Demographic and Health Survey Analysis
Also known as: DHS, Demographic and Health Survey, DHS Program survey, Standard DHS
The Demographic and Health Surveys (DHS) are nationally representative household surveys that provide standardised, internationally comparable data on population, health, and nutrition in low- and middle-income countries. Funded primarily by USAID and implemented through The DHS Program, they use model questionnaires, a complex multi-stage sample design, and a standardised wealth index to produce indicators of fertility, child and maternal mortality, family planning, child nutrition, and disease prevalence that drive health policy and programme monitoring worldwide.
Key highlights
- Standardised model questionnaires and methods yield indicators that are directly comparable across countries and over several decades.
- Birth and pregnancy histories allow estimation of fertility and child mortality without a functioning vital registration system.
- The asset-based wealth index provides a robust welfare proxy where reliable income or consumption data are unavailable.
- Publicly available, well-documented microdata and biomarkers support a vast secondary-research and equity-analysis literature.
Intuition
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How it works
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When to use it
Use DHS data and methods when you need nationally representative, internationally comparable indicators of fertility, mortality, family planning, maternal and child health, and nutrition, especially in settings lacking complete vital registration. They are ideal for monitoring health trends, benchmarking countries, and analysing socio-economic and geographic inequalities in health. They are less suited to measuring rare events in small subpopulations, to fine-grained small-area estimates, or to detecting short-term programme effects, since surveys run only every few years and cross-sectional designs limit causal inference.
Strengths & limitations
- Standardised model questionnaires and methods yield indicators that are directly comparable across countries and over several decades.
- Birth and pregnancy histories allow estimation of fertility and child mortality without a functioning vital registration system.
- The asset-based wealth index provides a robust welfare proxy where reliable income or consumption data are unavailable.
- Publicly available, well-documented microdata and biomarkers support a vast secondary-research and equity-analysis literature.
- Cross-sectional design limits causal inference and cannot directly attribute changes to specific programmes.
- Retrospective birth and mortality histories are subject to recall error, age heaping, and the omission or displacement of births and deaths.
- Sample sizes support national and regional estimates but not reliable district or small-area figures without modelling.
- Surveys are conducted only every few years, so they cannot track rapid or short-term changes in health indicators.
Common pitfalls
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Applications
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Frequently asked
How does the DHS build its wealth index?
The wealth index, formalised by Rutstein and Johnson (2004), is constructed from household ownership of durable assets and dwelling characteristics (e.g., radio, television, bicycle, type of water source, sanitation, floor material). Principal component analysis assigns weights to these variables, the first principal component gives each household a score, and households are ranked and divided into wealth quintiles. It is a relative measure of standing within that survey, not an absolute poverty line, and is used mainly to study equity gradients in health.
Why must DHS analysis account for the complex sample design?
DHS samples are stratified, clustered, and weighted, not simple random samples. Ignoring this produces biased point estimates (if weights are omitted) and understated standard errors and confidence intervals (if clustering and stratification are ignored), because households in the same cluster are correlated. Correct analysis applies the appropriate sampling weight and declares the survey design so that variance estimation reflects strata, clusters (PSUs), and the design effect.
How are child mortality rates estimated from a DHS?
Each interviewed woman gives a complete birth history listing every live birth, its date, survival status, and age at death if deceased. From these histories, the programme computes neonatal, infant, and under-five mortality for recent calendar periods using a synthetic-cohort life-table approach that combines age-specific mortality probabilities. This reconstructs mortality levels and trends for the years preceding the survey without relying on civil registration.
Sources
- 1.Croft, T. N., Marshall, A. M. J., Allen, C. K., et al. (2018). Guide to DHS Statistics: DHS-7. Rockville, MD: ICF, The DHS Program.
- 2.Rutstein, S. O., & Johnson, K. (2004). The DHS Wealth Index. DHS Comparative Reports No. 6. Calverton, MD: ORC Macro.
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Cite this page
ScholarGate. (2026, June 22). Demographic and Health Survey Analysis. ScholarGate. https://scholargate.app/development-studies/demographic-health-survey-analysis