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☐ ☆ ✇ BMJ Open

An assessment of data quality and sociodemographic variation in health service utilisation of general practice, emergency department and admitted services in a New South Wales linked health data asset: a retrospective cohort study of Lumos

Por: Bouckley · T. · Myton-Katieva · R. · Peiris · D. · Nambiar · D. · Prince · S. · Bishop · S. · Cordery · D. · Hill · F. R. · Correll · P. · Feyer · A.-M. · Schierhout · G. · Campain · A. — Julio 23rd 2025 at 04:48
Objectives

This study aimed to (1) assess Lumos data quality, a New South Wales (NSW) statewide linked health data asset; and (2) determine sociodemographic variation in health service utilisation of general practice, emergency department and admitted services.

Design

A retrospective cohort study using Lumos, a linked health data asset.

Setting

A representative statewide sample population of NSW, Australia.

Participants

People residing within NSW with an electronic health record at a Lumos participating general practice between January 2010 and June 2023.

Primary and secondary outcome measures

Data quality indicators of Lumos including completeness, representativeness against NSW population data, consistency and timeliness. Furthermore, variation in general practice visits, emergency department presentations and hospital admission rates stratified by age, sex, rurality and Index of Relative Socio-economic Disadvantage (IRSD)—a measure of socioeconomic status used in Australia, where lower values represent greater relative disadvantage across a range of metrics such as education and income.

Results

At the time of analysis, Lumos included records from 5.2 million unique patients, representing half (49.7%) of the NSW resident population. Limiting data to 2022, the Lumos population distribution broadly aligned with the 2021 Census except for IRSD quintile four and five which were under-represented (15.0% vs 20.4% (standardised difference –0.14)), and over-represented (29.7% vs 19.9% (standardised difference 0.23)), respectively. Age and greater relative disadvantage were associated with higher rates of general practice visits and hospital admissions. Greater relative disadvantage was also associated with higher rates of emergency department presentations.

Conclusions

Lumos’s ability to overcome historical limitations of separately managed health data in Australia and its demonstrated data quality present an opportunity to enhance health system policy and planning in NSW. The variation in service utilisation across primary and tertiary care by population and geography apparent in Lumos reinforces the need for tailored service planning.

☐ ☆ ✇ BMJ Open

Effects of interventional public health laws and regulations intended to reduce gambling-related harms: a realist review study protocol

Por: Fisher · M. · Piper · T. · Mavi · S. · Nambiar · J. · Sharma · P. K. · Kirby · J. · Melendez-Torres · G. J. · Montgomery · P. · Fewell · G. · Chandan · J. S. · Bedford · K. — Julio 16th 2025 at 09:42
Introduction

Gambling is now widely acknowledged to be a major public health (PH) issue. The Office for Health Improvement and Disparities conservatively estimated that gambling harm is associated with an annual cost of £1.05–£1.77 billion in England alone. Marionneau et al have categorised gambling harms into seven themes: (1) financial, (2) relationship/conflict, (3) emotional and psychological (mental health), (4) health decrements (physical health), (5) employment/education, (6) cultural and (7) criminal activity. In this understanding, gambling harms are not restricted to individual experiences: they also impact families, the wider community and society, and hence they require a whole systems, PH approach, anchored in population-level interventions to reduce harms. We aim to identify the effects of interventional PH laws and regulations on the harms associated with gambling.

Methods and analysis

We limit our focus to interventional PH laws and regulations within a comprehensive search of scientific and legal databases, grey literature and books. Following Population, Intervention, Comparator, Outcome, Study, Timing inclusion criteria, evidence will be screened and appraised in Covidence by two reviewers (MF and TP). Included evidence will be analysed and synthesised using a narrative synthesis approach. Methodological quality will be appraised using the relevant risk of bias tool. Randomised controlled trials will be assessed using the Cochrane risk of bias tool (RoB2), Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) will be used for other non-randomised studies. Qualitative studies will be appraised using the EPPI reviewer software for systematic reviewing.

Ethics and dissemination

The review protocol is registered with PROSPERO (International prospective register of systematic reviews) at the National Institute for Health Research and the Centre for Reviews and Dissemination (CRD) at the University of York (CRD42024574502). We aim to define a theory of change and produce a context-mechanism-outcome framework with relevant experts using the findings. We plan to disseminate the findings through peer-reviewed publications, meetings with relevant experts and international conference presentations.

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