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Racial Misclassification in Mortality Records Among American Indians/Alaska Natives in Oklahoma From 1991 to 2015

Dougherty, Tyler M. MPH, CPH; Janitz, Amanda E. PhD, BSN, RN; Williams, Mary B. PhD; Martinez, Sydney A. PhD; Peercy, Michael T. MPH, MT(ASCP)H; Wharton, David F. MPH, RN; Erb-Alvarez, Julie MPH, CPH; Campbell, Janis E. PhD, GISP

Author Information
Journal of Public Health Management and Practice: September/October 2019 - Volume 25 - Issue - p S36-S43
doi: 10.1097/PHH.0000000000001019
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Abstract

Racial misclassification among American Indians and Alaska Natives (AI/ANs) has been well documented in the literature for more than 40 years,1–4 and has resulted in a systematic underestimation of the burden of mortality. This misclassification influences states and tribes/nations both financially and administratively. It is essential that accurate data be available for national, state, and local agencies to determine where disparities exist in order to identify points of intervention, plan and evaluate programs, procure and allocate resources, and measure outcomes.

Misclassification, as well as other factors (ie, undersampling in surveys), of AI/ANs has been responsible for underestimating the presence and impact of HIV/AIDS, kidney disease, diabetes, cancer, neonatal and all-cause mortality in many locations throughout the United States.2,5–32 Misclassification varies by region, and the Southern Plains area (Oklahoma, Kansas, and Texas) may experience the greatest impact, with around 37% of AI/AN decedents misclassified as a different race (primarily as white) on death certificates.33 In Oklahoma, racial misclassification is associated with routinely underestimating morbidity and mortality rates of AI/ANs including areas such as infectious disease, sexually transmitted infections, injury, and cardiovascular disease.34–36

Despite the long-term recognition of racial misclassification, the problem has been slow to resolve. Multiple methods have been implemented to improve the problem, including regular administrative linkages with Indian Health Service (IHS) and birth, death, and cancer records; linkage with tribal membership or citizenship rolls; increased training of medical examiners/coroners, funeral home directors, and health facilities administrators; and using self-reported race categories.2,18,20,23,24,37–40 Studies concerning associations of racial misclassification among AI/ANs beyond the overall impact on morbidity and mortality, however, are virtually nonexistent. In addition, the National Death Index linkage with IHS records has not occurred on a national scale since 2009, resulting in nearly 10 years of mortality data that were not linked with IHS records (although an updated linkage is planned). Because of these data gap and the concern for inaccurate mortality data for AI/ANs in Oklahoma, staff from the University of Oklahoma Health Sciences Center, Oklahoma State Department of Health, the Oklahoma City Area IHS Office, the Oklahoma Area Tribal Epidemiology Center, and Centers for Disease Control and Prevention staff in New Mexico collaborated to update the linkage with IHS records for Oklahoma mortality files from 2010 to 2015, which were the most recent data available at the time of the linkage.

The primary purpose of this study was to compare age-adjusted mortality rates (AAMRs) before and after linkage with IHS records, adjusting for racial misclassification. We focused on differences in racial misclassification by gender, age, and geographic differences; substate planning districts (SSPDs); and cause of death. Our secondary purpose was to evaluate time trends in misclassification from 1991 to 2015.

Methods

Mortality

We obtained mortality rates per 100 000 population in the state of Oklahoma for AI/ANs and for each of the SSPDs using the Oklahoma State Department of Health's Web-based query system, Oklahoma Statistics on Health Available for Everyone (OK2SHARE).41 For our time-trend analysis, we used AAMRs for AI/ANs from 1991 to 2015. For all other analyses, we used crude, age-specific, and AAMRs for the last 5 years available (2011-2015). Mortality rates were age-adjusted by the direct method employing the 2000 US standard population using the bridged population estimates. This project was reviewed and approved by the University of Oklahoma Health Sciences Center Institutional Review Board. This article was submitted to the Oklahoma City Area Indian Health Service Institutional Review Board and was approved for publication.

