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The coronavirus disease 19 (COVID-19), the biggest health problem at present, doesn’t have uniform transmission and severity among the countries and communities therein. Knowledge of community vulnerability to the disease would facilitate interventions aimed at transmission control by the efficient deployment of available limited resources. Therefore, we assessed spatial variations and heterogeneity of disease vulnerability among the population in 753 municipal units of Nepal. We collected geospatial indicators representing the domain of socioeconomic inequalities, population dynamics, heterogeneity in accessibility and the information related to underlying health condition which potentially affect the severity of COVID-19 transmission. Those indicators were assembled to create three vulnerability indices using Geographic Information System (GIS); Social Vulnerability Index (SVI), Epidemiological Vulnerability Index (EVI) and a composite of the two- Social and Epidemiological Vulnerability Index (SEVI). The indicators were scaled to a common measurement scale and spatially overlaid via equally weighted arithmetic mean. The indices were classified into five level of risk and the municipal units and the population within vulnerabilities classes were quantified and visualized in the map. The index output indicated high vulnerability to epidemics in megacities like Kathmandu, Pokhara, Bharatpur, etc.; developing cities especially in the Province No 2; and, municipal units of Karnali and Sudoorpashchim provinces. Additionally, some other municipalities such as Dhulikhel, Beshishahar, Tansen etc. which have a higher prevalence of pulmonary and cardiovascular disorders are highly vulnerable. The SVI indicated that 174 municipal units and 41.5% population is highly vulnerable. The EVI identified 55 municipal units and 40.7% of the total population of the country highly vulnerable to COVID-19. The SEVI accounted that disease vulnerability is high in 105 municipal units and 40% population of Nepal. The vulnerability indices created are means for different tiers of the existing government in federal system of Nepal for prioritization and improved planning for disease intervention especially in highly vulnerable municipal units where the COVID-19 transmission could have high severity.

The novel coronavirus disease 2019 (COVID-19) has been the biggest public health problem of the present world. As the number of people suffering from the pandemic is rising, it is likely to claim more life and worsen the global health and economy. Nepal, one of the developing countries in the south Asia has been strongly influenced by the pandemic and struggling to contain it with multiple interventions, however, spatiotemporal dynamics of the epidemic and its linkage with various intervention strategies has not been studied yet. Here, we employed the prospective spatial-temporal analysis with SaTScan assessing dynamics of the COVID-19 cases from 23 January to 31 August 2020 at district level in Nepal. The results revealed that COVID-19 dynamics in the early stage of transmission was slower and confined in certain districts. However, from the third week of April, transmission spread rapidly across districts of Province No. 2 and Sudoorpaschim Province, primarily introduced by Nepalese citizens returning from India. Despite nationwide lockdown, nine statistically significant active and emerging clusters were detected between 23 January and 21 July 2020, whereas ten emerging clusters were observed for extended period to 31 August. The population density and population inflow from India crossing the sealed border had significant effects on the elevated risk of the epidemic. The capital city Kathmandu has become the highest-risk active cluster since August when travel restriction has been suspended. Movement restriction appears to be the most effective non-pharmaceutical intervention against the COVID-19 for resource-scarce countries with limited health care facilities. Our findings could be valuable to the health authorities within Nepal and beyond to better allocate resources and improve interventions on the pandemic for containing it efficiently.

Maternal blood glucose level is associated with fetal growth, therefore, its role in the associations between air pollution and birth weight deserves investigation. We examined the mediation effect of maternal blood glucose on the associations between maternal air pollution exposure and birth weight. A total of 10,904 pregnant women in Foshan, China during 2015–2019 were recruited. Oral glucose tolerance test (OGTT) was administered to each participant after late trimester 2. Air pollution data at the monitoring stations in residential districts was used to estimate exposures of each participant during trimester 1 and trimester 2. Mixed-effects linear models were used to estimate the associations between air pollution and birth weight. After controlling for ten covariates, the direct effect of PM2.5 and SO2 (each 10 μg/m3 increment) on birth weight was −15.7 g (95% CI: −29.4, −4.8 g) and −83.6 g (95% CI: −134.8, −33.0 g) during trimester 1. The indirect effect of PM2.5 and SO2 (each 10 μg/m3 increment) on birth weight by increasing maternal fasting glucose level was 6.6 g (95% CI: 4.6, 9.1 g) and 22.0 g (95% CI: 4.1, 44.0 g) during trimester 1. Our findings suggest that air pollution might affect the birth weight through direct and indirect pathway, and the indirect effect might be mediated by maternal blood glucose.

Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.

This study describes spatiotemporal distribution and geospatial diffusion patterns of dengue outbreak of 2013 in Jhapa district, Nepal. Laboratory-confirmed dengue cases were collected from the District Public Health Office, Government of Nepal. Choropleth mapping technique, Global Moran’s Index, SaTScan, and standard deviational ellipse were used to map and quantify the outbreak dynamics. The results revealed heterogeneous distribution and globally autocorrelated patterns. Local clusters were observed in 3 major urban centers. The standard deviational ellipse demonstrated the outbreak occurred from the east and diffused to the west along the east-west highway in different weeks. The results of this study could be useful to public health authorities to plan and execute dengue control strategies.

Background Most studies on the short-term health effects of air pollution have been conducted on a daily time scale, while hourly associations remain unclear. Methods We collected the hourly data of emergency ambulance calls (EACs), ambient air pollution, and meteorological variables from 2014 to 2016 in Luoyang, a central Chinese city in Henan Province. We used a generalized additive model to estimate the hourly effects of ambient air pollutants (PM2.5, PM10, SO2, and NO2) on EACs for all natural causes and cardiovascular and respiratory morbidity, with adjustment for potential confounding factors. We further examined the effect modification by temperature, relative humidity, wind speed, and atmospheric pressure using stratified analyses. Results In the single-pollutant models, PM2.5, PM10, SO2, and NO2 were associated with an immediate increase in all-cause morbidity at 0, 0, 12, 10 h, separately, after exposure to these pollutants (excess risks: 0.19% (95% confidence interval (CI): 0.03%, 0.35%), 0.13% (95% CI: 0.02%, 0.24%), 0.28% (95% CI: 0.01%, 0.54%) and 0.52% (95% CI: 0.06%, 0.99%), respectively). These effects remained generally stable in two-pollutant models. SO2 and NO2 were significantly associated with an immediate increase in risk of cardiovascular morbidity, but the effects on respiratory morbidity were relatively more delayed. The stratified analyses suggested that temperature could modify the association between PM2.5 and EACs, humidity and atmospheric pressure could modify the association between SO2 and EACs. Conclusions Our study provides new evidence that higher concentrations of PM2.5, PM10, SO2, and NO2 may have transiently acute effects on all-cause morbidity and subacute effects on respiratory morbidity. SO2 and NO2 may also have immediate effects on cardiovascular morbidity. Findings of this study have important implications for the formation of hourly air quality standards.

