Spatial risk adjustment between health insurances: using GWR in risk adjustment models to conserve incentives for service optimisation and reduce MAUP

Wende, Danny

doi:10.1007/s10198-019-01079-6

Cited by 11 publications

(5 citation statements)

References 49 publications

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“…In any case, the influence of scale on the resulting spatial patterns is known as the modifiable area unit problem. To minimise the effects of modifiable area unit problem, the smallest administrative areas are preferred, once more aggregated data have less variation, smaller variance, and standard deviation [20, 58, 59]. “Aggregation to larger areas should be avoided unless there are good reasons for doing so” [60].…”

Section: Discussionmentioning

confidence: 99%

Beyond Usual Geographical Scales of Analysis: Implications for Healthcare Management and Urban Planning

Morais¹,

Lopes²,

Rocha³

et al. 2022

Port J Public Health

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Introduction: In the context of climate emergency, advances in geographic information systems, geocoding, and geomedicine allow us to go beyond the conventional usual scales and be aligned with people’s needs, improving knowledge and accuracy of the spatial pattern of health outcomes. This study shows that the geographical scale of analysis affects the interpretation of health outcomes. Methods: All mortality that occurred in Portugal in 2014–2017 was geocoded. From 435,291 addresses, 412,608 were geocoded with success. As an example, we use the spatial patterns of the elderly’s heat-related cardiorespiratory mortality. Results: It is shown: (i) it is possible to have high quality and accuracy of spatial data used in health outcomes analysis; (ii) how geographic scales reveal different degrees of detail in health outcomes analysis; (iii) the neighbourhood scale revealed different patterns of cardiorespiratory mortality from the usually available scale (parish). Discussion: Our findings suggest the relevance of geocoding health outcomes with a finer scale in tackling the challenges of the healthcare sector, and in support of planning decision-making, closely matching citizens’ needs. Without running the risk of losing potentially major prospects, better healthcare management is achievable, with optimal resource allocation, and improved detailed and informed policymaking, allowing enhanced climate health equity in cities promotion.

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Section: Discussionmentioning

confidence: 99%

Beyond Usual Geographical Scales of Analysis: Implications for Healthcare Management and Urban Planning

Morais¹,

Lopes²,

Rocha³

et al. 2022

Port J Public Health

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“…Spatial modeling could be a powerful means for urban health practitioners to grasp geographical patterns and dynamics that otherwise remain unnoticed. While most obesity research relies either on linear aspatial [24,25] or linear spatial models [26][27][28][29][30][31], nonlinear spatially explicit modeling to investigate the relationships between tract-level obesity prevalence and socioenvironmental factors is lacking, as we are aware of. For example, Ferdowsy et al [32] used nonlinear random forest modeling to assess obesity risk by means of behavioral factors.…”

Section: Introductionmentioning

confidence: 99%

Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA

Lotfata

Georganos

Kalogirou³

et al. 2022

IJGI

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Some studies have established relationships between neighborhood conditions and health. However, they neither evaluate the relative importance of neighborhood components in increasing obesity nor, more crucially, how these neighborhood factors vary geographically. We use the geographical random forest to analyze each factor’s spatial variation and contribution to explaining tract-level obesity prevalence in Chicago, Illinois, United States. According to our findings, the geographical random forest outperforms the typically used nonspatial random forest model in terms of the out-of-bag prediction accuracy. In the Chicago tracts, poverty is the most important factor, whereas biking is the least important. Crime is the most critical factor in explaining obesity prevalence in Chicago’s south suburbs while poverty appears to be the most important predictor in the city’s south. For policy planning and evidence-based decision-making, our results suggest that social and ecological patterns of neighborhood characteristics are associated with obesity prevalence. Consequently, interventions should be devised and implemented based on local circumstances rather than generic notions of prevention strategies and healthcare barriers that apply to Chicago.

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“…GWR results depend on observations close to the subject point, revealing the relationship within the neighborhoods [ 12 ]. GWR has been widely used in geography, meteorology and economic territory [ 13 – 15 ]. However, with its unique advantages, GWR model is now being applied to the medical field, especially in public health.…”

Section: Introductionmentioning

confidence: 99%

Investigation on spatial variability and influencing factors of drinking water iodine in Xinjiang, China

et al. 2021

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Background and objectives Xinjiang is one of the areas in China with extremely severe iodine deficiency. The health of Xinjiang residents has been endangered for a long time. In order to provide reasonable suggestions for scientific iodine supplementation and improve the health and living standards of the people in Xinjiang, it is necessary to understand the spatial distribution of iodine content in drinking water and explore the influencing factors of spatial heterogeneity of water iodine content distribution. Methods The data of iodine in drinking water arrived from the annual water iodine survey in Xinjiang in 2017. The distribution of iodine content in drinking water in Xinjiang is described from three perspectives: sampling points, districts/counties, and townships/streets. ArcGIS was used for spatial auto-correlation analysis, mapping the distribution of iodine content in drinking water and visualizing the distribution of Geographically Weighted Regression (GWR) model parameter. Kriging method is used to predict the iodine content in water at non-sampling points. GWR software was used to build GWR model in order to find the factors affecting the distribution of iodine content in drinking water. Results There are 3293 sampling points in Xinjiang. The iodine content of drinking water ranges from 0 to 128 μg/L, the median is 4.15 μg/L. The iodine content in 78.6% of total sampling points are less than 10 μg/L, and only that in the 3.4% are more than 40 μg/L. Among 1054 towns’ water samples in Xinjiang, 88.9% of the samples’ water iodine content is less than 10 μg/L. Among the 94 studied areas, the median iodine content in drinking water in 87 areas was less than 10 μg/L, those values in 7 areas were between 10–40 μg/L, and the distribution of water iodine content in Xinjiang shows clustered. The GWR model established had found that the effects of soil type and precipitation on the distribution of iodine content in drinking water were statistically significant. Conclusions The iodine content of drinking water in Xinjiang is generally low, but there are also some areas which their drinking water has high iodine content. Soil type and precipitation are the factors affecting the distribution of drinking water iodine content, and are statistically significant (P<0.05).

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Spatial risk adjustment between health insurances: using GWR in risk adjustment models to conserve incentives for service optimisation and reduce MAUP

Cited by 11 publications

References 49 publications

Beyond Usual Geographical Scales of Analysis: Implications for Healthcare Management and Urban Planning

Beyond Usual Geographical Scales of Analysis: Implications for Healthcare Management and Urban Planning

Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA

Investigation on spatial variability and influencing factors of drinking water iodine in Xinjiang, China

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