Linkage methods

The IHS linkage process has been described elsewhere.33 Briefly, AI/AN individuals must provide proof of membership in a federally recognized tribe to receive health care from IHS, Tribal, and Urban Indian health (I/T/U) facilities. A public health database (in this case the Oklahoma vital records mortality data) was linked to the master patient file for IHS. There is a strict protocol and no health information was exchanged with the exception of basic demographic variables that are needed to conduct the record linkage (name, gender, date of birth, and address). Oklahoma has participated in this IHS linkage process, primarily for cancer surveillance data for more than 20 years7,34 and participated in the national linkage project described by Jim et al.33 For this study, the State of Oklahoma followed the same linkage process to update its death records from 2010 to 2015. OK2SHARE provides both the unadjusted (before linkage) and adjusted (after linkage) data. The unadjusted (original race code) on-death certificates were reported by funeral directors and physicians or their staff. These individuals frequently do not ask the family about the racial identification of the deceased individual. The IHS-linked race code classifies individuals as AI/AN if (1) the race reported on the death certificate was AI/AN or (2) the individual record linked to IHS regardless of the original race reported on the death certificate.

Statistical analyses

We calculated mortality rates prior to adjustment for racial misclassification using the original race code from OK2SHARE and mortality rates adjusted for racial misclassification using the IHS-linked race codes. We then calculated a misclassification rate ratio (MRR) and corresponding 95% confidence interval (CI) of the AAMR adjusted for misclassification compared with the unadjusted AAMR for racial misclassification for all racial groups (white, African American, AI/AN, and Asian/Pacific Islander) and for AI/ANs by gender, age group, and SSPD. We also obtained the AAMR by cause of death for the top 10 causes of death among non-Hispanic AI/ANs in the United States from 2015.42 We calculated the percent difference in the AAMR pre- and post-IHS linkage.

Time trends analysis

We used the Joinpoint Regression Program (version 4.6.0.0; National Cancer Institute, Bethesda, Maryland) to analyze yearly time trends of the percentage of AI/ANs that were misclassified from 1991 through 2015. Trends in misclassification over time were characterized using annual percent change (APC). We allowed a maximum of 4 joinpoints in the model. If the slope of the trend was significantly different from zero, an APC estimate was reported as increased or decreased; however, if there was no significant difference, the trend was reported as stable. To account for transition in trends, we also estimated the average annual percent change (AAPC).

Geography

For this study, we used the SSPDs in Oklahoma, which are voluntary associations of local governments formed under Oklahoma law that deal with problems and planning needs that cross the boundaries of individual local governments, such as counties, cities, and towns. Substate planning districts complete regional data collection and analysis, mapping, and coordination of environmental, economic, and social program plans, Rural Fire Defense, Capital Improvements Plan, Rural Economic Action Plan Grant programs, and Hazard Mitigation Planning. There are 11 SSPDs in Oklahoma with each SSPD encompassing between 3 counties in the Indian Nations Council of Governments (INCOG) and 10 counties in the Southern Oklahoma Development Association (Figure 1).

FIGURE 1
FIGURE 1:
Substate Planning Districts (SSPDs) in Oklahoma 2018Abbreviations: ACOG, Association of Central Oklahoma Governments; ASCOG, Association of South Central Oklahoma Governments; COEDD, Central Oklahoma Economic Development District; EODD, Eastern Oklahoma Development District; GGEDA, Grand Gateway Economic Development Association; INCOG, Indian Nations Council of Governments; KEDDO, Kiamichi Economic Development District of Oklahoma; NODA, Northern Oklahoma Development Authority; OEDA, Oklahoma Economic Development Authority; SODA, Southern Oklahoma Development Association; SWODA, South Western Oklahoma Developmental Authority.

Results

Time trends analysis

There was a decrease in the percentage of AI/ANs who were misclassified from 1991 to 2015 (Figure 2). The AAPC for the percentage of racially misclassified AI/ANs over the entire study period was −1.8% (95% CI: −3.1% to −0.5%) (Table 1). We identified 2 stable trends from 1991 to 2001 (APC: −0.2%; 95% CI: −1.4% to 1.0%) and from 2001 to 2005 (APC: −6.9%; 95% CI: −13.7% to 0.4%). However, the trend identified from 2005 to 2015 decreased significantly (APC: −1.4%; 95% CI: −2.5% to −0.2%).

