley, 2003). It is difficult to generalize cost offset effects from one year

PAGE 16

7 to another, due to pricing differences betw een years. This study used data from 2002, which was the most recent data available at study onset. This study combined previous years with 2002 data and prices of the previous years were inflated to reflect 2002 rates. Research Questions Research Question 1 After controlling for demographic characteristics, comorbidities, insurance status, and perceived mental and physic al health status, do pulmonary and cardiac patients with comorbid depression or anxiety have higher health care expenditures than those with pulmonary or cardiac conditions alone? The spec ific aim is to determine if, and to what extent, depression or anxiety in creases health care expenditures in pulmonary or cardiac patients. The hypothesis is that the presence of depression or a nxiety will correspond to an increase in health care expenditures. Research Question 2 After controlling for demographic characteristics, comorbidities, insurance status, and perceived mental and physic al health status, do pulmonary and cardiac patients with comorbid depression or anxiety who received me ntal health treatment have lower health care expenditures than those patients who did not receive mental health treatment? The specific aim is to determine if, and to what extent, the treatment of depression or anxiety affects health care expenditures in pulmonary or cardiac patients. Th e hypothesis is that mental health treatment will correspond to a decrease in health care expenditures. Research Question 3 After controlling for demographic characteristics, comorbidities, insurance status, and perceived mental and physic al health status, do pulmonary and cardiac patients with

PAGE 17

8 comorbid depression or anxiety who received mental health treatment have decreased health service utilization? The specific aim is to determine if, and to what extent, treatment of depression or anxi ety decreases health care serv ice utilization in pulmonary or cardiac patients. The hypothesis is that mental health treatment will correspond to a decrease in health care service utilization.

PAGE 18

9 CHAPTER 2 DATA AND METHODS Data Source Data were obtained from the Medical Expenditure Panel Survey (MEPS), a nationally representative survey of th e US non-institutionalized, civilian population, sponsored by the Agency for Healthcare Re search and Quality (AHRQ). The MEPS was created in 1996 and consists of information on health services u tilization, costs and payments of health services, and health insurance information of respondents. The MEPS Household Component (H C) obtains data from a sample of families and individuals across the countr y. The MEPS HC has an overl apping panel design in which each panel of households is interviewed five times during a two-year period. The HC obtains detailed information on demographic characteristics, health conditions, health status, use of medical care services, char ges and payments, access to care, satisfaction with care, health insurance coverage, inco me, and employment. During the second year of the original panel, a new sample is draw n to create a new panel. Thus, two separate panels are interviewed in the same year which makes for an overlapping sampling design. This thesis combined 1999, 2000, 2001, and 2002 MEPS data to assess the effect of mental health treatment on health care expenditures and hea lth services utilization. As a nationally representative survey, each respondent in the MEPS data represents a group of Americans that share similar ch aracteristics used to sample from the population. A sample weight for each case is developed to incorporate in the estimation processes, in order to account for sample design, including unequal probability sampling

PAGE 19

10 of the population (i.e., oversampling minority groups), as well as non-response rates and partial responses from some survey participan ts. To maintain national representation, this study used sample weights to test hypotheses. Variables Dependent Variables Health care expenditures were divided into two variables: total health care expenditures and medical expenditures. Expendi tures in MEPS are defined as the sum of direct payments for care pr ovided during the year, includi ng out-of-pocket payments and payments by private insurance, Medicaid, Medi care, and other sources. Not included in MEPS total expenditures are payments for overthe-counter drugs and for alternative care services, as well as indirect payments not re lated to specific medical events, such as Medicaid Disproportionate Share and Medicare Direct Medical Education subsidies. Total expenditures are defined as total payments for all health care services included in MEPS (outpatient department vi sits, office-based medical pr ovider visits, prescribed medicines, hospital inpatient visits, emergenc y room visits, home health, dental visits, and other medical expenses). Medical expend itures are defined as total payments for all health care services associat ed with medical conditions only. Put another way, any medical expense associated with a psyc hological condition was excluded from the calculation of the medical expend iture variable. When combin ing all four years of data (1999 to 2002), both total expenditures and medical expenditures from 1999, 2000, and 2001 data were inflated to 2002 dollars us ing the consumer price index (BLS, 19992002).

PAGE 20

11 Health services utilization wa s defined using four separate variables: total number of hospital outpatient visits, total hospital inpa tient nights at discharg e, total number of all emergency room visits, and total numbe r of office-based provider visits. Independent Variables The medical conditions of interest were identified using th e MEPS HC medical conditions file. The medical conditions f ile codes each self-reported medical condition the individual experiences during the y ear. In order to preserve respondent confidentiality, the condition c odes provided on this file have been collapsed from fullyspecified codes to 3-digit code categories. Medical conditions were coded using the International Classification of Diseases, Ni nth Revision (ICD-9) codes and classification codes (CC) as constructed us ing AHRQs Clinical Classifica tion Software (CCS). CCS aggregates ICD-9 codes into clinically mean ingful categories and these categories were collapsed based on the clinical significan ce of categories, acc urate reporting from respondents, and the frequency of the reported condition. From past research identifying spending a nd service use trends for various medical conditions, pulmonary conditions were identif ied from the MEPS HC medical conditions file using CC 127-134 and cardiac conditions were identified using CC 96, 97, 100-108 (Olin & Rhoades, 2005). For a breakdow n of CC categories, see Table A-1. Depression was identified using IC D-9 code 311. Although ICD-9 code 296 corresponds to depression, it al so includes individuals with bipolar disorder. When considering ICD-9 codes 296 and 311, 93 per cent of respondents had a code of 311, which corresponds to unspecified depression. The large number of patients with ICD-9 code 311 suggests that responde nts are likely self-reporti ng depression (as opposed to major depression), which then received a code of 311 instead of 296. Thus, ICD-9 code

PAGE 21

12 311 was used to identify respondents with depression and ICD-9 code 296 was excluded. Anxiety was identified using ICD-9 code 300. Mental health treatment was defined in th is study as psychothe rapy or psychotropic medications. Respondents who received psychotherapy were determined from the MEPS HC office-based medical provider visit file and outpatient visit file. In the office-based medical provider visit file, the best category for care that patien t received was coded. Respondents were considered to have under gone psychotherapy if the best category of care was psychotherapy/mental health counseling. In the MEPS HC prescribed medicines file, the presence of psychotropic me dications were determined. If particular anti-anxiety or anti-depressant drugs were coded under medica tion name (see Table A-2), respondents were considered to be taking psychotropic medications for their mental health condition. Control Variables Because some populations are at higher risk for poor health outcomes than others and thus, higher health care expenditures, we adjusted for these differences to compare health outcomes among different pati ent populations (Iezzoni, 2003). Patient demographic variables (age, sex, and race) and socioeconomic factors (education and income), obtained directly from pre-existing MEPS variables, were used to control for differences in mortality and morbidity. With regards to age, older persons generally have worse clinical outcomes than younger persons (Iezzoni, 2003). Sex is an important control variable because men and women face different risks for certain diseases. Among men and women 65 years of age and older, me n have higher death ra tes than women for cardiac disease and chronic lo wer respiratory disease (Ande rson, 2002). Furthermore, life spans for women tend to be longer on aver age than for men. Racial disparities in

PAGE 22

13 health care outcomes were also taken into ac count in this study because differences in disease prevalence and mortality exist am ong the races (Iezzoni, 2003). Because of socioeconomic disparities in he alth status and outcomes, we also controlled for income and education factors (Braveman & Tarimo, 2002). Proxy measures of illness severity were em ployed in the analysis to further control for differences among patient populations. Se lf-perceived mental and physical health status and number of comorbid ities were used to control for illness severity. Selfperceived mental health status and self-perceived physical he alth status were variables defined in MEPS and these are considered risk factors in health care outcomes (Iezzoni, 2003). Self-perceived mental and physical health status were reported by patients on a likert scale of excellent, very good, good, fair, and poor. Comorbidites were a significant consideration because patients with comorbid ities tend to have higher risks of death, complications, functional impairments, and hi gher health service use (Iezzoni, 2003). Comorbidities were determined from the MEPS HC medical conditions file in the number of different ICD-9 codes in an individuals file were tallied. Health insurance status was an additional variable that was created in order to control for health service util ization. The MEPS HC full year consolidated file was used to identify patients who were insured (i.e., insu red all months of the year), intermittently insured (i.e., at least one mont h of the year without health insurance), and uninsured (i.e., no health insurance for all months of the year). This was a control variable because it is expected that individuals insured throughout the year would have higher expenditures than those intermittently insured and uninsured throughout the year.

PAGE 23

14 Statistical Analyses In order to determine the relationship be tween comorbid depression or anxiety and health care expenditures in pulmonary and cardi ac patients, separate log-linear multiple regressions were used for pulmonary patients and cardiac patients, with total health care expenditures and medical expenditures as separate outcomes. Demographics, socioeconomic factors, physical and mental he alth status, insurance status, and number of comorbid conditions were control variables in each analysis. For significant results, smearing estimation was used to determine differences between groups in dollars. Next, the relationship between mental health treatment and health care expenditures in pulmonary or cardiac patients with depres sion or anxiety were determined with separate log-linear multiple regressions. De mographics, socioeconomic factors, physical and mental health status, insurance status, and number of comorbid conditions were control variables in each analysis. Smear ing estimation was employed for significant results to obtain group differences in dollars. Finally, the relationship between mental hea lth treatment and health care utilization in pulmonary or cardiac patients with depres sion or anxiety were determined with separate negative binomial regressions. Th e health care utilization variables were number of office-based provide r visits, number of outpatient hospital visits, number of inpatient nights at discharge, and numb er of emergency room visits. Again, demographics, socioeconomic factors, physical and mental health stat us, insurance status, and number of comorbid conditions were c ontrol variables in each analysis. For each of the above analyses, Stata stat istical software was used (StataCorp, 2002). Sample weights were employed to take into account the MEPS sampling procedures and to produce nationa lly representative estimates.

PAGE 24

15 CHAPTER 3 RESULTS Pulmonary Conditions Comorbidity and Expenditures Participant characteristics The pulmonary sample used to determine the relationship between comorbid depression or anxiety and health care expenditures consisted of 7,866 respondents. In the sa mple, 649 respondents had depression and 358 respondents had anxiety (see Table 33 for descriptive statistics). Results To determine the relationship between comorbid depression or anxiety and total health care expenditures after adjusting fo r demographics (age, sex, race/ethnicity), socioeconomic status (years of education a nd income), insurance status, and illness severity (perceived physical a nd mental health status, and comorbid conditions), a loglinear multiple regression was conducted. A significant positive relationship between the presence of depression in pulmonary patie nts and total health care expenditures was found (t = 2.60, p = .01), but anxiety was not si gnificantly related to total health care expenditures (t = 1.29, p = 0.10). That is, total health care expenditures of the group with comorbid depression was $8,338.52 more than the group without de pression (see Table 3-7). Despite non-significance, the total health care expenditures for the group with comorbid anxiety was $12,307 more than the group without anxiety. To determine the relationship between como rbid depression or anxiety and medical expenditures after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education a nd income), insurance status, a nd illness severity (perceived

PAGE 25

16 physical and mental health status, and co morbid conditions), a log-linear multiple regression was conducted. A significant nega tive relationship betw een the presence of anxiety and medical expenditures only was found (t = -1.91, p = 0.03), whereas the presence of depression yiel ded a non-significant relations hip to medical expenditures only (t = -.56, p = .29). That is, medical expenditures for the group with comorbid anxiety was $3,331.77 less than th e group without anxiety (see Table 3-8). Although not statistically significant, the medical expenditu res for the group with comorbid depression was $3,123 less than the group without comorbid depression. Depression Treatment and Expenditures Participant characteristics. The sample used to determine the relationship between treatment of depression and health care expenditures was 649 respondents with a pulmonary condition and depression. In th e sample, 100 respondents received mental health treatment (see Table 3-4 for sample characteristics). Results. To determine the relationship between depression treatment and total health care expenditures after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a log-linear multiple regression was conducted. The relationship between depression treatment and total health care expenditures was non-signi ficant (t = .54, p = .30) (see Table 3-7). The group who r eceived depression treatment cost $13,752, whereas the group who had not received depression treatment cost $5,413. To determine the relationship between depression treatment and medical expenditures only after adjusting for dem ographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity

PAGE 26

17 (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a log-linear multiple regression was conducted. A significant negative relationship between depression treatment a nd medical expenditures only was found (t = 3.31, p = .00). That is, with depression tr eatment, medical expenditures decreased by $6,208.39 (see Table 3-8). Depression Treatment and Health Care Utilization Using the same sample of pulmonary condition respondents with comorbid depression, the relationships between depres sion treatment and various measures of health care utilization (number of office-base d provider visits, outpa tient hospital visits, inpatient nights, and emergency room vi sits) were determined (see Table 3-9). Office-based provider visits results. To determine the relationship between depression treatment and number of office-b ased provider visits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and anxiety treatm ent, a negative binomial regression was conducted. The relationship between depression treatment and number of office-based provider visits was non-signi ficant (t = -.17, p = .43). The treatment group had 12.66 office-based provider visits whereas the non-treatment group had 13.26 visits. Outpatient hospital visits results. To determine the relationship between depression treatment and numb er of outpatient hospital vi sits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and anxiety treatm ent, a negative binomial regression was conducted. The relationship between depressi on treatment and number of outpatient

