title: Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries.
abstract: BACKGROUND
  Contraceptive prevalence rate (CPR) is a vital indicator used by country governments, international donors, and other stakeholders for measuring progress in family planning programs against country targets and global initiatives as well as for estimating health outcomes. Because of the need for more frequent CPR estimates than population-based surveys currently provide, alternative approaches for estimating CPRs are being explored, including using contraceptive logistics data.
  
  
  METHODS
  Using data from the Demographic and Health Surveys (DHS) in 30 countries, population data from the United States Census Bureau International Database, and logistics data from the Procurement Planning and Monitoring Report (PPMR) and the Pipeline Monitoring and Procurement Planning System (PipeLine), we developed and evaluated 3 models to generate country-level, public-sector contraceptive prevalence estimates for injectable contraceptives, oral contraceptives, and male condoms. Models included: direct estimation through existing couple-years of protection (CYP) conversion factors, bivariate linear regression, and multivariate linear regression. Model evaluation consisted of comparing the referent DHS prevalence rates for each short-acting method with the model-generated prevalence rate using multiple metrics, including mean absolute error and proportion of countries where the modeled prevalence rate for each method was within 1, 2, or 5 percentage points of the DHS referent value.
  
  
  RESULTS
  For the methods studied, family planning use estimates from public-sector logistics data were correlated with those from the DHS, validating the quality and accuracy of current public-sector logistics data. Logistics data for oral and injectable contraceptives were significantly associated (P<.05) with the referent DHS values for both bivariate and multivariate models. For condoms, however, that association was only significant for the bivariate model. With the exception of the CYP-based model for condoms, models were able to estimate public-sector prevalence rates for each short-acting method to within 2 percentage points in at least 85% of countries.
  
  
  CONCLUSIONS
  Public-sector contraceptive logistics data are strongly correlated with public-sector prevalence rates for short-acting methods, demonstrating the quality of current logistics data and their ability to provide relatively accurate prevalence estimates. The models provide a starting point for generating interim estimates of contraceptive use when timely survey data are unavailable. All models except the condoms CYP model performed well; the regression models were most accurate but the CYP model offers the simplest calculation method. Future work extending the research to other modern methods, relating subnational logistics data with prevalence rates, and tracking that relationship over time is needed.
authors.affiliation: John Snow, Inc., Arlington, VA, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: Cunningham
authors.affiliation: John Snow, Inc., Arlington, VA, USA abock@jsi.com.
authors.forename: Ariella
authors.initials: A
authors.lastname: Bock
authors.affiliation: University of Southern California, Los Angeles, CA, USA.
authors.forename: Niquelle
authors.initials: N
authors.lastname: Brown
authors.affiliation: John Snow, Inc., Arlington, VA, USA.
authors.forename: Suzy
authors.initials: S
authors.lastname: Sacher
authors.affiliation: JSI Research & Training Institute, Inc., Arlington, VA, USA.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Hatch
authors.affiliation: John Snow, Inc., Arlington, VA, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Inglis
authors.affiliation: John Snow, Inc., Arlington, VA, USA.
authors.forename: Dana
authors.initials: D
authors.lastname: Aronovich
mesh_terms: D000293:Adolescent; D000328:Adult; D003267:Contraception / Q000706:statistics & numerical data*; D003268:Contraception Behavior / Q000706:statistics & numerical data*; D003270:Contraceptive Agents; D003906:Developing Countries / Q000706:statistics & numerical data; D005193:Family Planning Services / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D038622:Internationality; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D055815:Young Adult
chemical_list: D003270:Contraceptive Agents