Chatterjee Bithika, Mande Shekhar C
National Centre for Cell Science, NCCS Complex, Ganeshkhind, Pune, 411007, India.
Bioinformatics Centre, Savitribai Phule Pune University, 411007, Ganeshkhind, Pune, India.
Sci Rep. 2025 Mar 25;15(1):10270. doi: 10.1038/s41598-025-93622-0.
The severity of COVID-19 has varied across regions, with a disproportionately higher case-fatality ratio in developed nations. In India, states with higher income have reported more COVID-19 related deaths compared to lower-income states. Understanding the underlying factors such as demographics, disease burden, urbanization, and sanitation can help in designing better public health policies to mitigate future pandemics. The objective of this study is to identify key predictors of COVID-19 mortality across Indian states by examining the role of disease prevalence, demographics, urbanization, and sanitation. We analysed data from the Global Burden of Diseases India 2019 and the National Health Profile 2019, correlating them with COVID-19 mortality during two peak periods of the pandemic. Spearman correlation analysis and multivariate regression models were employed to determine significant associations and build predictive models for COVID-19 deaths. Our analysis showed a positive correlation between COVID-19 mortality and demographic factors such as the percentage of the elderly population (ρ = 0.44, p < 0.05 for the first peak; ρ = 0.46, p < 0.05 for the second peak). Urbanization was also significantly associated with higher mortality (ρ = 0.71, p < 0.05 for the first peak; ρ = 0.57, p < 0.05 for the second peak). Additionally, the prevalence of autoimmune diseases and cancer correlated positively with deaths. An unexpected finding was the positive correlation between improved sanitation (e.g., closed drainage systems and indoor toilets) and COVID-19 mortality. The best-fit multivariate regression model, combining demographics, sanitation, autoimmune diseases, and cancer, achieved an adjusted R of 0.71 for the first peak and 0.85 for the second peak. Our findings suggest that as states become wealthier, they undergo urbanization and infrastructural improvements, including better sanitation. However, these changes may also be associated with a rise in autoimmune diseases and cancer, potentially reducing immune resilience to emerging infections. This study provides novel insights into how improved living conditions and lifestyle changes may paradoxically contribute to increased COVID-19 mortality. By emphasizing the role of immune training in pandemic preparedness, our research offers a new perspective on public health strategies for mitigating future infectious disease outbreaks.
新冠疫情的严重程度在不同地区有所不同，发达国家的病死率尤其高。在印度，与低收入邦相比，高收入邦报告的新冠相关死亡病例更多。了解人口统计学、疾病负担、城市化和卫生设施等潜在因素，有助于制定更好的公共卫生政策，以减轻未来的疫情。本研究的目的是通过考察疾病流行率、人口统计学、城市化和卫生设施的作用，确定印度各邦新冠死亡率的关键预测因素。我们分析了《2019年印度疾病负担》和《2019年国家卫生概况》的数据，并将它们与疫情两个高峰期的新冠死亡率相关联。采用斯皮尔曼相关性分析和多元回归模型来确定显著关联，并建立新冠死亡的预测模型。我们的分析表明，新冠死亡率与老年人口百分比等人口统计学因素呈正相关（第一个高峰期：ρ = 0.44，p < 0.05；第二个高峰期：ρ = 0.46，p < 0.05）。城市化也与较高的死亡率显著相关（第一个高峰期：ρ = 0.71，p < 0.05；第二个高峰期：ρ = 0.57，p < 0.05）。此外，自身免疫性疾病和癌症的流行率与死亡呈正相关。一个意外发现是，改善卫生设施（如封闭排水系统和室内厕所）与新冠死亡率呈正相关。结合人口统计学、卫生设施、自身免疫性疾病和癌症的最佳拟合多元回归模型，第一个高峰期的调整R值为0.71，第二个高峰期为0.85。我们的研究结果表明，随着各邦变得更加富裕，它们会经历城市化和基础设施改善，包括更好的卫生设施。然而，这些变化也可能与自身免疫性疾病和癌症的增加有关，可能会降低对新出现感染的免疫恢复力。这项研究为改善生活条件和生活方式变化可能反常地导致新冠死亡率上升提供了新的见解。通过强调免疫训练在大流行防范中的作用，我们的研究为减轻未来传染病爆发的公共卫生策略提供了新的视角。
