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A new look at weather-related health impacts through functional regression.

ABSTRACT: A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches.

SUBMITTER: Masselot P

PROVIDER: S-EPMC6189063 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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A new look at weather-related health impacts through functional regression.

Masselot Pierre P Chebana Fateh F Ouarda Taha B M J TBMJ Bélanger Diane D St-Hilaire André A Gosselin Pierre P

Scientific reports 20181015 1

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves ...[more]

PMID: 30323248

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Project description:BackgroundThe importance of viewing health from a broader perspective than the mere presence or absence of disease is critical at primary healthcare level. However, there is scanty evidence-based stratification of population health using other criteria than morbidity-related indicators in developing countries. We propose a novel stratification of population health based on cognitive, functional and social disability and its covariates at primary healthcare level in DR Congo.MethodWe conducted a community-based cross-sectional study in adults with diabetes or hypertension, mother-infant pairs with child malnutrition, their informal caregivers and randomly selected neighbours in rural and sub-urban health zones in South-Kivu Province, DR Congo. We used the WHO Disability Assessment Schedule 2.0 (WHODAS) to measure functional, cognitive and social disability. The study outcome was health status clustering derived from a principal component analysis with hierarchical clustering around the WHODAS domains scores. We calculated adjusted odds ratios (AOR) using mixed-effects ordinal logistic regression.ResultsOf the 1609 respondents, 1266 had WHODAS data and an average age of 48.3 (SD: 18.7) years. Three hierarchical clusters were identified: 9.2% of the respondents were in cluster 3 of high dependency, 21.1% in cluster 2 of moderate dependency and 69.7% in cluster 1 of minor dependency. Associated factors with higher disability clustering were being a patient compared to being a neighbour (AOR: 3.44; 95% CI: 1.93-6.15), residency in rural Walungu health zone compared to semi-urban Bagira health zone (4.67; 2.07-10.58), female (2.1; 1.25-2.94), older (1.05; 1.04-1.07), poorest (2.60; 1.22-5.56), having had an acute illness 30 days prior to the interview (2.11; 1.24-3.58), and presenting with either diabetes or hypertension (2.73; 1.64-4.53) or both (6.37; 2.67-15.17). Factors associated with lower disability clustering were being informally employed (0.36; 0.17-0.78) or a petty trader/farmer (0.44; 0.22-0.85).ConclusionHealth clustering derived from WHODAS domains has the potential to suitably classify individuals based on the level of health needs and dependency. It may be a powerful lever for targeting appropriate healthcare service provision and setting priorities based on vulnerability rather than solely presence of disease.

Dataset Information

A new look at weather-related health impacts through functional regression.

Publications

A new look at weather-related health impacts through functional regression.

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