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

Kauhl, Boris; Schweikart, Jürgen; Krafft, Thomas; Keste, Andrea; Moskwyn, Marita

doi:10.1186/s12942-016-0068-2

Cited by 39 publications

(42 citation statements)

References 54 publications

(86 reference statements)

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“…One final note is that MGWR and the best practices suggested here may hold merit for other health outcomes, data sources, and research questions. For example, GWR has already been applied to study obesity-related behaviors (Feuillet et al, 2015;Maroko et al, 2009), type 2 diabetes (Kauhl et al, 2016), and cancers (Cheng et al, 2011;St-Hilaire et al, 2010), and it could be beneficial to extend these inquiries through the use of MGWR. Furthermore, a finer measurement scale was pursued here than is typically utilized.…”

Section: Discussionmentioning

confidence: 99%

Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

Oshan¹,

Smith²,

Fotheringham³

2020

Preprint

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Background: Obesity rates are recognized to be at epidemic levels throughout much of the world, posing significant threats to both the health and financial security of many nations. The causes of obesity can vary but are often complex and multifactorial, and while many contributing factors can be targeted for intervention, an understanding of where these interventions are needed is necessary in order to implement effective policy. This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants, especially through the use of geographically weighted regression (GWR). Method: This paper provides a critical review of previous GWR models of obesogenic processes and then presents a novel application of multiscale (M)GWR using the Phoenix metropolitan area as a case study. Results: Though the MGWR model consumes more degrees of freedom than OLS, it consumes far fewer degrees of freedom than GWR, ultimately resulting in a more nuanced analysis that can incorporate spatial context but does not force every relationship to become local a priori. In addition, MGWR yields a lower AIC and AICc value than GWR and is also less prone to issues of multicollinearity. Consequently, MGWR is able to improve our understanding of the factors that influence obesity rates by providing determinant-specific spatial contexts. Conclusion:The results show that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants compared to both GWR and ordinary least squares.

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Section: Discussionmentioning

confidence: 99%

Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

Oshan¹,

Smith²,

Fotheringham³

2020

Preprint

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“…There was a de nite increase of continuity in age group 40-59, compared to other age groups. Continuity was distinctively lower in young age (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39), which can partly be explained by mild severity, less complication, and more frequent residential migration of the group. However, current phenomenon can pose an impending threat to the working population since legacy effect of diabetes has been recently reported 16 .…”

Section: Discussionmentioning

confidence: 99%

“…2019 American Diabetes Association guideline recommends that diabetes patients should meet their primary care givers every 2 to 3 months 2,25 . Moreover, prevalence of diabetes is closely related to residential location 26 , which implies local physicians can manage their patients in a more customized, communicative manner. In order to further improve continuity of care, we need a policy that would geographically align the supply and demand of chronic care needs.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Continuity of Care and Related Factors in Diabetes Patients in Korea

Shin

Kim

Cho

et al. 2020

Preprint

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Background: Diabetes is one of the fastest growing diseases with approximately 463 million patients worldwide. It is established that to manage diabetes, continuity of care in primary care setting is crucial. We aim to statistically define and analyze factors of continuity that are associated with patient, clinic and geographical relationship.Methods: We used 2014~2015 claim data from National Health Insurance Service (NHIS), with 39,096 eligible outpatient attendances across 29,912 office-based clinics. We applied multivariable logistic regression to analyze factors that may affect three kinds of continuity of care index for each patient: most frequent provider continuity index (MFPC), modified-modified continuity index (MMCI), and continuity of care index (COC). Results: Mean value of continuity of care indices were MFPC 0.90, MMCI 0.96, COC 0.85. Among patient factors, old age above 80 (MFPC 0.81 [0.74-0.89], MMCI 0.84 [0.76-0.92], COC 0.81 [0.74-0.89]) and disability were strongly associated with lower continuity of care. Another significant factor was residential area: further the patients lived from their primary care clinic, lower the continuity of diabetes care (MFPC 0.74 [0.70–0.78], MMCI 0.70 [0.66–0.73], COC 0.74 [0.70–0.78]). Patients who lived in metropolitan area had higher continuity of care compared to other areas (metropolitan area, MFPC 1.19 [1.17-1.27], MMCI 1.17 [1.10-1.25], COC 1.19 [1.12-1.27]). There was no statistical significance among clinic factors, such as number of hired physicians or nurses hired per clinic, between the lower and the higher continuity of care groups.Conclusion: Geographical proximity of patient’s residential area and clinic location showed highest correlation as factor of continuity. Political support is necessary to geographically align the imbalance of supply and demand of medical needs.

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“…Der Ansatz, der bei den angewendeten Kennzahlen verfolgt wird, ist immer raumorientiert (Wennberg und Gittelsohn 1973;Wennberg 2010Wennberg 2014 (Mangiapane 2014) genutzt. In den Routinedaten der Krankenkassen sind neben den krankheitsbezogenen Informationen auch Angaben zum Wohnort der Versicherten enthalten, sodass kleinräumig sehr differenzierte Auswertungen möglich sind (Kauhl et al 2016). Sofern eine Krankenkasse bundesweit flächendeckend tätig ist, können mit deren Routinedaten also kleinräumige Untersuchungen zum Gesundheitszustand durchgeführt werden.…”

Section: Nutzen Kleinräumiger Darstellungen Des Gesundheitszustandesunclassified

Schätzung kleinräumiger Krankheitshäufigkeiten für die deutsche Bevölkerung anhand von Routinedaten am Beispiel von Typ-2-Diabetes

Breitkreuz

Brückner

Burgard

et al. 2019

AStA Wirtsch Sozialstat Arch

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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

Cited by 39 publications

References 54 publications

Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

Analysis of Continuity of Care and Related Factors in Diabetes Patients in Korea

Schätzung kleinräumiger Krankheitshäufigkeiten für die deutsche Bevölkerung anhand von Routinedaten am Beispiel von Typ-2-Diabetes

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