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Assessing Preventable Hospitalisation InDicators (APHID): protocol for a data-linkage study using cohort study and administrative data.

ABSTRACT: INTRODUCTION:Potentially preventable hospitalisation (PPH) has been adopted widely by international health systems as an indicator of the accessibility and overall effectiveness of primary care. The Assessing Preventable Hospitalisation InDicators (APHID) study will validate PPH as a measure of health system performance in Australia and Scotland. APHID will be the first large-scale study internationally to explore longitudinal relationships between primary care and PPH using detailed person-level information about health risk factors, health status and health service use. METHODS AND ANALYSIS:APHID will create a new longitudinal data resource by linking together data from a large-scale cohort study (the 45 and Up Study) and prospective administrative data relating to use of general practitioner (GP) services, dispensing of pharmaceuticals, emergency department presentations, hospital admissions and deaths. We will use these linked person-level data to explore relationships between frequency, volume, nature and costs of primary care services, hospital admissions for PPH diagnoses, and health outcomes, and factors that confound and mediate these relationships. Using multilevel modelling techniques, we will quantify the contributions of person-level, geographic-level and service-level factors to variation in PPH rates, including socioeconomic status, country of birth, geographic remoteness, physical and mental health status, availability of GP and other services, and hospital characteristics. ETHICS AND DISSEMINATION:Participants have consented to use of their questionnaire data and to data linkage. Ethical approval has been obtained for the study. Dissemination mechanisms include engagement of policy stakeholders through a reference group and policy forum, and production of summary reports for policy audiences in parallel with the scientific papers from the study.

SUBMITTER: Jorm LR

PROVIDER: S-EPMC3533070 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Assessing Preventable Hospitalisation InDicators (APHID): protocol for a data-linkage study using cohort study and administrative data.

Jorm Louisa R LR Leyland Alastair H AH Blyth Fiona M FM Elliott Robert F RF Douglas Kirsty M A KM Redman Sally S

BMJ open 20121212 6

<h4>Introduction</h4>Potentially preventable hospitalisation (PPH) has been adopted widely by international health systems as an indicator of the accessibility and overall effectiveness of primary care. The Assessing Preventable Hospitalisation InDicators (APHID) study will validate PPH as a measure of health system performance in Australia and Scotland. APHID will be the first large-scale study internationally to explore longitudinal relationships between primary care and PPH using detailed per ...[more]

PMID: 23242247

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Project description:IntroductionThe aims of this program of research are to use linked health and law enforcement data to describe individuals presenting to emergency and inpatient healthcare services with an acute alcohol harm or problematic alcohol use; measure their health service utilisation and law enforcement engagement; and quantify morbidity, mortality, offending and incarceration.Methods and analysisWe will assemble a retrospective cohort of people presenting to emergency departments and/or admitted to hospitals between 1 January 2005 and 31 December 2014 in New South Wales, Australia with a diagnosis denoting an acute alcohol harm or problematic alcohol use. We will link these data with records from other healthcare services (eg, community-based mental healthcare data, cancer registry), mortality, offending and incarceration data sets. The four overarching areas for analysis comprise: (1) describing the characteristics of the cohort at their first point of contact with emergency and inpatient hospital services in the study period with a diagnosis indicating an acute alcohol harm and/or problematic alcohol use; (2) quantifying health service utilisation and law enforcement engagement; (3) quantifying rates of mortality, morbidity, offending and incarceration; and (4) assessing predictors (eg, age, sex) of mortality, morbidity, offending and incarceration among this cohort.Ethics and disseminationEthics approval has been provided by the New South Wales Population and Health Services Research Ethics Committee. We will report our findings in accordance with the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement and Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) where appropriate. We will publish data in tabular, aggregate forms only. We will not disclose individual results. We will disseminate project findings at scientific conferences and in peer-reviewed journals. We will aim to present findings to relevant stakeholders (eg, addiction medicine and emergency medicine specialists, policy makers) to maximise translational impact of research findings.

Project description:IntroductionCases of the novel coronavirus disease (COVID-19) continue to spread around the world even one year after the declaration of a global pandemic. Those with weakened immune systems, due to immunosuppressive medications or disease, may be at higher risk of COVID-19. This includes individuals with autoimmune diseases, cancer, transplants, and dialysis patients. Assessing the risk and outcomes of COVID-19 in this population has been challenging. While administrative databases provide data with minimal selection and recall bias, clinical and behavioral data is lacking. To address this, we are collecting self-reported survey data from a randomly selected subsample with and without COVID-19, which will be linked to administrative health data, to better quantify the risk of COVID-19 infection associated with immunosuppression.Methods and analysisUsing administrative and laboratory data from British Columbia (BC), Canada, we established a population-based case-control study of all individuals who tested positive for SARS-CoV-2. Each case was matched to 40 randomly selected individuals from two control groups: individuals who tested negative for SARS-CoV-2 (i.e., negative controls) and untested individuals from the general population (i.e., untested controls). We will contact 1000 individuals from each group to complete a survey co-designed with patient partners. A conditional logistic regression model will adjust for potential confounders and effect modifiers. We will examine the odds of COVID-19 infection according to immunosuppressive medication or disease type. To adjust for relevant confounders and effect modifiers not available in administrative data, the survey will include questions on behavioural variables that influence probability of being tested, acquiring COVID-19, and experiencing severe outcomes.Ethics and disseminationThis study has received approval from the University of British Columbia Clinical Research Ethics Board [H20-01914]. Findings will be disseminated through scientific conferences, open access peer-reviewed journals, COVID-19 research repositories and dissemination channels used by our patient partners.

Project description:IntroductionThe American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.MethodsUsing the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.ResultsOf the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.ConclusionExcluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.

Dataset Information

Assessing Preventable Hospitalisation InDicators (APHID): protocol for a data-linkage study using cohort study and administrative data.

Publications

Assessing Preventable Hospitalisation InDicators (APHID): protocol for a data-linkage study using cohort study and administrative data.

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