Understanding and Harnessing the Health Effects of Rapid Urbanization in China is a research paper published in Environmental Science & Technology (2011). On theSindex it has a DataRank of 0.749. It has been cited 146 times.
China is undergoing a rapid transition from a rural to an urban society. This societal change is a consequence of a national drive toward economic prosperity. Rapid urbanization impacts on infrastructure, environmental health and human wellbeing. Unlike many cases of urban expansion, Chinese urbanization has led to containment, rather than to increase, in the spread of infectious diseases. Conversely, the incidence of chronic conditions such as cardiovascular and metabolic diseases has risen, with higher rates occurring in urban regions. This rural-urban gradient in disease incidence seems not to be a reflection simply of more aggressive diagnosis or healthcare access. Other diseases exhibit little rural versus urban differences (e.g., liver cancer or respiratory disease), or even occur at a higher rate in the rural population (e.g., esophageal cancer). This article examines the impact of this changing demographic on environmental health and human wellbeing in China. Lessons learned from epidemiological studies mostly carried out in Europe and the U.S. may not be directly transferable to China. We advocate that there is now a need to establish robust systems of accurate data collection, a Chinese biobank network to facilitate the profiling of human health effects, and relevant randomized controlled trials to identify effective interventions in the Chinese urbanized setting. Such studies could allow for the future implementation of disease-preventive strategies.
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Base Score Contribution
0.749
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
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.