Identifying Community-Level Predictors of Depression Hospitalizations

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1 Identfyng Communty-Level Predctors of Depresson Hosptalzatons September 2005 John Fortney Gerard Rushton Scott Wood Lxun Zhang Kathryn Rost Western Interstate Commsson for Hgher Educaton Mental Health Program 3035 Center Green Drve Boulder, CO 80301

2 Executve Summary The goal of ths research was to dentfy rural areas that should be targeted for early adopton of evdence-based depresson treatments based on elevated rates of depresson related hosptalzatons. Usng county-level data from the Statewde Inpatent Database, Census, Department of Agrculture, and Area Resource Fle, predctors of elevated hosptalzatons rates were dentfed usng spatal regresson models. Ths nvestgaton demonstrated that: (1) rural countes have lower rates of depresson-related hosptalzaton than urban countes, (2) ruralty fals to predct depresson-related hosptalzaton n models that control for communty-level demographc, economc and health system rsk factors, (3) communty-level rsk factors explan a respectable ~30% of the varance n depresson-related hosptalzaton rates, and (4) whle these rsk factors dentfy hgh rsk areas n the 10 states we studed, they cannot be used to dentfy hgh rsk areas n other states. Ths s the frst nvestgaton of potentally preventable mental health hosptalzatons n a hgh qualty database that provdes systematcally coded nformaton on all publc and prvate hosptalzatons n 10 states across the country. Mergng ths database wth other natonal databases allowed us to dentfy communty-level rsk factors of unmet need for mental health care that are otherwse subsumed under the umbrella of ruralty. We dentfed rural countes from 10 states wth extremely elevated rates of depresson related hosptalzatons due to observable communty-level rsk factors. These countes should be prortzed for dssemnaton/mplementaton of evdence-based treatments for depresson usng desgns that evaluate whether mproved depresson treatment produces a cost offset for health plans because t reduces expensve hosptalzatons. Depresson Hosptalzatons - 2 -

3 Introducton Most efforts to mplement best-practces for depresson treatment target health systems servng predomnantly urban populatons. The goal of ths research was to dentfy rural areas that should be targeted for early adopton of enhanced depresson treatment models based on unmet need. Unmet need can be measured n a varety of ways, ncludng prevalence rates, sucde rates, and depresson related hosptalzaton rates. Prevalence rates n small areas must be collected va survey whch would be extremely expensve to collect for a natonally representatve sample. Sucde rates are avalable from the Centers for Dsease Control (Natonal Vtal Statstcs). (1) However, t would be dffcult to relably characterze patent need by sucde rates because, as relatvely rare events, sucde rates fluctuate greatly n small areas over short perods of tme. Therefore, n ths project, we used depresson related hosptalzatons as a proxy for unmet need, recognzng that the delvery of hgh qualty outpatent depresson treatment should lead to decreased rates of depresson-related hosptalzaton. (2) Geographc varaton n the populaton s need for mproved depresson care s multfaceted and depends on many factors ncludng demographc characterstcs of the populaton, characterstcs of the local economy, and characterstcs of the local health care system. We expected that rural and urban areas would dffer wth respect to these rsk factors and that rural areas would have greater need for the dssemnaton and mplementaton of bestpractces for depresson treatment. Although prevalence rates have not been found to dffer across rural and urban areas, (3) we expect that ruralty s assocated wth hgher hosptalzaton rates due to worse access to outpatent specalty mental health care and poor economc condtons. We also expect that hgher rates of hosptalzaton would be explaned by dfferences n demographc, economc, and health care system characterstcs. If these communty-level rsk factors explan a hgh proporton of the varance n unmet need, they can be used to dentfy geographc areas n rural Amerca that. Four hypotheses were tested. 1. Rural countes wll have sgnfcantly hgher depresson related hosptalzaton rates than urban countes. 2. Depresson related hosptalzaton rates wll be sgnfcantly correlated wth demographcs (e.g., ethncty, poverty, and educaton), economc condtons, and health system characterstcs. 3. Ruralty, demographcs, economc condtons, and health system characterstcs wll Depresson Hosptalzatons - 3 -