PAGE 27

18 hospital visits was non-significant (t = .55, p = .29). The treatment group had 1.67 outpatient hospital visits, whereas th e non-treatment group had 2.19 visits. Inpatient nights results. To determine the relationship between depression treatment and number of inpatient nights af ter adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health status, and comorbid conditions), and anxiety treatment, a negative binomial regression was conducted. The relationship between depression treatment a nd number of inpatient night s was non-significant (t = .11, p = .45). The group that received depr ession treatment ha d an average of 1.18 inpatient nights, whereas the group who did not receive treatment ha d an average of 1.49 inpatient nights. Emergency room results. To determine the relationship between depression treatment and number of emergency room vis its after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (y ears of education and income), insurance status, illness severity (per ceived physical and mental health status, and comorbid conditions), and anxiety treatment, a nega tive binomial regression was conducted. The relationship between depression treatment a nd number of emergency room visits was non-significant (t = -.13, p = .45). The group who had received depression treatment had .37 emergency room visits, whereas the group who had not received depression treatment had .47 emergency room visits. Anxiety Treatment and Expenditures Participant characteristics. The sample used to determine the relationship between anxiety treatment and expenditu res had 358 respondents with pulmonary

PAGE 28

19 conditions and anxiety. In the sample, ther e were 60 respondents who received mental health treatment (see Table 3-4 for sample characteristics). Results. To determine the relationship between anxiety treatment and total health care expenditures after adjusting for dem ographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health stat us, and comorbid conditions), and depression treatment, a log-linear multiple regression was conducted. A significant positive relationship between anxiety treatment and to tal health care expend itures was found (t = 1.83, p = .04). That is, the group who receiv ed treatment for anxiety had $4,442 more total expenditures than the group who had not received treatment (see Table 3-7). To determine the relationship between a nxiety treatment and medical expenditures only after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance st atus, illness severity (perceived physical and mental health status, and comorbid conditions ), and depression treatment, a log-linear multiple regression was conducted. The rela tionship between anxiety treatment and medical expenditures only was non-significan t (t = -.92, p = .18) (see Table 3-8). Although statistically non-signi ficant, the group who received anxiety treatment had $3,209 total health care expenditu res less than the group who had not received anxiety treatment. Anxiety Treatment and Health Care Utilization Using the same sample of pulmonary condition respondents with comorbid anxiety, the relationships between anxiety treatmen t and various measures of health care utilization (number of office-based provider visits, outpatient hospi tal visits, inpatient days, and emergency room visits) were determined (see Table 3-9).

PAGE 29

20 Office-based provider visits results. To determine the relationship between anxiety treatment and number of office-based provider visits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and depression trea tment, a negative binomial regression was conducted. The overall model in this anal ysis was non-significan t (F = 1.23, p = .29). Outpatient hospital visits results. To determine the relationship between anxiety treatment and number of outpatient hospital vi sits after adjusting fo r demographics (age, sex, race/ethnicity), socioeconomic status (y ears of education and income), insurance status, illness severity (per ceived physical and mental health status, and comorbid conditions), and depression treatment, a ne gative binomial regr ession was conducted. There was a significant negative relationship between anxiety treatment and the number of outpatient hospital visits (t = -2.96, p = .00). The incidenc e rate of outpatient hospital visits was .39 times lower with anxiety treatment. Inpatient nights results. To determine the relationship between anxiety treatment and number of inpatient nights af ter adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a negative binomial regressi on was conducted. The relationship between anxiety treatment and number of inpatient nights was non-si gnificant (t = 1.03, p = .15). The number of inpatient nights was 1.49 for th e group that received anxiety treatment, whereas the group who had not received anxiety treatment had 1.16 visits.

PAGE 30

21 Emergency room results. To determine the relationship between anxiety treatment and number of emergency room visits afte r adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health status, and comorbid conditions), and depression treatment, a negative binomial regression was conducted. The relationship between anxiety treatment and th e number of emergenc y room visits was non-significant (t = -.21, p = .42). The group who had received anxiety treatment had .48 emergency room visits and the group who had not received anxiety treatment had .52 visits. Cardiac Conditions Comorbidity and Expenditures Participant characteristics The cardiac conditions sample used to determine the relationship between comorbid depression or anxiety and health care expenditures consisted of 2,403 respondents. In the sample, 293 respondents had depression (see Table 3-5 for sample characteristics). Results. To determine the relationship between comorbid depression or anxiety and total health care expenditures after adjusting fo r demographics (age, sex, race/ethnicity), socioeconomic status (years of education a nd income), insurance status, and illness severity (perceived physical a nd mental health status, and comorbid conditions), a loglinear multiple regression was conducted. The relationship between the presence of depression and total health care expenditure s was non-significant (t = 1.30, p = .10), as was the relationship between anxiety and tota l health care expenditu res (t = 1.30, p = .10) (see Table 3-10). The depressed group cost $969 more than the non-depressed group, and the anxiety group cost $5,186 mo re than the non-anxiety group.

PAGE 31

22 To determine the relationship between comorbid depression or anxiety and medical expenditures only afte r adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education a nd income), insurance status, and illness severity (perceived physical a nd mental health status, and comorbid conditions), a loglinear multiple regression was conducted. The relationship between the presence of depression and medical expenditures only wa s non-significant (t = -.87, p = .19), as was the relationship between anxiet y and total health care expendi tures (t = .41, p = .34) (see Table 3-11). The depressed group cost $8,339 more than the non-depressed group, and the anxiety group cost $313 more than the non-anxiety group. Depression Treatment and Expenditures Participant characteristics The sample used to determine the relationship between treatment of depression and health care expenditures wa s 293 respondents with cardiac conditions and depressi on. In the sample, 34 respondents had mental health treatment for depression (see Table 3-6 for sample characteristics). Results To determine the relationship between depression treatment and total health care expenditures after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a log-linear multiple regression was conducted. The relationship between depression treatment and total health care ex penditures was non-signifi cant (t = -.08, p = .47) (see Table 3-10). The group that receiv ed depression treatment cost $7,466 less than the group who had not received treatment. To determine the relationship between depression treatment and medical expenditures only after adjusting for dem ographics (age, sex, race/ethnicity),

PAGE 32

23 socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a log-linear multiple regression was conducted. The relationship between depression treatment and medical expenditu res only was non-significant (t = -1.06, p = .15) (see Table 3-11). The group who had rece ived depression treatment cost $8,900 less than the group who had not received depression treatment. Depression Treatment and Health Care Utilization Using the same sample of cardiac conditi on respondents with comorbid depression, the relationships between depr ession treatment and various measures of health care utilization (number of office-based provider visits, outpatient hospital visits, hospital inpatient nights, and emergency room vi sits) were determined (see Table 3-12). Office-based provider visits results To determine the relationship between depression treatment and number of office-b ased provider visits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and anxiety treatm ent, a negative binomial regression was conducted. The relationship between depression treatment and number of office-based provider visits was non-significan t (t =.23, p = .41). The gro up that received depression treatment had 14.81 office-based provider visi ts and the group that did not receive depression treatment had 14.99 visits. Outpatient hospital visits results To determine the relationship between depression treatment and numb er of outpatient hospital vi sits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance status, illness severity (p erceived physical and mental health status,

PAGE 33

24 and comorbid conditions), and anxiety treatm ent, a negative binomial regression was conducted. The relationship between depressi on treatment and number of outpatient hospital visits was non-significant (t = -1.29, p = .10). The group that received depression treatment had 1.16 outpatient hos pital visits and the group who had not received treatment had 3.23 visits. Inpatient nights results. To determine the relationship between depression treatment and number of inpatient nights af ter adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health status, and comorbid conditions), and anxiety treatment, a negative binomial regression was conducted. The relationship between depression treatment and inpatient nights was non-significan t (t = .16, p = .44). The group who received depression treatment had 1.92 inpatient nights, whereas the group who had not received depression trea tment had 1.81 inpatient night stays. Emergency room results. To determine the relationship between depression treatment and number of emergency room vis its after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (y ears of education and income), insurance status, illness severity (per ceived physical and mental health status, and comorbid conditions), and anxiety treatment, a nega tive binomial regression was conducted. The overall model in this analysis was nonsignificant (F = 1.67, p = .10). The group who received depression treatment had 1.05 emerge ncy room visits and the group who had not received depression trea tment had .53 visits. Anxiety Treatment and Expenditures Participant characteristics The sample used to determine the relationship between anxiety treatment and expenditu res included 175 respondents with cardiac

PAGE 34

25 conditions and anxiety. There were 19 respondents who received mental health treatment for anxiety (see Table 3-6 for sample characteristics). Results To determine the relationship between anxiety treatment and total health care expenditures after adjusting for dem ographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health stat us, and comorbid conditions), and depression treatment, a log-linear multiple regression was conducted. The relationship between anxiety treatment and total health care e xpenditures was non-significant (t = .91, p = .19) (see Table 3-10). The group who had received anxiety treatment cost $5,186 more than the group who had not received anxiety treatment. To determine the relationship between a nxiety treatment and medical expenditures only after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (years of education and income), insurance st atus, illness severity (perceived physical and mental health status, and comorbid conditions ), and depression treatment, a log-linear multiple regression was conducted. The rela tionship between anxiety treatment and medical expenditures only was non-significan t (t = .91, p = .19) (see Table 3-11). The group who had received anxiety treatment co st $11,292 more than the group who had not received treatment. Anxiety Treatment and Health Care Utilization Using the same sample of respondents w ith cardiac conditions and anxiety, the relationships between an xiety treatment and various measur es of health care utilization (number of office-based provide r visits, outpatient hospital vi sits, inpatient nights, and emergency room visits) were determined (see Table 3-12).

PAGE 35

26 Office-based provider visits results To determine the relationship between anxiety treatment and number of office-based provider visits after adjusting for demographics (age, sex, race/ethnicity), so cioeconomic status (y ears of education and income), insurance status, illness severity (p erceived physical and mental health status, and comorbid conditions), and depression trea tment, a negative binomial regression was conducted. The relationship between anxiety treatment and number of office-based provider visits was non-significant (t = 1.33, p = .10). The group who received anxiety treatment had 9.98 office-based provider vi sits and the group w ho had not received treatment had 13.34 visits. Outpatient hospital visits results To determine the relationship between anxiety treatment and number of outpatient hospital vi sits after adjusting fo r demographics (age, sex, race/ethnicity), socioeconomic status (y ears of education and income), insurance status, illness severity (per ceived physical and mental health status, and comorbid conditions), and depression treatment, a ne gative binomial regr ession was conducted. The relationship between anxiety treatment and number of outpatient hospital visits was non-significant (t = 1.05, p = .15). The group who received anxiety treatment had 1.21 outpatient hospital visits and the group who had not receiv ed anxiety treatment had .89 visits. Inpatient nights results. To determine the relationship between anxiety treatment and number of inpatient nights af ter adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health st atus, and comorbid conditions), and anxiety treatment, a negative binomial regressi on was conducted. The relationship between

PAGE 36

27 anxiety treatment and inpatient nights was non-significant (t = -.65, p = .26). The group that received anxiety treatment had 1.93 inpa tient night stays, whereas the group who did not receive treatment had 2.21 inpatient night stays. Emergency room results. To determine the relationship between anxiety treatment and number of emergency room visits afte r adjusting for demographics (age, sex, race/ethnicity), socioeconomic status (years of education and income), insurance status, illness severity (perceived physical and mental health status, and comorbid conditions), and depression treatment, a negative binomial regression was conducted. The relationship between anxiety treatment and number of emergency room visits was nonsignificant (t = -.97, p = .17). The group who received anxiety treatment had .44 emergency room visits and the group who did not receive treatment had .74 visits.

PAGE 37

28 Table 3-1. Clinical Classification Codes and Diagnostic Categories. Medical Condition Classification Code Clinical Classification Software Diagnosis Category Pulmonary conditions 127 Chronic obstructive pulmonary disease and bronchiectasis 128 Asthma 129 Aspiration pneumonitis, food/vomitus 130 Pleurisy, pneumothorax, pulmonary collapse 131 Respiratory failure, insufficiency, arrest (adult) 132 Lung disease due to external agents 133 Other lower respiratory disease 134 Other upper respiratory disease Cardiac conditions 96 Heart valve disorders 97 Peri-, endo-, and myocarditis, cardiomyopathy (except that caused by tuberculosis) 100 Acute myocardial infarction 101 Coronary atherosclerosis and other heart disease 102 Nonspecific chest pain 103 Pulmonary heart disease 104 Other and ill-defined heart disease 105 Conduction disorders 106 Cardiac dysrhythmias 107 Cardiac arrest and ventricular fibrillation 108 Congestive heart failure, nonhypertensive

PAGE 38

29 Table 3-2. Antidepressant and Anti-anxiety Medication Names. Drug Class Generic Name Brand Name Antidepressant Imipramine Tofanil Desipramine Norpramin Amitriptyline Elavil Nortriptyline Aventyl, Pamelor Protriptyline Vivacil Trimipramine Surmontil Doxepin Sinequan, Adapin Maprotiline Ludiomil Amoxapine Asendin Trazodone Desyrel Fluoxetine Prozac Bupropion Wellbutrin Sertraline Zoloft Paroxetine Paxil Venlafaxine Effexor Nefazodone Serzone Fluvoxamine Luvox Phenelzine Nardil Tranylcypromine Parnate Anti-anxiety Diazepam Valium Chlordiazepoxide Librium Flurazepam Dalmane Azepam Centrax Clorazepate Tranxene Temazepam Klonopin Lorazepam Ativan Alprazolam Xanax Oxazepam Serax Triazolam Halcyon Estazolam ProSom Quazepam Doral Zolpidem Ambient Buspirone BuSpar Hydroxyzine Atarax, Vistaril Diphenhydramine Benadryl Propanolol Inderal Atenolol Tenormin Clonidine Catapres Source: Handbook of Clinical Psychopharmacology for Therapists