Background Ambient fine particulate matter pollution (PM2.5) is one leading cause of disease burden, but no study has quantified the association between daily PM2.5 exposure and life expectancy. We aimed to assess the potential benefits in life expectancy by attaining the daily PM2.5 standards in 72 cities of China during 2013–2016. Methods and findings We applied a two-stage approach for the analysis. At the first stage, we used a generalized additive model (GAM) with a Gaussian link to examine the city-specific short-term association between daily PM2.5 and years of life lost (YLL); at the second stage, a random-effects meta-analysis was used to generate the regional and national estimations. We further estimated the potential gains in life expectancy (PGLE) by assuming that ambient PM2.5 has met the Chinese National Ambient Air Quality Standard (NAAQS, 75 μg/m3) or the ambient air quality guideline (AQG) of the World Health Organization (WHO) (25 μg/m3). We also calculated the attributable fraction (AF), which denoted the proportion of YLL attributable to a higher-than-standards daily mean PM2.5 concentration. During the period from January 18, 2013 to December 31, 2016, we recorded 1,226,849 nonaccidental deaths in the study area. We observed significant associations between daily PM2.5 and YLL: each 10 μg/m3 increase in three-day–averaged (lag02) PM2.5 concentrations corresponded to an increment of 0.43 years of life lost (95% CI: 0.29–0.57). We estimated that 168,065.18 (95% CI: 114,144.91–221,985.45) and 68,684.95 (95% CI: 46,648.79–90,721.11) years of life lost can be avoided by achieving WHO’s AQG and Chinese NAAQS in the study area, which corresponded to 0.14 (95% CI: 0.09–0.18) and 0.06 (95% CI: 0.04–0.07) years of gain in life expectancy for each death in these cities. We observed differential regional estimates across the 7 regions, with the highest gains in the Northwest region (0.28 years of gain [95% CI: 0.06–0.49]) and the lowest in the North region (0.08 [95% CI: 0.02–0.15]). Furthermore, using WHO’s AQG and Chinese NAAQS as the references, we estimated that 1.00% (95% CI: 0.68%–1.32%) and 0.41% (95% CI: 0.28%–0.54%) of YLL could be attributable to the PM2.5 exposure at the national level. Findings from this study were mainly limited by the unavailability of data on individual PM2.5 exposure. Conclusions This study indicates that significantly longer life expectancy could be achieved by a reduction in the ambient PM2.5 concentrations. It also highlights the need to formulate a stricter ambient PM2.5 standard at both national and regional levels of China to protect the population’s health., Jinlei Qi and colleagues reveal the potential gains in life expectancy in China by reducing ambient PM2.5 concentrations., Author summary Why was this study done? Ambient fine particulate matter (PM2.5) pollution is a severe environmental health concern in China. Both short-term and long-term exposure to PM2.5 have been found to be associated with increased mortality and years of life lost. A few studies have estimated the association between annual PM2.5 concentration and life expectancy, but there is no report on the effects of daily PM2.5 exposure on life expectancy. What did the researchers do and find? This nationwide time-series study collected data on more than 1 million nonaccidental deaths in 72 Chinese cities from January 18, 2013 to December 31, 2016. We used a generalized additive model to explore the city-specific association between daily PM2.5 and years of life lost and then conducted random-effects meta-analyses to generate the regional and national estimates. During the study period from January 18, 2013 to December 31, 2016, we estimated that 168,065.18 (about 1.00% of the total) years of life lost can be avoided by achieving WHO’s guideline on daily PM2.5 concentrations (25 μg/m3) in the study area, which corresponded to 0.14 years of gains in life expectancy for each death. What do these findings mean? This is the first study to report the potential gains in life expectancy by attaining the daily standards of PM2.5, which provided important and useful information of the burden caused by ambient PM2.5 pollution. In the future, there should be a policy of stricter ambient PM2.5 standards at both national and regional levels in China, which would benefit the population’s health and life expectancy.

Although myopia has been largely ignored among the elderly population, there is an increased risk of myopia with advancing age. Ambient air pollution is one potential contributor to vision impairments, but few epidemiological studies have demonstrated such an association. This cross-sectional survey collected the information of 33,626 subjects aged ≥50 years in six developing countries during 2007–2010. Myopia was identified based on questions related to symptoms of myopia. The annual concentrations of fine particulate matter (PM2.5) and ozone (O3) were estimated with the satellite data and chemical transport model. We examined the associations between the two pollutants and myopia using mixed-effect Poisson regression models with robust variance estimation (sandwich estimation). We observed J-shaped associations between the two pollutants and myopia, and identified 12 and 54 μg/m3 as the threshold concentrations. The adjusted prevalence ratio was 1.12 (95% CI: 1.05, 1.21) and 1.26 (95% CI: 1.14, 1.38) for each standard deviation (SD) increase in PM2.5 and O3 concentrations above their threshold, respectively. In addition, the interaction analysis suggested a synergistic interaction of these two pollutants on myopia in the additive model, with a synergistic index of 1.81 (Bootstrapping 95% CI: 0.92, 4.94). Our results indicate that long-term exposures to PM2.5 and O3 might be important environmental risk factors of myopia in the elderly, and suggest that more efforts should be taken to reduce airborne PM2.5 and O3 levels to protect vision health.

