Abstract
Background
Although frequently used to track healthcare disparities, patient race/ethnicity data collected by hospitals can be unreliable, particularly for smaller minority groups. We sought to determine if the racial/ethnic distribution of hospitalized patients shifted after implementation of a statewide initiative to standardize data collection practices.
Methods
We conducted a difference-in-differences analysis of the State Inpatient Databases to estimate changes in the proportion of patients identified as non-Hispanic white, non-Hispanic black, Hispanic, Asian/Pacific Islander, and “other,” before (2005–2006) and after (2008–2009) standardized practices were implemented in New Jersey (NJ) relative to New York (NY), a state with similar demographics but no changes to data collection.
Results
Among 12,552,702 hospital discharges, modest relative changes were noted in the proportion of patients identified as non-Hispanic white (+1.1%; 95% CI +0.9 to +1.2) and non-Hispanic black (+1.6%; 95% CI +1.1 to +2.1) in NJ that were attributed to its use of standardized data collection practices as compared with NY. Larger relative changes were noted in the proportion of patients identified as Hispanic (−7.1%; 95% CI −7.8 to −6.4), Asian/Pacific Islander (+26.5%; 95% CI +25.1 to +27.9) and “other” (−24.6%; 95% CI −26.4 to −22.8). This pattern was largely consistent in analyses stratified by gender, age, and Major Diagnostic Category.
Conclusions
Measurement of healthcare disparities fundamentally depends on the racial/ethnic categorization of individuals. By redistributing substantial proportions of patients across smaller minority groups, standardized data collection could lead to shifts in estimates of healthcare disparities for these rapidly growing populations.
INTRODUCTION
In the landmark report Unequal Treatment, the Institute of Medicine (IOM) highlighted the need to track and compare healthcare utilization, delivery, and outcomes across racial and ethnic groups to ensure equitable care.1 Although information on the race/ethnicity of patients collected by hospitals is frequently used to track such measures, several studies suggest these data are unreliable due to faulty data collection practices.2–5 Hospitals frequently report patient race and/or ethnicity based on the observations of admitting clerks6 rather than self-identification which is considered the gold standard.7 Determinations of race/ethnicity by hospital staff are especially problematic for smaller minority groups.3–5,8,9 Additionally, in 2011, only 16 of 43 states reported using current Federal standard categories for race and ethnicity established by the Office of Management and Budget (OMB) in hospital claims, with most discrepancies occurring in the classification of smaller minority groups such as Hispanic and Asian.10 The absence of race and ethnicity fields on the national standard for hospital claims prior to 2007 and variable uptake of updates to the OMB standard in 1997 likely perpetuated these inconsistencies.10
To address these concerns, the IOM recommends that hospitals enable patients to self-identify their race and ethnicity using a uniform set of categories that expand on the current (1997) OMB standards.11 The New Jersey Hospital Association (NJHA) is one of a few healthcare systems that have recently implemented these recommendations. In 2007, the Health Research and Education Trust (HRET) of NJHA launched an innovative statewide strategy to standardize practices for collecting more specific race and Hispanic origin data from patients in order to better understand the healthcare needs of the state.12 Understanding if and how these changes to data collection practices affect assessments of the racial/ethnic distribution of hospitalized patients is unknown but of critical importance given the ubiquitous use of race/ethnicity data collected by hospitals to track healthcare disparities.
Accordingly, we sought to determine how patients may be shifted across different race/ethnicity categories in association with implementation of standardized data collection practices in New Jersey (NJ).
METHODS
Data Sources and the NJ HRET Program
We analyzed the State Inpatient Databases (SIDs) to compare the racial/ethnic distribution of hospitalized patients age 18 and older before (2005–2006) versus after (2008–2009) implementation of standardized data collection practices in NJ (intervention group) relative to New York (NY, comparison group). The SID, which is part of the Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project (HCUP), contains the universe of inpatient discharge abstracts provided by state data partners (the New Jersey Department of Health and Senior Services and the New York State Department of Health).13 Training and implementation of the new system in NJ occurred throughout 2007; therefore, data from 2007 was excluded. Individuals with missing age, gender, or race data (less than 2% of discharges) were excluded from the analysis. The study was deemed exempt from review by the Yale Institutional Review Board.
