The use of routinely collected administrative health data for research can provide unique insights to inform decision-making and, ultimately, support better public health outcomes. Yet, since these data are primarily collected to administer healthcare service delivery, challenges exist when using such data for secondary purposes, namely epidemiologic research. Many of these challenges stem from the researcher's lack of control over the quality and consistency of data collection, and - furthermore - a lessened understanding of the data being analyzed. That said, we assert that these challenges can be partly mitigated through careful, systematic use of these data in epidemiologic research. This article presents considerations derived from experiences analyzing administrative health data (e.g., healthcare practitioner billings, hospitalizations, and prescription medication data) in the Canadian province of British Columbia (population of over 5 million in 2024), though we believe the underlying principles generalize beyond this region. Key considerations were organized around four themes: 1) Know the data and their primary use (understand their scope and limitations); 2) Understand classification and coding systems (appreciate the nuances regarding classification systems, versions, how they are employed in the primary uses of the data, and querying the values); 3) Transform data into meaningful forms (process data and apply identification algorithms, when necessary); 4) Recognize the importance of validity when defining analytic variables (make meaningful inferences based on data/algorithms). Although this article is not an exhaustive list of all considerations, we believe that it will provide pragmatic insights for those interested in leveraging administrative health data for epidemiologic research.
Indiana, located in the Midwest region of the United States, faces significant challenges with respect to health, especially maternal and child health (MCH). These challenges include high rates of stillbirth, neonatal abstinence syndrome (NAS) and congenital syphilis (CS). Not only are these often-fatal conditions underreported, but it can also be difficult to track them longitudinally, as mothers and infants are not routinely linked through electronic health records (EHRs). This paper describes the process, structure and planned outcomes of a partnership between Indiana University, Regenstrief Institute and public health partners in support of the U.S. Centers for Disease Control and Prevention's Pregnant People-Infant Linked Longitudinal Surveillance (PILLARS) program. Together, academic, clinical and public health organisations are collaboratively developing an infrastructure and deploying novel methods to surveil stillbirth, CS and NAS longitudinally. The infrastructure includes: (a) deploying deterministic and probabilistic algorithms to link mothers and their infants using multiple, linked data sources; (b) creating and maintaining a registry of maternal-infant dyads; (c) using the registry to perform longitudinal surveillance in collaboration with Indiana public health authorities on stillbirth, NAS and CS and (d) translating information from surveillance activities into action by collaborating with public health and community-based organisations to improve and implement prevention activities in vulnerable Indiana communities. Our long-term goal is to improve outcomes for these conditions and other priority MCH outcomes by expanding our work to additional MCH use cases.
Background: The Danish National Patient Registry (DNPR) is a valuable resource for population-based research, but the validity of routine registration of advanced heart failure (HF) treatments within the registry is unknown. We, therefore, investigated the validity of HF, advanced HF treatments, and HF readmissions in the DNPR.
Methods: We randomly sampled patients registered at a Danish University Hospital during 2017-2021 from the DNPR. We identified 200 patients with first-time HF, 390 patients with one of eight advanced HF treatments, and 133 patients with HF admission after implantable cardioverter-defibrillator (ICD) or cardiac resynchronisation therapy (CRT). Compared with medical record reviews, we calculated positive predictive values (PPVs) with 95% confidence intervals (CIs).
Results: The PPV for first-time HF was 81% (95% CI: 74-86%). For advanced HF treatments, the PPV was 97% (95% CI: 91-99%) for ICD, 96% (95% CI: 86-100%) for CRT-pacemaker, 88% (95% CI: 76-95%) for CRT-defibrillator, 100% (95% CI: 83-100%) for left ventricular assist device, 43% (95% CI: 18-71%) for intra-aortic balloon pump, 38% (95% CI: 25-35%) for impella, 100% (95% CI: 93-100%) for cardiopulmonary support, and 100% (95% CI: 94-100%) for heart transplantation. The PPV for HF admission after ICD was 25% (95% CI: 16-37%) and 18% (95% CI: 9.2-30%) after CRT.
Conclusions: The PPV of routine registrations in the DNPR was moderate for first-time HF, high for most advanced HF treatments, and low for HF admissions after ICD or CRT. Thus, the DNPR is a valuable data source for population-based research on first-time HF and many advanced HF treatments.
Introduction: Research on substance use harm in Canada has been hampered by an absence of linked data to analyse and report on the social drivers of substance use harm.
Objectives: This study aims to address this gap by providing a fully annotated Stata do-file that links sociodemographic data to 11 years of hospitalisation and death outcomes. This do-file will greatly facilitate the creation of provincial and national substance use cohorts using line-level data available through Statistics Canada's Research Data Centres (RDC) program.
