Despite substantial declines in the last decade, pneumonia and diarrhea remain the leading causes of mortality in India among children, causing about 190,000 deaths at ages 1–59 months in 2015 (Fadel et al., 2017). Indian deaths are about a quarter of global totals from these conditions (Fadel et al., 2017; Liu et al., 2015). Mortality among children aged 5–14 years is less well studied and recent estimates suggest a combined burden of almost 30,000 pneumonia and diarrhea deaths in India in 2016 (Fadel et al., 2019). The declines in pneumonia and diarrhea mortality during the last decade or so have mostly been driven by expanding treatment, improved nutrition, and vaccination against measles and standard childhood antigens (Bhutta et al., 2013; Wong et al., 2019). Newer vaccines such as rotavirus and pneumococcal conjugate vaccines have been only recently added to India’s Universal Immunization Program (Arora and Swaminathan, 2016; Sachdeva, 2017). Assessing the impact of these newer vaccines requires understanding the baseline mortality and distribution of specific microbiologic etiologies.

Pneumonia and diarrhea exhibit distinct seasonal variation, and pathogen transmission patterns are commonly associated with climatic conditions such as temperature, rainfall, and humidity (Chadha et al., 2015; Chao et al., 2019; Fisman, 2012). Surveillance data from the United States has shown summer diarrhea peaks are associated with bacterial pathogens while winter peaks are associated with viral pathogens (Glass et al., 2012). Though similar nationally representative data are lacking in low and middle-income countries, pathogens such as rotavirus exhibit seasonal heterogeneity in India and are most prevalent in cold, dry months (Kumar et al., 2016; Mehendale et al., 2016).

Here, we describe estimates of pneumonia and diarrhea mortality in Indian children aged 1 month to 14 years from well-structured verbal autopsies in the nationally-representative Million Death Study (MDS; Aleksandrowicz et al., 2014; Jha et al., 2006), linked with seasonality, climate region-stratified analyses and clinical syndromes. We also combine mortality with prevalence surveys of Streptococcus pneumoniae, respiratory syncytial virus (RSV), influenza, Haemophilus influenzae type b (Hib), and rotavirus among clinically-confirmed cases of acute respiratory illness and gastroenteritis. We use these data to draw inferences regarding specific microbiologic etiologies and syndromes of these two diseases.

This study is the first to document the known etiologies of pneumonia and diarrhea using standardized, nationally representative physician-coded verbal autopsy data in India, the largest contributor to global child pneumonia and diarrhea mortality. Nationally, pneumonia and diarrhea both demonstrated bimodal seasonal variation and peaked in July and January. Pneumonia mortality rates were highest at ages 1–11 months, particularly during the cooler months of December and January. Diarrhea deaths with fever and bloody stool, indicative of enteroinvasive bacterial diarrhea and microbiologic etiologies such as Shigella spp. showed little seasonality. Climatic factors including temperature and rainfall drove regional differences in pneumonia mortality, including an excess 3500 deaths between June and August in India’s humid subtropical region (including most of the major states of Uttar Pradesh and Bihar). These same regions also showed marked geographic clustering of pneumonia deaths in children, whereas adult pneumonia deaths were dispersed. Verbal autopsies (mostly of home, medically unattended deaths) are by necessity crude in terms of defining exact etiologies (Aleksandrowicz et al., 2014). Despite the inherent misclassification of verbal autopsies, we demonstrate that carefully-conducted, nationally-representative verbal autopsies can be paired effectively with clinical syndromes, seasonality and climate region to yield novel insights into some of the etiologies of pneumonia and diarrhea, the two most common killers of children in the world.

The incidence of pneumonia mortality was 66% greater in January than the annual minimum in April. Infants aged 1–11 months contributed disproportionately to excess mortality in December and January, and viral etiologies such as RSV and influenza may contribute to this peak as they are known causes of respiratory infection in this age group during cooler months (Agrawal et al., 2009; Tang and Loh, 2014). Most RSV surveillance studies show the highest percent positivity between September and January (Benet et al., 2017; Bharaj et al., 2009; Choudhary et al., 2013), suggesting that RSV may be the primary or indirect cause of much of the excess pneumonia mortality in children during these months. Viral respiratory infections may precede bacterial pneumonia in children, and direct association has been established between RSV and influenza and invasive pneumococcal disease (Talbot et al., 2005; Weinberger et al., 2015). Globally, the prevalence of pneumococcal pneumonia is also known to peak in cool, dry months, though very little data describing pneumococcal seasonality is available from India and this warrants further study, ideally through multi-center surveillance studies (Numminen et al., 2015). Notably, no licensed vaccine for RSV currently exists and only Novavax’s RSV F maternal immunization has yet reached Phase three trials (Novavax, Inc, 2019; PATH, 2018). Hence, future introduction of RSV vaccines may be monitored indirectly by examining peak seasonal patterns by age and region.