PAGE 39

30 Table 3-3. Descriptive St atistics of Pulmonary Re spondents (Comorbidity) Variables No Depression Depression Comorbidity No Anxiety Anxiety Comorbidity N 7217 649 7508 358 Mean Age (SD) 33.14 (23.36) 46.81 (17.03) 33.72 (23.28) 45.86 (18.1) Mean Yrs Education (SD) 9.78 (4.81) 11.82 (3.61) 9.88 (4.79) 11.47 (3.67) Mean Income $ 16,889 19,385 17,025 18,556 % Male 45.4 25.6 44.7 24.3 % Female 54.6 74.4 55.3 75.7 % Caucasian 80.7 86.9 80.9 88.3 % African American 14.1 9.0 14.0 7.3 % Asian 3.7 1.8 3.6 1.7 % Other 1.5 2.2 1.5 2.8 % Hispanic 22.6 19.0 22.7 14.0 % No Comorbidity 53.7 41.6 51.7 32.7 % One Comorbidity 41.5 46.4 41.9 46.4 % Two + Comorbidity 4.8 12.1 6.4 21.0 % Uninsured 9.5 8.0 9.6 5.6 % Intermittent Insured 14.1 13.7 14.1 14.2 % Insured 76.3 78.3 76.3 80.2 Modal Physical Health Very Good Good Very Good Good Modal Mental Health Excellent Good Excellent Good

PAGE 40

31 Table 3-4. Descriptive St atistics of Pulmonary Respondents (Treatment) Variables No Depression Treatment Depression Treatment No Anxiety Treatment Anxiety Treatment N 549 100 298 60 Mean Age (SD) 47.89 (16.94) 40.90 (16.37) 46.30 (17.76) 43.67 (19.74) Mean Yrs Education (SD) 11.82 (3.57) 11.84 (3.85) 11.45 (3.69) 11.58 (3.60) Mean Income $ 19,139 20,737 18,913 16,780 % Male 25.9 24.0 23.8 26.7 % Female 74.1 76.0 76.2 73.3 % Caucasian 87.6 83.0 88.6 86.7 % African American 9.1 9.0 7.0 8.3 % Asian 1.5 4.0 1.7 1.7 % Other 1.8 4.0 2.7 3.3 % Hispanic 19.7 15.0 14.4 11.7 % No Comorbidity 40.1 50 30.9 41.7 % One Comorbidity 47.5 40 46.3 46.7 % Two + Comorbidity 12.4 10 22.8 11.6 % Uninsured 8.0 8.0 6.4 1.7 % Intermittent Insured 12.9 18.0 15.1 10.0 % Insured 79.1 74.0 78.5 88.3 Modal Physical Health Good Very Good Good Good/ Very Good Modal Mental Health Good Good Good Fair

PAGE 41

32 Table 3-5. Descriptive Statistics of Responde nts with Cardiac Cond itions (Comorbidity) Variables No Depression Depression Comorbidity No Anxiety Anxiety Comorbidity N 2110 293 2228 175 Mean Age (SD) 60.05 (19.45) 58.64 (16.95) 60.01 (19.15) 58.12 (19.38) Mean Yrs Education (SD) 11.15 (3.69) 11.50 (3.50) 11.19 (3.69) 11.29 (3.43) Mean Income $ 20,500 16,602 20,377 15,542 % Male 48.3 31.4 47.4 30.9 % Female 51.7 68.6 52.6 69.1 % Caucasian 82.7 85.3 82.6 88.6 % African American 14.0 10.9 14.1 8.0 % Asian 2.1 2.0 2.2 1.1 % Other 1.1 1.7 1.1 2.3 % Hispanic 13.4 17.1 13.7 16.0 % No Comorbidity 42.8 33.1 40.6 28.0 % One Comorbidity 43.5 16.0 43.3 41.1 % Two + Comorbidity 13.7 19.4 16.0 30.8 % Uninsured 6.3 7.2 6.6 4.6 % Intermittent Insured 11.9 5.1 12.3 12.6 % Insured 81.8 78.2 81.2 82.9 Modal Physical Health Good Fair Good Good Modal Mental Health Good Good Good Good

PAGE 42

33 Table 3-6. Descriptive Sta tistics of Pulmonary Condition Respondents (Treatment) Variables No Depression Treatment Depression Treatment No Anxiety Treatment Anxiety Treatment N 259 34 156 19 Mean Age (SD) 59.03 (16.58) 40.9 (19.74) 58.88 (18.69) 40.90 (16.37) Mean Yrs Education (SD) 11.48 (3.57) 11.58 (3.60) 11.40 (3.35) 11.84 (3.85) Mean Income $ 16,610 16,780 15,280 20,737 % Male 30.9 35.3 31.4 26.3 % Female 69.1 64.7 68.6 73.7 % Caucasian 84.9 88.2 87.8 94.7 % African American 11.6 5.9 9.0 0 % Asian 1.9 2.9 1.3 0 % Other 1.5 2.9 1.9 5.3 % Hispanic 17.0 17.6 17.3 5.3 % No Comorbidity 33.2 32.4 26.9 36.8 % One Comorbidity 46.7 52.9 41.0 42.1 % Two + Comorbidity 20.1 14.7 32.1 21.0 % Uninsured 6.9 8.8 5.1 0 % Intermittent Insured 15.1 11.8 12.2 15.8 % Insured 78.0 79.4 82.7 84.2 Modal Physical Health Fair Fair Good Fair Modal Mental Health Good Fair Good Very Good

PAGE 43

34 Table 3-7. Statistical Results of Pulmonary Condition Respondents (Total Expenditures) T p-value Predicted Expenditures ($) Depression 13,752 No Depression .20 2.60 .01** 5,413 Anxiety 17,848 No Anxiety .10 1.29 .10 5,541 Depression Treatment 13,752 No Depression Treatment .10 .54 .30 5,413 Anxiety Treatment 10,696 No Anxiety Treatment .33 1.83 .04** 6,254 Table 3-8. Statistical Results of Pulm onary Condition Respondents (Medical Expenditures) T p-value Predicted Expenditures ($) Depression 7,089 No Depression -.05 -.56 .29 3,966 Anxiety 8,347 No Anxiety -.17 -1.91 .03** 5,015 Depression Treatment 2,722 No Depression Treatment -.66 -3.31 .00** 8,931 Anxiety Treatment 6,140 No Anxiety Treatment -.28 -.92 .18 9,349

PAGE 44

35 Table 3-9. Statistical Results of Pulmona ry Condition Respondents (Health Care Utilization) Office-Based Provider Visits Incidence Rate Ratio Z p-value Predicted Visit Count Depression Treatment 12.66 No Depression Treatment .98 -.17 .43 13.26 Anxiety Treatment 14.33 No Anxiety Treatment .10 1.29 .10 11.87 Outpatient Hospital Visits Depression Treatment 1.67 No Depression Treatment 1.23 .55 .29 2.19 Anxiety Treatment .61 No Anxiety Treatment .39 -2.96 .00** 1.28 Inpatient Nights at Discharge Depression Treatment 1.18 No Depression Treatment .96 -.11 .45 1.49 Anxiety Treatment 1.49 No Anxiety Treatment 1.48 1.03 .15 1.16 Emergency Room Visits Depression Treatment .37 No Depression Treatment .97 -.13 .45 .47 Anxiety Treatment .48 No Anxiety Treatment .95 -.21 .42 .52

PAGE 45

36 Table 3-10. Statistical Results of Cardiac Condition Respondents (Total Expenditures) T p-value Predicted Expenditures ($) Depression 16,436 No Depression .13 1.30 .10 15,467 Anxiety 24,047 No Anxiety .18 1.30 .10 14,921 Depression Treatment 9,475 No Depression Treatment -.03 -.08 .47 16,941 Anxiety Treatment 18,881 No Anxiety Treatment .35 .91 .19 13,695 Table 3-11. Statistical Results of Cardiac Condition Respondents (Med ical Expenditures) T p-value Predicted Expenditures ($) Depression 13,752 No Depression -.11 -.87 .19 5,413 Anxiety 13,898 No Anxiety .05 .41 .34 13,585 Depression Treatment 5,181 No Depression Treatment -.40 -1.06 .15 14,081 Anxiety Treatment 22,077 No Anxiety Treatment .41 .91 .19 10,785

PAGE 46

37 Table 3-12. Statistical Results of Cardiac Recipients (Health Care Utilization) Office-Based Provider Visits Incidence Rate Ratio Z p-value Predicted Visit Count Depression Treatment 14.81 No Depression Treatment 1.04 .23 .41 14.99 Anxiety Treatment 9.98 No Anxiety Treatment .78 -1.33 .10 13.34 Outpatient Hospital Visits Depression Treatment 1.16 No Depression Treatment .49 -1.29 .10 3.23 Anxiety Treatment 1.21 No Anxiety Treatment 1.77 1.05 .15 .89 Inpatient Nights at Discharge Depression Treatment 1.92 No Depression Treatment 1.05 .16 .44 1.81 Anxiety Treatment 1.93 No Anxiety Treatment .72 -.65 .26 2.21 Emergency Room Visits Depression Treatment 1.05 No Depression Treatment 2.00 1.83 .04 (overall model not significant) .53 Anxiety Treatment .44 No Anxiety Treatment .66 -.97 .17 .74

PAGE 47

38 CHAPTER 4 DISCUSSION The present study examined the relations hip between comorbid depression or anxiety and health care expenditures in pulmona ry or heart patients. As expected, it was found that depression increased total expenditu res in pulmonary patients, but there was no corresponding increase in medical expendi tures only. Because medical expenditures only excluded any medical event associated with a psychological diagnosis, it appears that depressed patients may not use more me dical services for their medical conditions, but perhaps they do use more psychological serv ices. Depressed patients may have more diagnoses of other psychological conditi ons that prompt service-seeking. Contrary to expectation, th e presence of anxiety in pulmonary patients decreased medical expenditures only, but there was no difference in to tal expenditures. Thus, it appears that anxious pulmonary patients do not use more health care services overall and in fact, they seek less health care services for their medical conditions. This could be because their anxiety inhibits them from seeking needed care. The main aim of the study was to examin e the medical cost offset effect in pulmonary or heart patients who sought tr eatment for depression or anxiety. This analysis revealed that depresse d pulmonary patients showed a cost offset effect, in that depressed patients who received mental health treatment showed a decrease in medical expenditures only. Further analysis revealed that this effect was not explained by a decrease in the number of outpatient hospital visits, inpatient hospita l nights, office-based provider visits, or emergency room visits. T hus, this study suggests th at the treatment of

PAGE 48

39 pulmonary patients with comorbid depression wo uld result in a cost o ffset effect not due to cost shifting from medical treatment to psychological treatment. Anxious pulmonary patients who received mental health treatment showed an unexpected increase in total health care e xpenditures; however, th ere was a reduction in outpatient hospital visits, supporting the idea that added psychologica l care would show a reduction in health care utiliz ation. The number of hospital inpatient nights, office-based provider visits, and emergency room visits were not significantly different between the treated and untreated groups. These results mi ght suggest that anxiet y patients are getting the psychological services they need and added care costs more, but because needed care is provided, utilization in the medical sector is reduced. Furthermore, treated patients may also be more apt to recognize their an xiety symptoms as pa rt of a psychological disorder, as opposed to a medical problem. Heart disease patients did not show any signi ficant effects in any of the analyses. However, it should be noted that the number of heart disease patients who received psychological treatment was less than pulmonary patients, which limited the power of the results from the heart disease group. Neverthele ss, in this study, the variation in observed cost-offset effects suggests that the issue of cost-offset may be complex and variable across different psychological and medical conditions. Limitations Several limitations of the pr esent study should be consid ered. First, the data structure of MEPS seems to be unreliable. The present analysis included the years 1999 to 2002. A previous analysis using only the years 2000 to 2002 revealed different results. When 1999 was added, the results changed. Previ ous results showed a cost offset effect for both depression and anxiety treatment in pulmonary patients with comorbid

PAGE 49

40 depression or anxiety, whereas the present results reveal a cost offset effect for only depression treatment in pulmonary patients. The addition of data fr om 1999 appeared to have changed the structure of the data set. Part of this instability could be due to cohort effects, as well as a difference in power to detect statistical signi ficance. Second, only a relatively small number of patients received ment al health treatment, particularly for the heart disease groups. There were only 19 a nd 34 heart disease respondents who received mental health treatment for anxiety and depr ession, respectively. Methodologically, this poses a difficulty in terms of reliable estimat es. Third, the validity of diagnostic coding is somewhat questionable because data was obtained through self-report. Fourth, aggregating multiple classification c odes and psychotropic medication with psychotherapy reduces the precision of the anal ysis. Fifth, treatment efficacy could not be determined from the data. Finally, it is important to remember the cross-sectional and correlational nature of the present anal ysis does not address causality. Implications The demonstration of cost offset eff ects has implications for the field of psychology and its utility in reducing or contai ning rising health care costs in America. Although psychologists would like to believe th at a cost offset effect holds across medical conditions and psychological conditio ns, the present data suggests that the relationship between mental hea lth treatment and cost offsets is not clear-cut. Using data from the MEPS is a useful way to examine potential cost offset effects for specific medical conditions because it provides larg e numbers of subjects, is nationally representative, and allows fo r both cross-sectional and long itudinal analyses. Results from further analyses on other medical conditions may help to further refine the nature of

PAGE 50

41 cost offsets. Because the MEPS allows for l ongitudinal analyses, next steps would be to determine cost offsets longitudinally. An argument is that using cost offset as the only measure of the value of psychological services is incomplete (Coyne and Thompson, 2003). Patients and families who make treatment gains for depression or anxiety and employers who observe increased productivity in their workers treated for depression or anxiety may feel that these benefits are worth the additional co sts of psychological se rvices. Thus, the effectiveness of treatment as measured by quality of life and work performance and attendance would be important outcomes to consider in addi tion to cost issues. Although treatment efficacy information is not available from the MEPS data, future research will need to address the important issue of effectiv e treatment and cost offsets. However, the MEPS would allow for the analysis of employ ment variables releva nt to the present discussion. In conclusion, the present study provided preliminary results on the cost offset effects of specific medical and psychological populations. Results indicated that cost offset issues are complex and the future direction of cost offset research will be focused on teasing apart this complexity.