Methods: We used Canadian Census Health and Environment Cohorts (CanCHEC) 2006 to create a cohort of Saskatchewanians followed from 2006 to 2016. We linked sociodemographic information of the 2006 Census (long-form) respondents to their hospitalisation data captured in the Discharge Abstract Database (DAD) (2006 to 2016) and their mortality records in the Canadian Vital Statistics Death Database (CVSD) (2006 to 2016). We developed an algorithm to identify Saskatchewanians who experienced a substance use harm event. We validated the cohort by comparing our descriptive findings with those from other Canadian studies on substance use.
Results: We used CanCHEC, a national data resource, whereas most previous studies have used provincial data resources. Despite this difference in constructing the cohorts, our results showed trends consistent with previous studies, including an overrepresentation of individuals with lower socioeconomic status among the people who experienced substance use harm (PESUH). Similar to other Canadian studies, our results indicate an increasing rate of substance use harm from 2006 to 2016.
Conclusion: This study provides a Stata do-file that compiles a validated substance use cohort using CanCHEC, enabling comprehensive substance use research by linking sociodemographic data with health outcomes. The do-file is likely to save researchers hundreds of hours and accelerate research on the drivers of substance use harms in Canada.
Background: Sources of public and private data and ways to link them continue to evolve. This offers new opportunities for research, and new reasons for data-holding organisations to form partnerships. While research using these data can be beneficial, there is also a potential for negative consequences for some individuals or groups, including unintended or unanticipated effects. It is important to consult the public on how we might achieve both opportunities to link different types of data for research purposes, and protections against the misuse of data and the possibility of negative consequences.
Methods: Combining data sources for research was the topic of four days of deliberation held in British Columbia, Canada in late 2019. Public deliberation events bring diverse groups of people together to give direct input to policy makers, through carefully structured in-depth discussion on issues that are controversial and/or a source of public concern. Participants discussed whether data from electronic medical records should be used for research purposes, whether it is acceptable to combine data from public and private sources, who should authorise its use in research, and how a public advisory group on data use might be structured.
Results: Over four days, 29 residents of BC developed 17 deliberative conclusions that can be grouped into four broad topic areas: balancing benefit and potential harms when linking data; the protections that are expected to govern use of data; the type of authorisation required; and how the public should be involved in an ongoing way. Overall, the public is very supportive of research as long as oversight and controls are in place, including ongoing input from members of the public.
Conclusion: Deliberative conclusions from this event provide essential public input on the use of linked data for research, in particular when those data come from multiple sources. This is important information as policy-makers continue to develop legislation and practices around the use and linkage of both public and private sources of data.
The trade-off between the costs of childcare provision and the benefits of having an increased proportion of women, particularly women with dependent children, in employment is one of the most taxing social issues for Western governments. In countries like Northern Ireland, the limited subsidised childcare provision for preschool and primary school children has been partially offset by a rise in informal childcare though this has been considerably hard to assess both in terms of magnitude and effect. Using the entire 2011 Census cohort of mothers with children aged 1 to 16 years of age, we argue that co-resident grandparents have a substantial positive impact on maternal labour force participation in Northern Ireland. The presence of a co-resident grandparent was associated with an increase of 3.7 percentage points in employment for single-parent mothers and 2 percentage points for mothers in two-parent households. Mothers with co-resident grandparents report an increase of 2.7 percentage points for a single mother and of 3.7 percentage points for a mother in a two-parent household being in full-time employment than mothers without. Overall, the presence of a co-resident grandparent was associated with at least a 3.2 percentage point increase in labour force participation among mothers with primary-school-age children.
Introduction: The Canadian Institute of Health Information's (CIHI) Discharge Abstract Database (DAD) contains standardised administrative data on all hospitalisations in Canada, excluding Quebec.
Objectives: We aimed to validate preterm birth related perinatal and neonatal data in DAD by assessing its accuracy against the reference standard of the Canadian Neonatal Network (CNN) database.
Methods: We linked birth hospitalization data between the DAD and CNN databases for all neonates born <33 weeks gestational age (GA) admitted to the Neonatal Intensive Care Units in Winnipeg, Canada, between 2010 and 2022. A comprehensive list of maternal and neonatal variables relevant to preterm birth was chosen a priori for validation. For categorical variables, we measured correlation using Cohen's weighted kappa (k) and for continuous variables, we measured agreement using Lin's concordance correlation coefficient (LCCC).
