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

Demography volume 49, pages 857–865 (2012)Cite this article

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.

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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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Keywords

  • Influenza
  • Pandemic
  • India
  • Mortality
  • Population loss