1. Epidemiology and Global Health
Download icon

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 and Dalla Lana School of Public Health, University of Toronto, Canada
  2. King George's Medical University, India
  3. Post Graduate 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 and Dalla Lana School of Public Health, University of Toronto, Canada
Research Article
Cite this article as: eLife 2019;8:e46202 doi: 10.7554/eLife.46202
8 figures, 4 tables and 3 additional files

Figures

National mortality rates of (A) pneumonia and (B) diarrhea by sex, poorer/richer state, and residence type among Indian children aged 1 month to 4 years between 2005 and 2013.

Each x-axis represents 2005–2013. We used a three-year moving average of the weighted proportion of deaths to calculate mortality rates (per 1000 live births). We adjusted death data to reflect 2015 data from the United Nations Population Division and Inter-agency Group for Child Mortality Estimation. Poorer states include the Empowerment Action Group-Assam states of Assam, Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha (Orissa before 2011), Rajasthan, Uttarakhand, and Uttar Pradesh. Richer states include all other states and union territories.

https://doi.org/10.7554/eLife.46202.004
Figure 1—source data 1

National mortality rates and rate reductions of pneumonia and diarrhea by population subset.

1We adjusted death data to reflect 2015 data from the United Nations Population Division and Inter-agency Group for Child Mortality Estimation. 2We used a three-year moving average of the weighted proportion of deaths to calculate mortality rates. Mortality rates were per 1000 lives births at ages 1–11 months and 1–4 years and per 100,000 population for children at aged 5–14 years. 3We calculated rate reductions using the formula ARR = log(U5MRt2/U5MRt1) / (t1 – t2) as described by You et al. (2015). 4Poorer states include the Empowerment Action Group-Assam states of Assam, Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha (Orissa before 2011), Rajasthan, Uttarakhand, and Uttar Pradesh. Richer states include all other states and union territories.

https://doi.org/10.7554/eLife.46202.005
National mortality rates of (A) pneumonia and (B) diarrhea by sex, poorer/richer state, and residence type among Indian children aged 5 to 14 years between 2005 and 2013.

Each x-axis represents 2005–2013. We used a three-year moving average of the weighted proportion of deaths to calculate mortality rates (per 100,000 population). We adjusted death data to reflect 2015 data from the United Nations Population Division and Inter-agency Group for Child Mortality Estimation. Poorer states include the Empowerment Action Group-Assam states of Assam, Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha (Orissa before 2011), Rajasthan, Uttarakhand, and Uttar Pradesh. Richer states include all other states and union territories.

https://doi.org/10.7554/eLife.46202.006
Figure 3 with 2 supplements
Average seasonal patterns of pneumonia (A) and diarrhea (B).

Diarrhea deaths are split into subsets of cases with/without both fever and bloody stool, as well as typhoid and paratyphoid fever deaths. We defined pneumonia deaths using ICD-10 codes A37, H65-H68, H70, H71, J00-J22, J32, J36, J85, J86, P23, or U04. We defined diarrhea deaths using ICD-10 codes A00, A02-A09, and distinguished further based on symptoms reported in the VA (deaths exhibiting both fever and bloody stool; deaths not exhibiting both fever and bloody stool). We defined typhoid and paratyphoid fever deaths using ICD-10 code A01. Each horizontal axis represents an average yearly span from April to April. We determined seasonal patterns using monthly counts of death and modeled using Poisson regression. Rate ratios (RR) are calculated within each disease and are compared to annual minimum mortality in the month of April.

https://doi.org/10.7554/eLife.46202.007
Figure 3—source data 1

Monthly predicted values and rate ratios of the average annual pattern of pneumonia (total), diarrhea (by symptom profile; with fever and bloody stool, without fever and bloody stool), and typhoid/paratyphoid fever (total) mortality.

https://doi.org/10.7554/eLife.46202.012
Figure 3—figure supplement 1
Time series model by pneumonia and diarrhea total.

