Geographic Information Systems (GIS) in Public Health:How can GIS facilitate demand-based planning of healthcare and targeted prevention strategies?

Healthcare and prevention strategies are often planned based on the assumption that demand for healthcare and risk populations for diseases are the same everywhere. However, most health problems and associated risk factors vary widely across regions. The main question of this thesis was therefore: Can we plan healthcare and prevention strategies more effectively by acknowledging geographic aspects? Analysing four diseases with GIS answered this question. The results emphasize that a one-size-fits-all approach is not very effective for both, planning and allocation of healthcare and prevention strategies. Future public health policies need to acknowledge that geographic aspects are important determinants of health and should aim future interventions more towards local needs

Towards Sustainable Public Health Surveillance in India: Using Routinely Collected Electronic Emergency Medical Service Data for Early Warning of Infectious Diseases

Infectious disease surveillance, timely detection and early warning of outbreaks present a complex challenge to health authorities in India. Approaches based on the use of unexplored data sources, like emergency medical services (EMS) data, can contribute to the further advancement of public health surveillance capacities in India and support and strengthen the Integrated Disease Surveillance Programme (IDSP) strategy. This research followed a mixed method approach including a series of semi-structured interviews and fever data analysis of the EMS operating dispatch system in Andra Pradesh, India. In this paper, we explore whether routinely collected EMS health data can improve sustainable infectious disease surveillance and early warning capacity. The result highlights the need for improved surveillance systems for early warning of infectious diseases in India. The data availability at the EMS dispatch centre includes patient data and spatial information and can be used for near real-time analysis. Routine data relevant for health surveillance can be extracted to provide timely health information that supplements and enhances more traditional surveillance mechanisms and thus provides a cost-efficient, near real-time early warning system for the operating states. The designed intervention is sustainable and can improve infectious disease surveillance to potentially help the government officials to appropriately prioritize timely interventions to prevent infectious disease spread

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

Abstract 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

Who is where at risk for Chronic Obstructive Pulmonary Disease? A spatial epidemiological analysis of health insurance claims for COPD in Northeastern Germany.

Chronic obstructive pulmonary disease (COPD) has a high prevalence rate in Germany and a further increase is expected within the next years. Although risk factors on an individual level are widely understood, only little is known about the spatial heterogeneity and population-based risk factors of COPD. Background knowledge about broader, population-based processes could help to plan the future provision of healthcare and prevention strategies more aligned to the expected demand. The aim of this study is to analyze how the prevalence of COPD varies across northeastern Germany on the smallest spatial-scale possible and to identify the location-specific population-based risk factors using health insurance claims of the AOK Nordost.To visualize the spatial distribution of COPD prevalence at the level of municipalities and urban districts, we used the conditional autoregressive Besag-York-Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific ecological risk factors for COPD.The sex- and age-adjusted prevalence of COPD was 6.5% in 2012 and varied widely across northeastern Germany. Population-based risk factors consist of the proportions of insurants aged 65 and older, insurants with migration background, household size and area deprivation. The results of the GWR model revealed that the population at risk for COPD varies considerably across northeastern Germany.Area deprivation has a direct and an indirect influence on the prevalence of COPD. Persons ageing in socially disadvantaged areas have a higher chance of developing COPD, even when they are not necessarily directly affected by deprivation on an individual level. This underlines the importance of considering the impact of area deprivation on health for planning of healthcare. Additionally, our results reveal that in some parts of the study area, insurants with migration background and persons living in multi-persons households are at elevated risk of COPD

Results of the global OLS model.

<p>Significance levels: * <0.05, ** <0.01. ***<0.001.</p

Possible explanatory variables.

<p>Possible explanatory variables.</p

GWR regression coefficients of COPD.

<p>GWR regression coefficients of COPD.</p

Demographic composition of the AOK Nordost insurants in 2012.

<p>Demographic composition of the AOK Nordost insurants in 2012.</p

Poisson regression models for pertussis testing, stratified by age.

<p>Significance levels: * = 0.05; ** = 0.01; *** = 0.001. Only significant predictors are reported.</p

Rates of testing, test positivity and population incidence.

<p>SD = standard deviation.</p