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The impact of rapid urbanization on the spatiotemporal pattern of short-term extreme precipitation in China remains unclear at the subnational scale. In this study, we present a general framework that measures urbanization-induced variation in hourly extreme wet season precipitation (April–October) from 1985 to 2012, with reference to a dynamic urban–rural station classification based on annual changes in urban extent. We found that urbanization in south China (<29° N) brings more extreme precipitation to urban areas than to suburbs, and reduces extreme precipitation continually over urban areas in parts of the north and northeast. Over 60% of provincial capital cities show significant changes in extreme precipitation due to urbanization, including smaller size cities separated from large urban clusters. Urbanization enhances extreme precipitation mainly in the local main part of the rainy season, which refers to May in the south (e.g. urban–rural differences of 0.70 mm h −1 in Guangzhou) and July–September in the central and north (1.16 mm h −1 in August of Beijing). Urbanization also increases hourly extreme precipitation at peak times in diurnal cycles. The results indicate that urbanization has caused overall more and more heterogeneous spatial patterns over China and concentrated distributions during the rainy season and peak time. These patterns warrant attention when assessing the risk of increased waterlogging and flash flooding in urban areas.

Children's future academic success may depend on their readiness to learn and participate in preschool education. We examined school readiness differences in a sample of rural and urban preschool children (N = 82) from Zunyi, China, using the School Readiness Test Battery. The results indicated that school readiness differed between rural and urban children; rural children scored lower on emotional and social skills, basic knowledge, and drawing and language competence subtests than did urban students, but higher on sport skills, and understanding of both time and space. Thus, improving the early education of rural children will likely help to decrease the school readiness differences between rural and urban children.

Prior literature suggests that, among the so-called 'developed economies', residing in urban contexts is associated with lower life satisfaction. Using data from the European Social Survey (ESS) and Eurostat, we contribute to this literature by focusing on three different indicators of urbanity (subjective domicile, population density and living in a dominant urban region) in a multilevel modelling context in order to define where exactly the relatively lower life satisfaction can be found. Moreover, we account for the level of economic development at both regional and national levels. The results show that subjective domicile is strongly associated with life satisfaction, whereas regional gross domestic product (GDP) and other urbanity indicators are insignificant. Our results also highlight the relatively higher life satisfaction in rural surroundings in more developed countries. We conclude by noting that future contributions to the literature on urban-rural life satisfaction differences should utilize panel data, making it possible to address the spatial sorting versus contextual effects debate, and focus on investigating the higher level determinants at the country level that define the existence of urban-rural differences in life satisfaction within a country.

Background Regular physical examination contributes to early detection and timely treatment, which is helpful in promoting healthy behaviors and preventing diseases. The objective of this study is to compare the annual physical examination (APE) use between rural and urban elderly in China. Methods A total of 3,922 participants (60+) were randomly selected from three urban districts and three rural counties in Shandong Province, China, and were interviewed using a standardized questionnaire. We performed unadjusted and adjusted logistic regression models to examine the difference in the utilization of APE between rural and urban elderly. Two adjusted logistic regression models were employed to identify the factors associated with APE use in rural and urban seniors respectively. Results The utilization rates of APE in rural and urban elderly are 37.4% and 76.2% respectively. Factors including education level, exercise, watching TV, and number of non-communicable chronic conditions, are associated with APE use both in rural and urban elderly. Hospitalization, self-reported economic status, and health insurance are found to be significant ( p  < 0.05) predictors for APE use in rural elderly. Elderly covered by Urban Resident Basic Medical Insurance (URBMI) ( p  < 0.05, OR = 1.874) are more likely to use APE in urban areas. Conclusions There is a big difference in APE utilization between rural and urban elderly. Interventions targeting identified at-risk subgroups, especially for those rural elderly, are essential to reduce such a gap. To improve health literacy might be helpful to increase the utilization rate of APE among the elderly.

