Incidence and risk factors of diabetes mellitus in the Chinese population: a dynamic cohort study is a research paper published in BMJ Open (2022). On theSindex it has a DataRank of 0.617. It has been cited 12 times, with 12 citing works in its 1-hop citation network.
Objective Diabetes mellitus is a common condition often associated with an ageing population. However, only few longitudinal studies in China have investigated the incidence of diabetes and identified its risk factors. Therefore, this study aimed to investigate the incidence and risk factors of diabetes in Chinese people aged ≥45 years using the harmonised China Health and Retirement Longitudinal Study (CHARLS) data. Design A dynamic cohort study. Setting The harmonised CHARLS 2011–2018. Participants 19 988 adults aged ≥45 years. Primary outcome measure Incident diabetes from 2011 to 2018. Results The harmonised CHARLS is a representative longitudinal survey of people aged ≥45 years. Using data extracted from the harmonised CHARLS, we calculated the incidence of diabetes and used a competing risk model to determine risk factors of diabetes. In 2011–2013, 2013–2015, 2015–2018, the crude incidence of diabetes among middle-aged and older people in China was 1403.21 (1227.09 to 1604.19), 1673.22 (1485.73 to 1883.92) and 3919.83 (3646.01 to 4213.30) per 100 000 person‐years, respectively, with a significant increasing trend. There were no geographical variations in the incidence of diabetes. Age, obesity and alcohol consumption were associated with an increased risk of incident diabetes. Conclusion The incidence of diabetes increased annually, without any geographical differences. Age, obesity and alcohol consumption were found to be risk factors for incident diabetes.
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Base Score Contribution
0.385
From this paper's citation signal
Citation Network Contribution
0.232
From 8 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 62% comes from its base citations and 38% from the citation network (8 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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