Diarrhea deaths are split into subsets of cases with/without both fever and bloody stool, as well as typhoid and paratyphoid fever deaths. Pneumonia deaths are defined by ICD-10 codes A37, H65-H68, H70, H71, J00-J22, J32, J36, J85, J86, P23, or U04. Diarrhea deaths were defined by ICD-10 codes A00, A02-A09, and distinguished further based on symptoms reported in the verbal autopsy. Typhoid and paratyphoid fever deaths were defined by ICD-10 code A01. Each dashed vertical line designates December of each year. Data was modeled using a generalized additive model and Poisson regression, using monthly count of deaths from the Million Death Study. Each vertical dashed line designates December of each year.

https://doi.org/10.7554/eLife.46202.008
Figure 3—figure supplement 1—source data 1

Predicted values for pneumonia (total), diarrhea (by symptom profile; with fever and bloody stool, without fever and bloody stool), and typhoid/paratyphoid fever (total) mortality time series models; 2005-2013.

https://doi.org/10.7554/eLife.46202.009
Figure 3—figure supplement 2
Average seasonal patterns of pneumonia deaths by adult age subset.

Each horizontal axis represents an average yearly span from April to April. We determined seasonal patterns using monthly counts of death and modeled using Poisson regression. Rate ratios (RR) were calculated within each disease and were compared to annual minimum mortality in the month of April.

https://doi.org/10.7554/eLife.46202.010
Figure 3—figure supplement 2—source data 1

Monthly predicted values and rate ratios of the average annual pattern of pneumonia mortality, by adult age subset.

https://doi.org/10.7554/eLife.46202.011
Figure 4 with 1 supplement
Average seasonal patterns of pneumonia (A, C, E) and diarrhea lacking fever and bloody stool (B, D, F) deaths by child age subset.

Counts of diarrhea with fever and bloody stool were too small to model. Each horizontal axis represents an average yearly span from April to April. We determined seasonal patterns using monthly counts of death and modeled using Poisson regression. Rate ratios (RR) were calculated within each disease and were compared to annual minimum mortality in the month of April.

https://doi.org/10.7554/eLife.46202.013
Figure 4—source data 1

Monthly predicted values and rate ratios of the average annual pattern of pneumonia and diarrhea mortality, by child age subset.

https://doi.org/10.7554/eLife.46202.016
Figure 4—figure supplement 1
Time series model for (A) pneumonia and (B) diarrhea, by age subset.

Diarrhea cases shown here are only those that did not exhibit fever and bloody stool. Data was modeled using a generalized additive model and Poisson regression, using monthly count of deaths from the Million Death Study. Each dashed vertical line designates December of each year.

https://doi.org/10.7554/eLife.46202.014
Figure 4—figure supplement 1—source data 1

Predicted values for pneumonia and diarrhea mortality time series models by child age subset; 2005–2013.

https://doi.org/10.7554/eLife.46202.015
Map of India by state and Köppen-Geiger climate classification region.

Original climate region data was extracted from Kottek et al. (2006). The Köppen-Geiger map of India was created using ArcGIS 10.4. The three climate regions shown include hot semi-arid (Bsh; including hot desert or Bwh), humid subtropical (Cwa; including subtropical highland or Cwb), and tropical savannah (Aw; including tropical monsoon or Am). The hot desert, subtropical highland, and tropical monsoon regions are incorporated into similar, adjacent climate regions given insufficient sample size. Briefly, the hot semi-arid region exhibits large fluctuations in annual temperature, has little annual precipitation, and is geographically proximal to deserts; the tropical savannah region is characterized by distinct dry-wet seasonal variation; and the humid subtropical region is defined by hot, humid temperatures and consistent rainfall throughout the year. AN = Andaman and Nicobar Islands. AP = Andhra Pradesh. AR = Arunachal Pradesh. AS = Assam. BR = Bihar. CH = Chandigarh. CG = Chhattisgarh. DD = Daman and Diu. DN = Dadra and Nagar Haveli. DL = Delhi. GA = Goa. GJ = Gujarat. HP = Himachal Pradesh. HR = Haryana. JH = Jharkhand. JK = Jammu and Kashmir. KA = Karnataka. KL = Kerala. LD = Lakshadweep. MH = Maharashtra. ML = Meghalaya. MN = Manipur. MP = Madhya Pradesh. MZ = Mizoram. NL = Nagaland. OD = Odisha. PB = Punjab. PY = Puducherry. RJ = Rajasthan. SK = Sikkim. TN = Tamil Nadu. TR = Tripura. UP = Uttar Pradesh. UT = Uttarakhand. WB = West Bengal.

https://doi.org/10.7554/eLife.46202.017
Figure 6 with 1 supplement
Average seasonal pattern of deaths from pneumonia by age group and Köppen-Geiger climate region.