4 explan a substantal (e.g., >50%) amount of the varaton n depresson related hosptalzaton rates. 4. Predctve models can be used to accurately dentfy geographc areas wth elevated depresson related hosptalzaton rates. These hypotheses were tested by conductng an analyss of spatally-referenced datasets contanng county level nformaton about hosptalzatons, ruralty, demographcs, economcs, and health care systems. Our ultmate goal was to dentfy countes across the U.S. wth elevated rates of depresson related hosptalzatons. However, because comprehensve npatent datasets contanng both dagnostc and patent zpcode of orgn nformaton are not avalable at the natonal level for all adults, t was necessary to 1) dentfy a natonally representatve sub-set of U.S. countes wth avalable data, 2) dentfy sgnfcant predctors of depresson related hosptalzatons n ths sub-set of U.S. countes, 3) determne whether a predctve model wth external valdty could be developed, and f so, 4) predct hosptalzaton rates n all U.S. countes usng natonally avalable data about ruralty, demographcs, economcs, and health care systems. Methods Data Sources Depresson related hosptalzatons were extracted from the Statewde Inpatent Database (SID) whch contans the unverse of hosptal dscharge records from all communty hosptals n partcpatng states. States n the SID wth patent zpcode nformaton n 1995 ncluded Arzona, Colorado, Florda, Iowa, New Jersey, New York, Oregon, South Carolna, Washngton and Wsconsn. By 2000, data were also avalable from four other states (Kentucky, Mane, North Carolna, and West Vrgna). These states represent all regons of the US and have a wde range of demographc, economcs, and health system characterstcs. The SID database ncludes one record for each hosptalzaton and ncludes data about the age, gender, zpcode and up to 10 prmary and secondary dagnoses. Countes were defned as the unt of analyss because demographc, economc, and health system data are wdely avalable at ths unt of analyss and because a smaller geographcal unt would have yelded zero observed hosptalzaton too frequently to be analyzed effcently. Zpcodes were used to dentfy the patent s county of Depresson Hosptalzatons - 4 -

5 resdence usng a geocodng algorthm developed for the Mssour Census Data Center whch dentfes the county n whch the majorty of the land area of the zpcode s contaned (http://mcdc2.mssour.edu/webrepts/geography/zip.resources.html). Data about the demographc, economc and health system characterstcs of the countes were obtaned from the U.S. Census, the U.S. Department of Agrculture, and the Area Research Fle. Depresson Related Hosptalzaton Rates Followng tradtonal methods, age-sex adjusted depresson related hosptalzaton rates n 2000 were calculated for each county. 1 Usng categores defned by the U.S. Census, each npatent was categorzed nto on of 20 age-sex groups. 2 The observed hosptalzaton rate for each age-sex group n the ten states was calculated by dvdng the number of hosptalzatons n each group by the populaton n the group as reported n the 2000 U.S. Census. The expected number of hosptalzatons for each county was calculated as the product of the age-sex hosptalzaton rate for the ten-state area and the number of persons n the county n the age-sex category. The ndrectly standardzed hosptalzaton rate for each county k (SHR k ), s the rato of the observed to the expected number of hosptalzatons calculated accordng to the followng equaton: (4) x j jk O SHR k = n λ E = j jk where n jk s the populaton n the j k k th j age-sex group and the th k county, x jk s the number of depresson related hosptalzatons n the th j age-sex group and the th k county, and λ j s the depresson rate for n the th j age-sex group n the ten-state area. If O k denotes the observed number of hosptalzatons n each county and hosptalzatons, the SHR s k Ek denotes the expected number of O k. Values greater than one ndcate a greater number of E hosptalzatons than expected and values less than one ndcate fewer hosptalzatons than expected. 1 Admssons wth prmary or secondary dagnoses of ICDN x, 296.3x, 298.0, , 311 were extracted from the SID database. These are the dagnostc codes used for the HEDIS depresson performance measure. 2 The 10 age groups were 20-24, 25-29, 30-34, 35-44, 45-54, 55-59, 60-64, 65-74, 75-84, and 85+. Depresson Hosptalzatons - 5 -

6 Explanatory Varables Ruralty was measured at the county level usng two dfferent ndcators. The frst was the Offce of Management and Budget s (OMB) defnton of a non Metropoltan Statstcal Area (MSA). The OMB defnes a county as a MSA f t contans an urbanzed area (populaton greater 50,000) or t s adjacent to an MSA county and 25% of the employed populaton commutes to the urbanzed area (or vsa versa). The second measure of ruralty was Urban Influence Codes developed by the WWAMI Rural Health Research Center. (5) Countes are dvded nto 12 categores accordng to ther populaton and the proporton of workers commutng to urban areas (see Table 1). Demographc varables ncluded percent of populaton Afrcan Amercan, Hspanc, Asan Amercan, and Natve Amercan as defned by the U.S. Census. Other Census varables ncluded percent of populaton below the federally desgnated poverty level, percent wth a hgh school educaton, and percent of the populaton 16 and older who were unemployed. Economc data generated by the Department of Agrculture ncluded and ndcator of housng stress, and sx mutually exclusve dummy varables ndcatng whether the economy of county was dependent on farmng, mnng, manufacturng, federal government, servces, or not dependent on a specalzed sector. Health system data from the Area Resource Fle ncluded the number nonpsychatrst physcans per 1000 people, the number of psychatrsts per 1000 people, the number of psychologsts per 1000 people, the number of socal workers per 1000 people, the number of hosptal beds per 1000 people, and dummy varables ndcatng whether the county had a communty mental health center, whether the county was federally desgnated as a health professon shortage area, and the penetraton rate of health mantenance organzatons (HMO). Fnally, we also ncluded the longtude and lattude of the county, as the hosptalzaton rates vared seem to vary east to west and north to south. Statstcal Analyss The unt of the ecologcal regresson analyss was the county. The analyss was conducted n three stages. Frst, we conducted non-parametrc bvarate analyses of the mpact of ruralty on depresson related hosptalzaton rates. We used a Kruskal-Walls Rank Sum Tests to compare the SHR rankngs of MSA and non-msa countes and to compare the SHR rankngs of the 12 UIC categores of countes. Second, because the spatal regresson models Depresson Hosptalzatons - 6 -

