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Population-based health indexes: a systematic review
Eva Kaltenthaler1, Ravi Maheswaran, Catherine Beverley
1School of Health and Related Research (ScHARR), University of Sheffield, 30 Regent Street, Sheffield S1 4DA, UK. e.kaltenthaler@sheffield.ac.uk
Health Policy (Amsterdam, Netherlands)
|April 6, 2004
Summary
This systematic review examines population-based health indexes, finding wide variation in their design, indicators, and methods. Further research is needed to optimize these tools for community health monitoring.
Area of Science:
- Public Health
- Health Services Research
- Biostatistics
Background:
- Population-based health indexes are crucial for monitoring community health.
- Existing indexes exhibit significant heterogeneity in their development and application.
- Standardization is lacking in the selection and combination of health indicators.
Purpose of the Study:
- To systematically review the literature on the development and use of population-based health indexes.
- To identify variations in index purpose, geographical scope, indicator selection, weighting, data sources, and validation.
- To highlight areas requiring further research for improved health index construction.
Main Methods:
- Systematic literature review including studies from the UK, Europe, and North America.
- Inclusion criteria: population-based health indexes with at least two health indicators.
- Analysis of 17 individual studies and one review focusing on index development and application.
Main Results:
- Indexes were developed for diverse purposes, with geographical aggregation ranging from ward to national levels.
- Considerable variation observed in chosen health indicators and methods for combining them.
- Most studies used routine data sources; only four addressed index validity, and none justified weighting methods.
Conclusions:
- Population-based health indexes offer potential for community health monitoring.
- Significant heterogeneity in current index construction necessitates further research.
- Key areas for future research include optimal indicator selection and robust combination methodologies.