Tribal linkage and race data quality for American Indians in a state cancer registry

Life expectancy among Native Americans during the COVID-19 pandemic: estimates, uncertainty, and obstacles

Few reliable estimates have been available for assessing the impact of the COVID-19 pandemic on mortality among Native Americans. Using deidentified publicly available data on deaths and populations by age, we estimated life expectancy for the years 2019-2022 for single-race non-Hispanic Native Americans. Life expectancy in 2022 was 67.8 years, 2.3 years higher than in 2021 but a huge 4-year loss from 2019. Although our life expectancy estimates for 2022 varied under different assumptions about racial/ethnic classification and age misreporting errors, all estimates were lower than the average for middle-income countries. Estimates of losses and gains in life expectancy were consistent across assumptions. Large reductions in COVID-19 death rates between 2021 and 2022 were largely offset by increases in rates of death from unintentional injuries (particularly drug overdoses), chronic liver disease, diabetes, and heart disease, underscoring the difficulties facing Native Americans in achieving reductions in mortality, let alone returning to levels of mortality prior to the pandemic. Serious data problems have persisted for many years, but the scarcity and inadequacy of estimates during the pandemic have underscored the urgent need for timely and accurate demographic data on the Native American population.

Misracialization of Indigenous people in population health and mortality studies: a scoping review to establish promising practices

Indigenous people are often misracialized as other racial or ethnic identities in population health research. This misclassification leads to underestimation of Indigenous-specific mortality and health metrics, and subsequently, inadequate resource allocation. In recognition of this problem, investigators around the world have devised analytic methods to address racial misclassification of Indigenous people. We carried out a scoping review based on searches in PubMed, Web of Science, and the Native Health Database for empirical studies published after 2000 that include Indigenous-specific estimates of health or mortality and that take analytic steps to rectify racial misclassification of Indigenous people. We then considered the weaknesses and strengths of implemented analytic approaches, with a focus on methods used in the US context. To do this, we extracted information from 97 articles and compared the analytic approaches used. The most common approach to address Indigenous misclassification is to use data linkage; other methods include geographic restriction to areas where misclassification is less common, exclusion of some subgroups, imputation, aggregation, and electronic health record abstraction. We identified 4 primary limitations of these approaches: (1) combining data sources that use inconsistent processes and/or sources of race and ethnicity information; (2) conflating race, ethnicity, and nationality; (3) applying insufficient algorithms to bridge, impute, or link race and ethnicity information; and (4) assuming the hyperlocality of Indigenous people. Although there is no perfect solution to the issue of Indigenous misclassification in population-based studies, a review of this literature provided information on promising practices to consider.

Methods for retrospectively improving race/ethnicity data quality: a scoping review

Improving race and ethnicity (hereafter, race/ethnicity) data quality is imperative to ensure underserved populations are represented in data sets used to identify health disparities and inform health care policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary data sets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searches were conducted in the MEDLINE, Embase, and Web of Science Core Collection databases in July 2022. A total of 2 441 abstracts were dually screened, 453 full-text articles were reviewed, and 120 articles were included. Study characteristics were extracted and described in a narrative analysis. Six main method types for improving race/ethnicity data were identified: expert review (n = 9; 8%), name lists (n = 27, 23%), name algorithms (n = 55, 46%), machine learning (n = 14, 12%), data linkage (n = 9, 8%), and other (n = 6, 5%). The main racial/ethnic groups targeted for classification were Asian (n = 56, 47%) and White (n = 51, 43%). Some form of validation evaluation was included in 86 articles (72%). We discuss the strengths and limitations of different method types and potential harms of identified methods. Innovative methods are needed to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity are critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of health care practices and intervention.

A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms

Healthcare systems are hampered by incomplete and fragmented patient health records. Record linkage is widely accepted as a solution to improve the quality and completeness of patient records. However, there does not exist a systematic approach for manually reviewing patient records to create gold standard record linkage data sets. We propose a robust framework for creating and evaluating manually reviewed gold standard data sets for measuring the performance of patient matching algorithms. Our 8-point approach covers data preprocessing, blocking, record adjudication, linkage evaluation, and reviewer characteristics. This framework can help record linkage method developers provide necessary transparency when creating and validating gold standard reference matching data sets. In turn, this transparency will support both the internal and external validity of recording linkage studies and improve the robustness of new record linkage strategies.