PAGE 51

42 LIST OF REFERENCES Anderson, R. N. (2002). Deaths: Leading causes for 2000. Na tional vital statistics report, 50 (16). Hyattsville, MD: National Center for Health Statistics. Bickman, L. (1996). The evaluation of ch ildrens mental health managed care demonstration. Journal of the Mental He alth Administration, 23 : 7-15. Braveman, P., & Tarimo, E. (2002). Social in equalities in health within countries: Not only an issue for affluent nations. Social Science and Medicine, 54 (11): 1621-1635. Bromberg. J. I., Beasley, P. J., DAngelo, E. J., Landzberg, M., & DeMaso, D. R. (2003). Depression and anxiety in adults with congenital heart disease: A pilot study. Heart and Lung, 32 (2): 105-110. Bureau of Labor and Statistics. (1999-2002). Consumer price index for all urban consumers (CPI-U): U.S. city average, detailed expenditure categories (medical care). U.S. Department of Labor. Re trieved September, 2005, from http://www.bls.gov Carbone, L. A., Orav, E. J., Fricchione, G. L., & Borus, J. F. (2000). Psychiatric symptoms and medical utiliza tion in primary care patients. Psychosomatics, 41 (6): 512-518. Carlson, L. E., & Bultz, B. D. (2004). Efficacy and medical cost offset of psychological interventions in cancer care: Maki ng the case for economic analyses. PsychoOncology, 13 : 837-849. Chiles, J. A., Lambert, M. J., & Hatch, A. L. (1999). The impact of psychological interventions on medical cost o ffset: A meta-analytic review. Clinical Psychology: Science and Practice, 6 (2): 204-220. Collins, K. A., Westra, H. A., Dozois, D. J. A., & Burns, D. D. (2004). Gaps in accessing treatment for anxiety and depression: Challenges for the delivery of care. Clinical Psychology Review, 24 : 583-616. Coyne, J. C., & Thompson, R. (2003). Psychol ogists entering prim ary care: Manhattan cannot be bought for $24 worth of beads. Clinical Psychology: Science and Practice, 10 (1): 102-108. Follette, W. T., & Cummings, N. A. (1968). Ps ychiatric services a nd medical utilization in a prepaid health plan setting. Medical Care, 5 : 25-35.

PAGE 52

43 Friedman, R., Sobel, D., Myers, P., Ca udill, M., & Benson, H. (1995). Behavioral medicine, clinical health psychology, and cost offset. Health Psychology, 14 (6): 509-518. Harter, M. C., Conway, K. P., Merikangas, K. R. (2003). Associations between anxiety disorders and physical illness. European Archives of Psychiatry and Clinical Neuroscience, 253 : 313-320. Hunsley, J. (2003). Cost-effectiveness and medical cost-offset considerations in psychological service provision. Canadian Psychology, 44 (1): 61-73. Iezzoni, L. I. (2003). Risk adjustment for measuring health care outcomes, 3rd edition. Chicago, IL: Health Administration Press. Katon, W. J. (2003). Clinical and health services relationships between major depression, depressive symptoms, and general medical illness. Society of Biolog ical Psychiatry, 54 : 216-226. Kunik, M. E., Roundy, K., Veazey, C., Souche k, J., Richardson, P., Wray, N. P., & Stanley, M. A. (2005). Surp risingly high prevalence of anxiety and depression in chronic breathing disorders. Chest, 127 (4): 1205-1211. Mumford, E., Schlesinger, H. J., Glass, G. V., Patrick, C., & Cuerdon, T. (1984). A new look at evidence about reduced cost of medical utilizati on following mental health treatment. American Journal of Psychiatry, 141 : 1145-1158. Olin, G. L., & Rhoades, J. A. (2005). The five most costly medical conditions, 1997 and 2002: Estimates for the U.S. civilian noni nstitutionalized popul ation. Statistical brief #80. Agency for Healthcare Research and Quality. Rockville, MD: Retrieved August, 2005, from http://www.meps.ahrq.gov/papers/st80/stat80.pdf Otto, M. W. (1999). Psychol ogical interventions in the age of managed care: A commentary on medical cost offsets. Clinical Psychology: Science and Practice, 6 (2): 239-241. Simon, G. E., Revicki, D., Heiligenstein, J., Grothaus, L., VonKorff, M., Katon, W. J., & Hylan, T. R. (2000). Recovery from depr ession, work productivity, and health care costs among primary care patients. General Hospital Psychiatry, 22 : 153-162. StatCorp. Stata Statistical Software: Release 9.0 Special Edition. College Station, TX: Stata Corporation, 2002. Sturm, R. (2001). Economic grand rounds: The myth of medical cost offset. Psychiatric Services, 52 : 738-740. Thorpe, K. E., Florence, C. S., & Joski, P. (2004). Which medical conditions account for the rise in health care spending? Health Affairs web exclusive : Retrieved August, 2005, from http://content.healthaffairs.org/cgi/content/full/hlthaff.w4.437/DC1

PAGE 53

44 Van Ede, L., Yzermans, C. J., & Brouwer, H. J. (1999). Prevalence of depression in patients with chronic obstructive pulm onary disease: A systematic review. Thorax, 54 : 688-692. Wells, K. B., Sturm, R., Sherbourne, C. D., & Meredith, L. S. (1996). Caring for Depression. Cambridge, MA: Harvard University Press. World Health Organization. (2006). Chronic Conditions: The Economic Impact. Retrieved December, 2005, from http://www.who.int/chronic_condi tions/economics/en/index.html

PAGE 54

45 BIOGRAPHICAL SKETCH Andrea Meredith Lee graduated with a Bachel or of Arts (first class honors) degree in psychology in October 2004 from Simon Fraser University in Burnaby, British Columbia, Canada. She plans to pursue a doctoral degree in c linical and health psychology at the University of Florida. He r academic interests lie in health psychology and health policy.


Permanent Link: http://ufdc.ufl.edu/UFE0014363/00001

Material Information

Title: Medical Cost Offset Effects in Pulmonary and Cardiac Patients with Depression or Anxiety
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0014363:00001

Permanent Link: http://ufdc.ufl.edu/UFE0014363/00001

Material Information

Title: Medical Cost Offset Effects in Pulmonary and Cardiac Patients with Depression or Anxiety
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0014363:00001


This item has the following downloads:


Full Text












MEDICAL COST OFFSET EFFECTS IN PULMONARY AND CARDIAC PATIENTS
WITH DEPRESSION OR ANXIETY















By

ANDREA M. LEE


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2006


































Copyright 2006

by

Andrea M. Lee

































This document is dedicated to my parents, Jack and Ellen Lee, and to my grandparents,
Harvey Lim, Lan Chan Lim, Sonny Lee, and Laura Lee.















ACKNOWLEDGMENTS

I would first like to thank my mentors, Robert G. Frank and Jeffrey S. Harman, for

their support and guidance on this masters thesis. They have been a tremendous help

throughout the process. I would also like to thank my parents, Jack and Ellen Lee, for

their unwavering support and firm belief in my abilities. Their support enables my

successes and gives me the strength to continue on this academic journey.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES .............. ..... ............................................... vii

A B S T R A C T .......................................... .................................................. v iii

CHAPTER

1 INTRODUCTION ............... .................................................... 1

2 D A TA A N D M ETH O D S ............................................................. ....................... 9

D ata S o u rc e ....................................................... ................ .. 9
V a ria b le s ....................................................................................................... 1 0
D ep en d ent V ariab les ............................................ ......................................... 10
Independent V ariables ............................................................. ............... 11
C control V ariables........... .............................................................. .. .... .. ... .. 12
Statistical A analyses ............................................................. ....... ...... 14

3 R E S U L T S ........................................................................................................1 5

Pulm onary C conditions ............................................. ....................................... 15
Com orbidity and Expenditures..................................................................... 15
Depression Treatment and Expenditures.........................................................16
Depression Treatment and Health Care Utilization......................................17
Anxiety Treatm ent and Expenditures ............................ ............................... 18
Anxiety Treatment and Health Care Utilization......................................19
C ardiac C on edition s ............... ......................................................... .. .....2 1
Com orbidity and Expenditures..................................................................... 21
Depression Treatment and Expenditures..........................................................22
Depression Treatment and Health Care Utilization............................................23
Anxiety Treatm ent and Expenditures............................................................... 24
Anxiety Treatment and Health Care Utilization......................................25

4 D ISCU SSION ................................................................ ...... .......... 38

L im stations ...................................................................................................... ....... 39
Im p licatio n s ................................................................4 0









L IST O F R E F E R E N C E S ...................................... .................................... ....................42

B IO G R A PH IC A L SK E T C H ...................................................................... ..................45
















LIST OF TABLES


Table p

3-1. Clinical Classification Codes and Diagnostic Categories. .......................................28

3-2. Antidepressant and Anti-anxiety Medication Names..............................................29

3-3. Descriptive Statistics of Pulmonary Respondents (Comorbidity)..............................30

3-4. Descriptive Statistics of Pulmonary Respondents (Treatment)................................31

3-5. Descriptive Statistics of Respondents with Cardiac Conditions (Comorbidity) ........32

3-6. Descriptive Statistics of Pulmonary Condition Respondents (Treatment) ...............33

3-7. Statistical Results of Pulmonary Condition Respondents (Total Expenditures) ........34

3-8. Statistical Results of Pulmonary Condition Respondents (Medical Expenditures) ...34

3-9. Statistical Results of Pulmonary Condition Respondents (Health Care Utilization) .35

3-10. Statistical Results of Cardiac Condition Respondents (Total Expenditures) ..........36

3-11. Statistical Results of Cardiac Condition Respondents (Medical Expenditures).......36

3-12. Statistical Results of Cardiac Recipients (Health Care Utilization)......................37















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

MEDICAL COST OFFSET EFFECTS IN PULMONARY AND CARDIAC PATIENTS
WITH DEPRESSION OR ANXIETY

By

Andrea M. Lee

May 2006

Chair: Robert G. Frank
Major Department: Clinical and Health Psychology

An intervention that reduces or prevents usual costs to the health care system is

called a medical cost offset or the cost offset effect. This study examined a sample of

pulmonary patients and cardiac patients to determine if a cost offset effect was apparent.

Three research questions were examined in this study: (1) whether depression or anxiety

in pulmonary or heart patients increased health care expenditures, (2) whether depression

or anxiety treatment decreased health care expenditures, and (3) whether depression or

anxiety treatment decreased the number of emergency room visits, inpatient days,

outpatient visits, or office-based provider visits. Data were obtained from the Medical

Expenditure Panel Survey (MEPS), a nationally representative survey of the US non-

institutionalized, civilian population. The results of the study revealed that in pulmonary

patients, the presence of depression increased expenditures, whereas the presence of

anxiety decreased expenditures. Furthermore, depressed pulmonary patients showed a

decrease in expenditures and this effect was not explained by a decrease in the number of









outpatient hospital visits, inpatient hospital nights, office-based provider visits, or

emergency room visits. Anxious pulmonary patients who received mental health

treatment showed an increase in expenditures; however, there was a reduction in

outpatient hospital visits in this sample. The results suggest that the medical cost offset

effect is not a constant phenomenon and appears to vary across psychological and

medical conditions.














CHAPTER 1
INTRODUCTION

The health care system in the United States is in a state of fiscal crisis. Total health

care spending is on the rise each year and there is no evidence that this trend will subside.

Between 1987 and 2000, health care spending among the noninstitutionalized US

population increased by about $199 billion (about 3 percent per year) (Thorpe, Florence,

& Joski, 2004). Mental health treatment has been cited as a solution to reducing rising

costs in the health care system (Friedman et al., 1995). The cost offset effect occurs

when an intervention reduces or prevents usual costs to the health care system.

There are many reasons for the rise in health care expenditures, one of which is the

rise in the number of individuals with chronic diseases. With the aging of the population,

the rise in chronic diseases has seen a dramatic rise in recent years (World Health

Organization [WHO], 2006). The majority of this change was attributed to spending for

cardiac disease, psychological conditions, pulmonary disorders, cancer, and trauma. In a

report by the Agency for Healthcare Research and Quality (AHRQ), the most expensive

type of chronic condition in 1997 and 2002 was cardiac conditions and the greatest

increase in health care expenditures occurred for pulmonary conditions and psychological

conditions (Olin & Rhoades, 2005).