FIGURE 2
FIGURE 2:
Percentage of American Indians/Alaskan Natives Misclassified as Another Race by Year With 95% Confidence Intervals, Oklahoma 1991-2015
TABLE 1 - Percentage of American Indians/Alaska Natives Misclassified as Another Race by Year, Oklahoma 1991-2015a
Joinpointa Years APC (95% CI) P
Trend 1 1991-2001 −0.2 (−1.4 to 1.0) .74
Trend 2 2001-2005 −6.9 (−13.7 to 0.4) .06
Trend 3b 2005-2015 −1.4 (−2.5 to −0.2) .03
Overallc 1991-2005 −1.8 (−3.1 to −0.5) .007
Abbreviations: APC, annual percent change; CI, confidence interval.
aTwo joinpoints were identified for AI/ANs.
bAnnual percent change is significantly different from 0 at α of .05.
cAverage annual percent change is significantly different from 0 at α of .05.

Mortality rates

For the last 5 years available (2011-2015), the racial misclassification adjustment resulted in a higher all-cause mortality rate for AI/ANs reflecting an increase from 1008 per 100 000 to 1305 per 100 000 with the linkage process (Table 2). There were an estimated 3939 AI/ANs in Oklahoma who were misclassified as another race upon death in those 5 years, resulting in an underestimation of actual AI/AN deaths by nearly 29%. Prior to IHS record linkage, African Americans had the highest all-cause AAMR; however, after adjusting for misclassification through IHS linkage, the all-cause AAMR for AI/ANs was the highest of any racial group. When accounting for misclassification, AI/ANs had a significantly higher all-cause mortality rate than whites (868 per 100 000) with an all-cause mortality rate ratio of 1.5 (95% CI: 1.4-1.6). The AI/ANs experienced an all-cause mortality rate ratio of 1.3 (95% CI: 1.2-1.4) when compared with African Americans (all-cause AAMR of 1006 per 100 000) and an all-cause mortality rate ratio of 2.6 (95% CI: 2.6-2.9) when compared with Asian/Pacific Islanders (all-cause AAMR of 475 per 100 000) (data not shown).

