Proportion Of Urban Population Research Articles

Analysis of the multiple drivers of vegetation cover evolution in the Taihangshan-Yanshan region

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m2 favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

Impact of agricultural digitalization on carbon emission intensity of planting industry: Evidence from China

Studying the impact of agricultural digitalization (ADT) on the carbon emission intensity of planting industry (PCI) can help promote sustainable development and realize the “dual carbon” goal. Based on the panel data of 31 provinces in China from 2010 to 2020, this study uses the entropy weight method and emission coefficient method to measure the development level of ADT and PCI, respectively. By using the regression analysis method, as well as the robustness test, heterogeneity test, and spatial spillover effect and threshold tests, the impact of ADT on PCI was examined. The results are as follow: (1) PCI is high in the north and low in the south, and the north-south divide is becoming prominent. (2) ADT in China can significantly reduce PCI, as verified through the robustness test. (3) Regional differences exist in the impact of ADT on PCI, with the most significant effect observed in the northeast region, followed by the western and central regions. (4) ADT exerts a significant spatial spillover effect on PCI and an inhibitory effect on PCI of adjacent provinces. (5) The proportion of urban population exerts a threshold effect in the impact of ADT on PCI. When the urban population ratio crosses 69 %, the inhibitory effect of agricultural carbon emissions decreases marginally. Therefore, promoting the green and low-carbon development of the planting industry is highly recommended.

Estimating the incidence of Venezuelan migration and other socioeconomic factors on urban growth in Colombia.

This study investigates the intricate relationship between Venezuelan migration and urban growth in Colombia from 2018 to 2021. The study employs remote sensing data and social network metrics to uncover migration patterns and their impact on urban expansion. The methodology consists of three stages. Firstly, nighttime satellite imagery is used to analyze year-over-year urban growth in Colombia. Secondly, social network data estimates Venezuelan migration, overcoming challenges of underreporting and informal border crossings. Lastly, an econometric analysis explores the quantitative link between Venezuelan migration and urban growth, integrating socioeconomic variables to address endogeneity. The findings reveal the complex interplay of Venezuelan migration, socioeconomic factors, and urban growth. The study outlines remote sensing analysis, introducing the Anthropogenic Footprint Expansion Index (AFEI) to quantify urban growth. Facebook API data estimates migration trends and explores socioeconomic impacts on urban expansion. The analysis uncovers migration, poverty, aging, and urban population proportion as key factors affecting Colombia's urban landscape. Furthermore, the research underscores how Venezuelan migration affected short-term urban expansion pre- and post-COVID-19. Migration had a notable effect before the pandemic, but this influence waned afterward. The study highlights migration's short-term nature and emphasizes age demographics' role in medium-term dynamics.

Global burden of neoplasms attributable to specific occupational carcinogens over 30 years: a population-based study

ObjectivesThe study aimed to analyze the global burden of occupational neoplasms from various epidemiological perspectives. Study designIn this cross-sectional study, secondary analyses were conducted to assess the burden of neoplasms attributable to occupational carcinogens and their distribution characteristics using data from GBD 2019 and the World Bank database. MethodsBased on the GBD 2019 and the World Bank database, we analyzed the global burden of occupational neoplasms including the age-period-cohort model, decomposition analysis, health inequality analysis, and panel model. All analyses were conducted in R (version 4.0.3) and Joinpoint (version 4.9.1). ResultsThe absolute number of neoplasms burden attributable to occupational carcinogens has continued to rise over 30 years. In 2019, occupational neoplasms caused 333,867 [95% uncertainty interval (UI): 263,491 to 404,641] mortalities and 6,964,775 (95% UI: 5,467,884 to 8,580,431) disability-adjusted life years (DALYs) globally. Greenland, Monaco, the Netherlands, and Andorra suffered the highest burden. The burden was higher in countries with a higher sociodemographic index. The age effect was prominent in the elderly, and the 1925 birth cohort had the highest cohort effect. Population growth was the most significant driver of the mortalities (89%) and DALYs (111%) change. Moreover, the proportion of urban population was significantly positively associated with the disease burden, while GDP per capita was negatively correlated with the disease burden. ConclusionsThe burden of occupational neoplasms was unevenly distributed across locations and populations. The need for rational allocation of healthcare resources was urgent.

