Socio-demographic data sources for monitoring locality health profiles and geographical planning of primary health care in the UK

Child-to-adult body size change and risk of type 2 diabetes and cardiovascular disease

AIMS/HYPOTHESIS

Childhood overweight increases the risk of type 2 diabetes and cardiovascular disease in adulthood. However, the impact of childhood leanness on adult obesity and disease risk has been overlooked. We examined the independent and combined influences of child and adult body size on the risk of type 2 diabetes and cardiovascular disease.

METHODS

Data from the UK Biobank on 364,695 individuals of European ancestry and free of type 2 diabetes and cardiovascular disease were divided into nine categories based on their self-reported body size at age 10 and measured BMI in adulthood. After a median follow-up of 12.8 years, 33,460 individuals had developed type 2 diabetes and/or cardiovascular disease. We used Cox regression models to assess the associations of body size categories with disease incidence.

RESULTS

Individuals with low body size in childhood and high body size in adulthood had the highest risk of type 2 diabetes (HR 4.73; 95% CI 4.50, 4.99), compared to those with average body size in both childhood and adulthood. This was significantly higher than the risk in those with high body size in both childhood and adulthood (HR 4.05; 95% CI 3.84, 4.26). By contrast, cardiovascular disease risk was determined by adult body size, irrespective of childhood body size.

CONCLUSIONS/INTERPRETATION

Low body size in childhood exacerbates the risk of type 2 diabetes associated with adult obesity but not the risk of cardiovascular disease. Thus, promoting healthy weight management from childhood to adulthood, among lean children, is crucial.

Validating the Emergency Department Avoidability Classification (EDAC): A cluster randomized single-blinded agreement study

INTRODUCTION

The Emergency Department Avoidability Classification (EDAC) retrospectively classifies emergency department (ED) visits that could have been safely managed in subacute primary care settings, but has not been validated against a criterion standard. A validated EDAC could enable accurate and reliable quantification of avoidable ED visits. We compared agreement between the EDAC and ED physician judgements to specify avoidable ED visits.

MATERIALS AND METHODS

We conducted a cluster randomized, single-blinded agreement study in an academic hospital in Hamilton, Canada. ED visits between January 1, 2019, and December 31, 2019 were clustered based on EDAC classes and randomly sampled evenly. A total of 160 ED visit charts were randomly assigned to ten participating ED physicians at the academic hospital for evaluation. Physicians judged if the ED visit could have been managed appropriately in subacute primary care (an avoidable visit); each ED visit was evaluated by two physicians independently. We measured interrater agreement between physicians with a Cohen's kappa and 95% confidence intervals (CI). We evaluated the correlation between the EDAC and physician judgements using a Spearman rank correlation and ordinal logistic regression with odds ratios (ORs) and 95% CIs. We examined the EDAC's precision to identify avoidable ED visits using accuracy, sensitivity and specificity.

RESULTS

ED physicians agreed on 139 visits (86.9%) with a kappa of 0.69 (95% CI 0.59-0.79), indicating substantial agreement. Physicians judged 96.2% of ED visits classified as avoidable by the EDAC as suitable for management in subacute primary care. We found a high correlation between the EDAC and physician judgements (0.64), as well as a very strong association to classify avoidable ED visits (OR 80.0, 95% CI 17.1-374.9). The EDACs avoidable and potentially avoidable classes demonstrated strong accuracy to identify ED visits suitable for management in subacute care (82.8%, 95% CI 78.2-86.8).

DISCUSSION

The EDAC demonstrated strong evidence of criterion validity to classify avoidable ED visits. This classification has important potential for accurately monitoring trends in avoidable ED utilization, measuring proportions of ED volume attributed to avoidable visits and informing interventions intended at reducing ED use by patients who do not require emergency or life-saving healthcare.

Methods and tools for geographical mapping and analysis in primary health care

AIM

The purpose of this paper is to review methods and tools for mapping, visualising and exploring geographic information to aid in primary health care (PHC) research and development.

BACKGROUND

Mapping and spatial analysis of indicators of locality health profiles and healthcare needs assessment are well-established facets of health services research and development. However, while there are a range of different methods and tools used for these purposes, non-specialists responsible for managing the use of such information systems may find knowing where to start and what can be done a relatively steep learning curve. In this review, health and sociodemographic datasets are used to illustrate some key methods, tools and organisational issues, and builds upon two recent reviews in this journal, respectively, focusing on geographic data sources and geographic concepts. Those familiar with mapping and spatial analysis should find this a useful review of current matters.

METHOD

A thematic review is presented with illustrative case studies relevant to PHC. It begins with a section on visualising and interpreting geographic information. This is followed by a section critiquing analytical methods. Consideration is given to software and deployment issues in a third section. Content is based on domain knowledge of the authors as a team of geographic information scientists and a public health practitioner working in tandem, with its scope restricted to routine applications of mapping and analysis. Advanced techniques such as spatio-temporal modelling are not considered, neither are methodological technicalities, although guidance on further reading is provided.

SUMMARY

Geographical perspectives are now playing a significant role in PHC delivery, and for those engaged in informatics and/or managing population-level care, understanding key geographic information systems methods and terminologies are important as is gaining greater familiarity with institutional aspects of implementation.