Our climate region analysis demonstrates the July pneumonia peak evident nationally is mostly a result of a previously undocumented and unique peak in the humid subtropical region. We estimated that this peak was associated with 3500 excess deaths between April and September in 2015, though cannot define the etiology of this unique peak. However, the humid subtropical region is characterized by hot, humid summers, mild winters, and precipitation consistent throughout the year, conditions associated with viral etiologies such as influenza and RSV in tropical climates (Kamigaki et al., 2016; Paynter, 2015; Tang and Loh, 2014). Notably, adults aged 30–59 years exhibited excess mortality occurring around July, but these deaths were dispersed across all climate regions. The etiology of adult pneumonia deaths is more uncertain, in part as the agreement between dual independent physicians was lower than for children and because of the possible role of risk factors such as smoking and exposure to particulate matter (Jha et al., 2008; Kim et al., 2017).

The incidence of diarrhea mortality was 66% greater in July and 37% greater in January compared to the annual minimum. Previous evidence suggests that mid-year (July) diarrhea peaks are attributable to bacterial pathogens such as cholera and campylobacter species and parasitic pathogens such as cryptosporidium species whereas late-year (December-January) peaks are mostly attributable to rotavirus and other viral pathogens (Ajjampur et al., 2010; Fletcher et al., 2013; Sebastian et al., 2015). Diarrheal deaths with both fever and bloody stool may be clinically representative of enteroinvasive bacterial diarrhea, including microbiologic agents such as Shigella spp. and enteroinvasive E. coli (Liu et al., 2016).

Our age-stratified models suggest that rotavirus infection is the primary cause of diarrhea mortality during the December-January mortality peak. Though rotavirus infection circulates in India year-round, percent positivity among clinical cases is highest in cooler months between September and February (Mehendale et al., 2016). Rotavirus death is most common among children < 2 years but has a lower case-fatality rate at older ages, consistent with no observed December-January peak among children aged 5–14 years. Thus, we expect rotavirus deaths occurred disproportionately among the 58,600 all-cause diarrhea deaths that occurred in 2015 at ages 1–23 months. An earlier study using the MDS examined rotavirus mortality also at ages 1–59 months (Morris et al., 2012). The comparisons over time suggest about a 78% decline in rotavirus mortality (113,000 in 2005 versus 24,700 in 2013) versus a slightly smaller 67% decline in overall diarrheal mortality (334,000 versus 109,000). These declines occurred prior to the introduction of the Indian-made, low-cost oral rotavirus vaccine (RotaVac) in 2016. Hence, future diarrhea mortality may be expected to fall even faster. Future MDS data on age-specific and syndrome-specific (i.e. without fever and bloody stool) can help to assess these changes.

The MDS study enables comparison of mortality attributable to five microbiologic agents and two syndromes of pneumonia and diarrhea to modeled data. Compared to under-5 mortality estimates from the model-based Global Burden of Disease (GBD), we estimated 51% fewer pneumococcal pneumonia deaths and 66% fewer Hib pneumonia deaths in 2015 (GBD 2015 LRI Collaborators, 2017), though our estimates notably lack mortality from the first month of life. Though GBD estimates also utilize the vaccine probe approach used in this study, they also adjusted pneumococcal pneumonia estimates to a randomized controlled trial conducted in an elderly population from the Netherlands. This largely accounts for their etiologic fraction being twice as high as our estimate. For Hib pneumonia, GBD include Hib vaccine coverage as a covariate in their model rather than accounting for Hib vaccine coverage in a deterministic manner. This could lead to differences in the Hib pneumonia mortality estimates. Compared to the Maternal Child Epidemiology Estimation (MCEE) collaboration, we estimate 29% fewer pneumococcal pneumonia deaths and 40% fewer Hib pneumonia deaths in 2015 (Wahl et al., 2018). These differences are likely due to the higher estimates of all-cause pneumonia deaths by the MCEE collaboration.

By contrast, we estimated 146% more RSV pneumonia deaths and 425% more influenza deaths in 2015 than the GBD, as the modeled etiologic fractions in the GBD were calculated using a substantially lower proportion of test-positive cases than estimated in our meta-analyses (GBD 2015 LRI Collaborators, 2017). Our estimates of RSV and influenza are based on meta-analyses of directly tested specimens of hospitalized children in India. This has obvious limitations, but likely no more so than modeling results from the GBD (Rigby et al., 2019). The MDS study may be underestimating RSV mortality as we excluded neonates who are affected by this virus (Caballero and Polack, 2018; Scheltema et al., 2017). Our estimate of influenza mortality is also 50% lower than the recent nationally representative estimate of 27,825 deaths in a recent meta-analysis (Ram Purakayastha et al., 2018). However, those estimates also include neonates and use meta-analysis of studies outside India. For diarrhea, our estimates of rotavirus mortality are broadly consistent with GBD (Troeger et al., 2017) and our estimate for diarrhea cases with fever and bloody stool is similar to the GBD estimate of 4600 deaths from Shigella spp. (Khalil et al., 2018), suggesting that enteroinvasive bacterial pathogens are only a small minority of child diarrhea deaths in India (Troeger et al., 2017).