Each horizontal axis represents an average yearly span from April to April. We determined seasonal patterns using monthly counts of death and modeled using Poisson regression. Rate ratios (RR) were calculated within each disease and were compared to annual minimum mortality in the month of April. Given three regions also had a smaller sample size on which to model, they were bundled into regions with similar climatic characteristics (tropical monsoon into tropical savannah, hot desert into hot semi-arid, and subtropical highland into humid subtropical).

https://doi.org/10.7554/eLife.46202.018
Figure 6—source data 1

Monthly predicted values and rate ratios of the average annual pattern of pneumonia mortality, by age subset and climate region.

https://doi.org/10.7554/eLife.46202.021
Figure 6—figure supplement 1
Time series model for (A) pneumonia and (B) diarrhea by Köppen-Geiger climate region.

Diarrhea cases shown here are only those that did not exhibit fever and bloody stool. Data was modeled using a generalized additive model and Poisson regression, using monthly count of deaths from the Million Death Study. Each checked horizontal line designates December of each year. Given three regions also had a smaller sample size on which to model, they were bundled into regions with similar climatic characteristics (tropical monsoon into tropical savannah, hot desert into hot semi-arid, and subtropical highland into humid subtropical). Each dashed vertical line designates December of each year.

https://doi.org/10.7554/eLife.46202.019
Figure 6—figure supplement 1—source data 1

Predicted values for pneumonia and diarrhea mortality time series models by climate region; 2005–2013.

https://doi.org/10.7554/eLife.46202.020
Dot map of child (1 month – 14 years) and adult (30–59 years) pneumonia deaths captured within the Million Death Study between June and August, by Köppen-Geiger climate region.

The three climate regions shown include hot semi-arid (Bsh; including hot desert or Bwh), humid subtropical (Cwa; including subtropical highland or Cwb), and tropical savannah (Aw; including tropical monsoon or Am). The hot desert, subtropical highland, and tropical monsoon regions are incorporated into similar, adjacent climate regions given insufficient sample size to describe separately.

https://doi.org/10.7554/eLife.46202.022
Average seasonal pattern of deaths from diarrhea lacking fever and bloody stool by Köppen-Geiger climate region among children at ages 1 month to 14 years.

Counts of diarrhea with fever and bloody stool were too small to model. Each horizontal axis represents an average yearly span from April to April. We determined seasonal patterns using monthly counts of death and modeled using Poisson regression. Rate ratios (RR) were calculated within each disease and were compared to annual minimum mortality in the month of April. Given three regions also had a smaller sample size on which to model, they were bundled into regions with similar climatic characteristics (tropical monsoon into tropical savannah, hot desert into hot semi-arid, and subtropical highland into humid subtropical).

https://doi.org/10.7554/eLife.46202.023
Figure 8—source data 1

Monthly predicted values and rate ratios of the average annual pattern of child diarrhea mortality by climate region.

https://doi.org/10.7554/eLife.46202.024

Tables

Table 1
Study numbers and weighted percentages of pneumonia and diarrhea deaths recorded in the Million Death Study between 2005 and 2013, among Indian children aged 1 month to 14 years.
https://doi.org/10.7554/eLife.46202.002
Pneumonia1
n (%3)
Diarrhea2
n (%3)
Age
 1–11 Months4203 (57%)2291 (39%)
 1–4 Years2291 (34%)2297 (43%)
 5–14 Years640 (9%)1043 (18%)
Sex
 Female3645 (52%)3064 (55%)
 Male3489 (48%)2567 (45%)
Place of Death
 Home5093 (75%)4201 (77%)
 Health Facility1694 (20%)1112 (17%)
 Other287 (4%)264 (5%)
Type of Residence
 Rural6037 (84%)4766 (86%)
 Urban1097 (16%)865 (14%)
 Poorer States44352 (76%)3709 (81%)
 Richer States42782 (24%)1922 (19%)
Symptoms
 Fever5964 (84%)2842 (47%)
 Cough4482 (63%)827 (13%)
 Difficulty breathing5715 (81%)1876 (32%)
 Fast breathing54229 (61%)1085 (19%)
 Chest indrawing53623 (54%)728 (12%)
 Wheezing53773 (56%)901 (16%)
 Diarrhea (i.e. loose stools)1144 (17%)5122 (92%)
 Blood in stool695 (1%)787 (13%)
 Vomiting1913 (26%)3950 (71%)
 Abdominal pain1102 (15%)2291 (43%)
Treatment
 Antibiotics for breathing problems52946 (41%)722 (12%)
 ORS/other fluids6628 (9%)2963 (53%)
Both physicians agreed on initial assignment75431 (77%)4832 (86%)
Total deaths71345631
  1. 1We excluded 272 pneumonia deaths with a reported history of measles by the verbal autopsy respondent. 2We excluded 557 typhoid and paratyphoid fever deaths and 132 deaths with a reported history of measles by the verbal autopsy respondent. 3We calculated percentages usinga weighted proportion to account for differences in sampling probabilities. 4Poorer states include the Empowerment Action Group-Assam states of Assam, Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha (Orissa before 2011), Rajasthan, Uttarakhand, and Uttar Pradesh. Richer states include all other states and union territories. 5Only asked of those who reported difficulty breathing (n = 5715 pneumonia deaths, 1876 diarrhea deaths). 6Only asked of those who reported symptomatic diarrhea (i.e. loose stools; n = 1144 pneumonia deaths, 5122 diarrhea deaths). 7Physicians were blinded to each other’s diagnoses, and agreement is assessed prior to any adjudication or reconciliation of diagnoses.

