Impact of Income, Density, and Population Size on PM2.5 Pollutions: A Scaling Analysis of 254 Large Cities in Six Developed Countries

A review of factors influencing sensitive skin: an emphasis on built environment characteristics

Background

Sensitive skin (SS) is a condition characterized by hyperreactivity. Impacting around 37 percent of the worldwide population and exerting an influence on the quality of life for affected individuals. Its prevalence rate has increased due to factors such as elevating stress levels and deteriorating environmental conditions. The exposome factors influencing SS have extended from demographic, biological attributes, and lifestyle to external environments. Built environments (BEs) have demonstrated as root drivers for changes in behaviors and environmental exposure which have the potential to trigger SS, but the review of the associations between BEs and SS is currently lacking.

Objective

This review aims to achieve two primary objectives: (1) Examine exposome factors that exert influence on SS at the individual and environmental levels. (2) Develop a theoretical framework that establishes a connection between BEs and SS, thereby offering valuable insights into the impact of the built environment on this condition.

Methods

An extensive literature search was carried out across multiple fields, including sociology, epidemiology, basic medicine, clinical medicine, and environmental research, with a focus on SS. To identify pertinent references, renowned databases such as PubMed, Web of Science, and CNKI were utilized.

Results

SS is the outcome of interactions between individual attributes and environmental factors. These influencing factors can be categorized into five distinct classes: (1) demographic and socioeconomic characteristics including age, gender, and race; (2) physiological and biological attributes such as emotional changes, skin types, sleep disorders, and menstrual cycles in women; (3) behavioral factors, such as spicy diet, cosmetic use, alcohol consumption, and physical exercise; (4) natural environmental features, including climate conditions and air pollution; (5) built environmental features such as population density, green space availability, road network density, and access to public transportation, also have the potential to affect the condition.

Conclusion

The importance of interdisciplinary integration lies in its ability to ascertain whether and how BEs are impacting SS. By elucidating the role of BEs in conjunction with other factors in the onset of SS, we can provide guidance for future research endeavors and the formulation of interventions aimed at mitigating the prevalence of SS.

Spatial epidemiology of acute respiratory infections in children under 5 years and associated risk factors in India: District-level analysis of health, household, and environmental datasets

Background

In India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India.

Methods

Data on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM_2.5 and PM₁₀ datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi^* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors.

Results

Acute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM_2.5, PM₁₀, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM_2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI.

Conclusion

To decrease ARI in children, urgent interventions are required to reduce the levels of PM_2.5 and PM₁₀ (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.