The NJ HRET campaign included standardized guidelines and uniform protocols for the collection of self-identified patient race/ethnicity data which were disseminated statewide by: (1) conducting training programs for hospital intake workers, access managers, supervisors and registrars; (2) adapting information technology systems to be compatible with the standardized guidelines; and (3) designing and distributing educational tools, resources, and reference toolkits to hospital staff to ensure sustainability of the campaign.12 As part of this effort, HRET also collaborated with the New Jersey Department of Health and Senior Services to mandate adoption of revised standardized categories for reporting race and ethnicity in January 2007 which expanded on the OMB standards to include more specific race and ethnicity categories consistent with US Census Bureau classifications (see Appendix Table 1).12
Appendix Table 1.
Race/Ethnicity Categories by Data Source
| New Jersey SID | HCUP 2005–2009 | New York SID 2005–2009 | |
|---|---|---|---|
| 2005–2006 | 2008–2009 | ||
| Mexican | Mexican, Mexican American, Chicano | Hispanic | Spanish/Hispanic origin |
| Puerto Rican | Puerto Rican | ||
| Cuban | Cuban | ||
| Central or South American | Central or South American | ||
| Other and Unknown Hispanic | Other Spanish/Hispanic/Latino | ||
| White | White | White | White |
| Black | Black or African American | Black | African American (Black) |
| Asian Indian | Asian Indian | Asian or Pacific Islander | |
| Chinese | Chinese | ||
| Filipino | Filipino | ||
| Japanese | Japanese | ||
| Korean | Korean | ||
| Vietnamese | Vietnamese | Asian | |
| Native Hawaiian | Native Hawaiian | Native Hawaiian or Other Pacific Islander | |
| Guamian or Chamorro | Guamian or Chamorro | ||
| Samoan | Samoan | ||
| Other Asian or Pacific Islander | Other Asian | ||
| Other Pacific Islander | |||
| American Indian or Alaska Native | American Indian or Alaskan Native | Native American | Native American (American Indian, Eskimo, Aleut) |
| Multiracial: White and Black or African American | Multiracial: White and Black or African American | Other | Other |
| Multiracial: White and American Indian or Alaskan Native | Multiracial: White and American Indian or Alaskan Native | ||
| Multiracial: White and Asian | Multiracial: White and Asian | ||
| Multiracial: Black or African American and American Indian or Alaskan Native | Multiracial: Black or African American and American Indian or Alaskan Native | ||
| Other races | Other Race | ||
| Unknown, Missing Declined to Answer Blank | Unknown/Unavailable Declined to answer Blank | Missing | Missing Blank |
Study Outcomes
The outcome variable of interest was patient race/ethnicity, as reported according to categories used by HCUP. Because of known variation in coding race/ethnicity in claims data across states, HCUP uniformly reports the information received from all participating states using 6 mutually exclusive categories (see Appendix Table 1) equivalent to the 1977 OMB standards: non-Hispanic white, non-Hispanic black, Hispanic, Asian/Pacific Islander, Native American and “other”.10 If patients were identified as Hispanic for the NJ or NY SID ethnicity variable, these individuals were reported to be Hispanic for the HCUP race variable regardless of their racial classification in the NJ or NY SID. We included Native Americans in the “other” category because of their relatively small populations in both states. The categories used to collect race/ethnicity data in NY did not change between the study periods (see Appendix Table 1), making it an ideal comparator for NJ in this analysis.
