Local modelling of U.S. mortality rates: A multiscale geographically weighted regression approach

Cupido, Kyran; Fotheringham, A. Stewart; Jevtic, Petar

doi:10.1002/psp.2379

Cited by 24 publications

(14 citation statements)

References 46 publications

(54 reference statements)

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“…Previous studies have applied the MGWR technique to explore the determinants of health effects, such as obesity rate (Oshan et al., 2020 ), mortality rate (Cupido et al., 2020 ), incidence rate (Mollalo et al., 2020 ), and confirmed cases (Iyanda et al., 2020 ). To our knowledge, this study is the first application of the MGWR technique to examine the multiscale influences of the determinants on the resumption of work and production.…”

Section: Discussionmentioning

confidence: 99%

“…Multiscale geographically weighted regression (MGWR) removes the assumption of consistent spatial scale in GWR and allows that the explanatory variables have various specific bandwidths to represent spatial local heterogeneity at various spatial scales (Fotheringham, Yang, & Kang, 2017). The MGWR technique has been applied extensively to examine the multiscale influences of the determinants on health, socioeconomics, and natural environment (Cupido, Fotheringham, & Jevtic, 2020; Fotheringham, Yue, & Li, 2019; Hong & Yoo, 2020; Iyanda et al., 2020; Mollalo, Vahedi, & Rivera, 2020; Oshan, Smith, & Fotheringham, 2020; Yang, Zhan, Lv, & Liu, 2019). In this study, we further examined the determinants of the hospital resumption rate at various spatial scales using the MGWR model described as follows:

\begin{matrix} \log y_{s, t} = β_{b w 0, t}^{()(, s)} + \sum_{i} β_{b w i, t}^{()(, s)} \log x_{i, s, t} + ε_{s, t} \end{matrix}

where

β_{b w 0, t}^{(s)}

β_{b w i, t}^{(s)}

are the local intercepts and regression coefficients of explanatory variables with various optimal bandwidths, respectively, and bwi in

β_{b w i, t}^{(s)}

denotes the specific bandwidth used for calibration of the i th conditional relationship.…”

Section: Methodsmentioning

confidence: 99%

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Assessing work resumption in hospitals during the COVID‐19 epidemic in China using multiscale geographically weighted regression

Zhang

Tao³

et al. 2022

Transactions in GIS

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The resumption of work and production is one of the key issues during the novel coronavirus (COVID‐19) post‐epidemic phase. We used location‐based service data of mobile devices to assess the work resumption of 22,098 hospitals in mainland China. The multiscale influences of the determinants on work resumption in hospitals, including medical‐service capacity, human movement, and epidemic severity, were examined using the multiscale geographically weighted regression technique. This study provides a novel insight into the assessment of work resumption in hospitals and its determinants, and is flexible to be extended to evaluate the work resumption of other industries. The findings can introduce helpful information for other countries to implement the strategies of work recovery during the post‐epidemic phase.

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Section: Discussionmentioning

confidence: 99%

\begin{matrix} \log y_{s, t} = β_{b w 0, t}^{()(, s)} + \sum_{i} β_{b w i, t}^{()(, s)} \log x_{i, s, t} + ε_{s, t} \end{matrix}

where

β_{b w 0, t}^{(s)}

β_{b w i, t}^{(s)}

are the local intercepts and regression coefficients of explanatory variables with various optimal bandwidths, respectively, and bwi in

β_{b w i, t}^{(s)}

denotes the specific bandwidth used for calibration of the i th conditional relationship.…”

Section: Methodsmentioning

confidence: 99%

Assessing work resumption in hospitals during the COVID‐19 epidemic in China using multiscale geographically weighted regression