Background Blood pressure measurement is the first step in preventing and controlling hypertension. The objective of this study is to examine the rural–urban difference towards blood pressure measurement among elderly with hypertension. Methods A total of 2007 elderly (65+) were selected from the fifth Health Service Survey of Shandong Province in 2013. A standardized questionnaire was used to investigate the demographic characters, socioeconomic status, self-rated health, and blood pressure related index. Three logistic regression models were used to examine the difference in blood pressure measurement between rural and urban elderly. Unadjusted and adjusted logistic regression models were used to explore the associated factors of blood pressure measurement in both rural areas and urban areas. Results The prevalence of weekly blood pressure measurement in urban elderly was higher than that in rural elderly (63.9% vs 34.3%). The rural elderly had an odds ratio (OR) for weekly blood pressure measurement of 0.467 (95%CI = 0.380–0.575) compared with urban elderly. Binary logistic regression analysis showed that medication frequency and accepting health care professionals’ guidance were common associated factors of blood pressure measurement among both rural and urban elderly; personal income was unique associated factor of blood pressure measurement among rural elderly; marital status, education level, self-rated health, and blood pressure level currently were unique associated factors of blood pressure measurement among urban elderly. Conclusions There is a big difference in blood pressure measurement between rural and urban elderly. Interventions targeting identified at-risk subgroups, especially for those rural elderly, should be made to reduce such a gap.

This paper examines factors that determine differences in living standards as measured by a wealth index between rural and urban areas in Tanzania, by applying the Blinder‒Oaxaca decomposition method. The rural‒urban wealth gap has remained largely unchanged over time while rural‒urban differences in educational attainment play a significant role in explaining this gap. Further evidence shows that the wealth gap caused by a differential return to education is also significant. Our results stress the importance of improving the quality and quantity of education in rural Tanzania in attempting to decrease the rural‒urban wealth gap.

Background The rural–urban gap in socioeconomic and morbidity status among older adults is prevalent in India. These disparities may impact the levels and factors of self-rated health (SRH). The objective of the study is to compare the levels and determinants of SRH between rural and urban areas by considering the moderating effects of marital status and living arrangements. Subjects and methods The present study used data from the Longitudinal Ageing Study in India (LASI) wave 1 (2017–18). A total sample of 30,633 older adults aged 60 years and above were selected for the study. Descriptive statistics, bivariate chi-square test, the interaction effect of living arrangements and marital status, and logistic estimation were applied to accomplish the study objectives. Results The prevalence of poor SRH was found 7% higher in rural areas compared to urban counterparts. A substantial rural–urban disparity in the patterns of poor SRH was also observed. The interaction effect of marital status and living arrangement on self-rated health suggested that older adults who were currently unmarried and living alone were 38% more likely to report poor SRH than those who were currently married and co-residing in rural India. In addition to marital status and living situation, other factors that significantly influenced SRH include age, socio-cultural background (educational attainment and religion), economic background (employment status), health status (ADLs, IADLs, multi-morbidities), and geographic background (region). Conclusion The present study's findings demonstrated that, notwithstanding local variations, marital status and living circumstances significantly influenced SRH in India. In the present study, unmarried older people living alone were more susceptible to poor SRH in rural areas. The present study supports the importance of reinforcing the concepts of care and support for older individuals. There is a need for special policy attention to older individuals, particularly those unmarried and living alone. Although older individuals had difficulty performing ADLs and IADLs and had multi-morbidities, they reported poorer health. Therefore, offering them social support and top-notch medical assistance is crucial.

•We studied determinants of timely vaccination.•Education, poverty, seasonality, and area were associated with timely vaccination.•Rural children were more likely to fail timely BCG vaccination than urban children.•Urban children were more likely to fail timely Penta3 and measles vaccination.•Higher adherence of rural children might be related to outreach vaccination services. To identify the determinants of timely vaccination among young children in the North-West of Burkina Faso. This study included 1665 children between 12 and 23 months of age from the Nouna Health and Demographic Surveillance System, born between September 2006 and December 2008. The effect of socio-demographic variables on timely adherence to the complete vaccination schedule was studied in multivariable ordinal logistic regression with 3 distinct endpoints: (i) complete timely adherence, (ii) failure, and (iii) missing vaccination. Three secondary endpoints were timely vaccination with BCG, Penta3, and measles, which were studied with standard multivariable logistic regression. Mothers’ education, socio-economic status, season of birth, and area of residence were significantly associated with failure of timely adherence to the complete vaccination schedule. Year of birth, ethnicity, and the number of siblings was significantly related to timely vaccination with Penta3 but not with BCG or measles vaccination. Children living in rural areas were more likely to fail timely vaccination with BCG than urban children (OR=1.79, 95%CI=1.24–2.58 (proximity to health facility), OR=3.02, 95%CI=2.18–4.19 (long distance to health facility)). In contrast, when looking at Penta3 and measles vaccination, children living in rural areas were far less likely to have failed timely vaccinations than urban children. Mother's education positively influenced timely adherence to the vaccination schedule (OR=1.42, 95%CI 1.06–1.89). There was no effect of household size or the age of the mother. Additional health facilities and encouragement of women to give birth in these facilities could improve timely vaccination with BCG. Rural children had an advantage over the urban children in timely vaccination, which is probably attributable to outreach vaccination teams amongst other factors. As urban children rely on their mothers’ own initiative to get vaccinated, urban mothers should be encouraged more strongly to get their children vaccinated in time.