This research has several limitations. Firstly, our use of verbal autopsy may lead to uncertainties in cause of death coding. However, verbal autopsy remains the only viable way of monitoring causes of death in representative populations over time in India, and captures any changes since 2005 in oral rehydration treatment and improved access to care that may have reduced pneumonia and diarrhea mortality (Fadel et al., 2017). Moreover, our use of dual physician coding and adjudication minimizes potential misclassification bias and coding practices did not change over time. Notably, there was high agreement (>75%) for child pneumonia and diarrhea deaths while there was poor agreement for adult pneumonia deaths (38%). As such, greater misclassification may occur for adults due to the more disparate pathways of exposure and clinical disagreement resulting from potential comorbidities and further study of adult pneumonia is warranted. Secondly, our etiology-specific mortality calculations used several assumptions. Because in most cases verbal autopsy is unable to assign microbiologic etiology, our regional meta-analyses relied on prevalence surveys among hospitalized children and children seeking care in emergency departments. This will naturally differ from prevalence among dead children, but we believe that given most of the non-fatal cases comprised severe disease, that differences should be small with deceased children (Morris et al., 2012). Additionally, we generated EFs for influenza and RSV using data collected between 2005–2013 while we used pneumonia mortality envelopes from 2015. It is therefore possible that mortality trends attributable to these conditions diverged from those of all-cause pneumonia during this time. The health centers included in our meta-analyses were also almost entirely located in urban areas and the etiologic fraction may differ in more remote areas. However, we believe that for most regions the aggregation of studies through meta-analysis accounts for these regional differences. We were also unable to disaggregate deaths due to pandemic influenza and seasonal influenza A and B. Thirdly, Streptococcus pneumoniae and Hib also contribute to bacterial sepsis and meningitis, and these conditions would also be reduced with wide coverage of the newly-available conjugate vaccines (Wahl et al., 2018). Fourth, we are unable to attribute declining mortality to either a lower incidence of disease or a lower case-fatality rate. Fifth, though climate dynamics were a heavy focus of this research, we could not assess important risk factors such as malnutrition and zinc deficiency. Finally, we found no evidence of shifting seasonal patterns over the study period of 2005–13, though future seasonal patterns may be affected by changing socioeconomic conditions, climatic changes, and global warming (Burkart et al., 2011; Prüss-Ustün et al., 2017).

Notwithstanding these constraints, we believe our estimates in Table 3 to be the best available estimates of the etiology-specific mortality burden in India. Our results emphasize the need for more robust surveillance systems such as the Expanded National Rotavirus Surveillance System (Mehendale et al., 2016; INSPIRE investigators et al., 2017). They also emphasize the underappreciated robustness of carefully conducted and standardized verbal autopsies done on nationwide, representative populations (Jha, 2014). Mortality and surveillance data combined with seasonality, clinical syndromes, and examined over space and time can provide novel insights into specific etiologies of pneumonia and diarrhea and enable appropriate introduction of vaccination and treatment strategies.

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.

1. 1. Agrawal AS
   2. Sarkar M
   3. Chakrabarti S
   4. Rajendran K
   5. Kaur H
   6. Mishra AC
   7. Chatterjee MK
   8. Naik TN
   9. Chadha MS
   10. Chawla-Sarkar M

   (2009) Comparative evaluation of real-time PCR and conventional RT-PCR during a 2 year surveillance for influenza and respiratory syncytial virus among children with acute respiratory infections in Kolkata, India, reveals a distinct seasonality of infection

   Journal of Medical Microbiology 58:1616–1622.

   https://doi.org/10.1099/jmm.0.011304-0

   • PubMed
   • Google Scholar
2. 1. Ajjampur SS
   2. Liakath FB
   3. Kannan A
   4. Rajendran P
   5. Sarkar R
   6. Moses PD
   7. Simon A
   8. Agarwal I
   9. Mathew A
   10. O'Connor R
   11. Ward H
   12. Kang G

   (2010) Multisite study of cryptosporidiosis in children with diarrhea in India

   Journal of Clinical Microbiology 48:2075–2081.

   https://doi.org/10.1128/JCM.02509-09

   • PubMed
   • Google Scholar
3. 1. Aleksandrowicz L
   2. Malhotra V
   3. Dikshit R
   4. Gupta PC
   5. Kumar R
   6. Sheth J
   7. Rathi SK
   8. Suraweera W
   9. Miasnikof P
   10. Jotkar R
   11. Sinha D
   12. Awasthi S
   13. Bhatia P
   14. Jha P

   (2014) Performance criteria for verbal autopsy-based systems to estimate national causes of death: development and application to the indian million death study

   BMC Medicine 12:21.

   https://doi.org/10.1186/1741-7015-12-21

   • PubMed
   • Google Scholar
4. 1. Arora R
   2. Swaminathan S

   (2016) Ready to measure impact? The introduction of Rotavirus vaccine in India

   Indian Pediatrics 53:565–567.

   https://doi.org/10.1007/s13312-016-0889-x

   • PubMed
   • Google Scholar
5. 1. Benet T
   2. Picot S
   3. Messaoudi M
   4. Chou M
   5. Eap T
   6. Wang J
   7. Shen K
   8. Pape JW
   9. Rouzier V
   10. Awasthi S
   11. Pandey N
   12. Bavdekar A
   13. Sanghavi S
   14. Robinson A
   15. Rakoto-Andrianarivelo M
   16. Sylla M
   17. Diallo S
   18. Nymadawa P
   19. Naranbat N
   20. Russomando G
   21. Basualdo W
   22. Komurian-Pradel F
   23. Endtz H
   24. Vanhems P
   25. Paranhos-Baccala G

   (2017) Microorganisms associated with pneumonia in children

   Clinical Infectious Diseases : An Official Publication of the Infectious Diseases Society of America 65:604–612.

