Dataset Information

CenSoc: Public Linked Administrative Mortality Records for Individual-level Research.

ABSTRACT: In the United States, much has been learned about the determinants of longevity from survey data and aggregated tabulations. However, the lack of large-scale, individual-level administrative mortality records has proven to be a barrier to further progress. We introduce the CenSoc datasets, which link the complete-count 1940 U.S. Census to Social Security mortality records. These datasets-CenSoc-DMF (N = 4.7 million) and CenSoc-Numident (N = 7.0 million)-primarily cover deaths among individuals aged 65 and older. The size and richness of CenSoc allows investigators to make new discoveries into geographic, racial, and class-based disparities in old-age mortality in the United States. This article gives an overview of the technical steps taken to construct these datasets, validates them using external aggregate mortality data, and discusses best practices for working with these datasets. The CenSoc datasets are publicly available, enabling new avenues of research into the determinants of mortality disparities in the United States.

SUBMITTER: Breen CF

PROVIDER: S-EPMC10651897 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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CenSoc: Public Linked Administrative Mortality Records for Individual-level Research.

Breen Casey F CF Osborne Maria M Goldstein Joshua R JR

Scientific data 20231115 1

In the United States, much has been learned about the determinants of longevity from survey data and aggregated tabulations. However, the lack of large-scale, individual-level administrative mortality records has proven to be a barrier to further progress. We introduce the CenSoc datasets, which link the complete-count 1940 U.S. Census to Social Security mortality records. These datasets-CenSoc-DMF (N = 4.7 million) and CenSoc-Numident (N = 7.0 million)-primarily cover deaths among individuals a ...[more]

PMID: 37968265

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Project description:IntroductionCritical Care is a specialty in medicine providing a service for severely ill and high-risk patients who, due to the nature of their condition, may require long periods recovering after discharge. Consequently, focus on the routine data collection carried out in Intensive Care Units (ICUs) leads to reporting that is confined to the critical care episode and is typically insensitive to variation in individual patient pathways through critical care to recovery.A resource which facilitates efficient research into interactions with healthcare services surrounding critical admissions, capturing the complete patient's healthcare trajectory from primary care to non-acute hospital care prior to ICU, would provide an important longer-term perspective for critical care research.ObjectiveTo describe and apply a reproducible methodology that demonstrates how both routine administrative and clinically rich critical care data sources can be integrated with primary and secondary healthcare data to create a single dataset that captures a broader view of patient care.MethodTo demonstrate the INTEGRATE methodology, it was applied to routine administrative and clinical healthcare data sources in the Secure Anonymised Data Linking (SAIL) Databank to create a dataset of patients' complete healthcare trajectory prior to critical care admission. SAIL is a national, data safe haven of anonymised linkable datasets about the population of Wales.ResultsWhen applying the INTEGRATE methodology in SAIL, between 2010 and 2019 we observed 91,582 critical admissions for 76,019 patients. Of these, 90,632 (99%) had an associated non-acute hospital admission, 48,979 (53%) had an emergency admission, and 64,832 (71%) a primary care interaction in the week prior to the critical care admission.ConclusionThis methodology, at population scale, integrates two critical care data sources into a single dataset together with data sources on healthcare prior to critical admission, thus providing a key research asset to study critical care pathways.

Project description:Between 2012 and 2017, N = 2814 youth between the ages of 4 and 20 were in child protective services (CPS) custody in Hamilton County, Ohio, and placed in out-of-home care. Child welfare administrative records were extracted and linked to electronic health records for all encounters at Cincinnati Children's Hospital Medical Center, with n = 2787 (99.1%) of records successfully linked prior to de-identifying the data for research purposes. Child welfare administrative data fields in the dataset include demographics, dates of entry into and exit from protective custody and out-of-home care, reasons for entry into custody, dates of placement changes, reasons for placement changes, and types of placement (e.g., foster home, kinship home, group home, residential treatment, independent living). Electronic health records (EHR) data fields include demographics, all inpatient and outpatient encounters with medications, diagnoses, screening results, laboratory test results, flowsheet data, and problem list entries. Data have been coded to capture broader categories of health needs and encounter details, medications, and other health concerns. Due to the high representation of children in CPS custody and out-of-home care who are also represented in the EHR data, this dataset provides a comprehensive view of the medical needs and health concerns for school-aged children in CPS custody in an entire county. As a result, these data can be useful for understanding the emergence of global and specific health concerns, frequency of healthcare use, and placement stability for all youth in CPS custody in this community, accounting for variation due to other health and child welfare factors. These data are likely generalizable to other mid-sized urban communities where academic medical centers provide healthcare for children in CPS custody. De-identified data may be made available to other researchers with approved data transfer agreements between academic institutions in place.

Project description:BackgroundSocioeconomic inequalities in mortality are evident in all high-income countries, and ongoing monitoring is recommended using linked census-mortality data. Using such data, we provide the first estimates of education-related inequalities in cause-specific mortality in Australia, suitable for international comparisons.MethodsWe used Australian Census (2016) linked to 13 months of Death Registrations (2016-17). We estimated relative rates (RR) and rate differences (RD, per 100 000 person-years), comparing rates in low (no qualifications) and intermediate (secondary school) with high (tertiary) education for individual causes of death (among those aged 25-84 years) and grouped according to preventability (25-74 years), separately by sex and age group, adjusting for age, using negative binomial regression.ResultsAmong 13.9 M people contributing 14 452 732 person-years, 84 743 deaths occurred. All-cause mortality rates among men and women aged 25-84 years with low education were 2.76 [95% confidence interval (CI): 2.61-2.91] and 2.13 (2.01-2.26) times the rates of those with high education, respectively. We observed inequalities in most causes of death in each age-sex group. Among men aged 25-44 years, relative and absolute inequalities were largest for injuries, e.g. transport accidents [RR = 10.1 (5.4-18.7), RD = 21.2 (14.5-27.9)]). Among those aged 45-64 years, inequalities were greatest for chronic diseases, e.g. lung cancer [men RR = 6.6 (4.9-8.9), RD = 57.7 (49.7-65.8)] and ischaemic heart disease [women RR = 5.8 (3.7-9.1), RD = 20.2 (15.8-24.6)], with similar patterns for people aged 65-84 years. When grouped according to preventability, inequalities were large for causes amenable to behaviour change and medical intervention for all ages and causes amenable to injury prevention among young men.ConclusionsAustralian education-related inequalities in mortality are substantial, generally higher than international estimates, and related to preventability. Findings highlight opportunities to reduce them and the potential to improve the health of the population.

Dataset Information

CenSoc: Public Linked Administrative Mortality Records for Individual-level Research.

Publications

CenSoc: Public Linked Administrative Mortality Records for Individual-level Research.

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