Statistical Analysis
Changes in the proportion of patients identified in each racial/ethnic category that were associated with the new data collection practices in NJ were estimated using a nonlinear difference-in-differences model.14 We predicted the racial/ethnic distributions of hospitalized patients in NJ and NY as a function of the interaction of state and time (before versus after implementation of standardized data collection practices in NJ), adjusting for the two main effects of state and time, three age categories (18–44, 45–64, ≥65), and gender. For ease of reporting, we used multinomial logistic regression models15 to generate estimates and then expressed these as the proportion of individuals identified within each race/ethnicity category per 10,000 hospitalized patients. We clustered the models on survey year and used an unconditional variance estimator to carry the clustering through predictions. The statistical significance of the difference-in-differences was assessed with 95% confidence intervals constructed from delta-method standard errors and corrected for multiple testing within each race/ethnicity category.16,17 In order to assess the relative impact of standardized practices on the identification of patients within each racial/ethnic category, we divided the absolute difference-in-differences by the 2005–2006 proportions in NJ to report the percent change in the proportion of patients identified in each racial/ethnic category in NJ relative to NY (see Figure 1). This assessment of percent change was especially important given the smaller sizes of minority groups.
Figure 1.
Calculation of Percent Change in the Proportion of Patients Identified in Each Racial/Ethnic Category
Inherent in our use of the difference-in-differences approach is the identifying assumption that the racial/ethnic distributions of the underlying populations of NJ and NY would have trended in parallel over time in the absence of the NJ program. To test the robustness of this assumption, we replicated the analysis using the Public Use Microdata Sample of the American Community Survey (ACS). The ACS provides annual estimates on the race/ethnicity of the general population for each state.18 ACS respondents self-identify their race and ethnicity; additionally, since 2000, they have been able to identify multiple races if indicated.19 For this analysis, we included multiracial respondents to the ACS in the “other” category. Importantly, the process for collecting race/ethnicity data on the ACS did not change between 2005 and 2009 in either state. Therefore, we were able to use the ACS data to replicate the primary analysis of the SID to test the identifying assumption that any observed shifts in the racial/ethnic distribution of hospitalized patients were independent of demographic shifts in the general population of either state.
Finally, we conducted a series of sensitivity analyses to test the robustness of our results by replicating the primary analysis on different subsets of the SID. For these stratified analyses we analyzed the SID according to (1) Major Diagnostic Categories (MDCs) and (2) age categories (18–44, 45–64, and >65) separately for men and women.
All analyses were conducted in Stata 12 (College Station, TX).20
RESULTS
The final study population from the SIDs of NJ and NY included 12,552,702 discharges. Table 1 lists the racial/ethnic distribution, age, gender, and MDCs for hospitalized patients stratified by state prior to the implementation of the new data collection practices in NJ. The distribution of patients across these categories was similar between both states with the exception of NJ having a slightly higher percentage of patients classified as non-Hispanic white and NY having a greater percentage of patients classified as “other”.
Table 1.
Characteristics of 2005–2006 Hospital Discharges by State, Ages 18+
| State | New Jersey | New York |
|---|---|---|
| Number of hospital discharges | 1,833,110 | 4,313,490 |
| Demographic Characteristics | ||
| Non-Hispanic White | 66.7% | 61.0% |
| Non-Hispanic Black | 16.3 | 18.4 |
| Hispanic | 11.6 | 11.9 |
| Asian/Pacific Islander | 2.3 | 2.5 |
| Other Race/Ethnicity | 3.1 | 6.2 |
| Age 18–44 | 30.1 | 32.0 |
| Age 45–64 | 26.5 | 28.0 |
| Age 65 and above | 43.4 | 40.0 |
| Female | 59.4 | 58.1 |
| Male | 40.6 | 41.9 |
| Major Diagnostic Categories | ||
| Circulatory | 22.3 | 19.6 |
| Pregnancy/Childbirth | 12.7 | 12.1 |
| Digestive | 10.1 | 9.3 |
| Respiratory | 9.9 | 9.1 |
| Musculoskeletal | 7.3 | 7.8 |
| Other diagnostic category | 37.7 | 42.2 |
Note. Excludes hospital discharge abstracts missing data on gender, age, or race/ethnicity (less than 2% of the study sample).
