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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.

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.

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 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.

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.

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.

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.

Background Dyslipidemia is a major risk factor for cardiovascular diseases (CVD). The prevalence of dyslipidemia varies by geographic location, often higher in urban populations. Our study aimed to assess the prevalence of dyslipidemia in the aging Indian population and compare the rural–urban differences in lipid abnormalities. Methods We analyzed baseline cross-sectional data from two longitudinal aging cohorts in rural and urban southern India- 2,797 participants from the rural (CBR-SANSCOG) cohort in Srinivaspura, Karnataka, and, 430 participants from the urban (CBR-TLSA) cohort in Bangalore, Karnataka. Participants aged ≥ 45 years were included, and those with dementia, severe psychiatric/medical illnesses, and severe visual/hearing impairments were excluded. Data on sociodemographic variables, physical activity, tobacco/alcohol use, BMI, diagnosis of diabetes, hypertension, and other medical comorbidities were collected. Lipid profiles were measured from fasting peripheral venous blood samples using standard laboratory techniques and lipid abnormalities were classified based on the NCEP ATP-III criteria. Proportions of lipid abnormalities were compared between the two populations using the two-proportions Z-test, and risk factors associated with dyslipidemia were analyzed using multivariate logistic regression models. Results The prevalence of high total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-c) was significantly greater in the urban than rural population (TC: 37.0% vs. 28.4% p < 0.001 and LDL-c: 33.5% vs. 26.8%, p < 0.01, respectively), while the prevalence of low high-density lipoprotein cholesterol (HDL-c: 72.4% vs. 44.2%, p <0.001), high triglycerides (TG: 45.7% vs. 38.6%, p <0.01) and lipid risk ratios (TC/HDL-c, TG/HDL-c and LDL-c/HDL-c) was higher in the rural than urban population. Females in both urban and rural populations were at a higher risk of having multiple lipid abnormalities. For the other risk factors assessed, while diabetes, overweight, obesity, and physical inactivity were associated with increased risks for certain lipid abnormalities, these associations were less pronounced in the urban population. Conclusions Aging Indians, both in rural and urban settings, have an alarmingly high prevalence of lipid abnormalities. Considering an elevated cardiovascular disease risk associated with lipid abnormalities, targeted interventions towards these communities are necessary to reduce the disease burden.

Children in urban and eastern regions tend to be taller and have higher body mass index (BMI) compared to those in rural and central-western regions, partially due to better family resources. We examined urban‒rural areas, regional differences in growth trajectories, focusing on family influences. Longitudinal data on 8542 children from the China Health and Nutrition Survey (1991–2015) were used. Random effects models assessed the mean height/BMI growth trajectories across different regions, urban–rural areas, and sexes within cohorts born in 1980–1989, 1990–1999, and 2000–2009. In the 1980–1989 cohort, before adjusting for family dietary structure, children from eastern regions were on average 3.3 cm taller than those from central-western regions at age 6. After adjustment, the height difference decreased to 2.44 cm. In the 2000–2009 cohort, the urban–rural BMI difference at age 6 was initially 0.53 kg/m 2 , which narrowed to 0.40 kg/m 2 after adjusting for family socioeconomic factors. After adjusting for family environmental sanitation, the regional difference in the 2000–2009 cohort was attenuated by half before adjustment and was 0.44 kg/m 2 after adjustment. Family factors significantly account for the regional and urban–rural disparities in height and BMI. These disparities were driven by the family resource environment, like dietary structure and sanitation. However, with China’s socioeconomic changes, broader socioeconomic factors, including household income and parental education, have become more influential.

Perimenopause is a high-risk period for depressive symptoms in women. Investigating the urban–rural differences in key factors associated with depressive symptoms among Chinese perimenopausal women can provide a scientific basis for developing targeted intervention strategies. Using data from the 2020 wave of the China Health and Retirement Longitudinal Study (CHARLS), we employed a random forest model combined with Shapley value decomposition (a cooperative-game–theoretic approach that quantifies each variable’s marginal contribution to model accuracy), supplemented by logistic regression analysis, to systematically explore urban–rural differences in key factors influencing depressive symptoms in perimenopausal women. Among 1,105 perimenopausal women, the overall prevalence of depressive symptoms was 39.3%, with 34.4% in urban areas and 42.4% in rural areas. Life satisfaction, self-rated health, and sleep duration emerged as common factors affecting depressive symptoms in both urban and rural perimenopausal women. Chronic disease, hospitalization history, children’s financial support, and child contact were unique key factors influencing depressive symptoms in urban women. In contrast, activities of daily living (ADL), cognitive function, and total annual household income were unique key factors influencing depressive symptoms in rural women. Significant urban–rural differences exist in the key factors associated with depressive symptoms among Chinese perimenopausal women. Policy makers should therefore design context-specific mental-health programmes. for example, urban initiatives could integrate chronic-disease management with family-based psychosocial support, whereas rural programmes might combine economic-security improvements with community-level cognitive-health screening and rehabilitation of activities of daily living—to maximise intervention effectiveness.