Towards good practice for health statistics: lessons from the Millennium Development Goal health indicators
Open accessAbstract
Health statistics are at the centre of an increasing number of worldwide health controversies. Several factors are sharpening the tension between the supply and demand for high quality health information, and the health-related Millennium Development Goals (MDGs) provide a high-profile example. With thousands of indicators recommended but few measured well, the worldwide health community needs to focus its efforts on improving measurement of a small set of priority areas. Priority indicators should be selected on the basis of public-health significance and several dimensions of measurability. Health statistics can be divided into three types: crude, corrected, and predicted. Health statistics are necessary inputs to planning and strategic decision making, programme implementation, monitoring progress towards targets, and assessment of what works and what does not. Crude statistics that are biased have no role in any of these steps; corrected statistics are preferred. For strategic decision making, when corrected statistics are unavailable, predicted statistics can play an important part. For monitoring progress towards agreed targets and assessment of what works and what does not, however, predicted statistics should not be used. Perhaps the most effective method to decrease controversy over health statistics and to encourage better primary data collection and the development of better analytical methods is a strong commitment to provision of an explicit data audit trail. This initiative would make available the primary data, all post-data collection adjustments, models including covariates used for farcasting and forecasting, and necessary documentation to the public.
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Accurate interpretation and responsible use of health estimates requires understanding of the input data on which estimates were based, including their quality, and of the methods used to derive the estimates from the input data.
“Accurate interpretation and responsible use of health estimates requires understanding of the input data on which estimates were based, including their quality, and of the methods used to derive the estimates from the input data.”
From Guidelines for accurate and transparent health estimates reporting: the GATHER statement (2016) · cites this paper as [6]
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- Authors
- Murray, C. J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Murray, Christopher JL
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2007-03
- Volume
- 369
- Issue
- 9564
- Pages
- 862-873
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- PMC
- PMC7137868
- OpenAlex
- W2066141375
- Cited by
- 144
- References
- 21
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 5 · 2023: 6 · 2022: 6 · 2021: 5 · 2020: 4 · 2019: 3 · 2018: 4 · 2017: 10 · 2016: 6 · 2015: 11 · 2014: 7 · 2013: 3 · 2012: 6
- FWCI
- 56.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- The Lancet (publishedVersion, public-domain)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Human Rights and Development
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Health Professions › General Health Professions › Health and Medical Studies
- Keywords
- health statistics, Millennium Development Goals, health indicators, indicator measurement, primary data collection, forecasting, health monitoring, data quality
- MeSH terms
- Data Collection, Health Priorities, Health Status Indicators, Humans, Organizational Objectives, Social Change, Statistics as Topic, Global Health, Benchmarking
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