   https://doi.org/10.1093/cid/cix378

   • Google Scholar
6. 1. Bharaj P
   2. Sullender WM
   3. Kabra SK
   4. Mani K
   5. Cherian J
   6. Tyagi V
   7. Chahar HS
   8. Kaushik S
   9. Dar L
   10. Broor S

   (2009) Respiratory viral infections detected by multiplex PCR among pediatric patients with lower respiratory tract infections seen at an urban hospital in delhi from 2005 to 2007

   Virology Journal 6:89.

   https://doi.org/10.1186/1743-422X-6-89

   • PubMed
   • Google Scholar
7. 1. Bhaskaran K
   2. Gasparrini A
   3. Hajat S
   4. Smeeth L
   5. Armstrong B

   (2013) Time series regression studies in environmental epidemiology

   International Journal of Epidemiology 42:1187–1195.

   https://doi.org/10.1093/ije/dyt092

   • PubMed
   • Google Scholar
8. 1. Bhutta ZA
   2. Das JK
   3. Walker N
   4. Rizvi A
   5. Campbell H
   6. Rudan I
   7. Black RE

   (2013) Interventions to address deaths from childhood pneumonia and diarrhoea equitably: what works and at what cost?

   The Lancet 381:1417–1429.

   https://doi.org/10.1016/S0140-6736(13)60648-0

   • Google Scholar
9. 1. Burkart K
   2. Khan MH
   3. Krämer A
   4. Breitner S
   5. Schneider A
   6. Endlicher WR

   (2011) Seasonal variations of all-cause and cause-specific mortality by age, gender, and socioeconomic condition in urban and rural Areas of Bangladesh

   International Journal for Equity in Health 10:32.

   https://doi.org/10.1186/1475-9276-10-32

   • PubMed
   • Google Scholar
10. 1. Caballero MT
    2. Polack FP

    (2018) Respiratory syncytial virus is an "opportunistic" killer

    Pediatric Pulmonology 53:664–667.

    https://doi.org/10.1002/ppul.23963

    • PubMed
    • Google Scholar
11. 1. Chadha MS
    2. Potdar VA
    3. Saha S
    4. Koul PA
    5. Broor S
    6. Dar L
    7. Chawla-Sarkar M
    8. Biswas D
    9. Gunasekaran P
    10. Abraham AM
    11. Shrikhande S
    12. Jain A
    13. Anukumar B
    14. Lal RB
    15. Mishra AC

    (2015) Dynamics of influenza seasonality at sub-regional levels in India and implications for vaccination timing

    PLOS ONE 10:e0124122.

    https://doi.org/10.1371/journal.pone.0124122

    • PubMed
    • Google Scholar
12. 1. Chao DL
    2. Roose A
    3. Roh M
    4. Kotloff KL
    5. Proctor JL

    (2019) The seasonality of diarrheal pathogens: a retrospective study of seven sites over three years

    PLOS Neglected Tropical Diseases 13:e0007211.

    https://doi.org/10.1371/journal.pntd.0007211

    • PubMed
    • Google Scholar
13. 1. Choudhary ML
    2. Anand SP
    3. Wadhwa BS
    4. Chadha MS

    (2013) Genetic variability of human respiratory syncytial virus in Pune, western India

    Infection, Genetics and Evolution 20:369–377.

    https://doi.org/10.1016/j.meegid.2013.09.025

    • Google Scholar
14. 1. Fadel SA
    2. Rasaily R
    3. Awasthi S
    4. Begum R
    5. Black RE
    6. Gelband H
    7. Gerland P
    8. Kumar R
    9. Liu L
    10. Mathers C
    11. Morris SK
    12. Nair S
    13. Newcombe L
    14. Pandey A
    15. Ram F
    16. Ram U
    17. Rodriguez PS
    18. Sahu D
    19. Sati P
    20. Shah PJ
    21. Shet A
    22. Sheth J
    23. Singh JK
    24. Singh L
    25. Sinha A
    26. Suraweera W
    27. Jha P

    (2017) Changes in cause-specific neonatal and 1–59-month child mortality in India from 2000 to 2015: a nationally representative survey

    The Lancet 390:1972–1980.

    https://doi.org/10.1016/S0140-6736(17)32162-1

    • Google Scholar
15. 1. Fadel SA
    2. Boschi-Pinto C
    3. Yu S
    4. Reynales-Shigematsu LM
    5. Menon GR
    6. Newcombe L
    7. Strong KL
    8. Wang Q
    9. Jha P

    (2019) Trends in cause-specific mortality among children aged 5–14 years from 2005 to 2016 in India, China, Brazil, and Mexico: an analysis of nationally representative mortality studies

    The Lancet 393:1119–1127.

    https://doi.org/10.1016/S0140-6736(19)30220-X

    • Google Scholar
16. 1. Fisman D

    (2012) Seasonality of viral infections: mechanisms and unknowns

    Clinical Microbiology and Infection 18:946–954.

    https://doi.org/10.1111/j.1469-0691.2012.03968.x

    • PubMed
    • Google Scholar
17. 1. Fletcher SM
    2. McLaws ML
    3. Ellis JT

    (2013) Prevalence of gastrointestinal pathogens in developed and developing countries: systematic review and meta-analysis

    Journal of Public Health Research 2:9–53.

    https://doi.org/10.4081/jphr.2013.e9

    • PubMed
    • Google Scholar
18. 1. GBD 2015 LRI Collaborators

    (2017) Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory tract infections in 195 countries: a systematic analysis for the global burden of disease study 2015

    The Lancet Infectious Diseases 17:1133–1161.

    https://doi.org/10.1016/S1473-3099(17)30396-1

    • PubMed
    • Google Scholar
19. 1. Glass RI
    2. Parashar U
    3. Patel M
    4. Tate J
    5. Jiang B
    6. Gentsch J

    (2012)

    The control of Rotavirus gastroenteritis in the united states

    Transactions of the American Clinical and Climatological Association 123:36–52.

