Relevance of statistical techniques when using administrative health data: gender inequality in mortality from cardio-vascular disease

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1 Relevance of statstcal technques when usng admnstratve health data: gender nequalty n mortalty from cardo-vascular dsease Julan Perelman Céu Mateus June 2010 Escola Naconal de Saúde Públca, Unversdade Nova de Lsboa and Centro de Investgação e Estudos em Saúde Públca. Correspondence: Escola Naconal de Saúde Públca, Avenda Padre Cruz, Lsbon, Portugal. E-mal: jperelman@ensp.unl.pt, Phone: INTRODUCTION In most health care systems, data are routnely collected at hosptals for fnancng purposes. In partcular, the worldwde use of prospectve per case fnancng schemes requres nformaton on patents characterstcs to determne rembursement levels (Busse et al., 2006). These data are of prmary nterest for researchers. They are routnely collected for all patents n all hosptals; the data collecton s most often compulsory; hosptals have strong ncentves to commt to a hgh-qualty codng n order to get an adequate payment. Hence, large and exhaustve databases are avalable for n-patent dscharges n several countres. However, these databases are not desgned for research purposes but to quckly reveal the hosptal profle wthout generatng too much excess work. As a consequence, they cannot be compared to observatonal studes or clncal trals. There s thus a trade-off between usng large but ncomplete databases versus usng detaled but small and often poorly representatve ones. The man dffculty of usng admnstratve data for research s the nsuffcent nformaton about patents clncal characterstcs. In the present paper, we examne the specfc case of gender dfferences n n-patent mortalty from cardo-vascular dsease. We postulate that lmtatons of admnstratve databases can be overcome through a better understandng of how nsuffcent data bas results and can be compensated through approprate statstcal technques. To do so, we rely on a large admnstratve database ncludng all dscharges for cardo-vascular dseases at all NHS Portuguese hosptals durng the perod. Results from our dataset are compared to those obtaned n the lterature over the last 10 years n studes usng detaled surveys and admnstratve data.

2 RATIONALE Methodologcal ssue Suppose one seeks to estmate the mpact of revascularzaton, ether percutaneous coronary nterventon here-below PCI or bypass surgery, on npatent mortalty. Accordng to the lterature, canddates for nterventon are chosen based on a seres of patents characterstcs. Let us quote, among others, age, ST-segment elevaton on ntal echocardogram, medcal hstory (prevous dagnoses and nterventons), severty on admsson, and tme from the onset of symptoms to hosptal arrval (Vaccarno et al., 2005). However, admnstratve data are generally lmted to the patent s age, sex, and prmary and secondary dagnoses, whch defne the patent s Dagnoss Related Group (DRG) used to set payments. Hence, severty of dsease and, more generally, heterogenety among patents s mperfectly accounted for, leadng to based results. In addton, physcans decsons depend on expected outcomes. The Amercan College of Cardology/Amercan Heart Assocaton (ACC/AHA) gudelnes ndcate for example that, among AMI patents, PCI s approprate when performed on patents wth low mortalty rsk (<0.5%), and napproprate when the rsk s hgh (>=1%) (Epsten et al., 2003). In turn, mortalty rsk s related to severty of dsease whch s poorly observed n admnstratve databases. Unless one succeeds n controllng for severty dfferences, two statstcal problems occur whch lkely bas results on the mpact of treatment on mortalty: () selecton bas due to treated and untreated patents experencng dfferent unobservable characterstcs; () endogenety due to treatment decson beng based on expected outcome related to unobserved characterstcs. We are thus confronted to a classcal problem of selecton, but aggravated on the one hand by the treatment decson beng endogenous to expected outcome, and the crtera to select nto treatment beng partally non-observable. If gender and unobserved severty are correlated, then the measurement bas wll also affect the estmate of gender dfferences n mortalty and treatment use. Gender nequalty n treatment and mortalty Over the last ten years, several studes have been produced to measure gender dfferences n treatment use for cardo-vascular dsease and mortalty. The Table 1 summarzes the result of a systematc survey on these topcs for the perod. Studes are classfed n two categores accordng to the populaton they consder (wth or wthout AMI) and the rsk-adjustors they nclude (detaled clncal factors, medcal hstory, age and comorbdtes). Our search ncluded studes that measured gender dfferences n n-patent mortalty (several of them also measured gender dfferences n treatment). All selected studes were publshed n Englsh n peer-revewed journals n the year 2000 or after. We excluded the studes that consttuted ther cohorts based on nterventons, restrctng our search to cohorts based on dsease (CHD and/or AMI). The search covered the PUBMED bblographc databases. The followng