The global burden of schizophrenia and the impact of urbanization during 1990–2019: An analysis of the global burden of disease study 2019

Background and hypothesisThe burden of schizophrenia is increasing. Assessing the global distribution of schizophrenia and understanding the association between urbanization factors and schizophrenia are crucial. Study designWe conducted a two-stage analysis utilizing public data from GBD (global burden of disease) 2019 and the World Bank. First, the distribution of schizophrenia burden at the global, regional, and national levels as well as temporal trends was analyzed. Then, four composite indicators of urbanization (including demographic, spatial, economic, and eco-environment urbanization) were constructed from ten basic indicators. Panel data models were used to explore the relationship between urbanization indicators and the burden of schizophrenia. ResultsIn 2019, there were 23.6 million people with schizophrenia, an increase of 65.85% from 1990, and the country with the largest ASDR (age-standardized disability adjusted life years rate) was the United States of America, followed by Australia, and New Zealand. Globally, the ASDR of schizophrenia rose with the sociodemographic index (SDI). In addition, six basic urbanization indicators including urban population proportion, employment in industry/services proportion, urban population density, the population proportion in the largest city, GDP, and PM2.5 concentration were positively associated with ASDR of schizophrenia, with the largest coefficients being urban population density. Overall, demographic, spatial, economic, and eco-environment urbanization all had positive effects on schizophrenia, and the estimated coefficients indicated that demographic urbanization was the most significant influence. ConclusionsThis study provided a comprehensive description of the global burden of schizophrenia and explored urbanization as a factor contributing to the variation in the burden of schizophrenia, and highlighted policy priorities for schizophrenia prevention in the context of urbanization.

Regional Differences in Tourism Eco-Efficiency in the Beijing–Tianjin–Hebei Region: Based on Data from 13 Cities

In order to balance the economic development and ecological impact of tourism, it is essential to study tourism eco-efficiency in the context of sustainable development. This study analyzed regional tourism eco-efficiency based on the panel data of the 13 cities of the Beijing–Tianjin–Hebei region using the super-SBM DEA model. Then, we analyzed the driving factors, compared regional differences, and investigated influencing factors of tourism eco-efficiency by applying the global Malmquist–Luenberger (GML) index, Theil index, and geographically and temporally weighted regression (GTWR) models. The results demonstrate the following: (1) The overall tourism eco-efficiency in the Beijing–Tianjin–Hebei region between 2010 and 2019 was low, but it had an increasing trend. (2) The advancement of technological progress factors was mostly responsible for the increase in tourist eco-efficiency. (3) The results for tourism eco-efficiency were significantly polarizing, but the gap among the 13 cities is gradually narrowing. Regional differences are the main contributors to differences in tourism eco-efficiency. (4) Per capita GDP, the proportion of tertiary industry in GDP, the number of patents granted, and the proportion of urban population in the total population were the main factors affecting tourism eco-efficiency. This study could serve as a model for similar countries and regions seeking to enhance tourism eco-efficiency and achieve the Sustainable Development Goals.

Ethnic heterogeneity of the urban and rural population of the regions of Russia according to the results of the 2021 census

In connection with the complication of ethnic processes in Russia, which have a different nature of the course in urban settlements and rural areas, the importance of studying the various ethnic characteristics of the urban and rural population increases. The aim of the study is to identify regional differences in the ethnic mosaic of the urban and rural populations based on the results of the 2021 All-Russian Census. An additional task of the study is to compare the ethnic mosaic of regions with the proportion of urban and rural populations in them that did not indicate their nationality. In Russia as a whole, the value of the ethnic mosaic index calculated for rural areas almost doubled the corresponding indicator for urban settlements. But there are about two dozen regions where the ethnic mosaic of rural areas is lower than in urban areas. The share of people “without nationality” in Russian urban settlements is more than four times higher than the share of this category of the population in rural areas. Correlation analysis confirmed the direct dependence of the proportion of persons “without nationality” on the share of the urban population. A negative, albeit weak, correlation was also found between the proportion of people “without nationality” and the ethnic mosaic of regions. The lowest proportion of representatives of this category of the population in Russia had the republics of the North Caucasus and several republics of the Ural-Volga region. On the one hand, these republics are distinguished by a high level of ethnic mosaicism; on the other hand, they are characterized by the preservation of ethnic traditionalism against the backdrop of a low proportion of the urban population.

