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

  1. Daniel S Farrar
  2. Shally Awasthi
  3. Shaza A Fadel
  4. Rajesh Kumar
  5. Anju Sinha
  6. Sze Hang Fu
  7. Brian Wahl
  8. Shaun K Morris
  9. Prabhat Jha  Is a corresponding author
  1. St Michael's Hospital, Canada
  2. King George's Medical University, India
  3. Postgraduate Institute of Medical Education and Research, India
  4. Indian Council of Medical Research, India
  5. Johns Hopkins Bloomberg School of Public Health, United States
  6. 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

  1. Daniel S Farrar

    Centre for Global Health Research, St Michael's Hospital, Toronto, Canada
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7823-1912
  2. Shally Awasthi

    Department of Pediatrics, King George's Medical University, Lucknow, India
    Competing interests
    No competing interests declared.
  3. Shaza A Fadel

    Centre for Global Health Research, St Michael's Hospital, Toronto, Canada
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2336-6254
  4. Rajesh Kumar

    School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India
    Competing interests
    No competing interests declared.
  5. Anju Sinha

    Division of Reproductive Biology, Maternal and Child Health, Indian Council of Medical Research, New Dehli, India
    Competing interests
    No competing interests declared.
  6. Sze Hang Fu

    Centre for Global Health Research, St Michael's Hospital, Toronto, Canada
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4890-9339
  7. Brian Wahl

    International Vaccine Access Centre, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
    Competing interests
    No competing interests declared.
  8. Shaun K Morris

    Centre for Global Child Health, Division of Infectious Diseases, Hospital for Sick Children, Toronto, Canada
    Competing interests
    No competing interests declared.
  9. Prabhat Jha

    Center for Global Health Research, St Michael's Hospital, Toronto, Canada
    For correspondence
    jhap@smh.ca
    Competing interests
    Prabhat Jha, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7067-8341

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

  1. Mark Jit, London School of Hygiene & Tropical Medicine, and Public Health England, United Kingdom

Publication history

  1. Received: February 19, 2019
  2. Accepted: August 21, 2019
  3. Accepted Manuscript published: August 27, 2019 (version 1)
  4. 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.

Metrics

  • 1,322
    Page views
  • 204
    Downloads
  • 4
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Daniel S Farrar
  2. Shally Awasthi
  3. Shaza A Fadel
  4. Rajesh Kumar
  5. Anju Sinha
  6. Sze Hang Fu
  7. Brian Wahl
  8. Shaun K Morris
  9. Prabhat Jha
(2019)
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

    1. Epidemiology and Global Health
    Yochai Edlitz, Eran Segal
    Research Article

    Background: Type 2 diabetes (T2D) accounts for ~90% of all cases of diabetes, resulting in an estimated 6.7 million deaths in 2021, according to the International Diabetes Federation (IDF). Early detection of patients with high risk of developing T2D can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources or access to sophisticated computational resources, there is a need for accurate yet accessible prediction models.

    Methods: In this study, we analyzed data from 44,709 non-diabetic U.K. Biobank participants aged 40-69, predicting the risk of T2D onset within a selected timeframe (mean of 7.3 years with a standard deviation of 2.3 years). We started with 798 features that we identified as potential predictors for T2D onset. We first analyzed the data using gradient boosting decision trees, survival analysis, and logistic regression methods. We devised one non-laboratory model accessible to the general population and one more precise yet simple model that utilizes laboratory tests. We simplified both models to an accessible scorecard form, tested the models on normoglycemic and prediabetes sub cohorts, and compared the results to the results of the general cohort. We established the non-laboratory model using the following covariates: sex, age, weight, height, waist size, hip circumference, waist-to-hip Ratio (WHR), and Body-Mass Index (BMI). For the laboratory model, we used age and sex together with four common blood tests: HDL (high-density lipoprotein), gamma-glutamyl transferase, glycated hemoglobin, and triglycerides. As an external validation dataset, we used the electronic medical record database of Clalit Health Services.