Table 2
Study numbers and weighted percentages of pneumonia deaths recorded in the Million Death Study between 2005 and 2013, among Indian adults aged 15 to 69 years.
https://doi.org/10.7554/eLife.46202.003
Pneumonia1
n (%2)
Age
 15–29 Years1637 (27%)
 30–59 Years4155 (63%)
 60–69 Years695 (11%)
Sex
 Female3117 (48%)
 Male3370 (52%)
Place of Death
 Home5339 (85%)
 Health Facility935 (12%)
 Other90 (1%)
Type of Residence
 Rural5082 (75%)
 Urban1405 (25%)
 Poorer States32497 (52%)
 Richer States33990 (48%)
 Both physicians agreed on initial assignment42517 (38%)
Total deaths6487
  1. 1We defined pneumonia deaths using ICD-10 codes A37, H65-H68, H70, H71, J00-J22, J32, J36, J85, J86, P23, or U04. 2We calculated percentages using a weighted proportion to account for differences in sampling probabilities. 3Poorer states include the Empowerment Action Group-Assam states of Assam, Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha (Orissa before 2011), Rajasthan, Uttarakhand, and Uttar Pradesh. Richer states include all other states and union territories. 4Physicians were blinded to each other’s diagnoses, and agreement is assessed prior to any adjudication or reconciliation of diagnoses.

Table 3
Estimated deaths due to microbiologic etiologies and other syndromes of pneumonia and diarrhea in 2015 among Indian children aged 1–59 months.
https://doi.org/10.7554/eLife.46202.025
EtiologySource of estimationEtiologic fraction (%)
(uncertainty range)
Estimated deaths
(uncertainty range)
Streptococcus pneumoniae1Meta-analysis (clinical trials)38
(27–39)
40,600
(28,800–42,400)
Respiratory syncytial virus2Meta-analysis (prevalence surveys)19
(13–27)
20,700
(13,600–29,000)
Influenza2,3Meta-analysis (prevalence surveys)12
(7–19)
12,600
(7,200–20,600)
Haemophilus influenzae type b1Meta-analysis (clinical trials)7
(5–9)
7200
(5,000–9,400)
Humid subtropical summer excessSyndromic mortality estimate3
(no range)
3500
(no range)
Pneumonia total4108,000
Rotavirus5Meta-analysis (prevalence surveys)30
(21–40)
24,700
(17,200–32,800)
Diarrhea with fever and bloody stoolSyndromic mortality estimate8
(no range)
6200
(no range)
Diarrhea total482,000
  1. 1The proportion of pneumonia cases due to Streptococcus pneumoniae (pneumococcal) and Haemophilus influenzae type b (Hib) were determined using efficacy results from pneumococcal conjugate vaccine and Hib vaccine clinical trials. Proportions were then multiplied by all-cause pneumonia deaths among children aged 1–59 months. 2The proportion of pneumonia cases due to RSV and influenza was determined through a literature search of laboratory testing and surveillance studies containing etiologic fractions. Etiologic fractions were then meta-analyzed by region, and then multiplied by region-specific all-cause pneumonia deaths. 3Influenza includes influenza A (seasonal and pandemic) and influenza B. 4All-cause pneumonia and diarrhea deaths were previously reported in Fadel et al. (2017). Etiologic fractions between pathogens are not additive and do not have a sum of 100. 5The proportion of diarrhea cases due to rotavirus was determined through a literature search of laboratory testing and surveillance studies containing etiologic fractions. Etiologic fractions were then meta-analyzed by region, and then multiplied by region-specific all-cause diarrhea deaths.