3 terms were used n the search equaton: gender dff*, gender dspar*, sex dff*, gender bas, sex dspar*, cardo, coro*, myocardal, treatment, death, mortalty. Fnally, we also followed a snowball search, ncludng references from the 10 most recent studes. We observe on Table 1 that over the last ten years, most studes usng detaled rsk-adjustors do not fnd sgnfcant dfferences between men and women. Shaw et al. (2008) observe however a sgnfcant dfference aganst women n patents wth stable angna, Jned et al. (2008) n patents wth STEMI, and Rena et al. (2007) n patents wth AMI. Note also that when age groups are dstngushed, younger women experence worse outcome than younger men (Vaccarno et al., 1999, 2009, Smon et al., 2006). Fewer studes have been carred usng admnstratve data. Four n seven studes found that women have an excess rsk of death and one found that ths excess s lmted to younger women. However the number of studes s qute small, precludng the possblty to draw robust conclusons, studes usng admnstratve data seem more prone to fnd sgnfcant gender dfferences than do studes usng detaled clncal data. METHODS AND DATA Formally, the problem can be set as follows. We model the probablty of death from cardo-vascular dsease, m, as a functon of recevng treatment, t, the patent s gender, g, and other ndvdual characterstcs denoted by x, as a probt model: m " x +! * = 0 + " 1t + " 2 g + " 3 m = 1 f m * > 0 = 0 otherwse The probablty of recevng treatment s also a bnary dependent varable that can also be modeled as a probt model: t! x + µ * = 0 +! 1t +! 2 g +! 3 t = 1 f t * > 0 = 0 otherwse Ths has been the classcal way to deal wth ths ssue,.e., treatng both equatons as separate (generally assumng a logstc nstead of a normal dstrbuton). The presence of endogenety would however mply that error terms are correlated, whch we do assumng a jont dstrbuton of error terms,.e. (!, µ ) ~ "( 0,!). Ths bols down to adoptng a bvarate probt modelng approach. Ths model dffers from the tradtonal one n that the treatment varable appears n the frst equaton, whch renders the model recursve. However, the model (1) (2)

4 cannot be assmlated to a smultaneous-equaton model because otherwse we would have mortalty appearng as explanatory varable n equaton (2). Ths s not the case because obvously treatment s not nfluenced by mortalty, but well by unobservable factors that nfluence the probablty of death and also that of treatment (e.g. the rsk factors dentfed n the ACC/AHA gudelnes). As noted by Jones et al. (2006) n a dfferent context, we face an unobservable heterogenety bas rather than a smultaneous equatons bas. Followng these authors, ths allows us to adopt a type II approach approprate to the use of a recursve model treatment nfluences mortalty and not the mortalty-based propensty to be treated. The followng method s thus nspred on Jones et al. (2006). The complete model s one where error terms of equatons (1) and (2) depend on unobservable factors: $ + = # 1"! 1 and µ + = # 2 "! 2 Ths model s estmated usng a Full Informaton Maxmum Lkelhood (FIML) estmator (see Jones, 2000). If we denote by! the correlaton coeffcent between! and µ, then the statstcal sgnfcance of! wll allow detect the presence of endogenety. We studed patents admtted for cardo-vascular dsease at NHS hosptals n Portugal for the perod. Data were provded by the Central Authorty for Health Servces ( Autordade Central de Servços de Saúde, ACSS), a state agency that collects data on n-patent dscharges from medcal records for admnstratve purposes. Snce cardo-vascular dseases are mostly treated at NHS hosptals, ths offers us an exhaustve data set representatve of natonal patterns of treatment. The followng prncpal dagnoss were selected (usng codes from the Internatonal Classfcaton of Dseases, Nnth Revson, Clncal Modfcaton, ICD-9-CM): AMI (410.xx), stable (411.1x) or unstable angna (413.0x, 413.1x and 413.9x), chronc schemc heart dsease (414.xx), congestve heart falure (428.xx), hypertensve heart dsease wth congestve heart falure (402.91) and chest pan (786.5x). Transfers to another acute care faclty and patents admtted at hosptals not equpped to perform hgh-technology procedures are excluded, snce we cannot follow patents across admssons. Our fnal sample ncludes 255,953 dscharges from 59 hosptals (40.0 percent women, 60.0 percent men). We also analyse two sub-samples, ncludng patent wth the hghest severty of dsease (acute myocardal nfarcton, 64,546 dscharges) and the lowest severty of dsease (stable angna, 17,600 dscharges). We frst model the mpact of gender on n-patent mortalty usng a probt multvarate model, followng equaton (1). The treatment varable, t, s represented by revascularzaton procedures,.e PCI and stents (ICD-9-CM procedure codes 36.01, 36.02, and 36.06), and coronary artery bypass

