Seasonal variation and etiologic inferences of childhood pneumonia and diarrhea mortality in India

Farrar, Daniel; Awasthi, Sanjay; Fadel, Shaza A; Kumar, Rajesh; Sinha, Anju; Fu, Sze Hang; Wahl, Brian; Morris, Shaun K.; Jha, Prabhat

doi:10.7554/elife.46202

Cited by 22 publications

(33 citation statements)

References 51 publications

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“…These infectious diseases vary seasonally. Farrar et al (2019) find that diarrheal mortality is highest in the summer and monsoon months in India. On the other hand, pneumonia mortality is highest in the winter months.…”

Section: Seasonal Variation In Infant Mortality In India 1 Introductionmentioning

confidence: 88%

“…Studies on seasonal variation in child health, particularly those examining month of birth effects on height-for-age deficits using the Demographic and Health Surveys (Lokshin and Radyakin, 2012) have been limited by data quality concerns (Larsen et al, 2019) and methodological issues (Agarwal et al, 2017;Cummins, 2015). In contrast to these studies of month-of-birth effects on height-for-age, studies examining seasonal variation in child mortality using sample registration (Farrar et al, 2019;Burkart et al, 2011) or surveillance site data (Muhuri, 1996;Kampe et al, 2015;Delaunay et al, 2001) have found clearer evidence of seasonal influences. As a contribution to resolving this puzzle in the literature on seasonal variation in child health in developing countries, I estimate period mortality rates between the ages 0 and 1 ( 1 m 0 ) by calendar month using Demographic and Health Survey (DHS) data for India.…”

Section: Seasonal Variation In Infant Mortality In India 1 Introductionmentioning

confidence: 98%

“…Since then, a considerable literature in the economic, social, and health sciences has investigated monthly and seasonal variation in health (Rau, 2006b), especially early in life (Muhuri, 1996;Delaunay et al, 2001;Farrar et al, 2019).…”

Section: Seasonal Variation In Infant Mortality In India 1 Introductionmentioning

confidence: 99%

“…The DHS also make available richer information on determinants of child health compared to sample registration data. A key limitation of research based on sample registration data in India (for instance in Farrar et al 2019) is that the population exposed to the risk of mortality are not available in these data. Consequently, analysis is based on death counts or indirect methods of estimating population exposures (Gomes et al, 2017).…”

Section: Seasonal Variation In Infant Mortality In India 1 Introductionmentioning

confidence: 99%

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Seasonal variation in infant mortality in India

Gupta¹

2020

Preprint

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Investigating seasonal variation in health and mortality helps understand disease dynamics and environmental health exposures. Using four available rounds of India's Demographic and Health Surveys (DHS), this paper examines seasonality in infant mortality in India. I use information on the birth month-year, survival status within the first year of life, and age (in months) at death (if the infant died) of more than 330,000 children born between 1989 and 2014 to estimate period mortality rates between ages 0 and 1 for each calendar month. Relative to the spring months, infant mortality is higher in the summer, monsoon, and winter months. If the mortality conditions in the spring months were prevalent throughout the year, would have been less by 10.8 deaths per 1,000 infant alive per year in early 1990s and 4.1 deaths per 1,000 per year in the mid-2010s. Seasonal variation in infant mortality is higher among children born in less wealthy households, among children of less educated mothers, in rural areas, and in poorer regions. Although seasonality in infant mortality has attenuated over-time, seasonal variation in the early-childhood disease environment remains a concern, particularly in rural areas. These results highlight the multiple environmental health threats that infants in India face, and the limited period within a year when these threats are less salient.

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Section: Seasonal Variation In Infant Mortality In India 1 Introductionmentioning

confidence: 88%

Section: Seasonal Variation In Infant Mortality In India 1 Introductionmentioning

confidence: 98%

Section: Seasonal Variation In Infant Mortality In India 1 Introductionmentioning

confidence: 99%

Section: Seasonal Variation In Infant Mortality In India 1 Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Seasonal variation in infant mortality in India

Gupta¹

2020

Preprint

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“…We obtained weather data local to each site’s health centers during the GEMS study using NOAA’s Integrated Surface Database ( Smith et al, 2011 ). The incidence of many pathogens, including rotavirus ( Cook et al, 1990 ), norovirus ( Ahmed et al, 2013 ), cholera ( Emch et al, 2008 ), and Salmonella ( Mohanty et al, 2006 ), are known to have seasonal patterns, and other analyses have established climatic factors to be associated with diarrheal diseases ( Colwell, 1996 , Chao et al, 2019 , Farrar et al, 2019 ). Stations near GEMS sites such as in The Gambia exhibit seasonal patterns ( Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea

Brintz

Haaland

Howard

et al. 2021

eLife

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Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where 'pre-test’ epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.

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