A Comparison of the Mortality Experience of U.S. Adults Estimated With the 2006-2018 National Health Interview Survey Linked Mortality Files and the Annual U.S. Life Tables.

Objective-Linking data is a powerful mechanism to provide policy-relevant information. The National Center for Health Statistics' Data Linkage Program produces linked mortality files (LMFs) for research by linking data from the National Center for Health Statistics' surveys, including the National Health Interview Survey (NHIS), to mortality data from the National Death Index. Assessing the accuracy of the linked data is an important step in ensuring its analytic use. This report compares the cumulative survival probabilities estimated with the 2006-2018 NHIS LMFs to those from the annual U.S. life tables.

A methodological assessment of privacy preserving record linkage using survey and administrative data.

BACKGROUND: The National Center for Health Statistics (NCHS) links data from surveys to administrative data sources, but privacy concerns make accessing new data sources difficult. Privacy-preserving record linkage (PPRL) is an alternative to traditional linkage approaches that may overcome this barrier. However, prior to implementing PPRL techniques it is important to understand their effect on data quality. METHODS: Results from PPRL were compared to results from an established linkage method, which uses unencrypted (plain text) identifiers and both deterministic and probabilistic techniques. The established method was used as the gold standard. Links performed with PPRL were evaluated for precision and recall. An initial assessment and a refined approach were implemented. The impact of PPRL on secondary data analysis, including match and mortality rates, was assessed. RESULTS: The match rates for all approaches were similar, 5.1% for the gold standard, 5.4% for the initial PPRL and 5.0% for the refined PPRL approach. Precision ranged from 93.8% to 98.9% and recall ranged from 98.7% to 97.8%, depending on the selection of tokens from PPRL. The impact of PPRL on secondary data analysis was minimal. DISCUSSION: The findings suggest PPRL works well to link patient records to the National Death Index (NDI) since both sources have a high level of non-missing personally identifiable information, especially among adults 65 and older who may also have a higher likelihood of linking to the NDI. CONCLUSION: The results from this study are encouraging for first steps for a statistical agency in the implementation of PPRL approaches, however, future research is still needed.

Using Synthetic Data to Replace Linkage Derived Elements: A Case Study.

While record linkage can expand analyses performable from survey microdata, it also incurs greater risk of privacy-encroaching disclosure. One way to mitigate this risk is to replace some of the information added through linkage with synthetic data elements. This paper describes a case study using the National Hospital Care Survey (NHCS), which collects patient records under a pledge of protecting patient privacy from a sample of U.S. hospitals for statistical analysis purposes. The NHCS data were linked to the National Death Index (NDI) to enhance the survey with mortality information. The added information from NDI linkage enables survival analyses related to hospitalization, but as the death information includes dates of death and detailed causes of death, having it joined with the patient records increases the risk of patient re-identification (albeit only for deceased persons). For this reason, an approach was tested to develop synthetic data that uses models from survival analysis to replace vital status and actual dates-of-death with synthetic values and uses classification tree analysis to replace actual causes of death with synthesized causes of death. The degree to which analyses performed on the synthetic data replicate results from analysis on the actual data is measured by comparing survival analysis parameter estimates from both data files. Because synthetic data only have value to the degree that they can be used to produce statistical estimates that are like those based on the actual data, this evaluation is an essential first step in assessing the potential utility of synthetic mortality data.

Using supervised machine learning to identify efficient blocking schemes for record linkage.

Record linkage enables survey data to be integrated with other data sources, expanding the analytic potential of both sources. However, depending on the number of records being linked, the processing time can be prohibitive. This paper describes a case study using a supervised machine learning algorithm, known as the Sequential Coverage Algorithm (SCA). The SCA was used to develop the join strategy for two data sources, the National Center for Health Statistics' (NCHS) 2016 National Hospital Care Survey (NHCS) and the Center for Medicare & Medicaid Services (CMS) Enrollment Database (EDB), during record linkage. Due to the size of the CMS data, common record joining methods (i.e. blocking) were used to reduce the number of pairs that need to be evaluated to identify the vast majority of matches. NCHS conducted a case study examining how the SCA improved the efficiency of blocking. This paper describes how the SCA was used to design the blocking used in this linkage.