7 used n the thrd stage of the analyss were computatonally ntensve, t was necessary to frst dentfy a parsmonous model specfcaton. Therefore, we used backward selecton technques n conjuncton wth ordnary least squares regresson analyss to frst dentfy sgnfcant predctors of depresson related hosptalzatons. To reman n the parsmonous specfcaton, varables had to acheve a sgnfcance cutoff level of p<0.2. Because we had no a pror expectatons about how the ndependent varables would nteract to mpact hosptalzaton rates, we dd not test for nteracton effects. Because the dependent varable (SHR) does not conform to the dstrbutonal assumptons of ordnary least squares regresson, a more flexble spatal regresson model was used to test the hypotheses and generate predctons. In the thrd stage, we conducted a Posson regresson analyss to estmate the mpact of the explanatory varables on SHR. Two man analytcal problems complcated the analyss. Frst, the hosptalzaton rates among neghborng countes are lkely to be hghly correlated resultng n a volaton of the assumpton that observatons are ndependently dstrbuted. (6) Ths problem was addressed usng a Condtonal Autoregressve (CAR) model to account for potental spatal autocorrelaton among neghborng countes. Second, extremely hgh or low observed hosptalzaton rates are lkely to occur n countes wth small populatons due to random varaton. Ths problem was addressed usng Bayesan smoothng methods, whch took advantage of the spatal autocorrelaton present n the data. (7) To account for these two problems, we specfed the Bayesan Posson Condtonal Autoregressve (CAR) Model proposed by Besag et al. (8), as mplemented n the statstcal software GeoBUGS. (9) Ths model specfes the observed count of hosptalzatons to follow a Posson dstrbuton condtonal on the expected hosptalzaton count and the relatve rsk for the δ county: O δ ~ Posson( Ee ) Where denotes the -th county, O s the observed count of depresson related hosptalzatons n the -th county, county, e δ E s the expected count n the -th s the relatve rsk of the -th county. The followng equaton specfes the log δ relatve rsk ( ( ) log e = δ ): δ = β0 + β1x(1) + β2x(2) + K + βk x( k ) + Φ, where x are the county level varables and β are the regresson coeffcents. The spatal autocorrelaton s modeled as: Depresson Hosptalzatons - 7 -

8 Φ Φ m δ : 1 ~ N( Φ, ) δm 1 = m k Φ k : neghbor set of county : number of neghbors of county nverse of the overall varance parameter (controls the degree of smoothng mposed) External Valdaton The predctve valdty of the Bayesan Posson CAR model (wth Urban Influence Codes as the measure of ruralty) was determned usng addtonal data from the four states added to the SID database n Usng the estmated coeffcents from the Bayesan Posson CAR model and the values of the explanatory varables, the SHR was predcted for each county n Kentucky, Mane, North Carolna, and West Vrgna. Then for each state we used Pearson Rank Correlatons to compare the predcted and actual SHR for each state. Results In 2000, the 10 states had 520 countes and a populaton of 74,422,609. The SID database contaned 9,289,440 hosptal dscharges for the 10 states. Among the 54,012,886 resdents aged 20 and above, 448,752 cases of depresson related hosptalzatons were recorded n the SID database. The overall rate of depresson related hosptalzatons n these 520 countes was 8.3 per 1000 resdents ages 20 and above. Ths rate s slghtly lower than the natonal estmates based on the CDC Natonal Hosptal Dscharge Survey 2002 whch reports 9.2 depresson related hosptalzatons per 1000 resdents ages 15 and above (usng the same ICD9 codes). Table 1 presents the mean values of the county level rsk factors. The results of the bvarate analyses clearly demonstrate that rural countes have lower rates of depresson related hosptalzatons (MSA SHR=1.02 and non-msa SHR=0.94). Accordng to the Kruskal-Walls Rank Sum Test, non-msa countes have sgnfcantly (χ 2 = , p<0.01) lower rates of hosptalzaton than MSA countes. Lkewse, the 12 UIC classfcatons had sgnfcantly (χ 2 = , p<0.01) dfferent rates of depresson related hosptalzatons. These fndngs contradct our frst hypothess that ruralty s postvely correlated wth depresson related hosptalzatons. Depresson Hosptalzatons - 8 -

