Spatiotemporal Epidemiology of Malaria in India from 2007 to 2022.

India is a major contributor to the global burden of malaria, especially Plasmodium vivax infection. Understanding the spatiotemporal trends of malaria across India over the last two decades may assist in targeted intervention. The population-normalized spatiotemporal trends of malaria epidemiology in India from 2007 to 2022 were analyzed using a geographic information system with the publicly available "malaria situation" report of the National Vector Borne Disease Control Program (NVBDCP). The NVBDCP data showed malaria cases to have steeply declined from 1.17 million in 2015 to 0.18 million cases in 2022; this is 10.1 and 18.7 fold lower than the WHO's estimate of 11.93 million and 3.38 million cases in 2015 and 2022, respectively. From 2007 to 2022, Mizoram, Meghalaya, Tripura, Odisha, Chhattisgarh, and Jharkhand consistently reported high caseloads of Plasmodium falciparum. In the same period, the P. vivax caseload was high in Arunachal Pradesh, Mizoram, Nagaland, Jharkhand, Odisha, Chhattisgarh, Goa, Daman and Diu, Dadra and Nagar Haveli, and Andaman and Nicobar Islands. The distribution of forest cover, annual rainfall, and proportion of the Scheduled Tribe population (the most underprivileged in Indian society) spatially correlated with malaria cases and deaths. Mizoram is the only state where cases were higher in 2022 than in 2007. Overall, India has made tremendous progress in controlling malaria and malaria-related deaths in the last decade. The decline could be attributed to the effective vector and parasite control strategies implemented across the country.

Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India.

The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

Epidemiology of scrub typhus and other rickettsial infections (2018-22) in the hyper-endemic setting of Mizoram, North-East India.

BACKGROUND: In the past decade, scrub typhus cases have been reported across India, even in regions that had no previous history of the disease. In the North-East Indian state of Mizoram, scrub typhus cases were first recorded only in 2012. However, in the last five years, the state has seen a substantial increase in the scrub typhus and other rickettsial infections. As part of the public health response, the Mizoram Government has integrated screening and line listing of scrub typhus and other rickettsial infections across all its health settings, a first in India. Here we detail the epidemiology of scrub typhus and other rickettsial infections from 2018-2022, systematically recorded across the state of Mizoram. METHODOLOGY/PRINCIPAL FINDINGS: The line-listed data positive for scrub typhus and other rickettsial infections identified by rapid immunochromatographic test and/or Weil-Felix test from 2018-22 was used for the analysis. During this period, 22,914 cases of rickettsial infections were recorded, out of which 19,651 were scrub typhus cases. Aizawl is the worst affected, with 10,580 cases (46.17%). The average incidence of rickettsial infections is 3.54 cases per 1000 persons-year, and the case fatality rate is 0.35. Only ∼2% of the reported scrub typhus cases had eschar. Multivariate logistic regression analysis indicate patients with eschar (aOR = 2.5, p<0.05), occupational workers [farmers (aOR:3.9), businessmen (aOR:1.8), construction workers (aOR:17.9); p<0.05], and children (≤10 years) (aOR = 5.4, p<0.05) have higher odds of death due to rickettsial infections. CONCLUSION: The integration of systematic surveillance and recording of rickettsial diseases across Mizoram has shed important insights into their prevalence, morbidity, and mortality. This study underscores the importance of active surveillance of rickettsial infections across India, as the burden could be substantially higher, and is probably going undetected.

Seroprevalence of scrub typhus and other rickettsial diseases among the household rodents of Mizoram, North-East India.

In the last decade, scrub typhus, a zoonotic disease has emerged as a major health concern in Mizoram, a North-East Indian state that shares international borders with Myanmar and Bangladesh. Mizoram is a biodiversity hotspot and >85% of the state is under forest cover, which provides an ideal ecological niche for the rodents and mites to transmit scrub typhus and other rickettsial infections. Using the Weil-Felix test, a serosurvey of household rodents from 41 villages spread across all the 11 districts in Mizoram was undertaken to gather important insights on their role in disease transmission. Furthermore, the chigger and flea indexes were calculated from the captured rodents. The 163 rodents captured belonged to five species; the highest numbers were from Rattus tanezumi (87), followed by Rattus rattus (41), Mus musculus (17), Suncus murinus (16), and Bandicota bengalensis (2). The rickettsial seropositivity of the captured rodents was 66.26% (108 out of 163 were positive). Among the 163 rodents, sera of 75 (46.01%), 61 (37.42%), and 73 (44.78%) were reactive to OXK, OX19, and OX2 antigens, respectively. The chigger and flea index were 17.92 and 0.16, respectively. Overall, the study has given important insights into the risk of multiple rickettsial infections that household rodents could transmit in Mizoram. These findings indicate the need for the urgent implementation of effective rodent control strategies in Mizoram.

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 PM2.5 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 PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, 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 PM2.5 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.