Results: 2084 neonates were included (mean GA 29.4 ± 2.4 weeks; birth weight 1430 ± 461g). Baseline continuous maternal and neonatal variables showed excellent accuracy in DAD [Maternal age: LCCC = 0.99 (0.99, 0.99); GA: LCCC = 0.95 (0.95, 0.96); birth weight: LCCC = 0.97 (0.96, 0.97); sex: k = 0.99 (0.98-0.99)]. In contrast, the accuracy of the maternal baseline categorical variables and neonatal outcomes and interventions ranged from very good to poor [e.g., Caesarean section: k = 0.91 (0.89-0.93), pre-gestational diabetes: k = 0.04 (0.03-0.05), neonatal sepsis: k = 0.37 (0.31-0.42), bronchopulmonary dysplasia: k = 0.26 (0.19-0.33), neonatal laparotomy: k = 0.55 (0.43-067)].
Conclusion: Neonatal variables such as gestational age and birth weight had high accuracy in DAD, while the accuracy of maternal and neonatal morbidities and interventions were variable, with some being poor. Reasons for the inaccuracy of these variables should be identified and measures taken to improve them.
Introduction: In England, life expectancy has stalled and significant decreases observed in certain geographical areas and populations. The cause of this involves complex dynamics between an individual's health, characteristics, lifestyle, and their wider environment known as the wider determinants of health which are key to good life expectancy, healthy life expectancy, and prevention of long-term medical conditions. Knowing the availability, breadth, features, and linkage potential of datasets relevant to wider determinants of health is important for exploring trends and associations for policy and public health planning.
Methods: A systematic mapping of internet content identified accessible datasets relevant to wider determinants of health in England with town level geographical granularity or lower. Search terms were used in search engines and chatbots to identify weblinks subsequently examined for eligible datasets.
Results: 105 potential weblinks to datasets were identified. Of these, twenty-one weblinks were explored further after exclusion of those: not accessible or currently live (n = 13); duplicated across search engines (n = 17); providing information only (i.e. no raw data, n = 14); did not provide freely accessible data (n = 3); were not relevant to wider determinants of health (n = 17); lacked geographical granularity (n = 26). Eighty-nine datasets of interest were compiled with sub-town level data aggregation. Approximately half (n = 47, 52%) were from the England and Wales census 2021, with the remaining sources including government bodies, public services, and research datasets. Datasets covered many valuable categories of wider determinants of health. Key data gaps included food consumption, social care data and community/voluntary services.
Conclusion: In England, access to data relevant to wider determinants of health is good and available at relatively small geographical resolution. Accessible datasets were identified and compiled within multiple categories of wider determinants of health as a useful data resource to explore wider determinants of health at place if linked to relevant health data or population studies.
Key features: This data resource profile describes a systematic mapping of freely accessible population data on wider determinants of health in England. To the authors knowledge this is the first comprehensive compilation of freely accessible data resources of this kind.This data resource profile was created to support research into the mechanisms and impact of wider determinants on the health of populations in England but is applicable to research and populations studies wider than this.Eighty-nine datasets were identified that may be of use to researchers in health and other population data fields. Datasets are held separately but many have the potential to be linked through common geographical are
Introduction: The ORIGINS Project ("ORIGINS") is a longitudinal, population-level birth cohort with data and biosample collections that aim to facilitate research to reduce non-communicable diseases (NCDs) and encourage 'a healthy start to life'. ORIGINS has gathered millions of datapoints and over 400,000 biosamples over 15 timepoints, antenatally through to five years of age, from mothers, non-birthing partners and the child, across four health and wellness domains: 'Growth and development', 'Medical, biological and genetic', 'Biopsychosocial and cognitive', 'Lifestyle, environment and nutrition'.
Methods: Mothers, non-birthing partners and their offspring were recruited antenatally (between 18 and 38 weeks' gestation) from the Joondalup and Wanneroo communities of Perth, Western Australia from 2017 to 2024. Data come from several sources, including routine hospital antenatal and birthing data, ORIGINS clinical appointments, and online self-completed surveys comprising several standardised measures. Data are merged using the Medical Record Number (MRN), the ORIGINS Unique Identifier and the ORIGINS Pregnancy Number, as well as additional demographic data (e.g. name and date of birth) when necessary.
Results: The data are held on an integrated data platform that extracts, links, ingests, integrates and stores ORIGINS' data on an Amazon Web Services (AWS) cloud-based data warehouse. Data are linked, transformed for cleaning and coding, and catalogued, ready to provide to sub-projects (independent researchers that apply to use ORIGINS data) to prepare for their own analyses. ORIGINS maximises data quality by checking and replacing missing and erroneous data across the various data sources.
Conclusion: As a wide array of data across several different domains and timepoints has been collected, the options for future research and utilisation of the data and biosamples are broad. As ORIGINS aims to extend into middle childhood, researchers can examine which antenatal and early childhood factors predict middle childhood outcomes. ORIGINS also aims to link to State and Commonwealth data sets (e.g. Medicare, the National Assessment Program - Literacy and Numeracy, the Pharmaceutical Benefits Scheme) which will cater to a wide array of research questions.
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