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Mortality From the Influenza Pandemic of 1918–1919: The Case of India

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Demography

Abstract

Estimates of worldwide mortality from the influenza pandemic of 1918–1919 vary widely, from 15 million to 100 million. In terms of loss of life, India was the focal point of this profound demographic event. In this article, we calculate mortality from the influenza pandemic in India using panel data models and data from the Census of India. The new estimates suggest that for the districts included in the sample, mortality was at most 13.88 million, compared with 17.21 million when calculated using the assumptions of Davis (1951). We conclude that Davis’ influential estimate of mortality from influenza in British India is overstated by at least 24%. Future analyses of the effects of the pandemic on demographic change in India and worldwide will need to account for this significant downward revision.

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Acknowledgments

This research was made possible by Grant No. 1R21DA025917-01A1 from the National Institute on Drug Abuse (NIDA) of the National Institutes of Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIDA. The authors would also like to thank participants of the XXXIII Annual Conference of the Indian Association for the Study of Population (IASP) held in Lucknow, India, in 2011, for their input.

Author information

Authors and Affiliations

  1. Asian Studies Center, 301 International Center, Michigan State University, East Lansing, MI, 48824, USA

    Siddharth Chandra

  2. Department of Psychology, Michigan State University, East Lansing, MI, 48824, USA

    Goran Kuljanin

  3. Department of Sociology, Michigan State University, East Lansing, MI, 48824, USA

    Jennifer Wray

Authors
  1. Siddharth Chandra
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  2. Goran Kuljanin
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  3. Jennifer Wray
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Corresponding author

Correspondence to Siddharth Chandra.

Appendices

Appendix 1: List of Districts Used in the Analysis (colonial spellings)

Name

Name

Name

Name

Name

Name

Agra

Budaun

Garhwal

Kanara

Muzaffarpur

Satara

Ahmedabad

Bulandshahr

Garo Hills

Kangra

Mymensingh

Saugor

Ahmednagar

Buldana

Gaya

Karachi

Nadia

Shahabad

Ajmer Merwara

Burdwan

Ghazipur

Karnal

Nagpur

Shahjahanpur

Akola

Cachar

Goalpara

Kheri

NainiTal

Shahpur

Aligarh

Calcutta

Godavari East

Khulna

Nasik

Sheikhupura

Allahabad

Cawnpore

Godavari West

Kistna

Nellore

Sholapur

Almora

Champaran

Gonda

Kolaba

Nilgiris

Sialkot

Ambala

Chanda

Gorakhpur

Koraput

Nimar

Sibsagar

Amraoti

Chhindwara

Gujranwala

Kurnool

Noakhali

Singhbhum

Amritsar

Chingleput

Gujrat

Lahore

North Arcot

Sitapur

Anantapur

Chittagong

Guntur

Lakhimpur

Nowgong

SouthArcot

Attock

Chittoor

Gurdaspur

Larkana

Pabna

South Kanara

Azamgarh

Coimbatore

Gurgaon

Lucknow

Palamau

Sukkur

Bahraich

Cuddapah

Hamirpur

Ludhiana

24 Parganas

Sultanpur

Bakarganj

Cuttack

Hardoi

Madras

Partabgarh

Surat

Balaghat

Dacca

Hazaribagh

Madura

Pilibhit

Sylhet

Balasore

Darbhanga

Hissar

Mainpuri

Poona

Tanjore

Ballia

Darjeeling

Hooghly

Malabar

Puri

Thana

Banda

Darrang

Hoshangabad

Malda

Purnea

TharParkar

Bankura

Dehra Dun

Hoshiarpur

Manbhum

Rae Bareli

Tinnevelly

BaraBanki

Dera Ghazi Khan

Howrah

Mandla

Raipur

Tippera

Bareilly

Dharwar

Hyderabad

Meerut

Rajshahi

Trichinopoly

Basti

Dinajpur

Jalaun

Mianwali

Ramnad

Unao

Belgaum

Drug

Jalpaiguri

Midnapore

Ranchi

Upper Sind Frontier

Bellary

East Khandesh

Jaunpur

Mirzapur

Rangpur

Vizagapatam

Betul

Etah

Jessore

Monghyr

Ratnagiri

Wardha

Bhagalpur

Etawah

Jhang

Montgomery

Rawalpindi

West Khandesh

Bhandara

Faridpur

Jhansi

Moradabad

Rohtak

Yeotmal

Bijapur

Farrukhabad

Jhelum

Multan

Saharanpur

 

Bijnor

Fatehpur

Jubbulpore

Murshidabad

Salem

 

Birbhum

Ferozepore

Jullundur

Muttra

Sambalpur

 

Bogra

Fyzabad

Kaira

Muzaffargarh

Santal Parganas

 

Broach and Panch Mahals

Ganjam

Kamrup

Muzaffarnagar

Saran

 

Appendix 2: Details of Random-Coefficients Models

As discussed previously, the general model estimated is

$$ LPO{{P}_{{it}}} = {{\pi }_{{0i}}} + {{\pi }_{{1i}}}{{T}_{t}} + {{\pi }_{{2i}}}FL{{U}_{t}} + {{\pi }_{{3i}}}{{T}_{t}}FL{{U}_{t}} + {{\varepsilon }_{{it}}}, $$

where i and t index districts and time in years. The coefficient estimates π 0i , π 1i , π 2i , and π 3i are defined as

$$ \matrix{ {{\pi_{{0i}}} = {\gamma_{{00}}} + {\zeta_{{0i}}}} \hfill \\ {{\pi_{{1i}}} = {\gamma_{{10}}} + {\zeta_{{1i}}}} \hfill \\ {{\pi_{{2i}}} = {\gamma_{{20}}} + {\zeta_{{2i}}}} \hfill \\ {{\pi_{{3i}}} = {\gamma_{{30}}} + {\zeta_{{3i}}}} \hfill , \\ }<!end array> $$

where it is assumed that

$$ {\varepsilon_{{ij}}}\sim N(0,\sigma_{\varepsilon }^2) $$

and

$$ \left[ {\matrix{ {{\zeta_{{0i}}}} \hfill \\ {{\zeta_{{1i}}}} \hfill \\ {{\zeta_{{2i}}}} \hfill \\ {{\zeta_{{3i}}}} \hfill \\ }<!end array> } \right] \sim N\left( {\left[ {\matrix{ 0 \hfill \\ 0 \hfill \\ 0 \hfill \\ 0 \hfill \\ }<!end array> } \right],\left[ {\matrix{ {\sigma_0^2} \hfill &{{\sigma_{{01}}}} \hfill &{{\sigma_{{02}}}} \hfill &{{\sigma_{{03}}}} \hfill \\ {{\sigma_{{10}}}} \hfill &{\sigma_1^2} \hfill &{{\sigma_{{12}}}} \hfill &{{\sigma_{{13}}}} \hfill \\ {{\sigma_{{20}}}} \hfill &{{\sigma_{{21}}}} \hfill &{\sigma_2^2} \hfill &{{\sigma_{{23}}}} \hfill \\ {{\sigma_{{30}}}} \hfill &{{\sigma_{{31}}}} \hfill &{{\sigma_{{32}}}} \hfill &{\sigma_3^2} \hfill \\ }<!end array> } \right]} \right). $$

The coefficients are modeled as varying randomly across districts, and the estimates reported in Table 1 are the mean coefficients across all districts. Details of these models are provided in SAS (2011b).

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Chandra, S., Kuljanin, G. & Wray, J. Mortality From the Influenza Pandemic of 1918–1919: The Case of India. Demography 49, 857–865 (2012). https://doi.org/10.1007/s13524-012-0116-x

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