9 The results of the multvarate analyss for the MSA v.s. non-msa model dentfed 15 covarates (n addton to ruralty) wth p-values <0.2 ncludng: demographc characterstcs (percent of populaton Afrcan Amercan and Asan Amercan), economc condtons (percent of populaton below the federally desgnated poverty lne, percent of the populaton 16 and older who were unemployed, housng stress ndcator, manufacturng dependent economy, nondependent economy), and health system characterstcs (number non-psychatrst physcans per 1000 people, the number of psychatrsts per 1000 people, the number of hosptal beds per 1000 people, the HMO penetraton rate, presence of a communty mental health center, and federal desgnaton as a health professon shortage area). Longtude and lattude of the county was also a sgnfcant predctor n the bvarate analyses. Non-MSA status was not a sgnfcant predctor (p>0.2) n the multvarate analyss. The results of the multvarate analyss for the UIC model also dentfed 15 covarates wth p-values <0.2. In contrast to the MSA v.s. non-msa model, the Health Professon Shortage Area varable dropped out and percent of populaton wth a hgh school educaton was added. Only one of the UIC categores (non-metropoltan county not adjacent to metropoltan or mcropoltan county wthout own town) was a sgnfcant predctor (β<0, p<0.01). No varable had a varance nflaton factor >3.2 suggestng that mult-collnearty among the explanatory varables was not a problem. The R 2 for the MSA model was 0.30 (F= 9.74, p<0.01) and the R 2 for UIC model was 0.34 (F=-13.5, p<0.01). Whle these rsk factors explan much of the varance n ruralty s mpact on depresson related hosptalzaton rates, the results do not confrm our thrd hypothess that the explanatory varables can explan >50% of the varaton n depresson related hosptalzaton rates. The results of the Bayesan Posson CAR model are depcted n Table 2. The table presents the medan parameter estmate for each explanatory varable. If the upper and lower confdence lmts wthn whch 95% of the parameter estmates are ncluded nclude zero, the parameter estmate s not consdered statstcally sgnfcant at the α=0.05 level. Ruralty was not sgnfcantly correlated wth depresson related hosptalzaton rate n ether the MSA v.s. non- MSA model or the UIC model (although UIC category 12 had a negatve and sgnfcant coeffcent ndcatng that the most rural countes had the lowest rates of hosptalzaton). Both the MSA v.s. non-msa model and the UIC models ndcate that countes wth a hgher percentage of Afrcan Amercans had sgnfcantly (.e., the parameter estmate was postve 95% Depresson Hosptalzatons - 9 -

10 of the tme) lower rates of depresson related hosptalzaton. In the UIC model, countes wth hgher proportons of Asan Amercans also had sgnfcantly lower rates of hosptalzaton. In both models, countes wth hgher poverty rates and countes whose economes were dependent on manufacturng had sgnfcantly hgher rates of hosptalzaton. Results from both models ndcated that the non-psychatrst MDs per populaton and the presence of a communty mental health center were postvely and sgnfcantly correlated wth hosptalzaton rates. In the UIC model, the number of psychatrsts per populaton was negatvely correlated wth hosptalzaton rates. In the MSA v.s. non-msa model, the HMO penetraton was postvely assocated wth hosptalzaton rates. Overall, these results partally support our second hypothess that demographcs, economc condtons, and health system characterstcs are sgnfcantly correlated wth hosptalzaton rates. Extreme values of SHR were defned as <0.75 and > These cutoff values represent 25% less than the standardzed rate and 33% greater than the standardzed rate (note that 1/1.33=0.75). Maps 1 and 2 depct the countes wth extreme hosptalzaton rates based on the raw data and rates based on predctons from the Bayesan Posson CAR Model (UIC model whch had a lower devance nformaton crtera value than the MSA vs. Non-MSA model, 4, versus 4, ). The map of observed SHR depcts a large number of countes wth extreme observatons (153 countes wth SHR <0.75 and 80 countes wth SHR >1.33), hghlghtng the problem of estmatng hosptalzaton rates n countes wth small populatons. However, the map of estmated SHR from Bayesan Posson CAR Model depcts fewer countes wth extreme observatons (66 countes n the smallest group and 8 n the hghest group). Ths second map s more relable for detectng countes wth elevated SHRs because t reduces the rsk of dentfyng countes whch have extreme values due to ncreased random varaton resultng from small populatons. Specfcally, nformaton form neghborng countes s used to reduce ths random varaton. Ths fndng suggests that the predctve models can be used to dentfy geographc areas wth elevated hosptalzaton rates for countes ncluded n the analyss, whch partally supports our fourth hypothess. The external valdaton ndcated that the model estmated from the 10 states does not generalze to the four states added to the SID database n The rank correlaton between the observed SHR and the predcted SHRs from the Bayesan Posson CAR Model were all small and nsgnfcant: Kentucky (r=0.09, p=0.32), Mane (r=-0.48, p=0.06), North Carolna (r=0.02, Depresson Hosptalzatons

