Joint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia

Application of the Australian Bureau of Statistics Socio-Economic Indexes for Areas in cardiovascular disease research: a scoping review identifying implications for research

Objective To scope how the Australian Bureau of Statistics Socio-Economic Indexes for Areas (SEIFA) has been applied to measure socio-economic status (SES) in peer-reviewed cardiovascular disease (CVD) research. Methods The Joanna Briggs Institute's scoping review methodology was used. Results The search retrieved 2788 unique citations, and 49 studies were included. Studies were heterogeneous in their approach to analysis using SEIFA. Not all studies provided information as to what version was used and how SEIFA was applied in analysis. Spatial unit of analysis varied between studies, with participant postcode most frequently applied. Study quality varied. Conclusions The use of SEIFA in Australian CVD peer-reviewed research is widespread, with variations in the application of SEIFA to measure SES as an exposure. There is a need to improve the reporting of how SEIFA is applied in the methods sections of research papers for greater transparency and to ensure accurate interpretation of CVD research.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.

Risk Factors Associated with Preventable Hospitalisation among Rural Community-Dwelling Patients: A Systematic Review

Potentially preventable hospitalisations (PPHs) are common and increase the burden on already stretched healthcare services. Increasingly, psychosocial factors have been recognised as contributing to PPHs and these may be mitigated through greater attention to social capital. This systematic review investigates the factors associated with PPHs within rural populations. The review was designed, conducted, and reported according to PRISMA guidelines and registered with Prospero (ID: CRD42020152194). Four databases were systematically searched, and all potentially relevant papers were screened at the title/abstract level, followed by full-text review by at least two reviewers. Papers published between 2000-2022 were included. Quality assessment was conducted using Newcastle-Ottawa Scale and CASP Qualitative checklist. Of the thirteen papers included, eight were quantitative/descriptive and five were qualitative studies. All were from either Australia or the USA. Access to primary healthcare was frequently identified as a determinant of PPH. Socioeconomic, psychosocial, and geographical factors were commonly identified in the qualitative studies. This systematic review highlights the inherent attributes of rural populations that predispose them to PPHs. Equal importance should be given to supply/system factors that restrict access and patient-level factors that influence the ability and capacity of rural communities to receive appropriate primary healthcare.

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya

The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012–2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts.

Pairwise likelihood estimation of latent autoregressive count models

Latent autoregressive models are useful time series models for the analysis of infectious disease data. Evaluation of the likelihood function of latent autoregressive models is intractable and its approximation through simulation-based methods appears as a standard practice. Although simulation methods may make the inferential problem feasible, they are often computationally intensive and the quality of the numerical approximation may be difficult to assess. We consider instead a weighted pairwise likelihood approach and explore several computational and methodological aspects including estimation of robust standard errors and the role of numerical integration. The suggested approach is illustrated using monthly data on invasive meningococcal disease infection in Greece and Italy.

The effect of primary care on potentially avoidable hospitalizations in France: a cross-sectional study

Background

Potentially avoidable hospitalizations are an indirect measure of access to primary care. However, the role and quality of primary care might vary by geographical location. The main objective was to assess the impact of primary care on geographic variations of potentially avoidable hospitalizations in Occitanie, France.

Methods

We conducted a retrospective analysis of claims and socio-economic data for the French Occitanie region in 2014. In order to account for spatial heterogeneity, the region was split into two zones based on socio-economic traits: median pre-tax income and unemployment rate. Age- and sex-adjusted hospital discharge potentially avoidable hospitalization rates were calculated at the ZIP-code level. Demographic, socio-economic, and epidemiological determinants were retrieved, as well as data on supply of, access to and utilization of primary care.

Results

72% of PAH are attributable to two chronic conditions: chronic obstructive pulmonary disease and heart failure. In Zone 1, the potentially avoidable hospitalization rate was positively associated with premature mortality and with the number of specialist encounters by patients. It was negatively associated with the density of nurses. In Zone 2, the potentially avoidable hospitalization rate was positively associated with premature mortality, with access to general practitioners, and with the number of nurse encounters by patients. It was negatively associated with the proportion of the population having at least one general practitioner encounter and with the density of nurses.

Conclusions

This study suggests that the role of primary care in potentially avoidable hospitalizations might be geography dependent.

Risk factors for avoidable hospitalizations in Canada using national linked data: A retrospective cohort study

Hospitalizations for certain chronic conditions are considered avoidable for adult Canadians given effective and timely primary care management. Individual-level risk factors such as income and health behaviours are not routinely collected in most hospital databases and as a result, are largely uncharacterized for avoidable hospitalization at the national level. The aim of this study was to identify and describe demographic, socioeconomic, and health behavioural risk factors for avoidable hospitalizations in Canada using linked data. A national retrospective cohort study was conducted by pooling eight cycles of the Canadian Community Health Survey (2000/2001-2011) and linking to hospitalization records in the Discharge Abstract Database (1999/2000–2012/2013). Respondents who were younger than 18 years and older than 74 years of age, residing in Quebec, or pregnant at baseline were excluded yielding a final cohort of 389,065 individuals. The primary outcome measure was time-to index avoidable hospitalization. Sex-stratified Cox proportional hazard models were constructed to determine effect sizes adjusted for various factors and their associated 95% confidence intervals. Demographics, socioeconomic status, and health behaviours are associated with risk of avoidable hospitalizations in males and females. In fully adjusted models, health behavioural variables had the largest effect sizes including heavy smoking (Male HR 2.65 (95% CI 2.17–3.23); Female HR 3.41 (2.81–4.13)) and being underweight (Male HR 1.98 (1.14–3.43); Female HR 2.78 (1.61–4.81)). Immigrant status was protective in both sexes (Male HR 0.83 (0.69–0.98); (Female HR 0.69 (0.57–0.84)). Adjustment for behavioural and clinical variables attenuated the effect of individual-level socioeconomic status. This study identified several risk factors for time-to-avoidable hospitalizations by sex, using the largest national database of linked health survey and hospitalization records. The larger effect sizes of several modifiable risk factors highlights the importance of prevention in addressing avoidable hospitalizations in Canada.

Spatio-Temporal Variation of Gender-Specific Hypertension Risk: Evidence from China

Previous studies which have shown the existence of gender disparities in hypertension risks often failed to take into account the participants' spatial and temporal information. In this study, we explored the spatio-temporal variation for gender-specific hypertension risks in not only single-disease settings but also multiple-disease settings. From the longitudinal data of the China Health and Nutrition Survey (CHNS), 70,374 records of 21,006 individuals aged 12 years and over were selected for this study. Bayesian B-spline techniques along with the Besag, York, and Mollie (BYM) model and the Shared Component Model (SCM) model were then used to construct the spatio-temporal models. Our study found that the prevalence of hypertension in China increased from 11.7% to 34.5% during 1991 and 2015, with a higher rate in males than that in females. Moreover, hypertension was found mainly clustered in spatially adjacent regions, with a significant high-risk pattern in Eastern and Central China while a low-risk pattern in Western China, especially for males. The spatio-temporal variation of hypertension risks was associated with regional covariates, such as age, overweight, alcohol consumption, and smoking, with similar effects of age shared by both genders whereas gender-specific effects for other covariates. Thus, gender-specific hypertension prevention and control should be emphasized in the future in China, especially for the elderly population, overweight population, and females with a history of alcohol consumption and smoking who live in Eastern China and Central China.