Spatiotemporal variation in diabetes mortality in China: multilevel evidence from 2006 and 2012

Zhou, Maigeng; Astell-Burt, Thomas; Yin, Peng; Feng, Xiaoqi; Page, Andrew; Liu, Yunning; Liu, Jiangmei; Li, Yichong; Liu, Shiwei; Wang, Limin; Wang, Lijun; Wang, Lınhong

doi:10.1186/s12889-015-1982-0

Cited by 20 publications

(23 citation statements)

References 33 publications

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“…The results showed geographic variation in all-cause mortality and NCDs mortality varied among different area levels. These results are aligned with findings from previous studies [37][38][39]. Enhancing understandings of social determinants and inequities in all-cause and NCD-related mortality in China is critical to give a more complete, in-depth picture of the public health challenges that decision-makers face, while also providing data that can be used to evaluate specific policies and interventions.…”

Section: Discussionsupporting

confidence: 86%

Enhancing understandings of social determinants of health in China: Linkage and analysis of a national multilevel population health surveillance with routinely collected mortality records for 98 058 people in 31 provinces of mainland China

Liu¹,

Astell-Burt²,

Feng³

et al. 2019

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Background: The aim of this study was to enhance capability in research on social determinants of health in China by linking and analyzing routinely-collected death records over 5 years with national population health surveillance.Methods: Linkage of 98 058 participants in the 2010 China Chronic Disease and Risk Factor Surveillance (CCDRFS) to records in the national death surveillance data from 2011 to 2015 was conducted through a matching program involving identification numbers, name, gender and residential address, followed by a structured checking process. Multilevel regressions were used to investigate five-year odds of all-cause, non-communicable disease (NCD), infectious disease and injury mortality in relation to person- and county-level factors.Results: A total of 3,365 deaths were observed in the linked mortality and population health surveillance. Cross-checks and comparisons with national mortality distributions provided assurance that the linkage was reasonable. Geographic variation in mortality was observed via age and gender adjusted median odds ratios for all-cause mortality (>1.30), infectious disease (>2.01), NCD (>1.24) and injury (>1.12). Increased odds of all-cause and all three cause-specific mortality outcomes were higher with age and among men. Low educational attainment was a predictor of all-cause, NCD and injury mortality. Longer mean years of education at the county-level was only associated with lower injury mortality. Divorcees had a higher odd of all-cause and NCD mortality than singletons. Rurality was a predictor of all-cause and NCD mortality.Conclusion: The results of this study provide utility for future investigations of social determinants of health and mortality using linked data in China.

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Section: Discussionsupporting

confidence: 86%

Enhancing understandings of social determinants of health in China: Linkage and analysis of a national multilevel population health surveillance with routinely collected mortality records for 98 058 people in 31 provinces of mainland China

Liu¹,

Astell-Burt²,

Feng³

et al. 2019

Preprint

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“…Entre as doenças, encontra-se estudos acerca do Diabetes Mellitus (DM) (Zhou et al, 2015a;Zhou et al, 2015b;Pinto et al, 2014;Santana et al, 2014;Dávila-Cervantes;Pardo, 2014;Barker et al, 2011;Sánches-Barriga, 2010;Green et al, 2003).…”

Section: Introductionunclassified

“…Diante disso, considera-se importante a análise de áreas mais vulneráveis à mortalidade por DM, para subsidiar programas de prevenção e promoção à saúde, a fim de proporcionar maior qualidade de vida aos sujeitos e diminuir os gastos públicos relacionados (Brasil, 2006;Brasil, 2007;Zhou et al, 2015a;Zhou et al, 2015b;Pinto et al, 2014;Santana et al, 2014;Dávila-Cervantes;Pardo, 2014;Barker et al, 2011;Sánches-Barriga, 2010;Green et al, 2003). Tendo em vista que, os processos dinâmicos que ocorrem no ambiente podem influenciar o estilo de vida da população que ocupa determinado espaço (Brasil, 2006).…”

Section: Introductionunclassified

Health geography contribution to the analysis of diabetes mellitus mortality in Rio Grande do Sul