Zhang

Tao³

et al. 2022

Transactions in GIS

View full text Add to dashboard Cite

show abstract

“…The impacts of observational error on multivariate models are unpredictable; with SA, ignoring such errors may be more treacherous. When a conventional geospatial analysis produces anomalous or implausible results, e.g., that a higher rate of health insurance coverage increases mortality rates in southern Florida [ 64 ], researchers and reviewers ought to ask if observational error could be the cause of the findings. Our demonstration analysis shows that ignoring observational uncertainty in a single covariate measured with a fair degree of precision can impact coefficient estimates, model predictions, and posterior uncertainty of estimates.…”

Section: Discussionmentioning

confidence: 99%

Modeling Community Health with Areal Data: Bayesian Inference with Survey Standard Errors and Spatial Structure

Donegan

Chun

Griffith

2021

IJERPH

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Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet standard practice to use them for evaluating or modeling data reliability. ACS SEs vary systematically across regions, neighborhoods, socioeconomic characteristics, and variables. Failure to consider probable observational error may have substantial impact on the large bodies of literature relying on small-area estimates, including inferential biases and over-confidence in results. The issue is particularly salient for predictive models employed to prioritize communities for service provision or funding allocation. Leveraging the tenets of plausible reasoning and Bayes’ theorem, we propose a conceptual framework and workflow for spatial data analysis with areal survey data, including visual diagnostics and model specifications. To illustrate, we follow Krieger et al.’s (2018) call to routinely use the Index of Concentration at the Extremes (ICE) to monitor spatial inequalities in health and mortality. We construct and examine SEs for the ICE, use visual diagnostics to evaluate our observational error model for the ICE, and then estimate an ICE–mortality gradient by incorporating the latter model into our model of sex-specific, midlife (ages 55–64), all-cause United States county mortality rates. We urge researchers to consider data quality as a criterion for variable selection prior to modeling, and to incorporate data reliability information into their models whenever possible.

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“…MGWR uses a back-fitting algorithm for model calibration (Fotheringham et al, 2019) which is initialized with GWR parameter estimates and evaluates optimal bandwidths and locally estimated coefficients in an iterative manner Yu et al, 2019;Oshan et al, 2020). The bandwidth calibrated by the back-fitting algorithm varies with the independent variables indicating how different geographical factors affect longevity at different spatial scales and providing intuitive interpretations in terms of the affected area (Cupido et al, 2020;Harris et al, 2020). The bandwidth represents the number of counties affected by the variable, and the larger the number, the wider the influence scale of the geographical factor (Fan et al, 2020).…”

Section: The Multi-scale Geographically Weighted Regression Modelmentioning

confidence: 99%

Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis

Yang

Ren

et al. 2021

Geospat Health

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Longevity is a near-universal human aspiration that can affect moral progress and economic development at the social level. In rapidly developing China, questions about the geographical distribution and environmental factors of longevity phenomenon need to be answered more clearly. This study calculated the longevity index (LI), longevity index for females (LIF) and longevity index for males (LIM) based on the percentage of the long-lived population among the total number of elderly people to investigate regional and gender characteristics at the county level in China. A new multi-scale geographically weighted regression (MGWR) model and four possible geographical environmental factors were applied to explore environmental effects. The results indicate that the LIs of 2838 counties ranged from 1.3% to 16.3%, and the distribution showed obvious regional and gender differences. In general, the LI was high in the East and low in the West, and the LIF was higher than the LIM in 2614 counties (92.1%). The MGWR model performed well explaining that geographical environmental factors, including topographic features, vegetation conditions, human social activity and air pollution factors have a variable influence on longevity at different spatial scales and in different regions. These findings enrich our understanding of the spatial distribution, gender differences and geographical environmental effects on longevity in China, which provides an important reference for people interested in the variations in the associations between different geographical factors.

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Local modelling of U.S. mortality rates: A multiscale geographically weighted regression approach

Cited by 24 publications

References 46 publications

Assessing work resumption in hospitals during the COVID‐19 epidemic in China using multiscale geographically weighted regression

Assessing work resumption in hospitals during the COVID‐19 epidemic in China using multiscale geographically weighted regression

Modeling Community Health with Areal Data: Bayesian Inference with Survey Standard Errors and Spatial Structure

Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis

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