Challenges with misclassification of American Indian/Alaska Native race and Hispanic ethnicity on death records in North Carolina occupational fatalities surveillance

As frequently segregated and exploitative environments, workplaces are important sites in driving health and mortality disparities by race and ethnicity. Because many worksites are federally regulated, US workplaces also offer opportunities for effectively intervening to mitigate these disparities. Development of policies for worker safety and equity should be informed by evidence, including results from research studies that use death records and other sources of administrative data. North Carolina has a long history of Black/white disparities in work-related mortality and evidence of such disparities is emerging in Hispanic and American Indian/Alaska Native (AI/AN) worker populations. The size of Hispanic and AI/AN worker populations have increased in North Carolina over the last decade, and North Carolina has the largest AI/AN population in the eastern US. Previous research indicates that misidentification of Hispanic and AI/AN identities on death records can lead to underestimation of race/ethnicity-specific mortality rates. In this commentary, we describe problems and complexities involved in determining AI/AN and Hispanic identities from North Carolina death records. We provide specific examples of misidentification that are likely introducing bias to occupational mortality disparity documentation, and offer recommendations for improved data collection, analysis, and interpretation. Our primary recommendation is to build and maintain relationships with local community leadership, so that improvements in the ascertainment of race and ethnicity are grounded in the lived experience of workers from communities of color.

Racial Misclassification and Disparities in Neonatal Abstinence Syndrome Among American Indians and Alaska Natives

OBJECTIVES

Maternal substance misuse can result in neonatal abstinence syndrome (NAS), a drug withdrawal process in newborns exposed in utero to drugs. This study aimed to examine the effect of racial misclassification of American Indians and Alaska Natives (AI/AN) on rates of NAS in two hospital discharge datasets in the Pacific Northwest.

METHODS

We conducted probabilistic record linkages between the Northwest Tribal Registry and Oregon and Washington hospital discharge datasets to correct racial misclassification of AI/AN people. We assessed outcomes using International Classification of Disease, Ninth Revision/Tenth Revision, Clinical Modification (ICD-9-CM or ICD-10-CM) diagnosis codes.

RESULTS

Linkage increased ascertainment of NAS cases among AI/AN by 8.8% in Oregon and by 18.1% in Washington. AI/AN newborns were 1.5 and 3.9 times more likely to be diagnosed with NAS than NHW newborns in Oregon and Washington, respectively. The results showed that newborns residing in rural Washington were 1.4 times more likely to be diagnosed with NAS than those living in urban areas.

CONCLUSIONS

Correct racial classification is an important factor in improving data quality for AI/AN populations and establishing accurate surveillance to help address the disproportionate burden of neonatal abstinence syndrome among AI/AN. The results highlight the need for programing efforts tailored by insurance status and rurality for pregnant women using substances.

Indigenous health equity in health register ascertainment and data quality: a narrative review

Background

Health registers play an important role in monitoring distribution of disease and quality of care; however, benefit is limited if ascertainment (i.e., the process of finding and recruiting people on to a register) and data quality (i.e., the accuracy, completeness, reliability, relevance, and timeliness of data) are poor. Indigenous peoples experience significant health inequities globally, yet health data for, and about, Indigenous peoples is often of poor quality. This narrative review aimed to (i) identify perceived barriers for the ascertainment of Indigenous peoples on health registers, and (ii) collate strategies identified and used by health registers to support comprehensive ascertainment and high-quality data for Indigenous peoples.

Methods

A Kaupapa Māori theoretical framework was utilized to guide this work. Four electronic databases were systematically searched for original articles and screened for eligibility. Studies involving health registers with Indigenous population(s) identified were included if either ascertainment or data quality strategies were described. Data extraction focused on the reporting of research involving Indigenous peoples using the CONSIDER checklist domains, ascertainment, and data quality.

Results

Seventeen articles were included spanning publication between 1992 and 2020. Aspects of four of eight CONSIDER domains were identified to be included in the reporting of studies. Barriers to ascertainment were themed as relating to ‘ethnicity data collection and quality’, ‘systems and structures’, ‘health services/health professionals’, and ‘perceptions of individual and community-level barriers’. Strategies to support ascertainment were categorized as ‘collaboration’, ‘finding people’, and ‘recruitment processes’. Categorized strategies to support data quality were ‘collaboration’, ‘ethnicity data collection and quality’, ‘systems-level strategies’, and ‘health service/health professional-level strategies’.