The utilization pattern of patients with chronic medical diseases is complicated

when patients have comorbid psychological conditions. Due to the ongoing nature of

chronic diseases, patients who have one or more chronic diseases tend to be high utilizers

of the health care system and thereby expensive to the system. Although cardiac









conditions, pulmonary conditions, and psychological conditions have been identified as

expensive chronic conditions in the US health care system, when cardiac conditions and

pulmonary conditions are comorbid with psychological conditions, expenditures tend to

be greater than the cost of each condition alone. Primary care patients with psychological

conditions tend to utilize the health care system more often than patients without

comorbid psychological conditions. In studies of primary care patients, medical costs of

patients with depressive symptoms or major depression were higher than patients without

depression (Katon, 2003). For example, patients with congestive heart failure who also

present with depression have medical costs 26 to 29 percent higher than those with

congestive heart failure only.

The prevalence of psychological conditions comorbid with cardiac conditions or

pulmonary conditions is high. In particular, depression and anxiety are more frequent in

these medical populations. A study determining the associations between anxiety

disorders and physical illness found that both males and females with an anxiety disorder

have higher rates of cardiac disorders and pulmonary illnesses compared to individuals

without anxiety disorders (Harter, Conway, & Merikangas, 2003). In a pilot study on

individuals with congenital heart disease, 27.3 percent met the Diagnostic and Statistical

Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnostic criteria for depressive

episode and 9.1 percent met the DSM-IV criteria for generalized anxiety disorder (GAD)

(Bromberg et al., 2003). Depression comorbid with the respiratory condition, chronic

obstructive pulmonary disorder (COPD) is estimated to be up to four times more frequent

than in COPD alone (van Ede et al., 1999, as cited in Kunik et al., 2005). In a study









screening COPD patients for depression or anxiety, 80 percent screened positive for

depression, anxiety, or both (Kunik et al., 2005).

Despite the prevalence of depression and anxiety in medical populations, there is

evidence that the diagnosis and treatment of patients with depression or anxiety is

lacking. Epidemiological studies generally show that those with mental health problems

underutilize mental health care (Collins et al., 2004). Only 25 percent to 40 percent of

severely mentally ill patients access specialty mental health care and only 15.3 percent

receive adequate treatment (as cited in Collins et al., 2004). For those with anxiety and

mood disorders, the average delay for seeking professional treatment was 8 years.

Given the current trend toward inadequate detection and treatment of psychological

conditions, as well as the prevalence and high expenditures of patients with comorbid

psychological conditions and medical conditions, the question becomes whether treating

the mental health problems of medical patients would reduce expenditures in the health

care system. When an intervention reduces or prevents usual costs to the health care

system, this is called a medical cost offset or the cost offset effect (Carlson & Bultz,

2004).

Numerous studies have attempted to ascertain a medical cost-offset effect of mental

health care in primary care patients with psychological conditions. One of the first offset

studies was conducted by Follette and Cummings (1968). The medical records of 152

randomly selected adults who sought psychological services were examined. Data on

their health services utilization were collected one year prior to the beginning of

psychological treatment, as well as five years following treatment. Comparing the data to

a group matched for age, sex, socioeconomic status, and medical utilization rates who









had not received psychological treatment, it was found that this comparison group had

higher health care utilization rates over time, in addition to a reduction in health care

utilization for the group receiving psychological treatment.

Following the Follette and Cummings (1968) study, a series of cost-offset studies

were conducted. In 1984, two meta-analyses of the literature were published (Mumford

et al., 1984). One analysis was conducted on Blue Cross Blue Shield Federal Employee

Plan claims from 1974 to 1978, and the other analysis was conducted on 58 published

studies. The basic conclusion from these meta-analyses was that 85 percent of the studies

found a cost-offset effect, mainly observed in the reduction of inpatient days.

In another meta-analysis of 91 studies from 1967 to 1997, 90 percent of the studies

reported a reduction in medical utilization following mental health interventions (Chiles,

Lambert, & Hatch, 1999). Twenty-eight articles reported dollar savings and 31 percent

reported savings after taking into account the cost of mental health treatment. Overall, a

savings of about 20 to 30 percent was reported across the articles. The effect was most

evident for behavioral medicine and psychoeducational interventions.

Despite the evidence supporting the cost offset effect, several studies provide

evidence against the effect. The Medical Outcomes Study involved 22,000 outpatients

who were screened for several chronic medical conditions and these patients were

followed over time (Wells et al., 1996). One focus of the study was on the comparisons

between patients who received appropriate mental health treatment and those who had

not. The study produced no evidence of reduced inpatient or outpatient services. Instead,

cost-shifting occurred, in which the care received simply shifted from the patients'

general medical provider to a mental health provider.









The Fort Bragg Evaluation Project involved data collection of children and their

families over seven occasions to evaluate the effectiveness of comprehensive mental

health services to children and adolescents (Bickman, 1996). Compared to children

receiving care under traditional insurance, mental health expenditures were much higher

for children who received comprehensive care and this rise in cost was not offset by cost

savings elsewhere.

In two related studies looking at the medical utilization of patients with and without

psychological symptoms and the possible reduction in medical utilization in patients

referred to a psychiatry clinic, it was found that depressed and anxious patients who saw

a mental health provider had significantly more medical visits, emergency room visits,

and medical outpatient visits than patients with depression or anxiety who had not seen

mental health providers (Carbone et al., 2000). There were no significant differences in

medical costs between patients seeing mental health providers and those who had not.

However, both studies did not control for illness severity or comorbid medical or

psychiatric conditions. The patients in both studies had a relatively young median age

and one of the two studies had a small sample size. These factors may have made

medical cost-offset effects more difficult to demonstrate.

In a 2-year longitudinal study comparing adults who had major depression who had

remitted, improved but not remitted, or remained depressed, it was found that recovery

from depression was associated with increases in the probability of paid employment and

reductions in days missed from work due to illness (Simon et al., 2000). In terms of

health care costs, there were no significant differences in cost among groups in year one.

However, cost savings for patients with better outcomes in year two showed marginal









significance, suggesting the long-term nature of medical cost-offset. It should also be

noted that the study sample was derived from HMO clinics, which has implications for

cost offset effects. That is, to the extent that managed care restrictions reduce length of

treatment, dramatic cost offsets would also be reduced (Otto, 1999).

There are several limitations to cost offset studies and this study was an attempt to

address these limitations. First, when studies compare the costs of treated and untreated

patients, there may be a selection bias in which samples are not comparable (Sturm,

2001). That is, patients who received treatment may have different characteristics than

patients who did not receive treatment. If there is limited case-mix information in the

data, the selection bias is particularly pronounced. This is particularly problematic with

administrative datasets. In this study, the use of a large comprehensive dataset allowed

for greater control of potential confounding variables, such as illness severity and

comorbid medical conditions.

Second, cost offsets have traditionally been referred to as a general phenomenon

applying to all medical populations. Past cost offset research has not yet teased apart

which medical populations benefit from psychological interventions. This study is a

preliminary effort to identify specific cost offset effects in particular populations. The

populations of interest were pulmonary and cardiac patients who had comorbid

depression or anxiety. The dataset was a nationally representative sample, which allowed

for greater generalizability for the populations in question.

Rapid changes in healthcare financing and spending patterns necessitates frequent

review of offset effects refelcting current pricing in pharmacological and medical

treatments (Hunsley, 2003). It is difficult to generalize cost offset effects from one year









to another, due to pricing differences between years. This study used data from 2002,

which was the most recent data available at study onset. This study combined previous

years with 2002 data and prices of the previous years were inflated to reflect 2002 rates.

Research Questions

Research Question 1

After controlling for demographic characteristics, comorbidities, insurance status,

and perceived mental and physical health status, do pulmonary and cardiac patients with

comorbid depression or anxiety have higher health care expenditures than those with

pulmonary or cardiac conditions alone? The specific aim is to determine if, and to what

extent, depression or anxiety increases health care expenditures in pulmonary or cardiac

patients. The hypothesis is that the presence of depression or anxiety will correspond to

an increase in health care expenditures.

Research Question 2

After controlling for demographic characteristics, comorbidities, insurance status,

and perceived mental and physical health status, do pulmonary and cardiac patients with

comorbid depression or anxiety who received mental health treatment have lower health

care expenditures than those patients who did not receive mental health treatment? The

specific aim is to determine if, and to what extent, the treatment of depression or anxiety

affects health care expenditures in pulmonary or cardiac patients. The hypothesis is that

mental health treatment will correspond to a decrease in health care expenditures.

Research Question 3

After controlling for demographic characteristics, comorbidities, insurance status,

and perceived mental and physical health status, do pulmonary and cardiac patients with






8


comorbid depression or anxiety who received mental health treatment have decreased

health service utilization? The specific aim is to determine if, and to what extent,

treatment of depression or anxiety decreases health care service utilization in pulmonary

or cardiac patients. The hypothesis is that mental health treatment will correspond to a

decrease in health care service utilization.














CHAPTER 2
DATA AND METHODS

Data Source

Data were obtained from the Medical Expenditure Panel Survey (MEPS), a

nationally representative survey of the US non-institutionalized, civilian population,

sponsored by the Agency for Healthcare Research and Quality (AHRQ). The MEPS was

created in 1996 and consists of information on health services utilization, costs and

payments of health services, and health insurance information of respondents. The

MEPS Household Component (HC) obtains data from a sample of families and

individuals across the country. The MEPS HC has an overlapping panel design in which

each panel of households is interviewed five times during a two-year period. The HC

obtains detailed information on demographic characteristics, health conditions, health

status, use of medical care services, charges and payments, access to care, satisfaction

with care, health insurance coverage, income, and employment. During the second year

of the original panel, a new sample is drawn to create a new panel. Thus, two separate

panels are interviewed in the same year, which makes for an overlapping sampling

design. This thesis combined 1999, 2000, 2001, and 2002 MEPS data to assess the effect

of mental health treatment on health care expenditures and health services utilization.

As a nationally representative survey, each respondent in the MEPS data represents

a group of Americans that share similar characteristics used to sample from the

population. A sample weight for each case is developed to incorporate in the estimation

processes, in order to account for sample design, including unequal probability sampling









of the population (i.e., oversampling minority groups), as well as non-response rates and

partial responses from some survey participants. To maintain national representation, this

study used sample weights to test hypotheses.

Variables

Dependent Variables

Health care expenditures were divided into two variables: total health care

expenditures and medical expenditures. Expenditures in MEPS are defined as the sum of

direct payments for care provided during the year, including out-of-pocket payments and

payments by private insurance, Medicaid, Medicare, and other sources. Not included in

MEPS total expenditures are payments for over-the-counter drugs and for alternative care

services, as well as indirect payments not related to specific medical events, such as

Medicaid Disproportionate Share and Medicare Direct Medical Education subsidies.

Total expenditures are defined as total payments for all health care services included in

MEPS (outpatient department visits, office-based medical provider visits, prescribed

medicines, hospital inpatient visits, emergency room visits, home health, dental visits,

and other medical expenses). Medical expenditures are defined as total payments for all

health care services associated with medical conditions only. Put another way, any

medical expense associated with a psychological condition was excluded from the

calculation of the medical expenditure variable. When combining all four years of data

(1999 to 2002), both total expenditures and medical expenditures from 1999, 2000, and

2001 data were inflated to 2002 dollars using the consumer price index (BLS, 1999-

2002).









Health services utilization was defined using four separate variables: total number

of hospital outpatient visits, total hospital inpatient nights at discharge, total number of all

emergency room visits, and total number of office-based provider visits.

Independent Variables

The medical conditions of interest were identified using the MEPS HC medical

conditions file. The medical conditions file codes each self-reported medical condition

the individual experiences during the year. In order to preserve respondent

confidentiality, the condition codes provided on this file have been collapsed from fully-

specified codes to 3-digit code categories. Medical conditions were coded using the

International Classification of Diseases, Ninth Revision (ICD-9) codes and classification

codes (CC) as constructed using AHRQ's Clinical Classification Software (CCS). CCS

aggregates ICD-9 codes into clinically meaningful categories and these categories were

collapsed based on the clinical significance of categories, accurate reporting from

respondents, and the frequency of the reported condition.

From past research identifying spending and service use trends for various medical

conditions, pulmonary conditions were identified from the MEPS HC medical conditions

file using CC 127-134 and cardiac conditions were identified using CC 96, 97, 100-108

(Olin & Rhoades, 2005). For a breakdown of CC categories, see Table A-1.

Depression was identified using ICD-9 code 311. Although ICD-9 code 296

corresponds to depression, it also includes individuals with bipolar disorder. When

considering ICD-9 codes 296 and 311, 93 percent of respondents had a code of 311,

which corresponds to unspecified depression. The large number of patients with ICD-9

code 311 suggests that respondents are likely self-reporting depression (as opposed to

major depression), which then received a code of 311 instead of 296. Thus, ICD-9 code









311 was used to identify respondents with depression and ICD-9 code 296 was excluded.

Anxiety was identified using ICD-9 code 300.

Mental health treatment was defined in this study as psychotherapy or psychotropic

medications. Respondents who received psychotherapy were determined from the MEPS

HC office-based medical provider visit file and outpatient visit file. In the office-based

medical provider visit file, the best category for care that patient received was coded.

Respondents were considered to have undergone psychotherapy if the best category of

care was psychotherapy/mental health counseling. In the MEPS HC prescribed

medicines file, the presence of psychotropic medications were determined. If particular

anti-anxiety or anti-depressant drugs were coded under medication name (see Table A-2),

respondents were considered to be taking psychotropic medications for their mental

health condition.

