Socioeconomic and Demographic Effects on SARS-CoV-2 Testing: Evidence From the State of Uttar Pradesh, India.

Background The rapid global spread of SARS-CoV-2 highlighted critical challenges in healthcare systems worldwide, with differences in testing access and utilization becoming particularly evident. This study investigates the socioeconomic and demographic factors influencing SARS-CoV-2 testing service access and utilization during the second wave of the pandemic in Uttar Pradesh (UP), India. Methods The study was conducted from July to October 2023 in two districts of Uttar Pradesh (UP). These districts were chosen because one had the highest and the other the lowest SARS-CoV-2 testing rates per million population as reported from March to June 2021. The study population included consenting adult individuals with self-reported symptoms indicative of SARS-CoV-2 infection during March-June 2021. The study excluded individuals under 18 years, those who did not consent, pregnant or lactating mothers, and those with communication-impairing medical conditions. Data were collected using a structured questionnaire based on Andersen's Behavioural Model of Health Services Use. We used chi-squared tests for all categorical variables to obtain p-values and Poisson regression to identify factors influencing testing rates. Results We screened 4,595 individuals and identified 675 eligible participants for this study. Adjusted prevalence ratios derived from multiple variate Poisson regression models showed that participants in Sitapur had a 0.47 (95% CI: 0.39-0.57) times the prevalence of being tested than those in Lucknow. Furthermore, individuals from other backward castes and scheduled castes had a 1.15 (95% CI: 0.99-1.34) and 1.22 (95% CI: 0.95-1.56) times prevalence of being tested for SARS-CoV-2, respectively, when compared to the general caste population. Scheduled Tribes showed a higher prevalence of being tested, contrasting with existing literature. Households with low, middle, and high income showed a 1.46 (95% CI: 1.12-1.89), 1.52 (95% CI: 1.14-2.02), and 1.73 (95% CI: 1.23-2.45) times the prevalence of SARS-CoV-2 testing compared to those below the poverty line, respectively. Behavioral factors such as media use showed an inverse relationship with testing prevalence; individuals who did not watch TV at all had a 0.83 (95% CI: 0.70-0.99) times prevalence of being tested compared to frequent viewers, and similarly, those not using the internet on mobiles had a 0.82 (95% CI: 0.67-0.99) times prevalence than daily users. Individuals using private healthcare facilities had a 0.87 (95% CI: 0.77-0.99) times prevalence of SARS-CoV-2 testing compared to those using government facilities. Conclusions These findings highlight the importance of public health strategies that address socio-economic and behavioral disparities to ensure equitable testing access across all community groups.

Neonatal, Infant, and Child Mortality in India: Progress and Future Directions.

In India, considerable progress has been made in reducing child mortality rates. Despite this achievement, wide disparities persist across and socio-economic strata, and persistent challenges, such as malnutrition, poor sanitation, and lack of clean water. This paper provides a comprehensive review of the state of child health in India, examining key risk factors and causes of child mortality, assessing the coverage of child health interventions, and highlighting critical public health programs and policies. The authors also discuss future directions and recommendations for bolstering ongoing efforts to improve child health. These include state- and region-specific interventions, prioritizing social determinants of health, strengthening data systems, leveraging existing programs like the National Health Mission (NHM) and Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PM-JAY), and the proposed Public Health Management Cadre (PHMC). The authors argue that reducing child mortality requires not only scaled-up interventions but a comprehensive approach that addresses all dimensions of health, from social determinants to system strengthening.

The need for COVID-19 research in low- and middle-income countries.

In the early months of the pandemic, most reported cases and deaths due to COVID-19 occurred in high-income countries. However, insufficient testing could have led to an underestimation of true infections in many low- and middle-income countries. As confirmed cases increase, the ultimate impact of the pandemic on individuals and communities in low- and middle-income countries is uncertain. We therefore propose research in three broad areas as urgently needed to inform responses in low- and middle-income countries: transmission patterns of SARS-CoV-2, the clinical characteristics of the disease, and the impact of pandemic prevention and response measures. Answering these questions will require a multidisciplinary approach led by local investigators and in some cases additional resources. Targeted research activities should be done to help mitigate the potential burden of COVID-19 in low- and middle-income countries without diverting the limited human resources, funding, or medical supplies from response activities.