Conclusions

Poor-quality data for Indigenous peoples in health registers prevents the achievement of health equity and exemplifies inaction in the face of need. When viewed through a critical structural determinants lens, there are visible gaps in the breadth of strategies, particularly relating to the inclusion of Indigenous peoples in health register and research governance, and actions to identify and address institutional racism. Indigenous led research, meaningful collaboration, and a sharing of knowledge and experiences between health registers is recommended to enable research and health registers that support Indigenous self-determination and health equity.

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

OBJECTIVE

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

DESIGN

Retrospective, descriptive study.

SETTING

Oklahoma.

PARTICIPANTS

Persons contained in the Oklahoma State Health Department Vital Records.

MAIN OUTCOME MEASURES

To evaluate the age-adjusted mortality ratio pre- and post-Indian Health Service record linkage (misclassification rate ratio) and to evaluate the overall trend of racial misclassification on mortality records measured through annual percent change (APC) and average annual percent change (AAPC).

RESULTS

We identified 2 stable trends of racial misclassification upon death for American Indians/Alaska Natives (AI/ANs) from 1991 to 2001 (APC: -0.2%; 95% confidence interval: -1.4% to 1.0%) and from 2001 to 2005 (APC: -6.9%; 95% confidence interval: -13.7% to 0.4%). However, the trend identified from 2005 to 2015 decreased significantly (APC: -1.4%; 95% confidence interval: -2.5% to -0.2%). For the last 5 years available (2011-2015), the racial misclassification adjustment resulted in higher mortality rates for AI/ANs reflecting an increase from 1008 per 100 000 to 1305 per 100 000 with the linkage process. 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%.

CONCLUSIONS

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 disease in the AI/AN population.

Using Tribal Data Linkages to Improve the Quality of American Indian Cancer Data in Michigan

This study examines the extent to which data linkages between Indian Health Service, tribal data, and cancer registries affect cancer incidence rates among American Indians/Alaska Natives (AI/ANs) in Michigan. The incidence of tobacco- and alcohol-associated cancers for 1995-2012 was analyzed to compare rates of the Upper Peninsula (UP) and Lower Peninsula (LP) in Michigan and among AI/ANs and non-Hispanic Whites (NHWs). Complete linkage resulted in 1,352 additional AI/AN cases; 141 cases were linked via IHS records alone, while 373 were linked via tribal records alone; 838 were linked through both IHS and tribal records. Age-adjusted incidence rates for AI/ANs increased from 214.39 per 100,000 to 405.41 per 100,000, similar to that of NHWs after complete linkage (421.46 per 100,000). In the UP, AI/ANs had age-adjusted incidence rates 1.67 times higher than NHWs (596.69 per 100,000 vs. 356.32 per 100,000 respectively). This study indicates a substantial number of AI/AN cancer cases remain misclassified in Michigan.

A critical review of methods for assessing cancer survival disparities in indigenous population

PURPOSE

An increasing cancer burden among indigenous populations has led to a growing literature about survival disparities between indigenous and nonindigenous persons. We aim to describe and appraise methods used to measure cancer survival in indigenous persons in the United States, Canada, Australia, and New Zealand.

METHODS

We searched Medline, Web of Science, and EMBASE for articles published between 1990 and 2015 that estimated survival in populations indigenous to one of these four countries. We gathered information about data sources, analytical methods, and the extent to which threats to validity were discussed.

RESULTS

The search retrieved 83 articles. The most common approach to survival analysis was cause-specific survival (n = 49). Thirty-eight articles measured all-cause survival and 11 measured excess mortality attributable to cancer (relative survival). Three sources of information bias common to all studies (ethnic misclassification, incomplete case ascertainment, and incomplete death ascertainment) were acknowledged in a minority of articles.

CONCLUSIONS

The methodological considerations we present here are shared with studies of cancer survival across other subpopulations. We urge future researchers on this and related topics to clearly describe their data sources, to justify analytic choices, and to fully discuss the potential impact of those choices on the results and interpretation.