Control Variables

Because some populations are at higher risk for poor health outcomes than others

and thus, higher health care expenditures, we adjusted for these differences to compare

health outcomes among different patient populations (lezzoni, 2003). Patient

demographic variables (age, sex, and race) and socioeconomic factors (education and

income), obtained directly from pre-existing MEPS variables, were used to control for

differences in mortality and morbidity. With regards to age, older persons generally have

worse clinical outcomes than younger persons (lezzoni, 2003). Sex is an important

control variable because men and women face different risks for certain diseases. Among

men and women 65 years of age and older, men have higher death rates than women for

cardiac disease and chronic lower respiratory disease (Anderson, 2002). Furthermore,

life spans for women tend to be longer on average than for men. Racial disparities in









health care outcomes were also taken into account in this study because differences in

disease prevalence and mortality exist among the races (lezzoni, 2003). Because of

socioeconomic disparities in health status and outcomes, we also controlled for income

and education factors (Braveman & Tarimo, 2002).

Proxy measures of illness severity were employed in the analysis to further control

for differences among patient populations. Self-perceived mental and physical health

status and number of comorbidities were used to control for illness severity. Self-

perceived mental health status and self-perceived physical health status were variables

defined in MEPS and these are considered risk factors in health care outcomes (lezzoni,

2003). Self-perceived mental and physical health status were reported by patients on a

likert scale of excellent, very good, good, fair, and poor. Comorbidites were a significant

consideration because patients with comorbidities tend to have higher risks of death,

complications, functional impairments, and higher health service use (lezzoni, 2003).

Comorbidities were determined from the MEPS HC medical conditions file in the

number of different ICD-9 codes in an individual's file were tallied.

Health insurance status was an additional variable that was created in order to

control for health service utilization. The MEPS HC full year consolidated file was used

to identify patients who were insured (i.e., insured all months of the year), intermittently

insured (i.e., at least one month of the year without health insurance), and uninsured (i.e.,

no health insurance for all months of the year). This was a control variable because it is

expected that individuals insured throughout the year would have higher expenditures

than those intermittently insured and uninsured throughout the year.









Statistical Analyses

In order to determine the relationship between comorbid depression or anxiety and

health care expenditures in pulmonary and cardiac patients, separate log-linear multiple

regressions were used for pulmonary patients and cardiac patients, with total health care

expenditures and medical expenditures as separate outcomes. Demographics,

socioeconomic factors, physical and mental health status, insurance status, and number of

comorbid conditions were control variables in each analysis. For significant results,

smearing estimation was used to determine differences between groups in dollars.

Next, the relationship between mental health treatment and health care expenditures

in pulmonary or cardiac patients with depression or anxiety were determined with

separate log-linear multiple regressions. Demographics, socioeconomic factors, physical

and mental health status, insurance status, and number of comorbid conditions were

control variables in each analysis. Smearing estimation was employed for significant

results to obtain group differences in dollars.

Finally, the relationship between mental health treatment and health care utilization

in pulmonary or cardiac patients with depression or anxiety were determined with

separate negative binomial regressions. The health care utilization variables were

number of office-based provider visits, number of outpatient hospital visits, number of

inpatient nights at discharge, and number of emergency room visits. Again,

demographics, socioeconomic factors, physical and mental health status, insurance status,

and number of comorbid conditions were control variables in each analysis.

For each of the above analyses, Stata statistical software was used (StataCorp,

2002). Sample weights were employed to take into account the MEPS sampling

procedures and to produce nationally representative estimates.














CHAPTER 3
RESULTS

Pulmonary Conditions

Comorbidity and Expenditures

Participant characteristics. The pulmonary sample used to determine the

relationship between comorbid depression or anxiety and health care expenditures

consisted of 7,866 respondents. In the sample, 649 respondents had depression and 358

respondents had anxiety (see Table 3-3 for descriptive statistics).

Results. To determine the relationship between comorbid depression or anxiety and

total health care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, and illness

severity (perceived physical and mental health status, and comorbid conditions), a log-

linear multiple regression was conducted. A significant positive relationship between the

presence of depression in pulmonary patients and total health care expenditures was

found (t = 2.60, p = .01), but anxiety was not significantly related to total health care

expenditures (t = 1.29, p = 0.10). That is, total health care expenditures of the group with

comorbid depression was $8,338.52 more than the group without depression (see Table

3-7). Despite non-significance, the total health care expenditures for the group with

comorbid anxiety was $12,307 more than the group without anxiety.

To determine the relationship between comorbid depression or anxiety and medical

expenditures after adjusting for demographics (age, sex, race/ethnicity), socioeconomic

status (years of education and income), insurance status, and illness severity (perceived









physical and mental health status, and comorbid conditions), a log-linear multiple

regression was conducted. A significant negative relationship between the presence of

anxiety and medical expenditures only was found (t = -1.91, p = 0.03), whereas the

presence of depression yielded a non-significant relationship to medical expenditures

only (t = -.56, p = .29). That is, medical expenditures for the group with comorbid

anxiety was $3,331.77 less than the group without anxiety (see Table 3-8). Although not

statistically significant, the medical expenditures for the group with comorbid depression

was $3,123 less than the group without comorbid depression.

Depression Treatment and Expenditures

Participant characteristics. The sample used to determine the relationship

between treatment of depression and health care expenditures was 649 respondents with a

pulmonary condition and depression. In the sample, 100 respondents received mental

health treatment (see Table 3-4 for sample characteristics).

Results. To determine the relationship between depression treatment and total

health care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a log-linear multiple regression was conducted. The relationship between

depression treatment and total health care expenditures was non-significant (t = .54, p =

.30) (see Table 3-7). The group who received depression treatment cost $13,752,

whereas the group who had not received depression treatment cost $5,413.

To determine the relationship between depression treatment and medical

expenditures only after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity









(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a log-linear multiple regression was conducted. A significant negative

relationship between depression treatment and medical expenditures only was found (t

3.31, p = .00). That is, with depression treatment, medical expenditures decreased by

$6,208.39 (see Table 3-8).

Depression Treatment and Health Care Utilization

Using the same sample of pulmonary condition respondents with comorbid

depression, the relationships between depression treatment and various measures of

health care utilization (number of office-based provider visits, outpatient hospital visits,

inpatient nights, and emergency room visits) were determined (see Table 3-9).

Office-based provider visits results. To determine the relationship between

depression treatment and number of office-based provider visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and anxiety treatment, a negative binomial regression was

conducted. The relationship between depression treatment and number of office-based

provider visits was non-significant (t = -.17, p = .43). The treatment group had 12.66

office-based provider visits, whereas the non-treatment group had 13.26 visits.

Outpatient hospital visits results. To determine the relationship between

depression treatment and number of outpatient hospital visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and anxiety treatment, a negative binomial regression was

conducted. The relationship between depression treatment and number of outpatient









hospital visits was non-significant (t = .55, p = .29). The treatment group had 1.67

outpatient hospital visits, whereas the non-treatment group had 2.19 visits.

Inpatient nights results. To determine the relationship between depression

treatment and number of inpatient nights after adjusting for demographics (age, sex,

race/ethnicity), socioeconomic status (years of education and income), insurance status,

illness severity (perceived physical and mental health status, and comorbid conditions),

and anxiety treatment, a negative binomial regression was conducted. The relationship

between depression treatment and number of inpatient nights was non-significant (t = -

.11, p = .45). The group that received depression treatment had an average of 1.18

inpatient nights, whereas the group who did not receive treatment had an average of 1.49

inpatient nights.

Emergency room results. To determine the relationship between depression

treatment and number of emergency room visits after adjusting for demographics (age,

sex, race/ethnicity), socioeconomic status (years of education and income), insurance

status, illness severity (perceived physical and mental health status, and comorbid

conditions), and anxiety treatment, a negative binomial regression was conducted. The

relationship between depression treatment and number of emergency room visits was

non-significant (t = -.13, p = .45). The group who had received depression treatment had

.37 emergency room visits, whereas the group who had not received depression treatment

had .47 emergency room visits.

Anxiety Treatment and Expenditures

Participant characteristics. The sample used to determine the relationship

between anxiety treatment and expenditures had 358 respondents with pulmonary









conditions and anxiety. In the sample, there were 60 respondents who received mental

health treatment (see Table 3-4 for sample characteristics).

Results. To determine the relationship between anxiety treatment and total health

care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and depression

treatment, a log-linear multiple regression was conducted. A significant positive

relationship between anxiety treatment and total health care expenditures was found (t =

1.83, p = .04). That is, the group who received treatment for anxiety had $4,442 more

total expenditures than the group who had not received treatment (see Table 3-7).

To determine the relationship between anxiety treatment and medical expenditures

only after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status

(years of education and income), insurance status, illness severity (perceived physical and

mental health status, and comorbid conditions), and depression treatment, a log-linear

multiple regression was conducted. The relationship between anxiety treatment and

medical expenditures only was non-significant (t = -.92, p = .18) (see Table 3-8).

Although statistically non-significant, the group who received anxiety treatment had

$3,209 total health care expenditures less than the group who had not received anxiety

treatment.

Anxiety Treatment and Health Care Utilization

Using the same sample of pulmonary condition respondents with comorbid anxiety,

the relationships between anxiety treatment and various measures of health care

utilization (number of office-based provider visits, outpatient hospital visits, inpatient

days, and emergency room visits) were determined (see Table 3-9).









Office-based provider visits results. To determine the relationship between

anxiety treatment and number of office-based provider visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and depression treatment, a negative binomial regression was

conducted. The overall model in this analysis was non-significant (F = 1.23, p = .29).

Outpatient hospital visits results. To determine the relationship between anxiety

treatment and number of outpatient hospital visits after adjusting for demographics (age,

sex, race/ethnicity), socioeconomic status (years of education and income), insurance

status, illness severity (perceived physical and mental health status, and comorbid

conditions), and depression treatment, a negative binomial regression was conducted.

There was a significant negative relationship between anxiety treatment and the number

of outpatient hospital visits (t = -2.96, p = .00). The incidence rate of outpatient hospital

visits was .39 times lower with anxiety treatment.

Inpatient nights results. To determine the relationship between anxiety treatment

and number of inpatient nights after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a negative binomial regression was conducted. The relationship between

anxiety treatment and number of inpatient nights was non-significant (t = 1.03, p = .15).

The number of inpatient nights was 1.49 for the group that received anxiety treatment,

whereas the group who had not received anxiety treatment had 1.16 visits.









Emergency room results. To determine the relationship between anxiety treatment

and number of emergency room visits after adjusting for demographics (age, sex,

race/ethnicity), socioeconomic status (years of education and income), insurance status,

illness severity (perceived physical and mental health status, and comorbid conditions),

and depression treatment, a negative binomial regression was conducted. The

relationship between anxiety treatment and the number of emergency room visits was

non-significant (t = -.21, p = .42). The group who had received anxiety treatment had .48

emergency room visits and the group who had not received anxiety treatment had .52

visits.

Cardiac Conditions

Comorbidity and Expenditures

Participant characteristics. The cardiac conditions sample used to determine the

relationship between comorbid depression or anxiety and health care expenditures

consisted of 2,403 respondents. In the sample, 293 respondents had depression (see

Table 3-5 for sample characteristics).

Results. To determine the relationship between comorbid depression or anxiety and

total health care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, and illness

severity (perceived physical and mental health status, and comorbid conditions), a log-

linear multiple regression was conducted. The relationship between the presence of

depression and total health care expenditures was non-significant (t = 1.30, p = .10), as

was the relationship between anxiety and total health care expenditures (t = 1.30, p = .10)

(see Table 3-10). The depressed group cost $969 more than the non-depressed group,

and the anxiety group cost $5,186 more than the non-anxiety group.









To determine the relationship between comorbid depression or anxiety and

medical expenditures only after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, and illness

severity (perceived physical and mental health status, and comorbid conditions), a log-

linear multiple regression was conducted. The relationship between the presence of

depression and medical expenditures only was non-significant (t = -.87, p = .19), as was

the relationship between anxiety and total health care expenditures (t = .41, p = .34) (see

Table 3-11). The depressed group cost $8,339 more than the non-depressed group, and

the anxiety group cost $313 more than the non-anxiety group.

Depression Treatment and Expenditures

Participant characteristics. The sample used to determine the relationship

between treatment of depression and health care expenditures was 293 respondents with

cardiac conditions and depression. In the sample, 34 respondents had mental health

treatment for depression (see Table 3-6 for sample characteristics).

Results. To determine the relationship between depression treatment and total

health care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a log-linear multiple regression was conducted. The relationship between

depression treatment and total health care expenditures was non-significant (t = -.08, p =

.47) (see Table 3-10). The group that received depression treatment cost $7,466 less than

the group who had not received treatment.

To determine the relationship between depression treatment and medical

expenditures only after adjusting for demographics (age, sex, race/ethnicity),









socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a log-linear multiple regression was conducted. The relationship between

depression treatment and medical expenditures only was non-significant (t = -1.06, p =

.15) (see Table 3-11). The group who had received depression treatment cost $8,900 less

than the group who had not received depression treatment.

Depression Treatment and Health Care Utilization

Using the same sample of cardiac condition respondents with comorbid depression,

the relationships between depression treatment and various measures of health care

utilization (number of office-based provider visits, outpatient hospital visits, hospital

inpatient nights, and emergency room visits) were determined (see Table 3-12).

Office-based provider visits results. To determine the relationship between

depression treatment and number of office-based provider visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and anxiety treatment, a negative binomial regression was

conducted. The relationship between depression treatment and number of office-based

provider visits was non-significant (t =.23, p = .41). The group that received depression

treatment had 14.81 office-based provider visits and the group that did not receive

depression treatment had 14.99 visits.