11 p=0.84), and West Vrgna (r=0.10, p=0.48). Note that correlaton coeffcent for Mane was negatve. The correlaton coeffcent between the observed SHR n the 10 SID states and the predcted SHRs n the 10 SID states was much hgher and statstcally sgnfcant (r=0.47, (p<0.01). If the model had external valdty to the four states, the correlaton coeffcents would have approached ths value and level of sgnfcance. Ths fndng suggests that the predctve models cannot be used to dentfy geographc areas wth elevated hosptalzaton rates for countes not ncluded n the analyss. Ths result does not support our fourth hypothess. Dscusson Ths nvestgaton demonstrated that: 1) rural countes have lower rates of depressonrelated hosptalzaton than urban countes, 2) that ruralty fals to predct depresson-related hosptalzaton n models that control for communty-level demographcs, economc condtons, and health system rsk factors, 3) that these rsk factors explan about a thrd of the varance n depresson-related hosptalzaton rates, and 4) that whle these rsk factors dentfy hgh rsk areas n the 10 states we studed, they cannot be used to dentfy hgh rsk areas n other states. Our fndng that ruralty s not a rsk factor for depresson related hosptalzatons as hypotheszed s not consstent wth a prevous study of communty resdents wth depresson whch found that those located n rural areas were more lkely to be hosptalzed. (2) Wth respect to demographc characterstcs, our fndng that depresson related hosptalzaton rates are lower n countes wth hgher proportons of Afrcan Amercans s consstent wth epdemologcal studes whch fnd lower prevalence rates of depresson and lower treatment seekng rates among mnortes. (13-16) Wth respect to economc condtons, our fndng that poverty rates were sgnfcantly correlated wth hosptalzaton rates s consstent wth fndngs from observatonal studes that poverty s a rsk factor for depresson. (14;17-19) Our fndng that unemployment rates are not correlated wth depresson related hosptalzaton rates s not consstent wth prevous studes whch report that unemployment (measured at the level of the ndvdual) contrbutes drectly to an ncreased need for depresson treatment. (20-22) The fndng that countes wth manufacturng dependent economes have hgher rates of depresson related hosptalzatons has not been reported prevously n the lterature. Wth respect to health care system characterstcs, our fndngs pont to a rather complcated relatonshp between outpatent servce avalablty and hosptalzatons. On one Depresson Hosptalzatons

12 hand, hosptalzaton rates are hgher when the number of non-psychatrst physcans per person s hgher and when there s a communty mental health center n the county. The frst fndng s consstent wth a study reportng that ncreased access to prmary care (as measured at the level of the ndvdual) results n hgher npatent admsson rates for mental health problems. (23) The fndng that countes wth communty mental health centers have hgher rates of depresson related hosptalzatons supports a prevous study by Hendryx and Rohland that found that psychatrc npatent admsson rates were hgher n areas closer to a communty mental health center. (12) Hendryx and Rohland suggest that ths fndng may be due to suppler-nduced demand. It could also be that the greater avalablty of non-psychatrst physcans and Communty Mental Health Center staff ncreases the lkelhood that ndvduals n need of hosptalzaton are beng detected (a hypothess also put forth by Hendryx and Rohland). On the other hand, hosptalzaton rates are lower when the number of psychatrsts per person s greater, whch ndcates that when psychatrsts are avalable for referral or consultaton t reduces the rsk of hosptalzaton. In contrast, we found no sgnfcant relatonshp between hosptalzaton rates and the number of psychologsts or socal workers per person. Lkewse, we found no relatonshp between hosptalzaton rates and the number of hosptal beds per person. Ths latter fndng s nconsstent wth Wennberg s prevous fndngs that bed supply s correlated wth hosptalzaton rates across a range of physcal health dagnoses. (24) Our non-fndng may be due to the fact that we could only observe the total number of hosptal beds per person, rather than the number of psychatrc beds per person. The fndng that HMO penetraton rates are postvely correlated wth hosptalzaton rates for depresson seems counter-ntutve, although managed care cost contanment strateges tend to reduce costs per admsson rather number of admssons. (25;26) Lmtatons and Strengths There are several known lmtatons to the analyss. The frst lmtaton s the modfable areal unt problem, whch states that results may dffer dependng on the geographcal unt of analyss. Although countes are an approprate geographcal unt of analyss, results may have dffered f we had specfed zpcodes to be the unt of analyss. In partcular, the amount of border crossng (e.g., ndvduals usng outpatent health servces outsde the county) decreases as the sze of the geographc unt ncreases. A second known lmtaton s edge effects. (27) Depresson Hosptalzatons