An assessment of remotely sensed environmental variables on Dengue epidemiology in Central India.

In recent decades, dengue has been expanding rapidly in the tropical cities. Even though environmental factors and landscape features profoundly impact dengue vector abundance and disease epidemiology, significant gaps exist in understanding the role of local environmental heterogeneity on dengue epidemiology in India. In this study, we assessed the role of remotely sensed climatic factors (rainfall, temperature and humidity) and landscape variables (land use pattern, vegetation and built up density) on dengue incidence (2012-2019) in Bhopal city, Central India. Dengue hotspots in the city were assessed through geographical information system based spatial statistics. Dengue incidence increased from 0.59 cases in 2012 to 9.11 cases in 2019 per 10,000 inhabitants, and wards located in Southern Bhopal were found to be dengue hotspots. Distributed lag non-linear model combined with quasi Poisson regression was used to assess the exposure-response association, relative risk (RR), and delayed effects of environmental factors on dengue incidence. The analysis revealed a non-linear relationship between meteorological variables and dengue cases. The model shows that the risk of dengue cases increases with increasing mean temperature, rainfall and absolute humidity. The highest RR of dengue cases (~2.0) was observed for absolute humidity ≥60 g/m3 with a 5-15 week lag. Rapid urbanization assessed by an increase in the built-up area (a 9.1% increase in 2020 compared to 2014) could also be a key factor driving dengue incidence in Bhopal city. The study sheds important insight into the synergistic effects of both the landscape and climatic factors on the transmission dynamics of dengue. Furthermore, the study provides key baseline information on the climatic variables that can be used in the micro-level dengue prediction models in Bhopal and other cities with similar climatic conditions.

Social and housing indicators of dengue and chikungunya in Indian adults aged 45 and above: Analysis of a nationally representative survey (2017-18).

BACKGROUND: Dengue and chikungunya (CHIKV) are the two major vector-borne diseases of serious public health concern in India. Studies on socioeconomic and housing determinants of dengue and CHIKV at a pan-India level are lacking. Here, we took advantage of the recently carried out Longitudinal Ageing Study in India (LASI) carried out across all the states and Union Territories of India to study the social indicators of dengue and CHIKV in India. METHODS: LASI-1 (2017-2018) data on the self-reported period prevalence of dengue and CHIKV from 70,932 respondents aged ≥45 years were used for this analysis. The state-wise distribution of dengue and CHIKV was mapped. Prevalence was estimated for each study variable, and the difference was compared using the χ2 test. The adjusted odds ratios (AOR) of the socioeconomic and housing variables for dengue and CHIKV were estimated using the multiple logistic regression model. RESULTS: Urban residence is the major socio-economic indicator of dengue and CHIKV (dengue AOR: 1.57, 95% CI: 1.18-2.11; CHIKV AOR: 1.84, 95% CI: 1.36-2.49). The other notable indicator is wealth; rich respondents have higher odds of dengue and CHIKV. Adults older than 54 years and those with high school education and above are associated with a lower likelihood of dengue and CHIKV. In addition, CHIKV is associated with scheduled and forward castes, households with improper toilet facilities, open defecation, and kutcha house type. CONCLUSIONS: Despite the limitation that the data is only from adults ≥ 45, this analysis provides important insights into the socioeconomic and housing variables associated with higher odds of dengue and CHIKV in India. Understanding these determinants may assist in the national planning of prevention and control strategies for dengue and CHIKV.

Epidemiology, Hot Spots, and Sociodemographic Risk Factors of Alcohol Consumption in Indian Men and Women: Analysis of National Family Health Survey-4 (2015-16), a Nationally Representative Cross-Sectional Study.

Objectives: To map the alcohol hot spots and understand the Sociodemographic Indices (SDI) affecting alcohol consumption in Indian men and women. Methods: Data from National Family Health Survey-4 carried out from 2015 to 2016 with a sample size of 103,411 men and 699,686 women were used for Geographic Information System mapping, and hot spot identification by spatial statistics (Getis-Ord Gi*). Bivariate analyses and multiple logistic regressions were used to analyze SDI. Results: India has three major alcohol hot spots: (1) North-East (NE) states, (2) Eastern Peninsular states formed by Chhattisgarh, Odisha, Jharkhand, and Telangana, and (3) Southern states of Tamil Nadu and Kerala. Hot spot analysis strongly correlated with region-wise analysis of SDI. Respondents who consumed tobacco have higher odds (men adjusted odds ratio [aOR]: 5.42; women aOR: 4.30) of consuming alcohol. Except for religion and social category, other socioeconomic factors have a low to moderate effect on alcohol consumption. Conclusions: Hot spots and high-risk districts of alcohol consumption identified in this study can guide public health policies for targeted intervention. Alcohol use is at the discretion of individual states and union territories, and stringent anti-alcohol policies strictly enforced across India are the keys to control alcohol use.