Research on the Coupling and Coordination Relationship between Urbanization Development and Insurance Industry Development in Guangzhou City

This study aims to explore the coupling and coordination relationship between urbanization and insurance industry development in Guangzhou City from 2012 to 2021. By analyzing relevant indices, the study provides recommendations for the future advancement of urbanization and insurance industry development in Guangzhou. Thirteen indicators, including the urban population proportion, per capita built-up area, insurance density, and life insurance ratio, were selected for analysis. Based on these indicators, a systematic coupling model was established to describe the coupling degree and coordination degree of the system. The findings reveal a close linkage between urbanization and insurance industry development in Guangzhou: (1) At the city level, the coupling and coordination relationship between urbanization and insurance industry development has transitioned from a stalemate stage to a harmonization stage, demonstrating an overall positive development trend. (2) At different regional levels, the coupling and coordination of urbanization and insurance industry exhibit distinct geographical characteristics, with significant differences between the northern and southern parts of the city. This discrepancy can be attributed to the relatively lagging development of the insurance industry in these regions. However, in the central region, there is relatively better coupling and coordination between urbanization and the insurance industry. The significance of this study lies in the effective utilization of the coordinated development relationship between urbanization and the insurance industry, which contributes to the improvement of the social security system and promotes the reform and development of new insurance models. It also provides a safeguard for the social risks that may arise during the process of new urbanization construction.

Spatiotemporal Distribution of HIV Self-testing Kits Purchased on the Web and Implications for HIV Prevention in China: Population-Based Study.

BackgroundHIV self-testing (HIVST) holds great promise for expanding HIV testing. Nonetheless, large-scale data on HIVST behavior are scant. Millions of HIVST kits are sold through e-commerce platforms each year.ObjectiveThis study aims to analyze the spatiotemporal distribution of the HIVST kit–purchasing population (HIVSTKPP) in China.MethodsDeidentified transaction data were retrieved from a leading e-commerce platform in China. A joinpoint regression model was used to examine annual trends of the HIVSTKPP rates by calculating average annual percentage change. Bayesian spatiotemporal analysis was performed to locate hot spots with HIVSTKPP rates. Spatial autocorrelation analysis and space-time cluster analysis were conducted to identify clusters of HIVSTKPP. High-high clusters of HIVSTKPP can be identified by spatial autocorrelation analysis, and high-high clusters indicate that a region and its surrounding region jointly had a higher-than-average HIVSTKPP rate. Spatial regression analysis was used to elucidate the association between the number of HIV testing facilities, urbanization ratio (the proportion of urban population in the total population), and gross domestic product per capita and the HIVSTKPP.ResultsBetween January 1, 2016, and December 31, 2019, a total of 2.18 million anonymous persons in China placed 4.15 million orders and purchased 4.51 million HIVST kits on the web. In each of these 4 years, the observed monthly size of the HIVSTKPP peaked in December, the month of World AIDS Day. HIVSTKPP rates per 100,000 population significantly increased from 20.62 in 2016 to 64.82 in 2019 (average annual percentage change=48.2%; P<.001). Hot spots were mainly located in municipalities, provincial capitals, and large cities, whereas high-high clusters and high-demand clusters were predominantly detected in cities along the southeast coast. We found positive correlations between a region’s number of HIV testing facilities, urbanization ratio, and gross domestic product per capita and the HIVSTKPP.ConclusionsOur study identified key areas with larger demand for HIVST kits for public health policy makers to reallocate resources and optimize the HIV care continuum. Further research combining spatiotemporal patterns of HIVST with HIV surveillance data is urgently needed to identify potential gaps in current HIV-monitoring practices.

The epidemiology of diphtheria in Haiti, December 2014-June 2021: A spatial modeling analysis.