5 graft (ICD-9-CM procedure codes 36.03, 36.1, 36.2). The other patent characterstcs, x, are represented by a seres of rsk factors, ncludng age, comorbdtes, and year of admsson. Then n-patent mortalty s modeled usng a bvarate probt modelng approach, estmatng jontly equatons (1) and (2). Treatment s modeled n equaton (2) as a functon of gender and other patent characterstcs that are the same as those used n equaton (1). In order to avod dentfcaton problems, we nclude hosptal dummes n the treatment equaton (equaton (2)). Estmatons are performed frst for the complete sample, and then separately for patent wth AMI and stable angna. RESULTS On Table 2, we observe that women are more lkely to de and less lkely to be treated. They are older on average and more prone to suffer from all comorbdtes but malgnancy. Usng the probt model, we observe no sgnfcant gender dfferences n n-patent mortalty for the whole sample (Table 3) and stable angna (Table 5). By contrast, women are more lkely to de after AMI (Table 4). Usng the bvarate model, men are now sgnfcantly more lkely to de than women, for the all sample and for women wth stable angna. For patents wth AMI, gender dfferences are no more sgnfcant. The use of the bvarate model shows that, when accountng for unobserved heterogenety, outcomes are less favorable for men. For all samples, we observe that the mpact of treatment n reducng mortalty rsk s much smaller n magntude usng the probt model than usng the bvarate model. The correlaton coeffcent! s postve sgnfcant, whch ndcates the presence of endogenety. On average, treatment s more provded to hgh rsks, hence the mpact of treatment s under-estmated usng the probt model. When accountng for selecton, revascularzaton reveals more effectve. As women are on average more severely affected than women, accountng for selecton also reduces (resp. ncreases) the pro-men (resp. pro-women) gender gap. The Table 6 dsplays the predcted mortalty rates for men and women usng the dfferent models, wth all other varables set at ther mean value. For all patent types, we observe that women experence hgher death rates n the unadjusted and probt models, and a lower death rate when usng the bvarate model. DISCUSSION The use of admnstratve data on n-patent dscharges s wdespread n varous domans of research on health servces and health systems, and to desgn health polces. In partcular, they have been ncreasngly used to buld performance ndcators, whch are n turn used for benchmarkng and desgn fnancng ncentves (many examples are provded n Iezzon, 2003). Ths oblges us to thnk carefully about the lmtatons of such databases n order to avod based conclusons wth sgnfcant effects on practce. In partcular, t s crucal to be aware of the nsuffcent nformaton on ndvdual characterstcs. Iezzon (2003) largely dscusses ths dffculty, how t may nfluence results and the methods to