13 Although the statstcal analyss controlled for spatal autocorrelaton, we dd not account for hosptalzatons n adjacent countes located n states not n the SID database. Although, we could have mputed hosptalzatons from adjacent countes wth mssng data ths would have complcated the analyss consderably and t s not lkely that results would have changed substantally. The thrd potental lmtaton concerns varatons and naccuraces n the dagnoss of depresson durng hosptalzatons. It s possble that some of the varatons and correlatons observed n the data resulted from dfferences n dagnostc codng procedures across geographc areas rather than true dfferences n the need for npatent servces due to depresson or complcated by depresson. Fourth, whenever the unt of analyss s a geographc area (e.g., county) rather than an ndvdual, there s the potental for the ecologcal fallacy. Whle t would have been preferable to analyze ndvdual level data, ths was not feasble because large natonally representatve datasets contanng both clncal data and rsk factor data measured at the level of the ndvdual are not avalable. Despte the lmtatons descrbed above, these are the frst natonally representatve data that have been presented about the rsk factors for depresson related hosptalzatons. The analyss was conducted n a hgh qualty database that provded systematcally coded nformaton on all hosptalzatons n 10 states across the country. The depresson related hosptalzatons rates were very smlar to natonal estmates from the CDC. Usng spatal regresson technques, we were able to successfully dentfy countes from 10 states wth elevated rates of depresson related hosptalzatons. These countes should be prortzed for dssemnaton/mplementaton of evdence-based treatments for depresson. However, the predctve models were not found to have external valdty when compared to observed hosptalzaton rates n the four states added to the SID database n Therefore, we conclude that these models cannot be used to dentfy areas outsde the 10 states that are n greatest need for early adopton of evdence-based depresson treatment. Depresson Hosptalzatons