Knowledge and beliefs of African-American and American Indian parents and supporters about infant safe sleep

To investigate, by using qualitative methods, beliefs among African-American and American Indian families about infant safe sleep practices, barriers to acceptance of prevention recommendations, and more effective messaging strategies. Seventy-three mothers and supporters participated in focus groups. Participants discussed infant sleep practices and effectiveness of safe sleep messages. Data were coded, and themes were developed and revised in an iterative manner as patterns became more apparent. Themes included reasons for and influences on sleep decisions, and concerns about safe sleep recommendations. Parental sleep decisions seemed to be driven by perceptions of what would make their infant most comfortable and safe, and what would be most convenient. Parents were aware of safe sleep recommendations but unaware of the rationale. Because they generally did not believe that their infants were at risk for a sleep-related death, day-to-day decisions seemed to focus on what was most effective in getting their infant to sleep. There appeared to be no distinctions in opinions among African-American and American Indian families. African-American and American Indian families seemed to have similar concerns about infant comfort and safety, and their perceptions about what would be most effective in achieving these goals appeared to be important influences on their sleep practices. Adherence with safe sleep recommendations may be enhanced if health care providers and educational materials discussed rationale underlying recommendations and addressed common parental concerns. It may be beneficial to target educational interventions towards fathers, as they may be untapped sources in implementing safe sleep practices.

Using Record Linkage to Improve Race Data Quality for American Indians and Alaska Natives in Two Pacific Northwest State Hospital Discharge Databases

OBJECTIVE

To evaluate and adjust for American Indian and Alaska Native (AI/AN) racial misclassification in two hospital discharge datasets in the Pacific Northwest.

DATA SOURCES/STUDY SETTING

Oregon (2010-2011) and Washington (2011) hospital discharge datasets were linked with the Northwest Tribal Registry (NTR), a registry of AI/AN individuals who accessed services at Indian health facilities in the Northwest.

STUDY DESIGN

Record linkage was used to match state hospital records to the NTR. A state record was considered misclassified if it matched the NTR and was coded as non-AI/AN or missing race data. Effect of misclassification was evaluated by comparing prelinkage and postlinkage, age-adjusted hospital discharge rates.

DATA COLLECTION/EXTRACTION METHODS

Researchers used Link Plus 2.0 software (Atlanta, GA, USA) for linkages and SAS 9.4 (Cary, NC, USA) for statistical analyses.

PRINCIPAL FINDINGS

In Oregon, 55.4 percent of matching records were misclassified (66.5 percent miscoded white, and 22.1 percent were missing race information). In Washington, 44.9 percent of matching records were misclassified (61.8 percent miscoded white, and 32.7 percent were missing race information). Linkage increased ascertainment of AI/AN hospitalizations by 31.8 percent in Oregon and 33.9 percent in Washington. Linkage increased the rate ratio (RR) for AI/AN hospitalizations in comparison to non-Hispanic whites (NHW) from 0.81 to 1.07 in Oregon, and from 1.21 to 1.62 in Washington.

CONCLUSION

Correction of race in hospital discharge datasets through linkage with a reference file of known AI/AN individuals is an important first step before analytic research on AI/AN health care in the Pacific Northwest can be accomplished with administrative datasets.

Linkages to improve mortality data for American Indians and Alaska Natives: a new model for death reporting?

Racial misclassification is a well-documented weakness of mortality data taken from death certificates. As a result, mortality statistics for American Indians and Alaska Natives (AI/ANs) present, at best, an inaccurate and misleading assessment of mortality in this population. Studies evaluating the quality of race/ethnicity reporting on death certificates have linked data from death certificates to other data sources collected when the decedent was still alive (e.g., Census, Current Population Survey). Such studies have shown substantial misclassification of AI/AN decedents. Despite limitations, linking mortality data from death certificates with data from other sources collected when decedents were living provides opportunities to evaluate and correct misclassification of populations such as AI/AN persons and facilitates the calculation and presentation of more accurate mortality statistics.

Racial misclassification of American Indians and Alaska Natives by Indian Health Service Contract Health Service Delivery Area

OBJECTIVES

We evaluated the racial misclassification of American Indians and Alaska Natives (AI/ANs) in cancer incidence and all-cause mortality data by Indian Health Service (IHS) Contract Health Service Delivery Area (CHSDA).