Background To investigate the effects of air pollution exposure during pregnancy on the indicators of glucose homeostasis and gestational diabetes mellitus (GDM). Methods We conducted a birth cohort study in Foshan, China during 2015–2019. Oral glucose tolerance test (OGTT) was administered to each participant during pregnancy. GDM was defined according to the International Association of Diabetes and Pregnancy Study Groups criteria (IADPSG). Air pollutant (fine particulate matter (PM2.5), particulate matter with an aerodynamic diameter of 10 μm or less (PM10), sulfate dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3)) concentrations from the air monitoring stations in Foshan were used to estimate individual air pollutant exposure during the first two trimesters. Linear and logistic regression models were employed to estimate the associations between air pollution exposure during the first two trimesters and OGTT glucose levels and GDM. Results Of 12,842 pregnant women, 3055 (23.8%) had GDM. A 10 μg/m3 increase in PM2.5, PM10 and SO2 during trimester 1, trimester 2 and two trimesters were associated with 0.07 mmol/L to 0.29 mmol/L increment in OGTT-fasting glucose levels in single-pollutant model. A 10 μg/m3 increase in NO2 and O3 during two trimesters were associated with 0.15 mmol/L and 0.12 mmol/L decrease in OGTT-fasting glucose in single-pollutant model. However, no significant or weaker effects of O3 during two trimesters on OGTT-fasting glucose were observed in two-pollutant models. Moreover, exposure to PM2.5, PM10 and SO2 were associated with increased risk of GDM in both single- and two-pollutant models. Conclusions Our study suggests PM2.5, PM10 and SO2 exposure during the first two trimesters might increase the risk of GDM.

Objectives Prenatal exposure to air pollutant has been associated with congenital heart defect (CHD). However, no study has investigated this effect in pre-pregnancy overweighted women. This study aimed to evaluate gestational exposure to particulate pollutant (PM2.5) and gaseous air pollutants (O3 and NO2) on the risk of CHD, and explore the potential effect modifiers including maternal age, pre-pregnancy BMI and pregestational diseases. Methods In this birth cohort study, a total of 63,213 pregnant women in Foshan, China were initially recruited and followed from their first hospital visit for pregnancy to delivery during 2015–2019. CHD cases were confirmed by the records in hospital- and population- based birth defect surveillance systems. Air pollutant exposures were estimated by the daily concentrations measured in air monitoring stations in each participant's residential county. Mixed-effects regression models, adjusted for potential confounding factors were applied to estimate the associations between air pollutant and CHD during the first three months of the pregnancy. Results A total of 985 (1.6%) newborns were identified as CHD cases. For each 10 μg/m3 increase in ambient O3 during the 1st month, the OR values for CHD were 1.03 (95% CI: 0.94, 1.13) in pre-pregnancy normal weighted women and 1.24 (95% CI: 1.01, 1.53) in pre-pregnancy overweighted women. For each 10 μg/m3 increase in NO2 during the 3rd month, the OR values for CHD were 1.09 (95% CI: 1.01, 1.18) in pre-pregnancy normal weighted women and 1.27 (95% CI: 1.07, 1.51) in pre-pregnancy overweighted women. No significant associations were found between PM2.5 exposure and CHD in our analysis. Conclusions This study demonstrates that gaseous air pollutants (O3 and NO2) exposure during the cardiac embryogenesis period is associated with an increased risk of CHD, particularly for pre-pregnancy overweighted women.

Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users&rsquo, actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt&ndash, pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data.