TABLE 2 - Number of Deaths, Crude Death Rates, Age-Adjusted Mortality Rates per 100 000, and Misclassification Rate Ratios for American Indians/Alaska Natives Before and After Indian Health Service Linkage by Demographic Groups, Oklahoma 2011-2015
Category Before IHS Linkage After IHS Linkage Misclassification Rate Ratiob (95% CI)
Deaths Crude Rate AAMRa Deaths Crude Rate AAMRa
Race
White 163 083 1079.5 889.2 159 369 1054.9 868.3 1.0 (0.9-1.0)
African American 12 175 723.4 1023.3 11 959 710.6 1006.0 1.0 (0.9-1.1)
Asian/Pacific Islander 1217 267.8 477.4 1208 265.9 475.0 1.0 (0.9-1.1)
American Indian/Alaska Native 13 821 690.4 1008.2 17 760 887.2 1304.8 1.3 (1.2-1.4)
American Indian/Alaska Native only
Gender
Male 7503 757.8 1198.1 9399 949.3 1509.7 1.3 (1.2-1.3)
Female 6318 624.4 851.3 8361 826.3 1132.2 1.3 (1.3-1.4)
Age group, yc
<1 298 895.6 ... 315 946.7 ... 1.1 (0.9-1.2)
1-4 61 41.0 ... 73 49.1 ... 1.2 (0.8-1.6)
5-14 80 21.1 ... 105 27.7 ... 1.3 (0.8-1.9)
15-24 343 98.8 ... 457 131.6 ... 1.3 (1.1-1.6)
25-34 590 207.6 ... 758 266.7 ... 1.3 (1.1-1.5)
35-44 821 343.4 ... 1046 437.5 ... 1.3 (1.1-1.4)
45-54 1737 768.9 ... 2194 971.3 ... 1.3 (1.2-1.4)
55-64 2567 1401.1 ... 3242 1769.5 ... 1.3 (1.2-1.3)
65-74 2756 2671.6 ... 3544 3435.5 ... 1.3 (1.2-1.4)
75-84 2544 5813.8 ... 3319 7584.9 ... 1.3 (1.3-1.4)
≥85 2022 14 815.4 ... 2703 19 805.1 ... 1.3 (1.3-1.4)
Substate planning districtc
ACOG 1638 533.9 895.3 2312 753.5 1269.0 1.4 (1.3-1.5)
ASCOG 1068 753.9 1018.1 1275 900.0 1221.5 1.2 (1.1-1.3)
COEDD 1315 816.6 1184.5 1542 957.6 1387.2 1.2 (1.1-1.3)
EODD 2726 721.8 1038.2 3342 884.9 1282.2 1.2 (1.2-1.3)
GGEDA 2053 758.6 1056.8 2609 964.1 1362.2 1.3 (1.2-1.4)
INCOG 2085 649.7 1014.9 2899 903.3 1417.3 1.4 (1.3-1.5)
KEDDO 1063 675.0 852.2 1377 874.3 1107.2 1.3 (1.2-1.4)
NODA 375 676.7 1067.4 479 864.4 1396.1 1.3 (1.2-1.4)
OEDA 60 539.3 1068.4 72 647.1 1316.7 1.2 (0.9-1.5)
SODA 1243 734.2 1030.4 1593 940.9 1326.3 1.3 (1.2-1.4)
SWODA 187 627.3 952.4 249 835.3 1303.9 1.4 (1.2-1.6)
Abbreviation: AAMR, age-adjusted mortality rate; ACOG, Association of Central Oklahoma Governments; ASCOG, Association of South Central Oklahoma Governments; CI, confidence interval; COEDD, Central Oklahoma Economic Development District; EODD, Eastern Oklahoma Development District; GGEDA, Grand Gateway Economic Development Association; IHS, Indian Health Service; INCOG, Indian Nations Council of Governments; KEDDO, Kiamichi Economic Development District of Oklahoma; NODA, Northern Oklahoma Development Authority; OEDA, Oklahoma Economic Development Authority; SODA, Southern Oklahoma Development Association; SWODA, South Western Oklahoma Developmental Authority.
aAge-adjusted mortality rate calculated only for overall and by gender. Age-specific rates were calculated by age group and are, therefore, not age-adjusted.
bThe misclassification rate ratios by age group are not age-adjusted but age-specific rate ratios.
cUnknowns: n = 2 for age groups before IHS linkage, n = 4 after IHS linkage; n = 8 for substate planning districts before IHS linkage; and n = 11 after IHS linkage.

We observed little difference in racial misclassification by gender for AI/ANs, with a male MRR of 1.3 (95% CI: 1.2-1.3) compared with 1.3 (95% CI: 1.3-1.4) among females (Table 2). When stratifying by age for AI/ANs, we observed elevated MRR for all age groups above 1 year of age, with statistically significant increases among AI/ANs older than 15 years at death.

Of the 11 SSPDs, all districts had an increase in mortality after IHS linkage (Table 2). While the increase in the Oklahoma Economic Development Authority (rural northwestern Oklahoma) was not statistically significant, the number of deaths was smallest in this SSPD and the population was also smaller than other SSPDs. We observed the highest MRR in the 2 districts that encompass the 2 major metropolitan areas of Oklahoma: the Association of Central Oklahoma Governments (Oklahoma City, Oklahoma) (MRR: 1.42; 95% CI: 1.32-1.51) and the INCOG (Tulsa, Oklahoma) (MRR: 1.40; 95% CI: 1.31-1.49). In addition to experiencing one of the highest rates of misclassification, AI/ANs in the INCOG SSPD had the highest all-cause AAMR in the state after adjustment for racial misclassification. Before the IHS linkage, however, the INCOG area AI/ANs all-cause AAMR was in the bottom 50%.