METHODS

We evaluated data from 3 sources: IHS-National Vital Statistics System (NVSS), IHS-National Program of Cancer Registries (NPCR)/Surveillance, Epidemiology and End Results (SEER) program, and National Longitudinal Mortality Study (NLMS). We calculated, within each data source, the sensitivity and classification ratios by sex, IHS region, and urban-rural classification by CHSDA county.

RESULTS

Sensitivity was significantly greater in CHSDA counties (IHS-NVSS: 83.6%; IHS-NPCR/SEER: 77.6%; NLMS: 68.8%) than non-CHSDA counties (IHS-NVSS: 54.8%; IHS-NPCR/SEER: 39.0%; NLMS: 28.3%). Classification ratios indicated less misclassification in CHSDA counties (IHS-NVSS: 1.20%; IHS-NPCR/SEER: 1.29%; NLMS: 1.18%) than non-CHSDA counties (IHS-NVSS: 1.82%; IHS-NPCR/SEER: 2.56%; NLMS: 1.81%). Race misclassification was less in rural counties and in regions with the greatest concentrations of AI/AN persons (Alaska, Southwest, and Northern Plains).

CONCLUSIONS

Limiting presentation and analysis to CHSDA counties helped mitigate the effects of race misclassification of AI/AN persons, although a portion of the population was excluded.

Disparities of cancer incidence in Michigan's American Indians: spotlight on breast cancer

BACKGROUND

In American Indians (AIs), cancer is a leading cause of mortality, yet their disease burden is not fully understood due to unaddressed racial misclassification in cancer registries. This study describes cancer trends among AIs in Michigan, focusing on breast cancer, in a linked data set of Indian Health Service (IHS), tribal, and state cancer registry data adjusted for misclassification.

METHODS

AI status was based on reported race and linkage to IHS data and tribal registries. Data with complete linkage on all incident cancer cases in Michigan from 1995 to 2004 was used to calculate age-standardized incidence estimates for invasive all-site and female breast cancers stratified by racial group. For female breast cancers, stage- and age-specific incidence and percent distributions of early- versus late-stage cancers and age of diagnosis were calculated.

RESULTS

More than 50% of all AI cases were identified through IHS and/or tribal linkage. In the linked data, AIs had the lowest rates of all-sites and breast cancer. For breast cancers, AI women had a greater late-stage cancer burden and a younger mean age of diagnosis as compared to whites. Although the age-specific rate for whites was greater than for AI women in nearly all age groups, the difference in hazard ratio increased with increasing age.

CONCLUSIONS

Our state-specific information will help formulate effective, tailored cancer prevention strategies to this population in Michigan. The data linkages used in our study are crucial for generating accurate rates and can be effective in addressing misclassification of the AI population and formulating cancer prevention strategies for AI nationwide.

Pregnancy and birth outcome improvements for American Indians in the Healthy Start project of the Inter-Tribal Council of Michigan, 1998-2008

American Indians living in Michigan experience disproportionately high rates of infant mortality. This 11-year (1998-2008) cohort study evaluated impacts of a Healthy Start (HS) program administered by the Inter-Tribal Council of Michigan (ITCM) on perinatal outcomes. Women who enrolled in ITCM's HS program ("exposed") were compared with non-enrolled ("unexposed") for four outcomes: low birth weight (LBW), small for gestational age, preterm birth, and inadequate prenatal care. To classify exposed and unexposed women and their children, Michigan vital records data were linked with HS enrollment records to identify participants and non-participants among all American Indian births. Logistic regression was used to calculate odds ratios for the four outcomes of interest. Analyses were stratified for high and low access to care based on Medically Underserved Area (MUA) designation for a woman's county of residence. Of 4,149 American Indian births during the period, 872 were to women who enrolled prenatally in HS. Although unstratified analysis showed no differences between HS participants and non-participants, stratified analyses demonstrated that participants from MUA counties had decreased odds of LBW and inadequate prenatal care. Results suggest that in MUA counties where participants and non-participants are at similar risk for poor outcomes, HS may be reducing barriers and improving outcomes. In non-MUA counties participants had similar outcomes as non-participants. These results may reflect a wider disparity in risk factors between the two groups in non-MUA counties. The complex interplay among need, access, and benefit complicates analyses and suggests the importance of more in-depth and focused studies.