Overall, we found significant changes in the racial/ethnic distribution of hospitalized patients associated with the new standardized data collection practices in NJ when compared with NY; however, the magnitude of these changes varied substantially across racial/ethnic categories (see Table 2 and Figure 2). For example, the proportion of patients identified as non-Hispanic white and non-Hispanic black changed only modestly between the study periods in NJ relative to NY. In contrast, we observed much larger changes among smaller racial/ethnic categories related to the new data collection practices in NJ. The proportion of patients identified as Hispanic decreased in NJ but increased in NY leading to a relative decrease in the proportion of patients identified as Hispanic in NJ (−7.1%). The proportion of patients identified as Asian/Pacific Islander increased to a greater extent in NJ than in NY, leading to a relative increase in the proportion of patients identified as Asian/Pacific Islander in NJ (+26.5%). Finally, the proportion of patients identified as “other” increased to a lesser extent in NJ than in NY, leading to a relative decrease in the proportion of patients identified as “other” in NJ (−24.6%).
Table 2.
Adjusted Racial/Ethnic Distribution per 10,000 Hospitalizations in New Jersey and New York; State Inpatient Databases, Ages 18+ (N = 12,552,702)
| 2005–2006 | 2008–2009 | Difference | Difference-in- Differences |
Percent change | |
|---|---|---|---|---|---|
| Non-Hispanic White | |||||
| NJ | 6,669 (6660, 6677) | 6,520 (6512, 6529) | −148 (−159, −138) | +71 (59, 82)*** | +1.1 (+0.9, +1.2) |
| NY | 6,104 (6098, 6110) | 5,885 (5879, 5891) | −219 (−226, −212) | ||
| Non-Hispanic Black | |||||
| NJ | 1,633 (1627, 1640) | 1,663 (1656, 1669) | +29 (21, 38) | +26 (17, 35)*** | +1.6 (+1.1, +2.1) |
| NY | 1,838 (1833, 1842) | 1,841 (1836, 1845) | +3 (−3, 9) | ||
| Hispanic | |||||
| NJ | 1,153 (1148, 1159) | 1,114 (1109, 1120) | −39 (−46, −32) | −82 (−90, −74)*** | −7.1 (−7.8, −6.4) |
| NY | 1,185 (1182, 1189) | 1,228 (1225, 1232) | +43 (38, 48) | ||
| Asian/Pacific Islander | |||||
| NJ | 233 (231, 236) | 344 (340, 347) | +110 (107, 114) | +62 (58, 66)*** | +26.5 (+25.1, +27.9) |
| NY | 253 (251, 254) | 301 (299, 303) | +49 (46, 51) | ||
| Other Race/Ethnicity | |||||
| NJ | 311 (308, 315) | 359 (356, 363) | +48 (44, 52) | −76 (−81, −72)*** | −24.6 (−26.4, −22.8) |
| NY | 620 (617, 623) | 744 (741, 747) | +124 (120, 128) | ||
Notes. Adjustments are for gender and age categories (18/44, 45/64, 65+); Adjusted proportions are predictive margins (proportions), differences are contrasts of margins, and differences-in-differences are incremental effects of the interaction term in the model. Percent changes are DDs divided by 2005–2006 proportions for NJ, and multiplied by 100. 95% confidence intervals in parentheses were computed using the delta-method. CIs for each race are corrected for multiple testing (α/4 for the proportions and α/2 for the single differences). P-values are from chi-square tests of contrasts;
p < 0.001.
Figure 2.
Adjusted Percent Change for Each Racial/Ethnic Category
Table 3 reports results from our analysis of changes in the racial/ethnic distribution of the general populations of NJ and NY for the same time periods using ACS data. As expected, assessments of the distribution of race/ethnicity in both populations from these states remained relatively stable over time, indicating our results were unlikely to be explained by underlying demographic shifts. In sensitivity analyses, the percent changes in the proportions for each racial/ethnic category were largely consistent across MDCs as well as across age categories among men and women (see Supplemental Digital Content Figure 1 and Supplemental Digital Content Figure 2).
Table 3.