Penso¹,

Périco²

2016

Revista Brasileira De Geografia Física

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Compreender a relação entre o espaço geográfico e os eventos em saúde é relevante, uma vez que o ambiente influencia no processo de saúde e doença da população. Nesta perspectiva, a geografia da saúde surge como um contributo para a análise dos eventos em saúde. O presente estudo buscou utilizar a abordagem espacial, na perspectiva da geografia da saúde, para analisar o perfil epidemiológico da mortalidade por Diabetes Mellitus tipo 2, nas regiões de saúde do estado do Rio Grande do Sul, no período de 2003 a 2012. Foi realizado um estudo ecológico, observacional, de dados agregados humanos e séries temporais. Foi utilizado o Índice de Moran Global, o Índice de Moran Local, a correlação de Pearson e o teste G. A distribuição, e a análise espacial, possibilitou compreender o perfil geográfico da mortalidade pela doença nas regiões de saúde do estado. O Índice de Moran Local apontou para clusters significativo de regiões alto-alto, mas com tendência de variação destes clusters nas séries temporais em análise. Os coeficientes anuais apresentaram aumento de 59% na mortalidade por Diabetes Mellitus tipo 2 no período estudado. A população mais atingida em relação à mortalidade por Diabetes Mellitus tipo 2 foi a população do sexo feminino, de cor / raça branca e com escolaridade de 0 a 3 anos de estudo. A utilização de técnicas de análise espaço-temporal serviu como um contributo importante para a análise do perfil epidemiológico da mortalidade por Diabetes Mellitus tipo 2. Palavras-chave: Geografia Médica, Sistema de Informação Geográfica, Índice de Moran, Epidemiologia, Saúde Coletiva. Health geography contribution to the analysis of diabetes mellitus mortality in Rio Grande do Sul A B S T R A C TTo understand the relationship between the geographic area and the health event is relevant, since the environment influences the process of health and disease populations. In this perspective, health geography appears as a contribution to the analysis of health events. This study sought to use the spatial approach, from the perspective of health geography to analyze the epidemiology of mortality by type 2 diabetes mellitus, in the health regions of Rio Grande do Sul, in the period 2003-2012. An ecological study, observational, human aggregated data and time series was performed. Global Moran Index was used, the local Moran index, Pearson's correlation and G test. The distribution and spatial analysis, enabled us to understand the geographic profile of mortality from the disease in the state health regions. The local Moran index pointed to significant clusters of high-high regions, but with a tendency of variation of these clusters in time series analysis. The annual coefficient increased by 59% in mortality from type 2 diabetes during the study period. The population most affected in relation to mortality from type 2 diabetes mellitus is the female population, color / white race and education from 0 to 3 years of study. The use of space-time analysis techniques served as an important contribution to the analysis of the epide...

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“…China is not only known for its high concentrations of air pollution, 3 but also for the highest prevalence of diabetes worldwide, which affects more than 100 million people. 4,5 In fact, diabetes mortality is highest in the north eastern region of China 6 where the study 2 by Yang and colleagues took place.…”

mentioning

confidence: 99%

“…1 As a final point, it is conceivable that air pollution might not only increase the risk of diabetes, but also affect its progression; exploration of this hypothesis would be of particular value given the high prevalence of diabetes-related mortality in some regions of China. 6 Most public health decisions on environmental issues are made under uncertainty, especially when the stakes are high and prevention is better than cure. 10 In a time when daunting ecological threats tend to be dismissed in some public sector circles, evidence of even potentially detrimental metabolic effects of air pollution should be heralded and communicated to stakeholders to strengthen the societal impact of research.…”

mentioning

confidence: 99%

Ambient air pollution and diabetes in China

Dzhambov

2018

The Lancet Planetary Health

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Spatiotemporal variation in diabetes mortality in China: multilevel evidence from 2006 and 2012

Cited by 20 publications

References 33 publications

Enhancing understandings of social determinants of health in China: Linkage and analysis of a national multilevel population health surveillance with routinely collected mortality records for 98 058 people in 31 provinces of mainland China

Enhancing understandings of social determinants of health in China: Linkage and analysis of a national multilevel population health surveillance with routinely collected mortality records for 98 058 people in 31 provinces of mainland China

Health geography contribution to the analysis of diabetes mellitus mortality in Rio Grande do Sul

Ambient air pollution and diabetes in China

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