Seasonal variation and etiologic inferences of childhood pneumonia and diarrhea mortality in India
- St Michael's Hospital, Canada
- King George's Medical University, India
- Postgraduate Institute of Medical Education and Research, India
- Indian Council of Medical Research, India
- Johns Hopkins Bloomberg School of Public Health, United States
- Hospital for Sick Children, Canada
Abstract
Future control of pneumonia and diarrhea mortality in India requires understanding of their etiologies. We combined time series analysis of seasonality, climate-region, and clinical syndromes from 243,000 verbal autopsies in the nationally-representative Million Death Study. Pneumonia mortality at 1 month-14 years was greatest in January (Rate ratio (RR) 1.66, 99%CI 1.51-1.82; versus the April minimum). Higher RRs at 1-11 months suggested respiratory syncytial virus (RSV) etiology. India's humid subtropical region experienced a unique summer pneumonia mortality. Diarrhea mortality peaked in July (RR 1.66, 1.48-1.85) and January (RR 1.37, 1.23-1.48), while deaths with fever and bloody diarrhea (indicating enteroinvasive bacterial etiology) showed little seasonality. Combining mortality at ages 1-59-months in 2015 with prevalence surveys, we estimate 40,600 pneumonia deaths from Streptococcus pneumoniae, 20,700 from RSV, 12,600 from influenza, and 7,200 from Haemophilus influenzae type b and 24,700 diarrheal deaths from rotavirus. Careful mortality studies can elucidate etiologies and inform vaccine introduction.
Data availability
Data from the Million Death Study cannot be redistributed outside of the Centre for Global Health Research due to legal agreements with the Registrar General of India. Access to MDS data can be granted via data transfer agreements, upon request to the Office of the Registrar General, RK Puram, New Delhi, India (rgoffice.rgi@nic.in). The public census reports can be found at http://www.censusindia.gov.in/vital_statistics/SRS_Statistical_Report.html. Source data files have been provided for Figure 3, Figure 3 - figure supplement 1, Figure 3 - figure supplement 2, Figure 4, Figure 4 - figure supplement 1, Figure 6, Figure 6 - figure supplement 1, and Figure 8. Meta-analyses include only previously published data, and all data sources have been listed in supplemental reference lists within the article file.
Article and author information
Author details
Funding
Canadian Institutes of Health Research (FDN154277)
- Prabhat Jha
Bill and Melinda Gates Foundation
- Prabhat Jha
National Institutes of Health (R01TW05991-01)
- Prabhat Jha
The funders had no role in study design, data collection, analysis or interpretation, preparation of the manuscript or the decision to submit the work for publication.
Reviewing Editor
- Mark Jit, London School of Hygiene & Tropical Medicine, and Public Health England, United Kingdom
Version history
- Received: February 19, 2019
- Accepted: August 21, 2019
- Accepted Manuscript published: August 27, 2019 (version 1)
- Version of Record published: September 24, 2019 (version 2)
Copyright
© 2019, Farrar et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Seasonal variation and etiologic inferences of childhood pneumonia and diarrhea mortality in India
eLife 8:e46202.
https://doi.org/10.7554/eLife.46202
Further reading
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- Epidemiology and Global Health
Background:
In most of the world, the mammography screening programmes were paused at the start of the pandemic, whilst mammography screening continued in Denmark. We examined the mammography screening participation during the COVID-19 pandemic in Denmark.
Methods:
The study population comprised all women aged 50–69 years old invited to participate in mammography screening from 2016 to 2021 in Denmark based on data from the Danish Quality Database for Mammography Screening in combination with population-based registries. Using a generalised linear model, we estimated prevalence ratios (PRs) and 95% confidence intervals (CIs) of mammography screening participation within 90, 180, and 365 d since invitation during the pandemic in comparison with the previous years adjusting for age, year and month of invitation.
Results:
The study comprised 1,828,791 invitations among 847,766 women. Before the pandemic, 80.2% of invitations resulted in participation in mammography screening within 90 d, 82.7% within 180 d, and 83.1% within 365 d. At the start of the pandemic, the participation in screening within 90 d was reduced to 69.9% for those invited in pre-lockdown and to 76.5% for those invited in first lockdown. Extending the length of follow-up time to 365 d only a minor overall reduction was observed (PR = 0.94; 95% CI: 0.93–0.95 in pre-lockdown and PR = 0.97; 95% CI: 0.96–0.97 in first lockdown). A lower participation was, however, seen among immigrants and among women with a low income.
Conclusions:
The short-term participation in mammography screening was reduced at the start of the pandemic, whilst only a minor reduction in the overall participation was observed with longer follow-up time, indicating that women postponed screening. Some groups of women, nonetheless, had a lower participation, indicating that the social inequity in screening participation was exacerbated during the pandemic.
Funding:
The study was funded by the Danish Cancer Society Scientific Committee (grant number R321-A17417) and the Danish regions.
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- Epidemiology and Global Health
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Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.