Assessing Linkage Eligibility Bias in the National Health Interview Survey.

Background Linking health survey data to administrative records expands the analytic utility of survey participant responses, but also creates the potential for new sources of bias when not all participants are eligible for linkage. Residual differences-bias-can occur between estimates made using the full survey sample and the subset eligible for linkage. Objective To assess linkage eligibility bias and provide examples of how bias may be reduced by changes in questionnaire design and adjustment of survey weights for linkage eligibility. Methods Linkage eligibility bias was estimated for various sociodemographic groups and health-related variables for the 2000-2013 National Health Interview Surveys. Conclusions Analysts using the linked data should consider the potential for linkage eligibility bias when planning their analyses and use approaches to reduce bias, such as survey weight adjustments, when appropriate.

Comparative Analysis of the National Health Interview Survey Public-use and Restricted-use Linked Mortality Files.

Linking national survey data with administrative data sources enables researchers to conduct analyses that would not be possible with each data source alone. Recently, the Data Linkage Program at the National Center for Health Statistics (NCHS) released updated linked mortality files, including the National Health Interview Survey data linked to the National Death Index mortality files. Two versions of the files were released: restricted-use files available through NCHS and Federal Statistical Research Data Centers and public-use files. To reduce the reidentification risk, statistical disclosure limitation methods were applied to the public-use files before they were released. This included limiting the amount of mortality information available and perturbing cause of death and follow-up time for select records. To assess the comparability of the restricted-use and public-use files, relative hazard ratios for all-cause and cause-specific mortality using Cox proportional hazards models were estimated and compared. The comparative analysis found that the two data files yielded very similar descriptive and model results.

Nutritional interventions in primary mitochondrial disorders: Developing an evidence base.

In December 2014, a workshop entitled "Nutritional Interventions in Primary Mitochondrial Disorders: Developing an Evidence Base" was convened at the NIH with the goals of exploring the use of nutritional interventions in primary mitochondrial disorders (PMD) and identifying knowledge gaps regarding their safety and efficacy; identifying research opportunities; and forging collaborations among researchers, clinicians, patient advocacy groups, and federal partners. Sponsors included the NIH, the Wellcome Trust, and the United Mitochondrial Diseases Foundation. Dietary supplements have historically been used in the management of PMD due to their potential benefits and perceived low risk, even though little evidence exists regarding their effectiveness. PMD are rare and clinically, phenotypically, and genetically heterogeneous. Thus patient recruitment for randomized controlled trials (RCTs) has proven to be challenging. Only a few RCTs examining dietary supplements, singly or in combination with other vitamins and cofactors, are reported in the literature. Regulatory issues pertaining to the use of dietary supplements as treatment modalities further complicate the research and patient access landscape. As a preface to exploring a research agenda, the workshop included presentations and discussions on what PMD are; how nutritional interventions are used in PMD; challenges and barriers to their use; new technologies and approaches to diagnosis and treatment; research opportunities and resources; and perspectives from patient advocacy, industry, and professional organizations. Seven key areas were identified during the workshop. These areas were: 1) defining the disease, 2) clinical trial design, 3) biomarker selection, 4) mechanistic approaches, 5) challenges in using dietary supplements, 6) standards of clinical care, and 7) collaboration issues. Short- and long-term goals within each of these areas were identified. An example of an overarching goal is the enrollment of all individuals with PMD in a natural history study and a patient registry to enhance research capability. The workshop demonstrates an effective model for fostering and enhancing collaborations among NIH and basic research, clinical, patient, pharmaceutical industry, and regulatory stakeholders in the mitochondrial disease community to address research challenges on the use of dietary supplements in PMD.

Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010.