Adjusted Racial/Ethnic Distribution per 10,000 Individuals in New Jersey and New York; American Community Survey, Ages 18+ (N = 838,461)
| 2005–2006 | 2008–2009 | Difference | Difference-in- Differences |
Percent change | |
|---|---|---|---|---|---|
| Non-Hispanic White | |||||
| NJ | 6,489 (6486, 6492) | 6,343 (6339, 6346) | −146 (−150, −142) | −65 (−69, −60)*** | −1.0 (−1.1, −0.9) |
| NY | 6,276 (6274, 6278) | 6,194 (6192, 6197) | −82 (−85, −79) | ||
| Non-Hispanic Black | |||||
| NJ | 1,217 (1215, 1220) | 1,261 (1259, 1263) | +44 (41, 47) | +10 (7, 13)*** | +0.8 (+0.6, +1.1) |
| NY | 1,375 (1373, 1376) | 1,408 (1407, 1410) | +33 (31, 35) | ||
| Hispanic | |||||
| NJ | 1,438 (1435, 1440) | 1,516 (1514, 1519) | +79 (76, 82) | +44 (40, 47)*** | +3.0 (+2.8, +3.3) |
| NY | 1,500 (1498, 1501) | 1,535 (1533, 1536) | +35 (33, 37) | ||
| Asian/Pacific Islander | |||||
| NJ | 739 (737, 741) | 754 (752, 755) | +15 (13, 17) | +3 (1, 6)** | +0.4 (+0.1, +0.8) |
| NY | 692 (691, 693) | 704 (702, 705) | +12 (10, 13) | ||
| Other Race/Ethnicity | |||||
| NJ | 117 (117, 118) | 127 (126, 127) | +9 (8, 10) | +8 (7, 9)*** | +6.5 (+5.6, +7.3) |
| NY | 158 (157, 158) | 159 (159, 160) | +2 (1, 2) | ||
Notes. Rates are estimated using the American Community Survey frequency weights. Adjustments are for gender and age categories (18/44, 45/64, 65+); Adjusted proportions are predictive margins (proportions), differences are contrasts of margins, and differences-in-differences are incremental effects of the interaction term in the model. Percent changes are DDs divided by 2005–2006 proportions for NJ, and multiplied by 100. 95% confidence intervals in parentheses were computed using the delta-method. CIs for each race are corrected for multiple testing (α/4 for the proportions and α/2 for the single differences). P-values are from chi-square tests of contrasts;
p < 0.01,
p < 0.001.
DISCUSSION
We found substantial alterations in the racial/ethnic distribution of patients hospitalized in NJ following implementation of a statewide effort to standardize the processes and categories used to collect race/ethnicity data directly from patients in 2007. The most notable changes occurred among smaller minority groups with the proportion of patients identifying as Hispanic decreasing by 7%, “other” decreasing by 25%, and Asian/Pacific Islander increasing by 27%. These shifts may have occurred through several mechanisms. For example, the increase in the proportion of patients identified as Asian/Pacific Islander and decrease in the proportion of patients identified as Hispanic and “other” may have been due to misclassification of Asians/Pacific Islanders by hospital staff into the latter two categories in 2005–2006. Self-identification may have also allowed patients whose race/ethnicity was previously unassigned to identify themselves as Asians/Pacific Islanders. The decreases in the proportions of patients identifying as Hispanic and “other” were likely to have been additionally influenced by systemic issues with the conceptualization of race/ethnicity among these groups. A recent study found that individuals identifying as Hispanic, “other”, or multiracial (included in the “other” category in the NJ SID) were more likely to change their self-identified race and/or ethnicity than single-race non-Hispanic white, non-Hispanic black and Asian individuals when comparing responses on the 2000 and 2010 Census.21 Hispanic individuals often define both their race and ethnicity as Hispanic even though Hispanic is considered an ethnic classification by the federal government; thus, responses to questions of race often vary between white and “other” for Hispanics.21
NJ hospitals are among a growing number of healthcare institutions seeking to improve race/ethnicity data collection systems in order to better monitor healthcare disparities. A few studies have evaluated the effect of such interventions on the racial/ethnic identification of specific patients followed before and after data collection changes were made. One study found that as much as 70% of the race/ethnicity data collected for American Indian/Alaskan Native and Asian/Pacific Islander patients by the Department of Veterans Affairs may have been inaccurate prior to 2003 when they mandated the use of self-report for racial/ethnic identification.22 More recently, the “Expecting Success” program found that among a subset of patients seen both before and after changes were made to the collection of race/ethnicity data in participating hospitals there was no significant effect on the aggregate distribution of reported race and ethnicity for the OMB race categories.23 However, very few patients from smaller minority groups were included in these studies and neither study assessed the effects of data collection changes on the racial/ethnic distribution of patients at a population level.