6 reduce potental bases. Ths study presents an alternatve strategy, based on econometrc methods, and apples t to the study of gender dfferences n npatent mortalty from cardo-vascular dsease. Treatment decson s nfluenced by the mortalty rsk, whch s tself affected by unobservable factors n admnstratve health data, namely detaled clncal nformaton from dagnoss procedures. Selecton nto treatment and endogenety, both related to unobserved heterogenety, produce based estmates. Indeed, our study ndcates that results substantally dffer accordng to whether controls for unobserved heterogenety or not. As compared to the classcal approach, controllng for unobserved heterogenety dramatcally ncreases the postve mpact of treatment. Also, the women s excess mortalty dsappears, wth a gender gap becomng unfavorable to men or non sgnfcant. Treatment s more provded to those more at rsk, hence the effect of treatment s under-estmated f one fals to approprately measure rsk or severty. As women are more severely affected than men at admsson, the women excess mortalty s over-estmated. The hgher women s severty on admsson has already been nvestgated, wth several explanatons, n partcular related to a lower referral at earler stages of dsease. Women s dsease s detected later, for several reasons manly related to provders vews over cardovascular dsease n women and nterpretaton of symptoms (Schulman et al., 1999 and Arber et al., 2006). Other authors also argue that women have a dfferent way to present ther symptoms and a hgher reluctance to undergo nvasve procedures, although these hypothess have not receved emprcal valdaton (Saha et al., 1999, Schecter et al., 1996). Fnally, women are more severely affected when admtted because men are more lkely to de before hosptalzaton ( ). As regards gender dfferences n mortalty, relevant polcy mplcatons can be drawn. Frst, several authors justfy the lower treatment of women due to ther lower resstance to treatment (Vaccarno et al., 2003, Guru et al., 2006). Ths statement could be consdered as vald had we not controlled for unobserved heterogenety. Ths ndcates one very concrete consequence of not dealng adequately wth bas of admnstratve health data. Second, other authors state that women are dscrmnated ether at earler stages of dsease through lower referral (McKnlay., 1996) or durng admsson through lower treatment. Our analyss does not reject any of these statements, as women appear as more severely affected at admsson (hence more lkely to de) and however less treated even controllng for unobserved heterogenety.

7 TABLES Table 1. Publshed studes on gender dfferences n n-patent or short-term (<=30 days) death. Authors Populaton 1 Gender dfferences 2 Detaled rsk-adjustment 3 Alfredsson, 2009 NSTEMI None Berger, 2009 STEMI NSTEMI Unstable angna None None Men+ Vaccarno, 2009 AMI None for age>=55 Women+ for age<55 Zmmermann, 2009 STEMI None Jned, 2008 AMI STEMI None Women+ Morel, 2008 ACS None Setoguch, 2008 MI Men+ Shaw, 2008 Stable angna ACS Women+ None Alfredsson, 2007 NSTEMI None Radovanovc, 2007 ACS None Rena, 2007 AMI Women+ Koek, 2006 AMI None Smon, 2006 AMI None for age>=67 Women+ for age<67 Martnez-Salles, 2005 AMI None Vaccarno, 2005 MI None De Gevgney, 2001 AMI None Barakat, 2000 AMI None Bowker, 2000 ACS None Gan, 2000 AMI None Hochman, 1999 STEMI NSTEMI Unstable angna None None Men+ Scrca, 1999 Unstable angna None Vaccarno, 1999 AMI None for age>=75 Women+ for age<75 Non-detaled rsk-adjustment 3

8 Fang, 2006 AMI Women+ Hollenbeak, 2006 AMI None Mlcent, 2006 AMI Women+ Perers, 2005 ACS Women+ Ncolau, 2004 AMI None Canto, 2002 AMI Women+ MacIntyre, 2001 AMI None for age>=75 Women+ f age<75 1 ACS: Acute Coronary Syndrome, ncludng Non-ST Elevaton Myocardal Infarcton (NSTEMI) or unstable angna and ST Elevaton Myocardal Infarcton (STEMI). 2 Women+ : women are sgnfcantly more lkely to de; men+ : men are sgnfcantly more lkely to de; none : no sgnfcant dfferences between men and women. 3 Detaled rsk-adjustment means that gender dfferences are rsk-adjusted for detaled clncal factors from NIT (Q-waves, ST-segment elevaton) or CATH (locaton and extent of dsease - percentage dameter stenoss -, ejecton fracton, number of affected vessels), and/or medcal hstory (dabetes, hypertenson, pror myocardal nfarcton, hypercholesterolema, angna, prevous PCI or CABG, smokng habt). Non-detaled rsk-adjustment means that gender dfferences are rsk-adjusted for factors commonly avalable n admnstratve data: comorbdtes (congestve heart falure, pulmonary edema, shock, malgnancy, cardac dysrhythma, acute or chronc renal nsuffcency, or Charlson comorbdty ndex), soco-economc status (nsurance status or educaton, and ethncty or race), age.