Table 4
Age-specific mortality rates and etiologic fractions of microbiologic etiologies and other syndromes of pneumonia and diarrhea in 2015 among Indian children aged 1–59 months.
https://doi.org/10.7554/eLife.46202.026
RegionPneumonia mortality rate (per 1000 live births)1Etiologic fraction (%), (uncertainty range)2Diarrhea mortality rate (per 1000 live births)1Etiologic fraction (%), (uncertainty range)2
SPn3RSV4Influenza4Hib3Rotavirus4Diarrhea with fever and bloody stool3
Northeast9.5040 (28–41)11 (8–15)6 (<1–17)47 (5–9)6.9641 (33–49)12
Central5.3638 (27–40)22 (15–30)16 (11–21)9 (6–12)3.8022 (16–27)9
East4.4041 (29–43)12 (5–20)6 (<1–17)4 (3–6)4.3834 (19–52)5
North3.0142 (30–44)25 (20–30)9 (3–18)3 (2–4)2.5746 (37–56)8
West1.7740 (29–43)18 (16–21)9 (6–14)6 (4–8)1.1633 (25–41)7
South1.6341 (29–43)35 (23–47)13 (10–17)5 (3–6)0.8235 (30–41)4
India4.1738 (27–39)19 (13–27)12 (7–19)7 (5–9)3.1930 (21–40)8
EF Ratio (Highest: Lowest)1.113.182.673.002.093.00
Pearson (vs. pneumonia)−0.36−0.660.200.46Pearson (vs. diarrhea)0.120.73
  1. 11–59-month mortality rates were derived using estimates from Fadel et al. (2017). 2Etiologic fractions between pathogens are not additive and do not have a sum of 100. 3Etiologic fractions of Streptococcus pneumoniae (SPn), Haemophilus influenzae type b (Hib), and diarrhea with fever and bloody were calculated by dividing the estimated number of deaths by pathogen and administrative region by the total number of pneumonia or diarrhea deaths by administrative region. 4Etiologic fractions for respiratory syncytial virus (RSV), influenza, and rotavirus were determined with a meta-analysis of laboratory testing data by region. 5Given that no studies were identified describing influenza positivity in the Northeast region, the etiologic fraction for the East region (closest geographically) is substituted instead.

Table 4—source data 1

Regional meta-analysis of etiologic fractions for respiratory syncytial virus.

Studies reporting laboratory testing data were identified through a literature search of Ovid MEDLINE, Scopus, and Google Scholar. All studies described hospitalized children or children seeking care in emergency departments and reported data from 2005 onwards. Studies were meta-analyzed by administrative region using Stata’s metaprop package and visualized in RStudio. We weighted each study using the denominator of total number of laboratory tests in the respective study.

https://doi.org/10.7554/eLife.46202.027
Table 4—source data 2

Regional meta-analysis of etiologic fractions for influenza.

Studies reporting laboratory testing data were identified through a literature search of Ovid MEDLINE, Scopus, and Google Scholar. All studies described hospitalized children or children seeking care in emergency departments and reported data from 2010 onwards. All studies included describe pandemic and seasonal influenza A and influenza B. Studies were meta-analyzed by administrative region using Stata’s metaprop package and visualized in RStudio. We weighted each study using the denominator of total number of laboratory tests in the respective study.

https://doi.org/10.7554/eLife.46202.028
Table 4—source data 3

Regional meta-analysis of etiologic fractions for rotavirus.

Studies reporting laboratory testing data were identified through a literature search of Ovid MEDLINE, Scopus, and Google Scholar. All studies described hospitalized children or children seeking care in emergency departments and reported data from 2010 onwards. Studies were meta-analyzed by administrative region using Stata’s metaprop package and visualized in RStudio. We weighted each study using the denominator of total number of laboratory tests in the respective study.

https://doi.org/10.7554/eLife.46202.029

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.

Additional files

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)

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

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