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Measuring the Performance of Vaccination Programs Using Cross-Sectional Surveys: A Likelihood Framework and Retrospective Analysis


Background:
The performance of routine and supplemental immunization activities is usually measured by the administrative method: dividing the number of doses distributed by the size of the target population. This method leads to coverage estimates that are sometimes impossible (e.g., vaccination of 102% of the target population), and are generally inconsistent with the proportion found to be vaccinated in Demographic and Health Surveys (DHS). We describe a method that estimates the fraction of the population accessible to vaccination activities, as well as within-campaign inefficiencies, thus providing a consistent estimate of vaccination coverage.

Methods and Findings:
We developed a likelihood framework for estimating the effective coverage of vaccination programs using cross-sectional surveys of vaccine coverage combined with administrative data. We applied our method to measles vaccination in three African countries: Ghana, Madagascar, and Sierra Leone, using data from each country's most recent DHS survey and administrative coverage data reported to the World Health Organization. We estimate that 93% (95% CI: 91, 94) of the population in Ghana was ever covered by any measles vaccination activity, 77% (95% CI: 78, 81) in Madagascar, and 69% (95% CI: 67, 70) in Sierra Leone. “Within-activity” inefficiencies were estimated to be low in Ghana, and higher in Sierra Leone and Madagascar. Our model successfully fits age-specific vaccination coverage levels seen in DHS data, which differ markedly from those predicted by naïve extrapolation from country-reported and World Health Organization–adjusted vaccination coverage.

Conclusions:
Combining administrative data with survey data substantially improves estimates of vaccination coverage. Estimates of the inefficiency of past vaccination activities and the proportion not covered by any activity allow us to more accurately predict the results of future activities and provide insight into the ways in which vaccination programs are failing to meet their goals.

: Please see later in the article for the Editors' Summary


Vyšlo v časopise: Measuring the Performance of Vaccination Programs Using Cross-Sectional Surveys: A Likelihood Framework and Retrospective Analysis. PLoS Med 8(10): e32767. doi:10.1371/journal.pmed.1001110
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1001110

Souhrn

Background:
The performance of routine and supplemental immunization activities is usually measured by the administrative method: dividing the number of doses distributed by the size of the target population. This method leads to coverage estimates that are sometimes impossible (e.g., vaccination of 102% of the target population), and are generally inconsistent with the proportion found to be vaccinated in Demographic and Health Surveys (DHS). We describe a method that estimates the fraction of the population accessible to vaccination activities, as well as within-campaign inefficiencies, thus providing a consistent estimate of vaccination coverage.

Methods and Findings:
We developed a likelihood framework for estimating the effective coverage of vaccination programs using cross-sectional surveys of vaccine coverage combined with administrative data. We applied our method to measles vaccination in three African countries: Ghana, Madagascar, and Sierra Leone, using data from each country's most recent DHS survey and administrative coverage data reported to the World Health Organization. We estimate that 93% (95% CI: 91, 94) of the population in Ghana was ever covered by any measles vaccination activity, 77% (95% CI: 78, 81) in Madagascar, and 69% (95% CI: 67, 70) in Sierra Leone. “Within-activity” inefficiencies were estimated to be low in Ghana, and higher in Sierra Leone and Madagascar. Our model successfully fits age-specific vaccination coverage levels seen in DHS data, which differ markedly from those predicted by naïve extrapolation from country-reported and World Health Organization–adjusted vaccination coverage.

Conclusions:
Combining administrative data with survey data substantially improves estimates of vaccination coverage. Estimates of the inefficiency of past vaccination activities and the proportion not covered by any activity allow us to more accurately predict the results of future activities and provide insight into the ways in which vaccination programs are failing to meet their goals.

: Please see later in the article for the Editors' Summary


Zdroje

1. BurtonAMonaschRLautenbachBGacic-DoboMNeillM 2009 WHO and UNICEF estimates of national infant immunization coverage: methods and processes. Bull World Health Organ 87 535 541

2. MurrayCJLShengeliaBGuptaNMoussaviSTandonA 2003 Validity of reported vaccination coverage in 45 countries. Lancet 362 1022 1027

3. BrownJMonaschRBicegoGBurtonABoermaJT 2002 Assessment of the quality of estimates of child immunization coverage from population-based surveys: MEASURE Evaluation Working Paper series Chapel Hill (North Carolina) University of North Carolina at Chapel Hill

4. MunthaliAC 2007 Determinants of vaccination coverage in Malawi: evidence from the demographic and health surveys. Malawi Med J 19 79 82

5. WallingaJHeijneJCMKretzschmarM 2005 A measles epidemic threshold in a highly vaccinated population. PLoS Medicine 2 e316 doi:10.1371/journal.pmed.0020316

6. GraisRFDubrayCGerstlSGuthmannJPDjiboA 2007 Unacceptably high mortality related to measles epidemics in Niger, Nigeria, and Chad. PLoS Med 4 e16 doi:10.1371/journal.pmed.0040016

7. McBeanAMFosterSOHerrmannKLGateffC 1976 Evaluation of a mass measles immunization campaign in Yaoundé Cameroun. Trans R Soc Trop Med Hyg 70 206 212

8. World Health Organization 2010 WHO-UNICEF estimates of MCV coverage Geneva World Health Organization Available: http://apps.who.int/immunization_monitoring/en/globalsummary/timeseries/tswucoveragemcv.htm. Accessed 15 September 2010

9. Ghana Statistical Service, Ghana Health Service, ICF Macro 2009 Ghana demographic and health survey 2008 Accra (Ghana) Ghana Statistical Service

10. Institut National de la Statistique, ICF Macro 2010 Enquête démographique et de santé de Madagascar 2008–2009 Antananarivo (Madagascar) Institut National de la Statistique

11. Statistics Sierra Leone, Ministry of Health and Sanitation, ICF Macro 2009 Sierra Leone demographic and health survey 2008 Freetown (Sierra Leone) Statistics Sierra Leone

12. GelmanARubinDB 1992 Inference from iterative simulation using multiple sequences. Stat Sci 7 457 472

13. World Health Organization 2003 The immunization data quality audit (DQA) procedure. WHO/V&B/03.19 Geneva World Health Organization

14. RonveauxORickertDHadlerSGroomHLloydJ 2005 The immunization data quality audit: verifying the quality and consistency of immunization monitoring systems. Bull World Health Organ 83 504 510

15. LesslerJMossWJLowtherSACummingsDAT 2010 Maintaining high rates of measles immunization in Africa. Epidemiol and Infect E-pub ahead of print. doi: 10.1017/S0950268810002232

16. United Nations Economic Commission for Africa 2007 Reference regional strategic framework for statistical capacity building in Africa: baseline information—data development [database]. Available: http://www.uneca.org/eca_programmes/policy_analysis/rrsf/Baseline_datadev.htm. Accessed 4 November 2010

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Interné lekárstvo

Článok vyšiel v časopise

PLOS Medicine


2011 Číslo 10
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