To our knowledge, our study is the first to assess the impact of standardized data collection practices at the population, rather than individual, level. Use of the difference-in-differences approach, in particular, enabled us to better attribute specific changes in the racial/ethnic distribution of patients to implementation of standardized data collection practices in NJ. Additionally, our study overcame prior limitations by evaluating an initiative that collected self-reported data and used standardized processes and categories in a state with large minority populations. In doing so, our findings highlight the particular benefit of using standardized data collection practices to obtain self-identified race/ethnicity data for smaller minority groups within hospital discharge data. Sample size concerns for smaller minority groups often limit researchers’ ability to generate statistically reliable estimates for these groups in assessments of healthcare disparities.24 For example, despite the fact that data were collected to identify Asians or Asians/Pacific Islanders in all of the measures included in the 2003 National Healthcare Disparities Report, reliable estimates for Asian Americans could be generated for only 47% of utilization measures because of inadequate sample sizes of this group.24 Several recent articles have highlighted the need for better data on healthcare utilization and outcomes among Asian Americans.25–29 By increasing the identification of Asians/Pacific Islanders in healthcare data, the newly implemented data collection system in NJ could significantly improve our ability to recognize disparities that may be affecting these groups.
Our study should be interpreted in the context of its limitations. Since we used cross-sectional data for our analysis, it is impossible to know exactly how standardized practices redistributed patients across racial/ethnic categories. Also, our study does not account for additional factors that could have contributed to the observed shift in the racial/ethnic distribution of hospitalized patients during the study period. For example, variation in the extent to which standardized practices were implemented by hospitals might have influenced our results. Demographic shifts in the general population of NJ and NY would have also significantly influenced the racial/ethnic distribution of hospitalized patients between the study periods. However, our analysis of ACS data indicates our findings were unlikely to be due to demographic shifts in the general population.
Despite these limitations, our results have significant implications for policymakers and providers. Measurement of healthcare disparities fundamentally depends on the accurate and reliable racial/ethnic categorization of individuals. By redistributing substantial proportions of patients across smaller minority groups, standardized data collection practices could lead to significant shifts in estimates of healthcare disparities for these rapidly growing populations. Additionally, the Department of Health and Human Services recently released new standards for the collection of data on race/ethnicity in national population health surveys that closely resemble the NJ program.7,30 Our study demonstrates the significant effect these data collection changes could have if applied to data collected by hospitals as well. As healthcare institutions develop innovative methods of collecting high quality race/ethnicity data, we should continue to evaluate these strategies in order to identify “best practices” for monitoring healthcare disparities across all groups.
Supplementary Material
Figure 1. Adjusted Percent Change for Each Racial/Ethnic category, by Age and Gender
Figure 2. Adjusted Percent Change for Each Racial/Ethnic Category, by Major Diagnostic Category
Acknowledgments
Funding: Dr. Chakkalakal was supported by the Robert Wood Johnson Foundation Clinical Scholars Program at Yale University (award ID 70033) at the time this work was completed. This research was also supported by the Center for Cardiovascular Outcomes Research at Yale University (U01 HL105270-04) which is funded by the National Heart, Lung, and Blood Institute.
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Supplementary Materials
Figure 1. Adjusted Percent Change for Each Racial/Ethnic category, by Age and Gender
Figure 2. Adjusted Percent Change for Each Racial/Ethnic Category, by Major Diagnostic Category