    • PubMed
    • Google Scholar
20. 1. INSPIRE investigators
    2. Tang JW
    3. Lam TT
    4. Zaraket H
    5. Lipkin WI
    6. Drews SJ
    7. Hatchette TF
    8. Heraud JM
    9. Koopmans MP

    (2017) Global epidemiology of non-influenza RNA respiratory viruses: data gaps and a growing need for surveillance

    The Lancet Infectious Diseases 17:e320–e326.

    https://doi.org/10.1016/S1473-3099(17)30238-4

    • PubMed
    • Google Scholar
21. 1. Jha P
    2. Gajalakshmi V
    3. Gupta PC
    4. Kumar R
    5. Mony P
    6. Dhingra N
    7. Peto R
    8. RgiRGI-CGHR Prospective Study Collaborators

    (2006) Prospective study of one million deaths in India: rationale, design, and validation results

    PLOS Medicine 3:e18.

    https://doi.org/10.1371/journal.pmed.0030018

    • PubMed
    • Google Scholar
22. 1. Jha P
    2. Jacob B
    3. Gajalakshmi V
    4. Gupta PC
    5. Dhingra N
    6. Kumar R
    7. Sinha DN
    8. Dikshit RP
    9. Parida DK
    10. Kamadod R
    11. Boreham J
    12. Peto R
    13. RGI-CGHR Investigators

    (2008) A nationally representative case-control study of smoking and death in India

    New England Journal of Medicine 358:1137–1147.

    https://doi.org/10.1056/NEJMsa0707719

    • PubMed
    • Google Scholar
23. 1. Jha P

    (2014) Reliable direct measurement of causes of death in low- and middle-income countries

    BMC Medicine 12:19.

    https://doi.org/10.1186/1741-7015-12-19

    • PubMed
    • Google Scholar
24. 1. Kamigaki T
    2. Chaw L
    3. Tan AG
    4. Tamaki R
    5. Alday PP
    6. Javier JB
    7. Olveda RM
    8. Oshitani H
    9. Tallo VL

    (2016) Seasonality of influenza and respiratory syncytial viruses and the effect of climate factors in Subtropical-Tropical asia using Influenza-Like illness surveillance data, 2010 -2012

    PLOS ONE 11:e0167712.

    https://doi.org/10.1371/journal.pone.0167712

    • PubMed
    • Google Scholar
25. 1. Khalil IA
    2. Troeger C
    3. Blacker BF
    4. Rao PC
    5. Brown A
    6. Atherly DE
    7. Brewer TG
    8. Engmann CM
    9. Houpt ER
    10. Kang G
    11. Kotloff KL
    12. Levine MM
    13. Luby SP
    14. MacLennan CA
    15. Pan WK
    16. Pavlinac PB
    17. Platts-Mills JA
    18. Qadri F
    19. Riddle MS
    20. Ryan ET
    21. Shoultz DA
    22. Steele AD
    23. Walson JL
    24. Sanders JW
    25. Mokdad AH
    26. Murray CJL
    27. Hay SI
    28. Reiner RC

    (2018) Morbidity and mortality due to Shigella and enterotoxigenic Escherichia coli diarrhoea: the global burden of disease study 1990-2016

    The Lancet Infectious Diseases 18:1229–1240.

    https://doi.org/10.1016/S1473-3099(18)30475-4

    • PubMed
    • Google Scholar
26. 1. Kim SE
    2. Honda Y
    3. Hashizume M
    4. Kan H
    5. Lim Y-H
    6. Lee H
    7. Kim CT
    8. Yi S-M
    9. Kim H

    (2017) Seasonal analysis of the short-term effects of air pollution on daily mortality in northeast asia

    Science of the Total Environment 576:850–857.

    https://doi.org/10.1016/j.scitotenv.2016.10.036

    • Google Scholar
27. 1. Kottek M
    2. Grieser J
    3. Beck C
    4. Rudolf B
    5. Rubel F

    (2006) World map of the Köppen-Geiger climate classification updated

    Meteorologische Zeitschrift 15:259–263.

    https://doi.org/10.1127/0941-2948/2006/0130

    • Google Scholar
28. 1. Kumar A
    2. Basu S
    3. Vashishtha V
    4. Choudhury P

    (2016) Burden of Rotavirus diarrhea in under-five indian children

    Indian Pediatrics 53:607–617.

    https://doi.org/10.1007/s13312-016-0896-y

    • Google Scholar
29. 1. Levine OS
    2. Cherian T
    3. Hajjeh R
    4. Deloria Knoll M