Outpatient hospital visits results. To determine the relationship between

depression treatment and number of outpatient hospital visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,









and comorbid conditions), and anxiety treatment, a negative binomial regression was

conducted. The relationship between depression treatment and number of outpatient

hospital visits was non-significant (t = -1.29, p = .10). The group that received

depression treatment had 1.16 outpatient hospital visits and the group who had not

received treatment had 3.23 visits.

Inpatient nights results. To determine the relationship between depression

treatment and number of inpatient nights after adjusting for demographics (age, sex,

race/ethnicity), socioeconomic status (years of education and income), insurance status,

illness severity (perceived physical and mental health status, and comorbid conditions),

and anxiety treatment, a negative binomial regression was conducted. The relationship

between depression treatment and inpatient nights was non-significant (t = .16, p = .44).

The group who received depression treatment had 1.92 inpatient nights, whereas the

group who had not received depression treatment had 1.81 inpatient night stays.

Emergency room results. To determine the relationship between depression

treatment and number of emergency room visits after adjusting for demographics (age,

sex, race/ethnicity), socioeconomic status (years of education and income), insurance

status, illness severity (perceived physical and mental health status, and comorbid

conditions), and anxiety treatment, a negative binomial regression was conducted. The

overall model in this analysis was non-significant (F = 1.67, p = .10). The group who

received depression treatment had 1.05 emergency room visits and the group who had not

received depression treatment had .53 visits.

Anxiety Treatment and Expenditures

Participant characteristics. The sample used to determine the relationship

between anxiety treatment and expenditures included 175 respondents with cardiac









conditions and anxiety. There were 19 respondents who received mental health treatment

for anxiety (see Table 3-6 for sample characteristics).

Results. To determine the relationship between anxiety treatment and total health

care expenditures after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and depression

treatment, a log-linear multiple regression was conducted. The relationship between

anxiety treatment and total health care expenditures was non-significant (t = .91, p = .19)

(see Table 3-10). The group who had received anxiety treatment cost $5,186 more than

the group who had not received anxiety treatment.

To determine the relationship between anxiety treatment and medical expenditures

only after adjusting for demographics (age, sex, race/ethnicity), socioeconomic status

(years of education and income), insurance status, illness severity (perceived physical and

mental health status, and comorbid conditions), and depression treatment, a log-linear

multiple regression was conducted. The relationship between anxiety treatment and

medical expenditures only was non-significant (t = .91, p = .19) (see Table 3-11). The

group who had received anxiety treatment cost $11,292 more than the group who had not

received treatment.

Anxiety Treatment and Health Care Utilization

Using the same sample of respondents with cardiac conditions and anxiety, the

relationships between anxiety treatment and various measures of health care utilization

(number of office-based provider visits, outpatient hospital visits, inpatient nights, and

emergency room visits) were determined (see Table 3-12).









Office-based provider visits results. To determine the relationship between

anxiety treatment and number of office-based provider visits after adjusting for

demographics (age, sex, race/ethnicity), socioeconomic status (years of education and

income), insurance status, illness severity (perceived physical and mental health status,

and comorbid conditions), and depression treatment, a negative binomial regression was

conducted. The relationship between anxiety treatment and number of office-based

provider visits was non-significant (t = -1.33, p = .10). The group who received anxiety

treatment had 9.98 office-based provider visits and the group who had not received

treatment had 13.34 visits.

Outpatient hospital visits results. To determine the relationship between anxiety

treatment and number of outpatient hospital visits after adjusting for demographics (age,

sex, race/ethnicity), socioeconomic status (years of education and income), insurance

status, illness severity (perceived physical and mental health status, and comorbid

conditions), and depression treatment, a negative binomial regression was conducted.

The relationship between anxiety treatment and number of outpatient hospital visits was

non-significant (t = 1.05, p = .15). The group who received anxiety treatment had 1.21

outpatient hospital visits and the group who had not received anxiety treatment had .89

visits.

Inpatient nights results. To determine the relationship between anxiety treatment

and number of inpatient nights after adjusting for demographics (age, sex, race/ethnicity),

socioeconomic status (years of education and income), insurance status, illness severity

(perceived physical and mental health status, and comorbid conditions), and anxiety

treatment, a negative binomial regression was conducted. The relationship between









anxiety treatment and inpatient nights was non-significant (t = -.65, p = .26). The group

that received anxiety treatment had 1.93 inpatient night stays, whereas the group who did

not receive treatment had 2.21 inpatient night stays.

Emergency room results. To determine the relationship between anxiety treatment

and number of emergency room visits after adjusting for demographics (age, sex,

race/ethnicity), socioeconomic status (years of education and income), insurance status,

illness severity (perceived physical and mental health status, and comorbid conditions),

and depression treatment, a negative binomial regression was conducted. The

relationship between anxiety treatment and number of emergency room visits was non-

significant (t = -.97, p = .17). The group who received anxiety treatment had .44

emergency room visits and the group who did not receive treatment had .74 visits.

















Table 3-1. Clinical Classification Codes and Diagnostic Categories.
Medical Condition Classification Code Clinical Classification
Software Diagnosis
Category
Pulmonary conditions 127 Chronic obstructive
pulmonary disease and
bronchiectasis
128 Asthma
129 Aspiration pneumonitis,
food/vomitus
130 Pleurisy, pneumothorax,
pulmonary collapse
131 Respiratory failure,
insufficiency, arrest (adult)
132 Lung disease due to
external agents
133 Other lower respiratory
disease
134 Other upper respiratory
disease
Cardiac conditions 96 Heart valve disorders
97 Peri-, endo-, and
myocarditis,
cardiomyopathy (except
that caused by tuberculosis)
100 Acute myocardial infarction
101 Coronary atherosclerosis
and other heart disease
102 Nonspecific chest pain
103 Pulmonary heart disease
104 Other and ill-defined heart
disease
105 Conduction disorders
106 Cardiac dysrhythmias
107 Cardiac arrest and
ventricular fibrillation
108 Congestive heart failure,
nonhypertensive









Table 3-2. Antidepressant and Anti-anxiety Medication Names.
Drug Class Generic Name Brand Name
Antidepressant Imipramine Tofanil
Desipramine Norpramin
Amitriptyline Elavil
Nortriptyline Aventyl, Pamelor
Protriptyline Vivacil
Trimipramine Surmontil
Doxepin Sinequan, Adapin
Maprotiline Ludiomil
Amoxapine Asendin
Trazodone Desyrel
Fluoxetine Prozac
Bupropion Wellbutrin
Sertraline Zoloft
Paroxetine Paxil
Venlafaxine Effexor
Nefazodone Serzone
Fluvoxamine Luvox
Phenelzine Nardil
Tranylcypromine Parnate
Anti-anxiety Diazepam Valium
Chlordiazepoxide Librium
Flurazepam Dalmane
Azepam Centrax
Clorazepate Tranxene
Temazepam Klonopin
Lorazepam Ativan
Alprazolam Xanax
Oxazepam Serax
Triazolam Halcyon
Estazolam ProSom
Quazepam Doral
Zolpidem Ambient
Buspirone BuSpar
Hydroxyzine Atarax, Vistaril
Diphenhydramine Benadryl
Propanolol Inderal
Atenolol Tenormin
Clonidine Catapres
Source: Handbook of Clinical Psychopharmacology for Therapists









Table 3-3. Descriptive Statistics of Pulmonary Respondents (Comorbidity)
No Depression Anxiety
Variables Depression Comorbidity No Anxiety Comorbidity
N 7217 649 7508 358
Mean Age (SD) 33.14 (23.36) 46.81 (17.03) 33.72 (23.28) 45.86 (18.1)
Mean Yrs 9.78(4.81) 11.82(3.61) 9.88(4.79) 11.47
Education (SD) (3.67)
Mean Income $ 16,889 19,385 17,025 18,556
% Male 45.4 25.6 44.7 24.3
% Female 54.6 74.4 55.3 75.7
% Caucasian 80.7 86.9 80.9 88.3
% African 14.1 9.0 14.0 7.3
American
% Asian 3.7 1.8 3.6 1.7
% Other 1.5 2.2 1.5 2.8
% Hispanic 22.6 19.0 22.7 14.0
% No 53.7 41.6 51.7 32.7
Comorbidity
% One 41.5 46.4 41.9 46.4
Comorbidity
% Two + 4.8 12.1 6.4 21.0
Comorbidity
% Uninsured 9.5 8.0 9.6 5.6
% Intermittent 14.1 13.7 14.1 14.2
Insured
% Insured 76.3 78.3 76.3 80.2
Modal Physical Very Good Good Very Good Good
Health
Modal Mental Excellent Good Excellent Good
Health









Table 3-4. Descriptive Statistics of Pulmonary Respondents (Treatment)
No
Variables Depression Depression No Anxiety Anxiety
Treatment Treatment Treatment Treatment
N 549 100 298 60
Mean Age (SD) 47.89 40.90 (16.37) 46.30 43.67
(16.94) (17.76) (19.74)
Mean Yrs 11.82 11.84 (3.85) 11.45 11.58
Education (SD) (3.57) (3.69) (3.60)
Mean Income $ 19,139 20,737 18,913 16,780
% Male 25.9 24.0 23.8 26.7
% Female 74.1 76.0 76.2 73.3
% Caucasian 87.6 83.0 88.6 86.7
% African 9.1 9.0 7.0 8.3
American
% Asian 1.5 4.0 1.7 1.7
% Other 1.8 4.0 2.7 3.3
% Hispanic 19.7 15.0 14.4 11.7
% No Comorbidity 40.1 50 30.9 41.7
% One 47.5 40 46.3 46.7
Comorbidity
% Two + 12.4 10 22.8 11.6
Comorbidity
% Uninsured 8.0 8.0 6.4 1.7
% Intermittent 12.9 18.0 15.1 10.0
Insured
% Insured 79.1 74.0 78.5 88.3
Modal Physical Good Very Good Good Good/
Health Very Good
Modal Mental Good Good Good Fair
Health










Table 3-5. Descriptive Statistics of Respondents with Cardiac Conditions (Comorbidity)
No Depression Anxiety
Variables Depression Comorbidity No Anxiety Comorbidity
N 2110 293 2228 175
Mean Age (SD) 60.05 58.64 60.01 (19.15) 58.12
(19.45) (16.95) (19.38)
Mean Yrs 11.15 11.50 11.19 (3.69) 11.29
Education (SD) (3.69) (3.50) (3.43)
Mean Income $ 20,500 16,602 20,377 15,542
% Male 48.3 31.4 47.4 30.9
% Female 51.7 68.6 52.6 69.1
% Caucasian 82.7 85.3 82.6 88.6
% African 14.0 10.9 14.1 8.0
American
% Asian 2.1 2.0 2.2 1.1
% Other 1.1 1.7 1.1 2.3
% Hispanic 13.4 17.1 13.7 16.0
% No 42.8 33.1 40.6 28.0
Comorbidity
% One 43.5 16.0 43.3 41.1
Comorbidity
% Two + 13.7 19.4 16.0 30.8
Comorbidity
% Uninsured 6.3 7.2 6.6 4.6
% Intermittent 11.9 5.1 12.3 12.6
Insured
% Insured 81.8 78.2 81.2 82.9
Modal Physical Good Fair Good Good
Health
Modal Mental Good Good Good Good
Health









Table 3-6. Descriptive Statistics of Pulmonary Condition Respondents (Treatment)
No Depression No Anxiety Anxiety
Variables Depression Treatment Treatment Treatment
Treatment
N 259 34 156 19
Mean Age (SD) 59.03 (16.58) 40.9 (19.74) 58.88 (18.69) 40.90 (16.37)
Mean Yrs 11.48 (3.57) 11.58 (3.60) 11.40 (3.35) 11.84 (3.85)
Education (SD)
Mean Income $ 16,610 16,780 15,280 20,737
% Male 30.9 35.3 31.4 26.3
% Female 69.1 64.7 68.6 73.7
% Caucasian 84.9 88.2 87.8 94.7
% African 11.6 5.9 9.0 0
American
% Asian 1.9 2.9 1.3 0
% Other 1.5 2.9 1.9 5.3
% Hispanic 17.0 17.6 17.3 5.3
% No Comorbidity 33.2 32.4 26.9 36.8
% One 46.7 52.9 41.0 42.1
Comorbidity
% Two + 20.1 14.7 32.1 21.0
Comorbidity
% Uninsured 6.9 8.8 5.1 0
% Intermittent 15.1 11.8 12.2 15.8
Insured
% Insured 78.0 79.4 82.7 84.2
Modal Physical Fair Fair Good Fair
Health
Modal Mental Good Fair Good Very Good
Health









Table 3-7. Statistical Results of Pulmonary Condition Respondents (Total Expenditures)


p-value


Predicted
Expenditures
($)


Depression .20 2.60 .01** 13,752
No Depression 5,413
Anxiety .10 1.29 .10 17,848
No Anxiety 5,541
Depression 13,752
Treatment .10 .54 .30
No Depression 5,413
Treatment
Anxiety 10,696
Treatment .33 1.83 .04**
No Anxiety 6,254
Treatment



Table 3-8. Statistical Results of Pulmonary Condition Respondents (Medical
Expenditures)
Predicted
P T p-value Expenditures
________~________________ ($)
Depression -.05 -.56 .29 7,089
No Depression 3,966
Anxiety -.17 -1.91 .03** 8,347
No Anxiety 5,015
Depression 2,722
Treatment -.66 -3.31 .00**
No Depression 8,931
Treatment
Anxiety 6,140
Treatment -.28 -.92 .18
No Anxiety 9,349
Treatment









Table 3-9. Statistical Results of Pulmonary Condition Respondents (Health Care
Utilization)
Office-Based Provider Visits


