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Opportunities and Challenges in Public Health Data Collection in Southern Asia: Examples from Western India and Kathmandu Valley, Nepal

Author

Listed:
  • Amruta Nori-Sarma

    (School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA)

  • Anobha Gurung

    (Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX 78712, USA
    International Centre for Mountain Development, Kathmandu 44700, Nepal)

  • Gulrez Shah Azhar

    (Pardee RAND Graduate School, Santa Monica, CA 90401, USA)

  • Ajit Rajiva

    (School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA)

  • Dileep Mavalankar

    (Public Health Foundation of India, Indian Institute of Public Health—Gandhinagar, Gandhinagar, Gujarat 382042, India)

  • Perry Sheffield

    (Department of Environmental Medicine and Public Health and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA)

  • Michelle L. Bell

    (School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA)

Abstract

Small-scale local data resources may serve to provide a highly resolved estimate of health effects, which can be spatially heterogeneous in highly populated urban centers in developing countries. We aim to highlight the challenges and opportunities of health data registries in a developing world context. In western India, government-collected daily mortality registry data were obtained from five cities, along with daily hospital admissions data from three government hospitals in Ahmedabad. In Nepal, individual-level data on hospital admissions were collected from six major hospitals in Kathmandu Valley. Our process illustrates many challenges for researchers, governments, and record keepers inherent to data collection in developing countries: creating and maintaining a centralized record-keeping system; standardizing the data collected; obtaining data from some local agencies; assuring data completeness and availability of back-ups to the datasets; as well as translating, cleaning, and comparing data within and across localities. We suggest that these “small-data” resources may better serve the analysis of health outcomes than exposure-response functions extrapolated from data collected in other areas of the world.

Suggested Citation

  • Amruta Nori-Sarma & Anobha Gurung & Gulrez Shah Azhar & Ajit Rajiva & Dileep Mavalankar & Perry Sheffield & Michelle L. Bell, 2017. "Opportunities and Challenges in Public Health Data Collection in Southern Asia: Examples from Western India and Kathmandu Valley, Nepal," Sustainability, MDPI, vol. 9(7), pages 1-9, June.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1106-:d:102678
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    References listed on IDEAS

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    1. Prabhat Jha, 2006. "Prospective Study of One Million Deaths in India: Rationale, Design, and Validation Results," Working Papers id:669, eSocialSciences.
    2. Simina M. Boca & Héctor Céorrada Bravo & Brian Caffo & Jeffrey T. Leek & Giovanni Parmigiani, 2013. "A Decision-Theory Approach to Interpretable Set Analysis for High-Dimensional Data," Biometrics, The International Biometric Society, vol. 69(3), pages 614-623, September.
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    Cited by:

    1. Amruta Nori-Sarma & Tarik Benmarhnia & Ajit Rajiva & Gulrez Shah Azhar & Prakash Gupta & Mangesh S. Pednekar & Michelle L. Bell, 2019. "Advancing our Understanding of Heat Wave Criteria and Associated Health Impacts to Improve Heat Wave Alerts in Developing Country Settings," IJERPH, MDPI, vol. 16(12), pages 1-13, June.
    2. Matthew C. LaFevor & Alexandra G. Ponette-González & Rebecca Larson & Leah M. Mungai, 2021. "Spatial Targeting of Agricultural Support Measures: Indicator-Based Assessment of Coverages and Leakages," Land, MDPI, vol. 10(7), pages 1-17, July.

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