BackgroundHaiti has been experiencing a resurgence of diphtheria since December 2014. Little is known about the factors contributing to the spread and persistence of the disease in the country. Geographic information systems (GIS) and spatial analysis were used to characterize the epidemiology of diphtheria in Haiti between December 2014 and June 2021.MethodsData for the study were collected from official and open-source databases. Choropleth maps were developed to understand spatial trends of diphtheria incidence in Haiti at the commune level, the third administrative division of the country. Spatial autocorrelation was assessed using the global Moran’s I. Local indicators of spatial association (LISA) were employed to detect areas with spatial dependence. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were built to identify factors associated with diphtheria incidence. The performance and fit of the models were compared using the adjusted r-squared (R2) and the corrected Akaike information criterion (AICc).ResultsFrom December 2014 to June 2021, the average annual incidence of confirmed diphtheria was 0.39 cases per 100,000 (range of annual incidence = 0.04–0.74 per 100,000). During the study period, diphtheria incidence presented weak but significant spatial autocorrelation (I = 0.18, p<0.001). Although diphtheria cases occurred throughout Haiti, nine communes were classified as disease hotspots. In the regression analyses, diphtheria incidence was positively associated with health facility density (number of facilities per 100,000 population) and degree of urbanization (proportion of urban population). Incidence was negatively associated with female literacy. The GWR model considerably improved model performance and fit compared to the OLS model, as indicated by the higher adjusted R2 value (0.28 v 0.15) and lower AICc score (261.97 v 267.13).ConclusionThis study demonstrates that GIS and spatial analysis can support the investigation of epidemiological patterns. Furthermore, it shows that diphtheria incidence exhibited spatial variability in Haiti. The disease hotspots and potential risk factors identified in this analysis could provide a basis for future public health interventions aimed at preventing and controlling diphtheria transmission.

The Influence of Demographic Factors on Urban Medical Insurance Expenditure under Big Data

As the urban basic medical insurance with the most widespread area and age coverage, a series of research is conducive to the optimization and better promotion of policies. As the main receptor of medical insurance, people analyze the impact of population factors on the expenditure of urban basic medical insurance, which is conducive to the improvement and implementation of the medical insurance system. In this paper, birth rate, total dependency ratio, urban population proportion, and educational population proportion were selected as independent variables, urban basic medical insurance expenditure was selected as dependent variables, and a regression model was established after data processing and analysis. The results show that the birth rate, the proportion of the urban population, and the expenditure of the urban basic medical insurance fund will change in the opposite direction; the expenditure of the urban basic medical insurance fund, the total dependency ratio, and the illiteracy rate will change in the same direction.

Comparison and Economic Envelope Structure Schemes for Deep Foundation Pit of Subway Stations Based on Fuzzy Logic.

Since the 1990s, with the continuous advancement of urbanization, the proportion of urban population has gradually increased. There is a serious shortage of land resources, and people's demand for underground space is increasing day by day. The construction of subway stations has developed into an inevitable trend in the future construction engineering industry, and it is also necessary to select the best solution from various solutions. The purpose of this paper is to study how to evaluate and analyze the economy of the deep foundation pit envelope structure of subway stations based on fuzzy logic, so as to choose the optimal and most economical plan. This paper proposes a fuzzy comprehensive evaluation method based on fuzzy logic, which is a reasonable method for the classic evaluation index. The experimental results of this paper show that in 2015 about 8% of people chose to travel by subway. By 2020, 54.5% of people chose to travel by subway, an increase of 46.5% during this period. It can be seen that more and more people are willing to take the subway, and subway transportation is a public transportation mode with large transportation volume. It has obvious public welfare, and it can relieve the urban traffic pressure very well, so the investment in subway construction in various cities is also increasing.