Incidence Rate
Ratio


p-value


Predicted Visit
Count


Depression 12.66
Treatment .98 -.17 .43
No Depression 13.26
Treatment
Anxiety 14.33
Treatment .10 1.29 .10
No Anxiety 11.87
Treatment
Outpatient Hospital Visits
Depression 1.67
Treatment 1.23 .55 .29
No Depression 2.19
Treatment
Anxiety .61
Treatment .39 -2.96 .00**
No Anxiety 1.28
Treatment
Inpatient Nights at Discharge
Depression 1.18
Treatment .96 -.11 .45
No Depression 1.49
Treatment
Anxiety 1.49
Treatment 1.48 1.03 .15
No Anxiety 1.16
Treatment
Emergency Room Visits
Depression .37
Treatment .97 -.13 .45
No Depression .47
Treatment
Anxiety .48
Treatment .95 -.21 .42
No Anxiety .52
Treatment












Condition Respondents (Total Expenditures)


p-value


Predicted
Expenditures
($)


Depression .13 1.30 .10 16,436
No Depression 15,467
Anxiety .18 1.30 .10 24,047
No Anxiety 14,921
Depression 9,475
Treatment -.03 -.08 .47
No Depression 16,941
Treatment
Anxiety 18,881
Treatment .35 .91 .19
No Anxiety 13,695
Treatment



Table 3-11. Statistical Results of Cardiac Condition Respondents (Medical Expenditures)
Predicted
P T p-value Expenditures
($)
Depression -.11 -.87 .19 13,752
No Depression 5,413
Anxiety .05 .41 .34 13,898
No Anxiety 13,585
Depression 5,181
Treatment -.40 -1.06 .15
No Depression 14,081
Treatment
Anxiety 22,077
Treatment .41 .91 .19
No Anxiety 10,785
Treatment


Table 3-10. Statistical Results of Cardiac











Table 3-12. Statistical Results of Cardiac Recipients (Health Care Utilization)
Office-Based Provider Visits


Incidence Rate
Ratio


p-value


Predicted Visit
Count


Depression 14.81
Treatment 1.04 .23 .41
No Depression 14.99
Treatment
Anxiety 9.98
Treatment .78 -1.33 .10
No Anxiety 13.34
Treatment
Outpatient Hospital Visits
Depression 1.16
Treatment .49 -1.29 .10
No Depression 3.23
Treatment
Anxiety 1.21
Treatment 1.77 1.05 .15
No Anxiety .89
Treatment
Inpatient Nights at Discharge
Depression 1.92
Treatment 1.05 .16 .44
No Depression 1.81
Treatment
Anxiety 1.93
Treatment .72 -.65 .26
No Anxiety 2.21
Treatment
Emergency Room Visits
Depression .04 (overall 1.05
Treatment 2.00 1.83 model not
No Depression significant) .53
Treatment
Anxiety .44
Treatment .66 -.97 .17
No Anxiety .74
Treatment














CHAPTER 4
DISCUSSION

The present study examined the relationship between comorbid depression or

anxiety and health care expenditures in pulmonary or heart patients. As expected, it was

found that depression increased total expenditures in pulmonary patients, but there was

no corresponding increase in medical expenditures only. Because medical expenditures

only excluded any medical event associated with a psychological diagnosis, it appears

that depressed patients may not use more medical services for their medical conditions,

but perhaps they do use more psychological services. Depressed patients may have more

diagnoses of other psychological conditions that prompt service-seeking.

Contrary to expectation, the presence of anxiety in pulmonary patients decreased

medical expenditures only, but there was no difference in total expenditures. Thus, it

appears that anxious pulmonary patients do not use more health care services overall and

in fact, they seek less health care services for their medical conditions. This could be

because their anxiety inhibits them from seeking needed care.

The main aim of the study was to examine the medical cost offset effect in

pulmonary or heart patients who sought treatment for depression or anxiety. This

analysis revealed that depressed pulmonary patients showed a cost offset effect, in that

depressed patients who received mental health treatment showed a decrease in medical

expenditures only. Further analysis revealed that this effect was not explained by a

decrease in the number of outpatient hospital visits, inpatient hospital nights, office-based

provider visits, or emergency room visits. Thus, this study suggests that the treatment of









pulmonary patients with comorbid depression would result in a cost offset effect not due

to cost shifting from medical treatment to psychological treatment.

Anxious pulmonary patients who received mental health treatment showed an

unexpected increase in total health care expenditures; however, there was a reduction in

outpatient hospital visits, supporting the idea that added psychological care would show a

reduction in health care utilization. The number of hospital inpatient nights, office-based

provider visits, and emergency room visits were not significantly different between the

treated and untreated groups. These results might suggest that anxiety patients are getting

the psychological services they need and added care costs more, but because needed care

is provided, utilization in the medical sector is reduced. Furthermore, treated patients

may also be more apt to recognize their anxiety symptoms as part of a psychological

disorder, as opposed to a medical problem.

Heart disease patients did not show any significant effects in any of the analyses.

However, it should be noted that the number of heart disease patients who received

psychological treatment was less than pulmonary patients, which limited the power of the

results from the heart disease group. Nevertheless, in this study, the variation in observed

cost-offset effects suggests that the issue of cost-offset may be complex and variable

across different psychological and medical conditions.

Limitations

Several limitations of the present study should be considered. First, the data

structure of MEPS seems to be unreliable. The present analysis included the years 1999

to 2002. A previous analysis using only the years 2000 to 2002 revealed different results.

When 1999 was added, the results changed. Previous results showed a cost offset effect

for both depression and anxiety treatment in pulmonary patients with comorbid









depression or anxiety, whereas the present results reveal a cost offset effect for only

depression treatment in pulmonary patients. The addition of data from 1999 appeared to

have changed the structure of the data set. Part of this instability could be due to cohort

effects, as well as a difference in power to detect statistical significance. Second, only a

relatively small number of patients received mental health treatment, particularly for the

heart disease groups. There were only 19 and 34 heart disease respondents who received

mental health treatment for anxiety and depression, respectively. Methodologically, this

poses a difficulty in terms of reliable estimates. Third, the validity of diagnostic coding

is somewhat questionable because data was obtained through self-report. Fourth,

aggregating multiple classification codes and psychotropic medication with

psychotherapy reduces the precision of the analysis. Fifth, treatment efficacy could not

be determined from the data. Finally, it is important to remember the cross-sectional and

correlational nature of the present analysis does not address causality.

Implications

The demonstration of cost offset effects has implications for the field of

psychology and its utility in reducing or containing rising health care costs in America.

Although psychologists would like to believe that a cost offset effect holds across

medical conditions and psychological conditions, the present data suggests that the

relationship between mental health treatment and cost offsets is not clear-cut. Using data

from the MEPS is a useful way to examine potential cost offset effects for specific

medical conditions because it provides large numbers of subjects, is nationally

representative, and allows for both cross-sectional and longitudinal analyses. Results

from further analyses on other medical conditions may help to further refine the nature of









cost offsets. Because the MEPS allows for longitudinal analyses, next steps would be to

determine cost offsets longitudinally.

An argument is that using cost offset as the only measure of the value of

psychological services is incomplete (Coyne and Thompson, 2003). Patients and families

who make treatment gains for depression or anxiety and employers who observe

increased productivity in their workers treated for depression or anxiety may feel that

these benefits are worth the additional costs of psychological services. Thus, the

effectiveness of treatment as measured by quality of life and work performance and

attendance would be important outcomes to consider in addition to cost issues. Although

treatment efficacy information is not available from the MEPS data, future research will

need to address the important issue of effective treatment and cost offsets. However, the

MEPS would allow for the analysis of employment variables relevant to the present

discussion.

In conclusion, the present study provided preliminary results on the cost offset

effects of specific medical and psychological populations. Results indicated that cost

offset issues are complex and the future direction of cost offset research will be focused

on teasing apart this complexity.















LIST OF REFERENCES


Anderson, R. N. (2002). Deati/h Leading causes for 2000. National vital statistics report,
50(16). Hyattsville, MD: National Center for Health Statistics.

Bickman, L. (1996). The evaluation of children's mental health managed care
demonstration. Journal of the Mental Health Administration, 23: 7-15.

Braveman, P., & Tarimo, E. (2002). Social inequalities in health within countries: Not
only an issue for affluent nations. Social Science and Medicine, 54(11): 1621-1635.

Bromberg. J. I., Beasley, P. J., D'Angelo, E. J., Landzberg, M., & DeMaso, D. R. (2003).
Depression and anxiety in adults with congenital heart disease: A pilot study. Heart
andLung, 32(2): 105-110.

Bureau of Labor and Statistics. (1999-2002). Consumer price index for all urban
consumers (CPI-U): U.S. city average, detailed expenditure categories (medical
care). U.S. Department of Labor. Retrieved September, 2005, from
http://www.bls.gov

Carbone, L. A., Orav, E. J., Fricchione, G. L., & Borus, J. F. (2000). Psychiatric
symptoms and medical utilization in primary care patients. Psychosomatics, 41(6):
512-518.

Carlson, L. E., & Bultz, B. D. (2004). Efficacy and medical cost offset of psychological
interventions in cancer care: Making the case for economic analyses. Psycho-
Oncology, 13: 837-849.

Chiles, J. A., Lambert, M. J., & Hatch, A. L. (1999). The impact of psychological
interventions on medical cost offset: A meta-analytic review. Clinical Psychology:
Science and Practice, 6(2): 204-220.

Collins, K. A., Westra, H. A., Dozois, D. J. A., & Burns, D. D. (2004). Gaps in accessing
treatment for anxiety and depression: Challenges for the delivery of care. Clinical
Psychology Review, 24: 583-616.

Coyne, J. C., & Thompson, R. (2003). Psychologists entering primary care: Manhattan
cannot be bought for $24 worth of beads. Clinical Psychology: Science and
Practice, 10(1): 102-108.

Follette, W. T., & Cummings, N. A. (1968). Psychiatric services and medical utilization
in a prepaid health plan setting. Medical Care, 5: 25-35.









Friedman, R., Sobel, D., Myers, P., Caudill, M., & Benson, H. (1995). Behavioral
medicine, clinical health psychology, and cost offset. Health Psychology, 14(6):
509-518.

Harter, M. C., Conway, K. P., Merikangas, K. R. (2003). Associations between anxiety
disorders and physical illness. European Archives of Psychiatry and Clinical
Neuroscience, 253: 313-320.

Hunsley, J. (2003). Cost-effectiveness and medical cost-offset considerations in
psychological service provision. Canadian Psychology, 44(1): 61-73.

lezzoni, L. I. (2003). Risk adjustment for measuring health care outcomes, 3rd edition.
Chicago, IL: Health Administration Press.

Katon, W. J. (2003). Clinical and health services relationships between major depression,
depressive symptoms, and general medical illness. Society ofBiological Psychiatry,
54: 216-226.

Kunik, M. E., Roundy, K., Veazey, C., Souchek, J., Richardson, P., Wray, N. P., &
Stanley, M. A. (2005). Surprisingly high prevalence of anxiety and depression in
chronic breathing disorders. Chest, 127(4): 1205-1211.

Mumford, E., Schlesinger, H. J., Glass, G. V., Patrick, C., & Cuerdon, T. (1984). A new
look at evidence about reduced cost of medical utilization following mental health
treatment. American Journal ofPsychiatry, 141: 1145-1158.

Olin, G. L., & Rhoades, J. A. (2005). The five most costly medical conditions, 1997 and
2002: Estimates for the U.S. civilian noninstitutionalized population. Statistical
brief #80. Agency for Healthcare Research and Quality. Rockville, MD: Retrieved
August, 2005, from http://www.meps.ahrq.gov/papers/st80/stat80.pdf

Otto, M. W. (1999). Psychological interventions in the age of managed care: A
commentary on medical cost offsets. Clinical Psychology: Science and Practice,
6(2): 239-241.

Simon, G. E., Revicki, D., Heiligenstein, J., Grothaus, L., VonKorff, M., Katon, W. J., &
Hylan, T. R. (2000). Recovery from depression, work productivity, and health care
costs among primary care patients. General Hospital Psychiatry, 22: 153-162.

StatCorp. Stata Statistical Software: Release 9.0 Special Edition. College Station, TX:
Stata Corporation, 2002.

Sturm, R. (2001). Economic grand rounds: The myth of medical cost offset. Psychiatric
Services, 52: 738-740.

Thorpe, K. E., Florence, C. S., & Joski, P. (2004). Which medical conditions account for
the rise in health care spending? Health Affairs web exclusive: Retrieved August,
2005, from http://content.healthaffairs.org/cgi/content/full/hlthaff.w4.437/DC1






44


Van Ede, L., Yzermans, C. J., & Brouwer, H. J. (1999). Prevalence of depression in
patients with chronic obstructive pulmonary disease: A systematic review. Thorax,
54: 688-692.

Wells, K. B., Sturm, R., Sherbourne, C. D., & Meredith, L. S. (1996). Caringfor
Depression. Cambridge, MA: Harvard University Press.

World Health Organization. (2006). Chronic Conditions: The Economic Impact.
Retrieved December, 2005, from
http://www.who.int/chronic_conditions/economics/en/index.html















BIOGRAPHICAL SKETCH

Andrea Meredith Lee graduated with a Bachelor of Arts (first class honors) degree

in psychology in October 2004 from Simon Fraser University in Burnaby, British

Columbia, Canada. She plans to pursue a doctoral degree in clinical and health

psychology at the University of Florida. Her academic interests lie in health psychology

and health policy.