Spatial analysis to locate new clinics for diabetic kidney patients in the underserved communities in Alberta

Geographic hot spots of kidney transplant candidates wait-listed post-dialysis

INTRODUCTION

Preemptive wait-listing of deceased donor kidney transplant (DDKT) candidates before maintenance dialysis increases the likelihood of transplantation and improves outcomes among transplant patients. Previous studies have identified substantial disparities in rates of preemptive listing, but a gap exists in examining geographic sources of disparities, particularly for sub-regional units. Identifying small area hot spots where delayed listing is particularly prevalent may more effectively inform both health policy and regionally appropriate interventions.

METHODS

We conducted a retrospective cohort study utilizing 2010-2020 Scientific Registry of Transplant Recipients (SRTR) data for all DDKT candidates to examine overall and race-stratified geospatial hot spots of post-dialysis wait-listing in U.S. zip code tabulation areas (ZCTA). Three geographic clustering methods were utilized to identify robust statistically significant hot spots of post-dialysis wait-listing.

RESULTS

Novel sub-regional hot spots were identified in the southeast, southwest, Appalachia, and California, with a majority existing in the southeast. Race-stratified results were more nuanced, but broadly reflected similar patterns. Comparing transplant candidates in hot spots to candidates in non-clusters indicated a strong association between residence in hot spots and high area deprivation (OR: 6.76, 95%CI: 6.52-7.02), indicating that improving access healthcare in these areas may be particularly beneficial.

CONCLUSION

Our study identified overall and race-stratified hot spots with low rates of preemptive wait list placement in the U.S., which may be useful for prospective healthcare policy and interventions via targeting of these narrowly defined geographical areas.

Evidence-Based Decision Making 6: Administrative Databases as Secondary Data Source for Epidemiologic and Health Service Research

Health-care systems require reliable information on which to base health-care planning and make decisions, as well as to evaluate their policy impact. Administrative data, predominantly captured for non-research purposes, provide important information about health services use, expenditures, and clinical outcomes and may be used to assess quality of care. With increased digitalization and accessibility of administrative databases, this data is more readily available for health service research purposes, aiding evidence-based decision making. This chapter discusses the utility of administrative data for population-based studies of health and health care.

Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression

BACKGROUND

The provision of general practitioners (GPs) in Germany still relies mainly on the ratio of inhabitants to GPs at relatively large scales and barely accounts for an increased prevalence of chronic diseases among the elderly and socially underprivileged populations. Type 2 Diabetes Mellitus (T2DM) is one of the major cost-intensive diseases with high rates of potentially preventable complications. Provision of healthcare and access to preventive measures is necessary to reduce the burden of T2DM. However, current studies on the spatial variation of T2DM in Germany are mostly based on survey data, which do not only underestimate the true prevalence of T2DM, but are also only available on large spatial scales. The aim of this study is therefore to analyse the spatial distribution of T2DM at fine geographic scales and to assess location-specific risk factors based on data of the AOK health insurance.

METHODS

To display the spatial heterogeneity of T2DM, a bivariate, adaptive kernel density estimation (KDE) was applied. The spatial scan statistic (SaTScan) was used to detect areas of high risk. Global and local spatial regression models were then constructed to analyze socio-demographic risk factors of T2DM.

RESULTS

T2DM is especially concentrated in rural areas surrounding Berlin. The risk factors for T2DM consist of proportions of 65-79 year olds, 80 + year olds, unemployment rate among the 55-65 year olds, proportion of employees covered by mandatory social security insurance, mean income tax, and proportion of non-married couples. However, the strength of the association between T2DM and the examined socio-demographic variables displayed strong regional variations.

CONCLUSION

The prevalence of T2DM varies at the very local level. Analyzing point data on T2DM of northeastern Germany's largest health insurance provider thus allows very detailed, location-specific knowledge about increased medical needs. Risk factors associated with T2DM depend largely on the place of residence of the respective person. Future allocation of GPs and current prevention strategies should therefore reflect the location-specific higher healthcare demand among the elderly and socially underprivileged populations.

Evidence-based decision-making 6: Utilization of administrative databases for health services research

Health-care systems require reliable information on which to base health-care planning and make decisions, as well as to evaluate their policy impact. Administrative data provide important information about health services use, expenditures, clinical outcomes, and may be used to assess quality of care. With increased digitalization and accessibility of administrative databases, these data are more readily available for health service research purposes, aiding evidence-based decision-making. This chapter discusses the utility of administrative data for population-based studies of health and health care.