INTRODUCTION: The increase in chronic health conditions among Medicare beneficiaries has implications for the Medicare system. The objective of this study was to use the US Department of Health and Human Services Strategic Framework on multiple chronic conditions as a basis to examine the prevalence of multiple chronic conditions among Medicare beneficiaries. ANALYSIS: We analyzed Centers for Medicare and Medicaid Services administrative claims data for Medicare beneficiaries enrolled in the fee-for-service program in 2010. We included approximately 31 million Medicare beneficiaries and examined 15 chronic conditions. A beneficiary was considered to have a chronic condition if a Medicare claim indicated that the beneficiary received a service or treatment for the condition. We defined the prevalence of multiple chronic conditions as having 2 or more chronic conditions. RESULTS: Overall, 68.4% of Medicare beneficiaries had 2 or more chronic conditions and 36.4% had 4 or more chronic conditions. The prevalence of multiple chronic conditions increased with age and was more prevalent among women than men across all age groups. Non-Hispanic black and Hispanic women had the highest prevalence of 4 or more chronic conditions, whereas Asian or Pacific Islander men and women, in general, had the lowest. SUMMARY: The prevalence of multiple chronic conditions among the Medicare fee-for-service population varies across demographic groups. Multiple chronic conditions appear to be more prevalent among women, particularly non-Hispanic black and Hispanic women, and among beneficiaries eligible for both Medicare and Medicaid benefits. Our findings can help public health researchers target prevention and management strategies to improve care and reduce costs for people with multiple chronic conditions.

Mortality experience of the 1986-2000 National Health Interview Survey Linked Mortality Files participants.

The National Center for Health Statistics (NCHS) has produced the 1986-2000 National Health Interview Survey (NHIS) Linked Mortality Files by linking eligible adults in the 1986-2000 NHIS cohorts through probabilistic record linkage to the National Death Index to obtain mortality follow-up through December 31, 2002. The resulting files contain more than 120,000 deaths and an average of 9 years of survival time. To assess how well mortality was ascertained in the linked mortality files, NCHS has conducted a comparison of the mortality experience of the 1986-2000 NHIS cohorts with that of the U.S. population. This report presents the results of this comparative mortality assessment. Methods The survival of each annual NHIS cohort was compared with that of the U.S. population during the same period. Cumulative survival probabilities for each annual NHIS cohort were derived using the Kaplan-Meier product limit method, and corresponding cumulative survival probabilities were computed for the U.S. population using information from annual U.S. life tables. The survival probabilities were calculated at various lengths of follow-up for each age-race-sex group of each NHIS cohort and for the U.S. population. Results As expected, mortality tended to be underestimated in the NHIS cohorts because the sample includes only civilian, noninstitutionalized persons, but this underestimation generally was not statistically significant. Statistically significant differences increased with length of follow-up, occurred more often for white females than for the other race-sex groups, and occurred more often in the oldest age groups. In general, the survival experience of the age-race-sex groups of each NHIS cohort corresponds quite closely to that of the U.S. population, providing support that the ascertainment of mortality through the probabilistic record linkage accurately reflects the mortality experience of the NHIS cohorts.

John and Jane Doe: the epidemiology of unidentified decedents.

The number of people who cannot be identified at the time of death, sometimes referred to as John or Jane Does, is unknown, and little is known about them as a group. The study's objectives were to estimate the number of annual unidentified deaths, to identify demographic characteristics associated with dying unidentified, to determine whether the rates of such deaths vary geographically or over time, and to better characterize the causes of death. This was a population-based surveillance study of data collected from death certificates from 1979 to 2004 in the U.S. Subjects were selected by the absence of name, date of birth, and Social Security Number on their certificates. Main outcome measures were distributions by age, sex, and underlying cause of death and rates by sex, race, year, and state of death. An average of 413 unidentified persons died each year. The peak year was 1987 with 691 deaths, a rate of 28.5 per 10 million people. The rate declined to 9.7 per 10 million in 2004. Most unidentified decedents were male (80.6%). Unidentified death rates were highest among black people and in the Southwest. Among deaths for which the cause was known, 82.7% were due to injuries. Among injury deaths, 31.8% were homicides. Improvement in identification technology may have reduced rates of unidentified death since the 1980s. In addition, variations in rates of unidentified decedents may reflect changes in risk factors such as homelessness and substance abuse.