House Price Prediction Model of Zhaoqing City Based on Correlation Analysis and Multiple Linear Regression Analysis

Situated in southern China, Zhaoqing City is a part of Guangdong Province, China. The total administrative area of the city covers 14,891 square kilometers. The data of China’s seventh population census in 2020 showed that the permanent resident population in Zhaoqing City reached up to 4,413,594. Meanwhile, Zhaoqing is one of the cities in the Guangdong-Hong Kong-Macao Greater Bay Area. House price analysis and prediction carried out against Zhaoqing City will have directive significance for relevant policies formulated by the local government, residential investment or purchase of consumers, and prediction of house price trend as well as business decisions made by enterprises. By virtue of machine learning and statistical theory, the house price in Zhaoqing City from 2010 to 2020 will be researched, and the house price prediction model of Zhaoqing City will be constructed in this paper with several variables including GDP, proportion of tertiary industry, income of urban residents, fiscal revenue, land price, investment volume in real estate development, permanent resident population, population density, and proportion of urban population in net migration. First of all, the methods of correlation analysis will be utilized, to select variables that are highly correlated with house price data based on correlation coefficients. Then, the model will be constructed for predicting the house price on the basis of multiple linear regression analysis that is conducted with selected variables. Finally, the prediction model will be adjusted gradually based on data with different correlations selected from available data, to realize better imitative effect and more precise predictive effect and select optimum prediction model. By means of the above model, the house prices of Zhaoqing City in 2021 and beyond will be predicted accurately, with preferable fitting effect and prediction effect.

A Cross-National Multilevel Analysis of Fear of Crime: Exploring the Roles of Institutional Confidence and Institutional Performance

Drawing upon Wave 7 of the World Value Survey, this study performed multilevel analysis on fear of crime among 47,996 residents from 36 diverse nations. Confidence in the government and perceived government performance were identified as significant predictors of crime fear. Further analysis showed the effect of confidence in the government stemmed primarily from between-country variation, whereas both within-country and between-country variation in perceived government performance shaped fear of crime. In addition, macroeconomic factors and the proportion of urban population, but not national-level homicide rate, were found to be salient country-level predictors. The findings contextualize the elevated fear of crime in some countries despite declining crime rates and inform the recommendation that institutional confidence and performance be prioritized in crime fear reduction efforts.

Analysis of Global Road Traffic Death Data Using a Clustering Approach

Three years of global traffic death data were analyzed to identify significant socio-economic determinants of global traffic death. Due to wide variation of data elements, the collected data were standardized and then four clusters namely Medium, Low, High and Medium-High were formed. The stepwise regression analysis method was used to find significant determinants. Proportion of urban population, alcohol, number of registered vehicles per million of population and human development index were appeared as significant determinants. However, income was noticed as a significant variable in the case of only cluster Low. The involvement of older drivers in traffic death incident was very low.

Analysis of Spatio-Temporal Heterogeneity and Socioeconomic driving Factors of PM2.5 in Beijing–Tianjin–Hebei and Its Surrounding Areas

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of “high–high” and “low–low”. The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective.

Assessment and mapping of demographic potential of urbanized territories of the Baikal-Mongol region

This article provides an assessment of the demographic potential of the Baikal-Mongolian region, which unites the adjacent territories of the two countries. The cores of the research site are the urbanized territories of Irkutsk, Ulan-Ude and Ulan-Bator, and communications are railways and highways connecting the main centres. The demographic potential is characterized by the level and possibilities for the development of demographic processes and population structures, and mainly numerous quantitative characteristics of the population of the territory are used. The authors limited themselves to using quantitative characteristics of the demographic potential according to statistical data for 2019–2020 within the territories of the municipal districts and urban districts of the Irkutsk region, the Republic of Buryatia and aimags of Mongolia. Data on density and proportion of urban population reflect the size of the main urban areas. Data on demographic processes reflect the characteristics of the natural and migration movement of the population. Demographic structures are represented by the age structure and the demographic load of the working-age population, which is minimal throughout Mongolia and in the suburbs of Russian regional centres. Generalizing characteristics of demographic potential calculated from the average sum of individual indicators.

Explaining COVID-19 mortality rates in the first wave in Europe

The beta regression has been received considerable attention in the last decade because of its applications to proportional data in several fields. We study the variability of coronavirus death rates in the first wave of twenty European countries using the beta regression with two systematic components for the mean and dispersion parameters. We prove empirically that the population density, proportion of urban population, hospital beds per 100 thousand and running time explain the variability of the COVID-19 death rates in the first wave of these countries.