Linking cancer registry and hospital discharge data for treatment surveillance

Cancer registry data often lack complete chemotherapy and radiation therapy information. To conduct treatment disparity surveillance, we linked 2005–2009 Nebraska Cancer Registry data with Nebraska hospital discharge data. Due to the high quality of both datasets and the proposed linkage procedure, we had a linkage rate of 97 percent. We demonstrate the utilization of the linked dataset in case finding, treatment update, and treatment surveillance. The results show that the linked dataset is likely to identify up to 5 percent of potential missed cases. We investigated the use of radiation therapy in treating colorectal and breast cancers as case-finding examples. The linked dataset found 12 percent and 14 percent more treatment cases for colorectal and breast cancer patients, respectively.

Probabilistic linkage of assisted reproductive technology information with vital records, Massachusetts 1997-2000

To assess the validity of probabilistic linkage (PL) in combining national surveillance data on assisted reproductive technology (ART) with Massachusetts birth and infant death data, for the purpose of monitoring maternal and child health outcomes of ART. A study conducted in 2006 utilized direct identifiers to match Massachusetts birth records with records on ART procedures performed to Massachusetts residents in fertility clinics located in Massachusetts and Rhode Island, achieving a linkage rate of 87.5%. The present study employed PL using the program Link Plus, without access to direct identifiers. The primary linking variables were maternal and infant dates of birth, and plurality. Ancillary variables such as maternal ZIP code and gravidity helped resolve duplicate matches and capture additional matches. PL linked 5,390 (87.8%) of 6,139 deliveries, correctly identifying 96.4% of the matches previously obtained using deterministic linkage methods. PL yielded a high linkage rate with satisfactory validity; this method may be applied in other states to help monitor the maternal and child health outcomes of ART.

Regional differences and tribal use of American Indian/Alaska Native cancer data in the Pacific Northwest

In the Pacific Northwest, cancer is a leading cause of morbidity and mortality for American Indians and Alaska Natives (AI/AN). Misclassification of AI/AN race in state cancer registries causes cancer burden to be underestimated. Furthermore, local-level data are rarely available to individual tribes for use in health assessment and program planning. We corrected race coding in the cancer registries of Idaho, Oregon, and Washington using probabilistic record linkage to a file derived from patient registration records from Indian Health Service and a large urban clinic. We calculated cancer incidence and mortality measures by state, comparing AI/AN to non-Hispanic White (NHW) race. Record linkages identified a high prevalence of misclassified race. Differences in AI/AN cancer patterns were identified across the three state region. Compared to NHW, AI/AN experienced disproportionate late stage rates of some screen-detectable cancers. The correct classification of race is a crucial factor in cancer surveillance and can reveal regional differences even within a relatively small area. The availability of local-level cancer data can help inform tribes in appropriate intervention efforts.

Process for determining the cancer burden of the Hopi Tribe

The Hopi Tribe is located in the northeastern part of Arizona on more than one million acres of federally reserved land. Tribally based community research, conducted in collaboration with University of Arizona researchers, has been successfully implemented on Hopi beginning with a cross-sectional community survey in 1993 and continuing with a second survey in 2006. Both surveys identified a strong community interest in cancer. This article reports on the process involved in a third study, in which official Hopi enrollment data were matched with Arizona Cancer Registry data. The process involved bringing in a new partner and obtaining tribal, state, and university approvals, as well as a signed data exchange agreement between the state and the Hopi Tribe. Technical implementation of the data match required computer programming and epidemiologic expertise, as well as an understanding of the community and the culture. Close collaboration among Hopi residents and university epidemiologists was critical.

Research training of students in minority and international settings: lessons learned from cancer epidemiology education in special populations

This article describes the development and evaluation of an NCI-sponsored short-term summer cancer research education program. The study questions examined: the feasibility of conducting a cancer education program in special populations at multiple US and international field sites for masters students; the merit and worth that students and faculty attribute to the program; and students' scholarly and cancer-related career outcomes. Developing a new curriculum, increasing the pool of mentors, utilizing and increasing the number of field sites, and program dissemination were also evaluated. Evidence of the program's success included students' completion of field experiences at multiple sites and their subsequent 70% project-related publication rate, with 79% of trainees reporting themselves as likely to pursue future cancer-related careers. Evaluation-guided future plans for the program include implementing faculty development to further enhance the program outcomes.