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New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening.

ABSTRACT: To optimally allocate health resources, policy planners require an indicator reflecting the inequality. Currently, health inequalities are frequently measured by area-based indices. However, methodologies for constructing the indices have been hampered by two difficulties: 1) incorporating the geographical relationship into the model and 2) selecting appropriate variables from the high-dimensional census data. Here, we constructed a new area-based health coverage index using the geographical information and a variable selection procedure with the example of gastric cancer. We also characterized the geographical distribution of health inequality in Japan. To construct the index, we proposed a methodology of a geographically weighted logistic lasso model. We adopted a geographical kernel and selected the optimal bandwidth and the regularization parameters by a two-stage algorithm. Sensitivity was checked by correlation to several cancer mortalities/screening rates. Lastly, we mapped the current distribution of health inequality in Japan and detected unique predictors at sampled locations. The interquartile range of the index was 0.0001 to 0.354 (mean: 0.178, SD: 0.109). The selections from 91 candidate variables in Japanese census data showed regional heterogeneities (median number of selected variables: 29). Our index was more correlated to cancer mortalities/screening rates than previous index and revealed several geographical clusters with unique predictors.

SUBMITTER: Yoneoka D

PROVIDER: S-EPMC4877577 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening.

Yoneoka Daisuke D Saito Eiko E Nakaoka Shinji S

Scientific reports 20160524

To optimally allocate health resources, policy planners require an indicator reflecting the inequality. Currently, health inequalities are frequently measured by area-based indices. However, methodologies for constructing the indices have been hampered by two difficulties: 1) incorporating the geographical relationship into the model and 2) selecting appropriate variables from the high-dimensional census data. Here, we constructed a new area-based health coverage index using the geographical inf ...[more]

PMID: 27215347

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Project description:ObjectivesIn epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability.Material and methodsTwo improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents.ResultsThe proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures.ConclusionsThe newly proposed procedures improve the identification of significant variables and enable us to derive a new insight into epidemiological association analysis.

Dataset Information

New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening.

Publications

New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening.

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