City green infrastructure (CGI) makes cities more resilient and sustainable, as required by the United Nations’ (UN) Sustainable Development Goal 11–Sustainable Cities and Communities. Based on the CGI policies of Beijing, land use/land cover (LULC) changes of two Asian capitals, Beijing, China and Islamabad, Pakistan, are simulated. LULC maps for 2010 and 2015 are developed by applying object-based image analysis (OBIA) to Landsat imagery. Dynamics of land system (DLS) model was used to simulate the LULC changes for 2020 and 2025 under three scenarios: (1) business-as-usual (BAU); (2) urban green space work plan (UGWP); and (3) landscape and greening policies (LGP). Results reveal that DLS is efficient than other simulation models. The BAU scenario predicts an overall expansion in Beijing’s greenery, while Islamabad will encounter a decline by 7.3 km2 per year. Under the UGWP scenario, urban green spaces and other vegetation area of Beijing will expand by 7.6 km2, while, for Islamabad, vegetation degradation rate will slow down to 6.9 km2 per year. The LGP scenario envisage a massive increase of 23.5 km2 per year in green resources of Beijing and Islamabad’s green land loss rate will further slowdown to 6.1 km2 per year. It is inferred from the results that vegetation degradation in Islamabad need to lessen by implementing LGP policy after basic amendments according to the local conditions and available resources.

Although ambient air pollution has been consistently associated with morbidity and mortality of stroke, there is limited evidence on whether the control of air pollution would associate with a reduced risk of stroke. The citywide air pollution controlling measures during the 2010 Asian Games in Guangzhou provided such an opportunity to answer this question. We collected daily data on the concentrations of air pollutants and mortality from ischemic and hemorrhagic stroke, and compared them for 51 intervention days in 2010 with the corresponding days in 2006–2009 and 2011 (reference period). Poisson-based interrupted time series analysis was employed to estimate the relative risk (RR) and 95% confidence interval (CI). The daily mean concentrations of PM10 (particles with an aerodynamic diameter smaller than 10 μm) was 88.64 μg/m3 during the reference period, and 80.47 μg/m3 during the intervention period. Daily mortality number of total stroke, ischemic and hemorrhagic stroke decreased from 3.67, 2.00 and 1.67 during the reference period, to 3.00, 1.76 and 1.24 during the intervention period; the corresponding RR was 0.79 (95% CI: 0.66–0.94), 0.87 (95% CI: 0.69–1.11) and 0.69 (95% CI: 0.52–0.91), respectively. Our findings show that ambient air pollution controlling measures during the 2010 Asian Games in Guangzhou were associated with a decreased risk of stroke mortality, which suggests a need to reduce air pollution to improve public health.

Background Due to recent emergence, dengue is becoming one of the major public health problems in Nepal. The numbers of reported dengue cases in general and the area with reported dengue cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of dengue have not been investigated yet. This study aims to fill this gap by analyzing spatiotemporal patterns based on monthly surveillance data aggregated at district. Methods Dengue cases from 2010 to 2014 at district level were collected from the Nepal government’s health and mapping agencies respectively. GeoDa software was used to map crude incidence, excess hazard and spatially smoothed incidence. Cluster analysis was performed in SaTScan software to explore spatiotemporal clusters of dengue during the above-mentioned time period. Results Spatiotemporal distribution of dengue fever in Nepal from 2010 to 2014 was mapped at district level in terms of crude incidence, excess risk and spatially smoothed incidence. Results show that the distribution of dengue fever was not random but clustered in space and time. Chitwan district was identified as the most likely cluster and Jhapa district was the first secondary cluster in both spatial and spatiotemporal scan. July to September of 2010 was identified as a significant temporal cluster. Conclusion This study assessed and mapped for the first time the spatiotemporal pattern of dengue fever in Nepal. Two districts namely Chitwan and Jhapa were found highly affected by dengue fever. The current study also demonstrated the importance of geospatial approach in epidemiological research. The initial result on dengue patterns and risk of this study may assist institutions and policy makers to develop better preventive strategies.