14 References: 1. Gbbons RD, Hur K, Bhaumk DK, Mann JJ. The relatonshp between antdepressant medcaton use and rate of sucde. Arch Gen Psychatry. 2005; 62: Rost K, Zhang M, Fortney J, Smth J, Smth GR, Jr. Rural-urban dfferences n depresson treatment and sucdalty. Med Care. 1998; 36(7): Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S et al. Lfetme and 12-month prevalence of DSM-III-R psychatrc dsorders n the Unted States: Results from the Natonal Comorbdty Survey. Arch Gen Psychatry. 1994; 51(1): Gotway C, Waller L. Appled Spatal Statstcs for Publc Health Data. Hoboken, NJ: John Wley and Sons, Hart LG, Larson EH, Lshner DM. Rural defntons for health polcy and research. Am J Publc Health. 2005; 95(7): Clayton DG, Bernardnell L, Montomol C. Spatal correlaton n ecologcal analyss. Int J Epdemol. 1993; 22(6): Clayton D, Kaldor J. Emprcal Bayes estmates of age-standardzed relatve rsks for use n dsease mappng. Bometrcs. 1987; 43(3): Besag J, York J, Molle A. Bayesan mage restoraton, wth two applcatons n spatal statstcs. Annals of the Insttute of Statstcal Mathematcs. 1991; 43: Thomas A, Best N, Arnold R, Spegelhalter D. GeoBugs User Manual (Verson 1.1 Beta) Ref Type: Electronc Ctaton 10. Wsconsn Department of Health and Famly Servces DoPHMHP. The health of racal and ethnc populatons n Wsconsn: (Wsconsn Mnorty Health Report). PPH Madson, WI, Department of Health and Famly Servces. Ref Type: Report 11. Rchman VV, Rchman EM, Rchman A. Patterns of hosptal costs for depresson n general hosptal wards and specalzed psychatrc settngs. Psychatr Serv. 2000; 51(2): Hendryx MS, Rohland BM. A small area analyss of psychatrc hosptalzatons to general hosptals: Effects of communty mental health centers. Gen Hosp Psychatry. 1994; 16: Kessler RC, Berglund P, Demler O, Jn R, Walters EE. Lfetme prevalence and age-of-onset dstrbutons of DSM-IV dsorders n the Natonal Comorbdty Survey Replcaton. Arch Gen Psychatry. 2005; 62: Rolo SA, Nguyen TA, Greden JF, Kng CA. Prevalence of depresson by race/ethncty: Fndngs from the Natonal Health and Nutrton Examnaton Survey III. Am J Publc Health. 2005; 95(6): Wang PS, Lane M, Olfson M, Pncus HA, Wells KB, Kessler RC. Twelve-month use of mental health servces n the Unted States: Results from the Natonal Comorbdty Survey Replcaton. Arch Gen Psychatry. 2005; 62: Wang PS, Berglund P, Olfson M, Pncus HA, Wells KB, Kessler RC. Falure and delay n ntal treatment contact after frst onset of mental dsorders n the Natonal Comorbdty Survey Replcaton. Arch Gen Psychatry. 2005; 62: Ebner C, Sturn R, Gresenz CR. Does relatve deprvaton predct the need for mental health servces? Journal of Mental Health Polcy and Economcs. 2004; 7(4): Egede LE, Zheng D. Independent factors assocated wth major depressve dsorder n a natonal sample of ndvduals wth dabetes. Dab Care. 2003; 26(1): Mrowsky J, Ross CE. Age and the effect of economc hardshp on depresson. J Health Soc Behav. 2001; 42(2): Dooley D, Cotalano R. Recent research on the psychologcal effects of unemployment. J Soc Issues. 1988; 44(4): Fergusson DM, Horwood LJ, Lynskey MT. The effects of unemployment on psychatrc llness durng young adulthood. Psychol Med. 1997; 27(2): Montgomery SM, Cook DG, Bartley MJ, Wadsworth ME. Unemployment pre-dates symptoms of depresson and anxety resultng n medcal consultaton n young men. Int J Epdemol. 1999; 28(1): Fortney J, Steffck D, Burgess J, Macejewsk M, Petersen L. Are prmary care servces a substtute or complement for specalty and npatent servces? Health Serv Res. In press. Depresson Hosptalzatons

15 24. Dartmouth Medcal School, Center for the Evaluatve Clncal Scences. The Dartmouth Atlas of Health Care. Chcago: Amercan Hosptal Publshng, Inc., Merrck EL. Treatment of major depresson before and after mplementaton of a behavoral health carveout plan. Psychatr Serv. 1998; 49(12): Lesle DL, Rosenheck R. Shftng to outpatent care? Mental health care use and cost under prvate nsurance. Am J Psychatry. 1999; 156: Vdal Rodero C, Lawson A. An evaluaton of the edge effects n dsease map modelng. Computatonal Statstcs and Data Analyss. 2005; 49: Depresson Hosptalzatons

16 Table 1 Defnton of Urban Influence Codes Urban Influence Code Defnton 1 large metropoltan county (populaton greater than 1,000,000) 2 small metropoltan county (1,000,000>populaton>50,000) 3 mcropoltan county (50,000>populaton>10,000) adjacent to large metropoltan county 4 non-metropoltan county (populaton <10,000) adjacent to large metropoltan county 5 mcropoltan county (50,000>populaton>10,000) adjacent to small metropoltan county 6 non-metropoltan county (populaton <10,000) adjacent to small metropoltan county wth own town (populaton >2,500) 7 non-metropoltan county (populaton <10,000) adjacent to small metropoltan county wthout own town (populaton >2,500) 8 mcropoltan county (50,000>populaton>10,000) not adjacent to a metropoltan county 9 non-metropoltan county (populaton <10,000) adjacent to mcropoltan county wth own town (populaton >2,500) 10 non-metropoltan county (populaton <10,000) adjacent to mcropoltan county wthout own town (populaton >2,500) 11 non-metropoltan county (populaton <10,000) not adjacent to metropoltan or mcropoltan county wth own town (populaton >2,500) 12 non-metropoltan county (populaton <10,000) not adjacent to metropoltan or mcropoltan county wthout own town (populaton >2,500) Depresson Hosptalzatons