    (2009) Progress and future challenges in coordinated surveillance and detection of pneumococcal and hib disease in developing countries

    Clinical Infectious Diseases 48:S33–S36.

    https://doi.org/10.1086/596479

    • Google Scholar
30. 1. Liu L
    2. Oza S
    3. Hogan D
    4. Perin J
    5. Rudan I
    6. Lawn JE
    7. Cousens S
    8. Mathers C
    9. Black RE

    (2015) Global, regional, and national causes of child mortality in 2000–13, with projections to inform post-2015 priorities: an updated systematic analysis

    The Lancet 385:430–440.

    https://doi.org/10.1016/S0140-6736(14)61698-6

    • Google Scholar
31. 1. Liu J
    2. Platts-Mills JA
    3. Juma J
    4. Kabir F
    5. Nkeze J
    6. Okoi C
    7. Operario DJ
    8. Uddin J
    9. Ahmed S
    10. Alonso PL
    11. Antonio M
    12. Becker SM
    13. Blackwelder WC
    14. Breiman RF
    15. Faruque ASG
    16. Fields B
    17. Gratz J
    18. Haque R
    19. Hossain A
    20. Hossain MJ
    21. Jarju S
    22. Qamar F
    23. Iqbal NT
    24. Kwambana B
    25. Mandomando I
    26. McMurry TL
    27. Ochieng C
    28. Ochieng JB
    29. Ochieng M
    30. Onyango C
    31. Panchalingam S
    32. Kalam A
    33. Aziz F
    34. Qureshi S
    35. Ramamurthy T
    36. Roberts JH
    37. Saha D
    38. Sow SO
    39. Stroup SE
    40. Sur D
    41. Tamboura B
    42. Taniuchi M
    43. Tennant SM
    44. Toema D
    45. Wu Y
    46. Zaidi A
    47. Nataro JP
    48. Kotloff KL
    49. Levine MM
    50. Houpt ER

    (2016) Use of quantitative molecular diagnostic methods to identify causes of diarrhoea in children: a reanalysis of the GEMS case-control study

    The Lancet 388:1291–1301.

    https://doi.org/10.1016/S0140-6736(16)31529-X

    • Google Scholar
32. 1. Mehendale S
    2. Venkatasubramanian S
    3. Girish Kumar CP
    4. Kang G
    5. Gupte MD
    6. Arora R

    (2016) Expanded indian national Rotavirus surveillance network in the context of Rotavirus vaccine introduction

    Indian Pediatrics 53:575–581.

    https://doi.org/10.1007/s13312-016-0891-3

    • PubMed
    • Google Scholar
33. 1. Moïsi JC
    2. Saha SK
    3. Falade AG
    4. Njanpop-Lafourcade BM
    5. Oundo J
    6. Zaidi AK
    7. Afroj S
    8. Bakare RA
    9. Buss JK
    10. Lasi R
    11. Mueller J
    12. Odekanmi AA
    13. Sangaré L
    14. Scott JA
    15. Knoll MD
    16. Levine OS
    17. Gessner BD

    (2009) Enhanced diagnosis of pneumococcal meningitis with use of the binax NOW immunochromatographic test of Streptococcus pneumoniae antigen: a multisite study

    Clinical Infectious Diseases 48:S49–S56.

    https://doi.org/10.1086/596481

    • PubMed
    • Google Scholar
34. 1. Morris SK
    2. Bassani DG
    3. Awasthi S
    4. Kumar R
    5. Shet A
    6. Suraweera W
    7. Jha P
    8. MDS Collaborators

    (2011) Diarrhea, pneumonia, and infectious disease mortality in children aged 5 to 14 years in India

    PLOS ONE 6:e20119.

    https://doi.org/10.1371/journal.pone.0020119

    • PubMed
    • Google Scholar
35. 1. Morris SK
    2. Awasthi S
    3. Khera A
    4. Bassani DG
    5. Kang G
    6. Parashar UD
    7. Kumar R
    8. Shet A
    9. Glass RI
    10. Jha P
    11. Million Death Study Collaborators

    (2012) Rotavirus mortality in India: estimates based on a nationally representative survey of diarrhoeal deaths

    Bulletin of the World Health Organization 90:720–727.

    https://doi.org/10.2471/BLT.12.101873

    • PubMed
    • Google Scholar
36. 1. Novavax, Inc

    (2019) Novavax Announces Topline Results from Phase 3 PrepareTM Trial of ResVaxTM for Prevention of RSV Disease in Infants via Maternal Immunization

    Novavax Announces Topline Results from Phase 3 PrepareTM Trial of ResVaxTM for Prevention of RSV Disease in Infants via Maternal Immunization, http://ir.novavax.com/news-releases/news-release-details/novavax-announces-topline-results-phase-3-preparetm-trial.

    http://ir.novavax.com/news-releases/news-release-details/novavax-announces-topline-results-phase-3-preparetm-trial

    • Google Scholar
37. 1. Numminen E
    2. Chewapreecha C
    3. Turner C
    4. Goldblatt D
    5. Nosten F
    6. Bentley SD
    7. Turner P
    8. Corander J

    (2015) Climate induces seasonality in pneumococcal transmission

    Scientific Reports 5:11344.

    https://doi.org/10.1038/srep11344

    • PubMed
    • Google Scholar
38. Software

    1. PATH

    (2018)