9 Table 2. Patent characterstcs Men, % (n=153,759) Women, % (n=102,374) In-patent mortalty 7.41 (11,376) (12,086) Revascularzaton (43,659) (13,979) Age (Mean, SD) (12.60) (11.68) Comorbdtes Congestve heart falure 7.23 (142,475) 9.03 (9,242) Cardac dysrhytmas (26,755) (26,464) Cerebrovascular dsease 4.20 (6,451) 5.45 (5,579) Pulmonary edema 0.40 (615) 0.61 (622) Dabetes wth complcaton 3.92 (6,027) 6.13 (6,280) Chronc renal falure 5.10 (7,829) 5.99 (6,130) Acute renal falure 2.51 (3,850) 3.34 (3,420) Malgnancy 1.73 (2,651) 1.24 (1,268) Shock 0.93 (1,422) 1.22 (1,248)

10 Table 3. Adjusted margnal effects for n-hosptal death and revascularzaton use among patents all sample 1. Probt model In-patent death Bvarate model Revascularzaton use Probt model Bvarate model Revascularzaton * **.. Female ** ** Congestve heart falure 0.034** 0.014** ** 0.069** Cardac dysrhytmas 0.034** 0.008** ** 0.369** Cerebrovascular dsease 0.064** 0.053** ** 0.108** Pulmonary edema 0.049** 0.028** ** Dabetes wth complcaton 0.025** 0.011** ** Chronc renal falure 0.034** 0.009** ** Acute renal falure 0.133** 0.105** ** 0.517** Malgnancy 0.066** 0.027** ** 0.140** Shock 0.662** 0.608** ** 1.649** Pseudo-R (d) (d) rho **. (d) Notes: 1 All regressons nclude 11 dummes for age groups and 5 dummes for year, whch are not reported. *p-value<0.10**p-value<0.05.

11 Table 4. Adjusted margnal effects for n-hosptal death and revascularzaton use among patents wth acute myocardal nfarcton 1. Probt model In-patent death Bvarate model Revascularzaton use Probt model Bvarate model Revascularzaton * **.. Female 0.010** ** ** Congestve heart falure 0.051** 0.016** ** ** Cardac dysrhytmas 0.098** 0.092** ** ** Cerebrovascular dsease 0.060** 0.025** ** ** Pulmonary edema ** ** Dabetes wth complcaton 0.036** ** ** Chronc renal falure 0.023** ** ** Acute renal falure 0.158** 0.144** ** ** Malgnancy 0.078** 0.033** ** ** Shock 0.646** 0.568** Pseudo-R (d) (d) rho **. (d) Notes: 1 All regressons nclude 11 dummes for age groups and 5 dummes for year, whch are not reported. *p-value<0.10**p-value<0.05.

12 Table 5. Adjusted margnal effects for n-hosptal death and revascularzaton use patents wth stable angna 1. Probt model In-patent death Bvarate model Revascularzaton use Probt model Bvarate model Revascularzaton ** **.. Female ** ** ** Congestve heart falure 0.011** ** ** Cardac dysrhytmas 0.022** 0.197** ** ** Cerebrovascular dsease 0.026** 0.037** ** ** Pulmonary edema Dabetes wth complcaton * ** ** Chronc renal falure 0.013** < ** ** Acute renal falure 0.047** 0.056** ** ** Malgnancy 0.022** ** ** Shock 0.585** 0.567** Pseudo-R (d) (d) rho **. (d) Notes: 1 All regressons nclude 11 dummes for age groups and 5 dummes for year, whch are not reported. *p-value<0.10**p-value<0.05.

13 Table 6. Adjusted n-hosptal death rates. Men Women Dfference All sample Unadjusted model Probt Model Bvarate model AMI Unadjusted model Probt Model Bvarate model Stable angna Unadjusted model Probt Model Bvarate model

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