Assessing Wealth-Related Inequalities in Demand for Family Planning Satisfied in 43 African Countries.

Background: Around 80% of the African population lives in urban areas, and a rapid urbanization is observed in almost all countries. Urban poverty has been linked to several sexual and reproductive health risks, including high levels of unintended pregnancies. We aim to investigate wealth inequalities in demand for family planning satisfied with modern methods (mDFPS) among women living in urban areas from African countries.Methods: We used data from 43 national health surveys carried out since 2010 to assess wealth inequalities in mDFPS. mDFPS and the share of modern contraceptive use were stratified by groups of household wealth. We also assessed the ecological relationship between the proportion of urban population living in informal settlements and both mDFPS and inequalities in coverage.Results: mDFPS among urban women ranged from 27% (95% CI: 23–31%) in Chad to 87% (95% CI: 84–89%) in Eswatini. We found significant inequalities in mDFPS with lower coverage among the poorest women in most countries. In North Africa, inequalities in mDFPS were identified only in Sudan, where coverage ranged between 7% (95% CI: 3–15%) among the poorest and 52% (95% CI: 49–56%) among the wealthiest. The largest gap in the Eastern and Southern African was found in Angola; 6% (95% CI: 3–11%) among the poorest and 46% (95% CI: 41–51%) among the wealthiest. In West and Central Africa, large gaps were found for almost all countries, especially in Central African Republic, where mDFPS was 11% (95% CI: 7–18%) among the poorest and 47% (95% CI: 41–53%) among the wealthiest. Inequalities by type of method were also observed for urban poor, with an overall pattern of lower use of long-acting and permanent methods. Our ecological analyses showed that the higher the proportion of the population living in informal settlements, the lower the mDFPS and the higher the inequalities.Conclusion: Our results rise the need for more focus on the urban-poorer women by public policies and programs. Future interventions developed by national governments and international organizations should consider the interconnection between urbanization, poverty, and reproductive health.

Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities

Management of coronavirus disease 2019 (COVID-19) in India is a top government priority.However, there is a lack of COVID-19 adjusted case fatality risk (aCFR) estimates and information on states with high aCFR. Data on COVID-19 cases and deaths in the first pandemic wave and 17 state-specific geodemographic, socio-economic, health and comorbidity-related factors were collected. State-specific aCFRs were estimated, using a 13-day lag for fatality. To estimate country-level aCFR in the first wave, state estimates were meta-analysed based on inverse-variance weighting and aCFR as either a fixed- or random-effect. Multiple correspondence analyses, followed by univariable logistic regression, were conducted to understand the association between aCFR and geodemographic, health and social indicators.Based on health indicators, states likely to report a higher aCFR were identified. Using random- and fixed-effects models, cumulative aCFRs in the first pandemic wave on 27 July 2020 in India were 1.42% (95% CI 1.19%–1.70%) and 2.97% (95% CI 2.94%–3.00%), respectively. At the end of the first wave, as of 15 February 2021, a cumulative aCFR of 1.18% (95% CI 0.99%–1.41%) using random and 1.64% (95% CI 1.64%–1.65%) using fixed-effects models was estimated. Based on high heterogeneity among states, we inferred that the random-effects model likely provided more accurate estimates of the aCFR for India. The aCFR was grouped with the incidence of diabetes, hypertension, cardiovascular diseases and acute respiratory infections in the first and second dimensions of multiple correspondence analyses. Univariable logistic regression confirmed associations between the aCFR and the proportion of urban population, and between aCFR and the number of persons diagnosed with diabetes, hypertension, cardiovascular diseases and stroke per 10,000 population that had visited NCD (Non-communicable disease) clinics. Incidence of pneumonia was also associated with COVID-19 aCFR. Based on predictor variables, we categorised 10, 17 and one Indian state(s) expected to have a high, medium and low aCFR risk, respectively. The current study demonstrated the value of using meta-analysis to estimate aCFR. To decrease COVID-19 associated fatalities, states estimated to have a high aCFR must take steps to reduce co-morbidities.