    Results: The non-laboratory scorecard model achieved an Area Under the Receiver Operating Curve (auROC) of 0.81 (0.77-0.84 95% Confidence Interval (CI)) and an odds ratio (OR) between the upper and fifth prevalence deciles of 17.2 (5-66 95% CI). Using this model, we classified three risk groups, a group with 1% (0.8-1%), 5% (3-6%), and the third group with a 9% (7-12%) risk of developing T2D. We further analyzed the contribution of the laboratory-based model and devised a blood-test model based on age, sex and the four common blood tests noted above. In this scorecard model, we included age, sex, glycated hemoglobin (HbA1c%), gamma glutamyl-transferase, triglycerides, and HDL cholesterol. Using this model, we achieved an auROC of 0.87 (0.85-0.90 95% CI) and a deciles' OR of x48 (12-109 95% CI). Using this model, we classified the cohort into four risk groups with the following risks: 0.5% (0.4%-7%); 3% (2-4%); 10% (8-12%) and a high-risk group of 23% (10-37%) of developing T2D. When applying the blood tests model using the external validation cohort (Clalit), we achieved an auROC of 0.75 (0.74-0.75 95% CI). We analyzed several additional comprehensive models, which included genotyping data and other environmental factors. We found that these models did not provide cost-efficient benefits over the four blood test model. The commonly used German Diabetes Risk Score (GDRS) and Finnish Diabetes Risk Score (FINDRISC) models, trained using our data, achieved an auROC of 0.73 (0.69-0.76) and 0.66 (0.62-0.70), respectively, inferior to the results achieved by the four blood test model and by the Anthropometry models.

    Conclusions: The four blood tests and anthropometric models outperformed the commonly used non-laboratory models, the FINDRISC and the GDRS. We suggest that our models be used as tools for decision-makers to assess populations at elevated T2D risk and thus improve medical strategies. These models might also provide a personal catalyst for changing lifestyle, diet, or medication modifications to lower the risk of T2D onset.

    Funding: No Funders. The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

    1. Epidemiology and Global Health
    Guillermo Bosch et al.
    Research Article Updated

    Background:

    To assess the effect of the COVID-19 pandemic on performance indicators in the population-based breast cancer screening program of Parc de Salut Mar (PSMAR), Barcelona, Spain.

    Methods:

    We conducted a before-and-after, study to evaluate participation, recall, false positives, the cancer detection rate, and cancer characteristics in our screening population from March 2020 to March 2021 compared with the four previous rounds (2012–2019). Using multilevel logistic regression models, we estimated the adjusted odds ratios (aORs) of each of the performance indicators for the COVID-19 period, controlling by type of screening (prevalent or incident), socioeconomic index, family history of breast cancer, and menopausal status. We analyzed 144,779 invitations from 47,571women.

    Results:

    During the COVID-19 period, the odds of participation were lower in first-time invitees (aOR = 0.90 [95% CI = 0.84–0.96]) and in those who had previously participated regularly and irregularly (aOR = 0.63 [95% CI = 0.59–0.67] and aOR = 0.95 [95% CI = 0.86–1.05], respectively). Participation showed a modest increase in women not attending any of the previous rounds (aOR = 1.10 [95% CI = 1.01–1.20]). The recall rate decreased in both prevalent and incident screening (aOR = 0.74 [95% CI = 0.56–0.99] and aOR = 0.80 [95% CI = 0.68–0.95], respectively). False positives also decreased in both groups (prevalent aOR = 0.92 [95% CI = 0.66–1.28] and incident aOR = 0.72 [95% CI = 0.59–0.88]). No significant differences were observed in compliance with recall (OR = 1.26, 95% CI = 0.76–2.23), cancer detection rate (aOR = 0.91 [95% CI = 0.69–1.18]), or cancer stages.

    Conclusions:

    The COVID-19 pandemic negatively affected screening attendance, especially in previous participants and newcomers. We found a reduction in recall and false positives and no marked differences in cancer detection, indicating the robustness of the program. There is a need for further evaluations of interval cancers and potential diagnostic delays.

    Funding:

    This study has received funding by grants PI19/00007 and PI21/00058, funded by Instituto de Salud Carlos III (ISCIII) and cofunded by the European Union and Grant RD21/0016/0020 funded by Instituto de Salud Carlos III and by the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia (MRR).