    Vaccine Resource Library

    RSV Vaccine and mAb Snapshot.
39. 1. Paynter S

    (2015) Humidity and respiratory virus transmission in tropical and temperate settings

    Epidemiology and Infection 143:1110–1118.

    https://doi.org/10.1017/S0950268814002702

    • PubMed
    • Google Scholar
40. 1. Prüss-Ustün A
    2. Wolf J
    3. Corvalán C
    4. Neville T
    5. Bos R
    6. Neira M

    (2017) Diseases due to unhealthy environments: an updated estimate of the global burden of disease attributable to environmental determinants of health

    Journal of Public Health 39:464–475.

    https://doi.org/10.1093/pubmed/fdw085

    • PubMed
    • Google Scholar
41. 1. Ram Purakayastha D
    2. Vishnubhatla S
    3. Rai SK
    4. Broor S
    5. Krishnan A

    (2018) Estimation of burden of influenza among under-Five children in India: a Meta-Analysis

    Journal of Tropical Pediatrics 64:441–453.

    https://doi.org/10.1093/tropej/fmx087

    • PubMed
    • Google Scholar
42. 1. Rigby M
    2. Deshpande S
    3. Blair M

    (2019) Credibility in published data sources

    The Lancet 393:225–226.

    https://doi.org/10.1016/S0140-6736(18)32844-7

    • Google Scholar
43. 1. Sachdeva A

    (2017) Pneumococcal conjugate vaccine introduction in India's Universal Immunization Program

    Indian Pediatrics 54:445–446.

    https://doi.org/10.1007/s13312-017-1044-z

    • PubMed
    • Google Scholar
44. 1. Scheltema NM
    2. Gentile A
    3. Lucion F
    4. Nokes DJ
    5. Munywoki PK
    6. Madhi SA
    7. Groome MJ
    8. Cohen C
    9. Moyes J
    10. Thorburn K
    11. Thamthitiwat S
    12. Oshitani H
    13. Lupisan SP
    14. Gordon A
    15. Sánchez JF
    16. O'Brien KL
    17. Gessner BD
    18. Sutanto A
    19. Mejias A
    20. Ramilo O
    21. Khuri-Bulos N
    22. Halasa N
    23. de-Paris F
    24. Pires MR
    25. Spaeder MC
    26. Paes BA
    27. Simões EAF
    28. Leung TF
    29. da Costa Oliveira MT
    30. de Freitas Lázaro Emediato CC
    31. Bassat Q
    32. Butt W
    33. Chi H
    34. Aamir UB
    35. Ali A
    36. Lucero MG
    37. Fasce RA
    38. Lopez O
    39. Rath BA
    40. Polack FP
    41. Papenburg J
    42. Roglić S
    43. Ito H
    44. Goka EA
    45. Grobbee DE
    46. Nair H
    47. Bont LJ
    48. PERCH Study Group

    (2017) Global respiratory syncytial virus-associated mortality in young children (RSV GOLD): a retrospective case series

    The Lancet Global Health 5:e984–e991.

    https://doi.org/10.1016/S2214-109X(17)30344-3

    • PubMed
    • Google Scholar
45. 1. Sebastian T
    2. Anandan S
    3. Jeyaseelan V
    4. Jeyaseelan L
    5. Ramanathan K
    6. Veeraraghavan B

    (2015) Role of seasonality and rainfall in Vibrio cholerae infections: a time series model for 11 years surveillance data

    Clinical Epidemiology and Global Health 3:144–148.

    https://doi.org/10.1016/j.cegh.2015.05.001

    • Google Scholar
46. 1. Talbot TR
    2. Poehling KA
    3. Hartert TV
    4. Arbogast PG
    5. Halasa NB
    6. Edwards KM
    7. Schaffner W
    8. Craig AS
    9. Griffin MR

    (2005) Seasonality of invasive pneumococcal disease: temporal relation to documented influenza and respiratory syncytial viral circulation

    The American Journal of Medicine 118:285–291.

    https://doi.org/10.1016/j.amjmed.2004.09.016

    • PubMed
    • Google Scholar
47. 1. Tang JW
    2. Loh TP

    (2014) Correlations between climate factors and incidence--a contributor to RSV seasonality

    Reviews in Medical Virology 24:15–34.

    https://doi.org/10.1002/rmv.1771

    • PubMed
    • Google Scholar
48. 1. Troeger C
    2. Forouzanfar M
    3. Rao PC
    4. Khalil I
    5. Brown A
    6. Reiner RC
    7. Fullman N
    8. Thompson RL
    9. Abajobir A
    10. Ahmed M
    11. Alemayohu MA
    12. Alvis-Guzman N
    13. Amare AT
    14. Antonio CA
    15. Asayesh H
    16. Avokpaho E
    17. Awasthi A
    18. Bacha U
    19. Barac A
    20. Betsue BD
    21. Beyene AS
    22. Boneya DJ
    23. Malta DC
    24. Dandona L
    25. Dandona R
    26. Dubey M
    27. Eshrati B
    28. Fitchett JRA
    29. Gebrehiwot TT
    30. Hailu GB
    31. Horino M
    32. Hotez PJ
    33. Jibat T
    34. Jonas JB
    35. Kasaeian A
    36. Kissoon N
    37. Kotloff K
    38. Koyanagi A
    39. Kumar GA
    40. Rai RK
    41. Lal A
    42. El Razek HMA
    43. Mengistie MA
    44. Moe C
    45. Patton G
    46. Platts-Mills JA
    47. Qorbani M
    48. Ram U
    49. Roba HS
    50. Sanabria J
    51. Sartorius B
    52. Sawhney M
    53. Shigematsu M
    54. Sreeramareddy C
    55. Swaminathan S
    56. Tedla BA
    57. Jagiellonian RT-M
    58. Ukwaja K
    59. Werdecker A
    60. Widdowson M-A
    61. Yonemoto N
    62. El Sayed Zaki M
    63. Lim SS
    64. Naghavi M
    65. Vos T
    66. Hay SI
    67. Murray CJL
    68. Mokdad AH