When stratifying by the top 10 causes of death, the percentage difference in the AAMR pre- to post-IHS records linkage ranged from a 15.2% increase (chronic liver disease and cirrhosis) to 40.1% increase (chronic lower respiratory disease) in the AAMR for AI/ANs (Table 3). For whites, the AAMR decreased from 1.6% (cerebrovascular disease) to 4.1% (unintentional injuries and chronic liver disease and cirrhosis). Preceding the IHS record linkage, the white population had a higher AAMR for chronic lower respiratory disease, cerebrovascular disease, and suicide compared with AI/ANs. However, after the IHS linkage, the data now reflect a more accurate burden of death due to disease and injury for AI/ANs (Table 3).

TABLE 3 - Number of Deaths, Crude Death Rates, Age-Adjusted Mortality Rates per 100 000, and Rate Ratios of Before and After Indian Health Service Linkage for the Top 10 Causes of Death in American Indians/Alaska Natives, Oklahoma 2011-2015
Cause of Death White American Indian/Alaska Native
Before IHS Linkage After IHS Linkage Percent Difference in AAMR Before IHS Linkage After IHS Linkage Percent Difference in AAMR
Deaths AAMR Deaths AAMR Deaths AAMR Deaths AAMR
Diseases of the heart 42 112 225.0 41 277 221.0 −2.0 3136 246.0 4019 316.2 28.6
Cancer 34 753 184.9 33 932 181.0 −2.4 2692 193.0 3560 256.1 32.8
Unintentional injuries 9951 61.4 9584 58.9 −4.1 1218 72.2 1605 95.6 32.4
Diabetes 4895 26.3 4765 25.6 −2.7 789 58.2 927 69.1 18.7
Chronic liver disease and cirrhosis 2194 12.3 2111 11.8 −4.1 539 32.3 624 37.4 15.8
Chronic lower respiratory disease 12 444 65.9 12 186 64.6 −2.0 667 51.4 930 72.0 40.1
Cerebrovascular disease 8096 43.4 7967 42.7 −1.6 519 42.0 659 53.9 28.3
Suicide 3050 19.8 2934 19.1 −3.5 307 16.7 429 23.3 39.5
Kidney disease and infections 2319 12.4 2265 12.1 −2.4 277 21.4 336 26.5 23.8
Pneumonia and influenza 3150 16.9 3087 16.6 −1.8 273 22.3 342 27.7 24.2
Abbreviations: AAMR, age-adjusted mortality rate per 100 000 population; IHS, Indian Health Service.

Discussion and Conclusions

Mortality data are a primary source of information used in public health to understand the population burden of disease and injury, identify disparities among population subgroups, monitor trends over time, and prioritize programs and resource allocation. With many decisions relying on the assumption of accurate data, misclassification of race on death certificates is a significant problem when describing health disparities in Oklahoma and throughout the United States. Although this most recent study shows that racial misclassification on death certificates has improved through time, it continues to be a problem in Oklahoma with 30% more deaths among AI/AN residents being identified through linkage with IHS records. Misclassification remains high in all regions of Oklahoma and in most disease categories. Furthermore, we know that there is still misclassification for which the process of IHS linkage does not account, as not all AI/ANs seek care at I/T/Us. Other mortality linkage studies2,43 and commentaries3 have previously described the limitations of using IHS-linked data to correct for racial misclassification, particularly in places where IHS services are less accessible and for AI/ANs who were eligible for but never accessed IHS services.

Oklahoma has invested in continued IHS linkage to improve mortality data and, to our knowledge, is the only state that makes these misclassified data available on a public Web query site (OK2SHARE). The Centers for Disease Control and Prevention makes its IHS-linked cancer incidence data publicly available through Wide-ranging ONline Data for Epidemiologic Research (CDC WONDER); however, the unlinked data are not available to examine misclassification. Publicly available IHS-linked data have been very important for tribal epidemiology centers, tribes, and researchers and even more important for program managers and policy makers who are unable to measure the extent of conditions or the impacts of their programs.