Background Globally, middle-aged and older adults who are employed are prone to experiencing depressive symptoms, a phenomenon particularly pronounced in China due to its significant ageing population. Despite this, there is a relative scarcity of research focusing on depressive symptoms among this community, especially in China. The purpose of this study is to explore and measure the factors contributing to variations in depressive symptoms among employed middle-aged and older adults in urban and rural areas. Methods The study utilized data from 3,156 employed middle-aged and older adults, derived from the fourth wave of The 2018 China Health and Retirement Longitudinal Study (CHARLS). The 10-item short version of the Center for Epidemiological Studies Depression Scale (CES-D-10) was used to assess depressive symptoms. Urban-rural differences were examined using chi-square tests and binary logistic regression analyses, while the Oaxaca-Blinder decomposition technique was applied to explore the disparities and their respective contributions. Results The study comprised a sample of 783 employed individuals of middle and older age residing in urban areas, alongside 2,373 counterparts in rural areas. The gender distribution within the sample was 68.1% male and 31.9% female. The prevalence of depressive symptoms was observed in 23.2% of the urban cohort, in contrast to 30.4% of the rural cohort, a difference that was statistically significant ( P  < 0.001). In urban settings, significant determinants of depressive symptoms included self-assessed health status, sleep duration, and life satisfaction. In contrast, in rural areas, factors such as gender, age, education, self-assessed health status, chronic diseases, sleep duration, and life satisfaction were identified. The Oaxaca-Blinder decomposition analysis identified age, education, self-assessed health status, and sleep duration as key factors in the disparity of depressive symptoms between urban and rural areas among employed middle-aged and older adults. Conclusions Depressive symptoms are more common in rural areas due to various factors found in these communities. It is imperative to create focused and advanced intervention plans to improve the mental well-being of this community and reducing the urban-rural disparity.

Epidemiological studies found that exposure to air pollution increases cardiovascular hospitalizations. However, studies on rural-urban differences in associations between hospitalizations for cardiovascular diseases and air pollution are limited. The generalized linear model (GLM) was applied to investigate the associations between cardiovascular hospitalizations and air pollution (SO 2 , NO 2 , PM 2.5 , PM 10 , CO, and O 3 ) in Guangxi, southwest China, in 2015 (January 1–December 31). The relative risk of pollutants (SO 2 , NO 2 ) on cardiovascular hospital admissions was significantly different between urban and rural areas. The effect of SO 2 on cardiovascular hospitalizations was higher in urban areas than in rural areas at lag0 to lag3 and cumulative lag01 to lag03. In urban areas, there were positive associations between NO 2 and cardiovascular hospitalizations at lag0, lag1 and cumulative lag01, lag02. In contrast, the effect of NO 2 on cardiovascular hospitalizations was not significant in rural areas. Urban residents were more sensitive than rural residents to SO 2 and NO 2 . Subgroup analyses showed statistically significant differences between rural and urban areas in the association between SO 2 and NO 2 and cardiovascular hospitalizations for males. For age groups, people aged ≥ 65 years appeared to be more vulnerable to SO 2 and NO 2 in urban areas. The effects of PM 2.5 PM 10 , CO, and O 3 on cardiovascular hospitalizations were consistently negative for all groups. Our findings indicated that there were rural-urban differences in associations between cardiovascular hospitalizations and air pollutants. In rural areas, the risk of cardiovascular hospitalizations was mainly influenced by SO 2 . Therefore, we expect to pay attention to protecting people from air pollution, particularly for those aged ≥ 65 years in urban areas.