    (2017) Estimates of global, regional, and national morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic analysis for the global burden of disease study 2015

    The Lancet Infectious Diseases 17:909–948.

    https://doi.org/10.1016/S1473-3099(17)30276-1

    • Google Scholar
49. 1. Wahl B
    2. O'Brien KL
    3. Greenbaum A
    4. Majumder A
    5. Liu L
    6. Chu Y
    7. Lukšić I
    8. Nair H
    9. McAllister DA
    10. Campbell H
    11. Rudan I
    12. Black R
    13. Knoll MD

    (2018) Burden of Streptococcus pneumoniae and Haemophilus influenzae type b disease in children in the era of conjugate vaccines: global, regional, and national estimates for 2000-15

    The Lancet Global Health 6:e744–e757.

    https://doi.org/10.1016/S2214-109X(18)30247-X

    • PubMed
    • Google Scholar
50. 1. Wahl B
    2. Sharan A
    3. Deloria Knoll M
    4. Kumar R
    5. Liu L
    6. Chu Y
    7. McAllister DA
    8. Nair H
    9. Campbell H
    10. Rudan I
    11. Ram U
    12. Sauer M
    13. Shet A
    14. Black R
    15. Santosham M
    16. O'Brien KL
    17. Arora NK

    (2019) National, regional, and state-level burden of Streptococcus pneumoniae and Haemophilus influenzae type b disease in children in India: modelled estimates for 2000–15

    The Lancet Global Health 7:e735–e747.

    https://doi.org/10.1016/S2214-109X(19)30081-6

    • Google Scholar
51. 1. Weinberger DM
    2. Klugman KP
    3. Steiner CA
    4. Simonsen L
    5. Viboud C

    (2015) Association between respiratory syncytial virus activity and pneumococcal disease in infants: a time series analysis of US hospitalization data

    PLOS Medicine 12:e1001776.

    https://doi.org/10.1371/journal.pmed.1001776

    • PubMed
    • Google Scholar
52. 1. Wong BK
    2. Fadel SA
    3. Awasthi S
    4. Khera A
    5. Kumar R
    6. Menon G
    7. Jha P

    (2019) The impact of measles immunization campaigns in India using a nationally representative sample of 27,000 child deaths

    eLife 8:e43290.

    https://doi.org/10.7554/eLife.43290

    • PubMed
    • Google Scholar
53. Report

    1. World Health Organization

    (2010) ICD-10 : International Statistical Classification of Diseases and Related Health Problems

    World Health Organization.

    https://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf

    • Google Scholar
54. 1. You D
    2. Hug L
    3. Ejdemyr S
    4. Idele P
    5. Hogan D
    6. Mathers C
    7. Gerland P
    8. New JR
    9. Alkema L
    10. United Nations Inter-agency Group for Child Mortality Estimation (UN IGME)

    (2015) Global, regional, and national levels and trends in under-5 mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Inter-agency group for child mortality estimation

    The Lancet 386:2275–2286.

    https://doi.org/10.1016/S0140-6736(15)00120-8

    • PubMed
    • Google Scholar

Author details

1. Daniel S Farrar

   Centre for Global Health Research, St. Michael’s Hospital and Dalla Lana School of Public Health, University of Toronto, Ontario, Canada

   Contribution

   Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "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

   Contribution

   Conceptualization, Resources, Investigation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

3. Shaza A Fadel

   Centre for Global Health Research, St. Michael’s Hospital and Dalla Lana School of Public Health, University of Toronto, Ontario, Canada

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2336-6254

4. Rajesh Kumar

   Department of Community Medicine, School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India

   Contribution

   Resources, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

5. Anju Sinha

   Division of Reproductive Biology, Maternal and Child Health, Indian Council of Medical Research, New Delhi, India

   Contribution

   Resources, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

6. Sze Hang Fu

   Centre for Global Health Research, St. Michael’s Hospital and Dalla Lana School of Public Health, University of Toronto, Ontario, Canada

   Contribution

   Resources, Data curation, Visualization, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4890-9339

7. Brian Wahl

   International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States

   Contribution

   Resources, Formal analysis, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

8. Shaun K Morris

   Centre for Global Child Health, Division of Infectious Diseases, Hospital for Sick Children and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Resources, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

9. Prabhat Jha

   Centre for Global Health Research, St. Michael’s Hospital and Dalla Lana School of Public Health, University of Toronto, Ontario, Canada

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   jhap@smh.ca

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0001-7067-8341

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