ArticlePloS one2013
Using spatial analysis to predict health care use at the local level: a case study of type 2 diabetes medication use and its association with demographic change and socioeconomic status.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 37 citations in OpenAlex.
- Spatial inequalities and contextual correlates of methylphenidate dispensing in Jiangsu Province, China.Frontiers in pharmacology · 2026Article
- Article
- Impact of Contextual-Level Social Determinants of Health on Newer Antidiabetic Drug Adoption in Patients with Type 2 Diabetes.International journal of environmental research and public health · 2023Article
- Article
- Spatial epidemiology of diabetes: Methods and insights.World journal of diabetes · 2021Review
- Spatiotemporal analyses of the epidemiological characteristics of diabetes mellitus.Epidemiology and health · 2021Article
- Mapping Census Tract Clusters of Type 2 Diabetes in a Primary Care Population.Preventing chronic disease · 2019Article
- Exploration of Risk Factors for Type 2 Diabetes among Arabs in Israel.Annals of global health · 2019Article
- Exploring the small-scale spatial distribution of hypertension and its association to area deprivation based on health insurance claims in Northeastern Germany.BMC public health · 2018Article
- Do long-term care services match population needs? A spatial analysis of nursing homes in Chile.PloS one · 2018Article
- Article
- Spatial Analysis of Ambient PMInternational journal of environmental research and public health · 2017Article
- Article
- Spatio-temporal Analysis for New York State SPARCS Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Article
- 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.International journal of health geographics · 2016Article
- Integrative Spatial Data Analytics for Public Health Studies of New York State.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Article
- Geographical distribution patterns of iodine in drinking-water and its associations with geological factors in Shandong Province, China.International journal of environmental research and public health · 2014Article
- Identification of optimum scopes of environmental factors for snails using spatial analysis techniques in Dongting Lake Region, China.Parasites & vectors · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Local health status and health care use may be negatively influenced by low local socio-economic profile, population decline and population ageing. To support the need for targeted local health care, we explored spatial patterns of type 2 diabetes mellitus (T2DM) drug use at local level and determined its association with local demographic, socio-economic and access to care variables. We assessed spatial variability in these associations. We estimated the five-year prevalence of T2DM drug use (2005-2009) in persons aged 45 years and older at four-digit postal code level using the University of Groningen pharmacy database IADB.nl. Statistics Netherlands supplied data on potential predictor variables. We assessed spatial clustering, correlations and estimated a multiple linear regression model and a geographically weighted regression (GWR) model. Prevalence of T2DM medicine use ranged from 2.0% to 25.4%. The regression model included the extent of population ageing, proportion of social welfare/benefits, proportion of low incomes and proportion of pensioners, all significant positive predictors of local T2DM drug use. The GWR model demonstrated considerable spatial variability in the association between T2DM drug use and above predictors and was more accurate. The findings demonstrate the added value of spatial analysis in predicting health care use at local level.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.