Health measures must be accurate for states and health care systems to plan for the use and costs of health care services. Accurate data are needed to evaluate the trends related to the quality and delivery of health care services, as well as the capacity of various components of health care. Although the corrected data are available in OK2Share, not all health care providers are familiar with or access these data source when planning service delivery. This also demonstrates the need to educate health care providers on the impacts of racial misclassification in Oklahoma and on data sources, such as OK2Share, where they can obtain the adjusted mortality rates.

Mortality records are not the only health care record system affected by racial misclassification. Morbidity data (hospital discharge data, emergency department data, etc) can be affected, as well. To date, hospital discharge data have not been linked with IHS records in Oklahoma. Underestimating AI/AN morbidity can cause additional problems for health care entities, health program managers, and policy makers because they are unable to obtain accurate data to evaluate and measure the effects of public health initiatives for the AI/AN population. Accurate hospital discharge data and emergency department data could be used to illustrate where AI/ANs seek acute care outside of the IHS system and allow for IHS to better respond to needs.

There are several limitations in this study. One limitation is the change in reporting quality over time. During the reporting period, there have been changes in the quality of some variables collected by the Oklahoma Vital Records. There have also been changes to categorization within certain variables, one of which is how race is collected. In particular, we observed that after 2000, when multiple race categories could be reported, we observed dramatic improvement in racial misclassification. Furthermore, there continues to be misclassification in the AI/AN population in Oklahoma. Not all AI/ANs seek care through IHS or I/T/U facilities during their life and may still have their race reported as another race as opposed to AI/AN. A final limitation was the lack of tribal specific data. Because of the large number of tribal headquarters and tribal heterogeneity in the state, tribal members live throughout the state not just in tribal jurisdictional areas. The geographic dispersion of the AI/AN population leads to an additional challenge in reviewing any health data for AI/AN populations in Oklahoma.

This analysis highlights the need for several additional studies and recommendations. Estimates of the number of racially misclassified cases among individuals who do not use IHS services are necessary. The use of geographic information systems analysis could be implemented to determine accuracy of coding by tribal boundaries. The most important recommendation from this analysis is to continue the enhancement systems that support IHS linkages with surveillance systems throughout the United States. Finally, we recommend public access to linked data so that the data remain accessible. The linkage projects are of no value to program planners, evaluators, and researchers if the data are not accessible. As a final point, it is important to determine how to handle the adjusted race variable and determine whether we provide dual estimates (as in OK2Share) or whether there is a more appropriate method. In addition, tribal nations need AI/AN-specific information to plan health services and outreach. Strides cannot be made to improve the health status of a population when inaccurate information prevents effective health planning. Public and private entities require this information so that the conditions that most profoundly affect the health of AI/ANs can effectively be targeted to provide higher quality of care; improve access, targeted health facilities, and cost containment; and allow the pursuit of more resources for prevention and treatment. A final issue that needs to be made clear is that these data reflect statewide population-based information. They do not represent tribal health services or the IHS user population and thus do not reflect the quality of care provided by tribal health services, tribal programs, or IHS.

As mentioned previously, an important result of this study is that misclassification is improving; however, this effort needs to be maintained and further improved. Continued linkage efforts and public access to linked data are essential throughout the United States to better understand the burden of chronic disease in the AI/AN population. Accurately recorded mortality and morbidity records can also allow for better understanding of acute causes of illness (ie, influenza), injuries, and newly emerging epidemics such as the current opioid crisis. Having more accurate data for AI/ANs will help inform health care providers and funders about the true burden of disease and other health outcomes to ultimately improve the health of AI/ANs.

Implications for Policy & Practice

  • American Indian and Alaska Native surveillance data are important for program planning and evaluation of health services, particularly those services designed to reduce health disparities.
  • Linkages to Indian Health Service data systems are an established way of improving the well-known issues of misclassification of American Indians and Alaska Natives in the United States.
  • Rapid and ongoing linkages to Indian Health Service data systems would allow for a more accurate identification and can support effort to improve access to health care resources, particularly for vulnerable populations.
  • Access to linked data is important for program planning and evaluation of staff throughout the United States.

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Keywords:

IHS record linkage; misclassification rate ratio; racial misclassification

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