17 Table 2 Descrptve Statstcs Varables Mean (s.d.)/proporton Dependent Varable Standardzed Hosptalzaton Rato 0.96 (0.38) Explanatory Varables Non-MSA 65.8% Urban Influence Code % Urban Influence Code % Urban Influence Code 3 3.9% Urban Influence Code 4 1.9% Urban Influence Code % Urban Influence Code % Urban Influence Code 7 5.8% Urban Influence Code 8 5.4% Urban Influence Code 9 5.0% Urban Influence Code % Urban Influence Code % Urban Influence Code % Covarates % Afrcan Amercan 6.89 (12.62) % Hspanc 6.97 (10.48) % Asan Amercan 1.12 (1.74) % Natve Amercan 1.50 (5.94) % Poverty (4.64) % Unemployed 2.10 (1.03) % Hgh School Educaton (7.15) House Stress Indcator 31.35% Mnng Dependent 0.96% Farmng Dependent 8.65% Federal Government Dependent 12.88% Servces Dependent 21.35% Manufacturng Dependent 25.58% Not Dependent 30.58% Health Professonal Shortage Area 0.66 (0.67) Physcans Per 1000 Pop 1.38 (1.23) Psychatrsts Per 1000 Pop (0.10) Psychologsts Per 1000 Pop 0.21 (0.39) Socal workers per 1000 Pop 0.84 (1.22) Hosptal Beds Per 1000 Pop 3.4 (3.36) CMHC n County 21.2% HMO Penetraton Rate 0.17 (0.16) Lattude (5.57) Longtude (14.97) Depresson Hosptalzatons

18 Table 3 Bayesan Posson CAR Model Results Varable Model 1 Coeffcents Model 2 Coeffcents Intercept * Non-MSA UIC UIC UIC UIC UIC UIC UIC UIC UIC UIC UIC UIC * % Afrcan Amercan * * % Asan Amercan * % Poverty 0.019* 0.023* % Unemployed % Hgh School Educaton _ House Stress Indcator * Manufacturng Dependent 0.147* 0.119* Not Dependent * HPSA Physcans Per Pop 0.102* 0.090* Psychatrsts Per Pop * Hosptal Beds Per Pop CMHC n County * HMO Penetraton Rate 0.373* Lattude Longtude MSA Metropoltan Statstcal Area UIC Urban Influence Code * p<0.05 Depresson Hosptalzatons

19 Map 1 Indrectly Standardzed Depresson Related Hosptalzaton Rate Legend Standardzed Hosptalzaton Rates Lowest Hghest Depresson Hosptalzatons

20 Map 2 Depresson Related Hosptalzaton Rates from Bayesan Posson CAR Model Legend Standardzed Hosptalzaton Rates Lowest Hghest Depresson Hosptalzatons

21 The WICHE Center for Rural Mental Health Research was establshed n 2004 to develop and dssemnate scentfc knowledge that can be readly appled to mprove the use, qualty and outcomes of mental health care provded to rural populatons. As a General Rural Health Research Center n the Offce of Rural Health Polcy, the WICHE center s supported by the Federal Offce of Rural health Polcy, Health Resources and Servces Admnstraton (HRSA), Publc Health Servces, grant number U1CRH The WICHE Center selected mental health as ts area of concentraton because: (1) although the prevalence and entry nto care for mental health problems s generally comparable n rural and urban populatons, the care that rural patents receve for mental health problems may be of poorer qualty, partcularly for resdents n outlyng rural areas and (2) efforts to ensure that rural patents receve smlar qualty care to ther urban counterparts generally requres restructurng treatment delvery models to address the unque problems rural delvery settngs face. Wthn mental health, the Center proposes to conduct the research development/dssemnaton efforts needed to ensure rural populatons receve hgh qualty depresson care. Wthn mental health, the Center wll concentrate on depresson because: (1) depresson s one of the most prevalent and mparng mental health condtons n both rural and urban populatons, (2) most depressed patents fal to receve hgh qualty care when they enter rural or urban treatment delvery systems, (3) outlyng rural patents are more lkely to receve poorer qualty care than ther urban counterparts, (4) urban team settngs are adoptng new evdence-based care models to assure that depressed patents receve hgh qualty care for the condton that wll ncrease the rural-urban qualty chasm even further, and (5) urban care models can and need to be refned for delvery to rural populatons. The WICHE Center s based at the Western Interstate Commsson for Hgher Educaton. For more nformaton about the Center and ts publcatons, please contact: WICHE Center for Rural Mental Health Research 3035 Center Green Dr. Boulder, CO Phone: (303) Fax: (303) The WICHE Center for Rural Mental Health Research s one of seven Rural Health Research Centers supported by the Federal Offce of Rural Health Polcy (ORHP), Grant No. 1 U1CRH Ths project s funded by ORHP, Health Resources and Servces Admnstraton, U.S. Department of Health and Human Servces. The specfc content of ths paper s the sole responsblty of the authors. Depresson Hosptalzatons

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