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ORIGINAL ARTICLE
Open Access

Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries

Marc Cunningham, Ariella Bock, Niquelle Brown, Suzy Sacher, Benjamin Hatch, Andrew Inglis and Dana Aronovich
Global Health: Science and Practice September 2015, 3(3):462-481; https://doi.org/10.9745/GHSP-D-15-00116
Marc Cunningham
aJohn Snow, Inc., Arlington, VA, USA
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Ariella Bock
aJohn Snow, Inc., Arlington, VA, USA
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  • For correspondence: abock{at}jsi.com
Niquelle Brown
bUniversity of Southern California, Los Angeles, CA, USA
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Suzy Sacher
aJohn Snow, Inc., Arlington, VA, USA
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Benjamin Hatch
cJSI Research & Training Institute, Inc., Arlington, VA, USA
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Andrew Inglis
aJohn Snow, Inc., Arlington, VA, USA
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Dana Aronovich
aJohn Snow, Inc., Arlington, VA, USA
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Figures & Tables

Figures

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  • Additional Files
  • FIGURE 1
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    FIGURE 1

    Public‐Sector Injectables Prevalence Rate Estimates

  • FIGURE 2
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    FIGURE 2

    Public‐Sector Oral Contraceptive Prevalence Rate Estimates

  • FIGURE 3
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    FIGURE 3

    Public‐Sector Male Condom Prevalence Rate Estimates

  • FIGURE 4
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    FIGURE 4

    CPR Estimates for Public‐Sector Short‐Acting Methods

  • APPENDIX FIGURE 1.
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    APPENDIX FIGURE 1.

    Difference Between Model‐Generated and Referent DHS Public Injectables Prevalence Rate

  • APPENDIX FIGURE 2.
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    APPENDIX FIGURE 2.

    Difference Between Model‐Generated and Referent DHS Public Oral Contraceptives Prevalence Rate

  • APPENDIX FIGURE 3.
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    APPENDIX FIGURE 3.

    Difference Between Model‐Generated and Referent DHS Public Condoms Prevalence Rate

Tables

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    TABLE 1 Countries Included in the Analysis, Data Sources, and Periods of Analysis
    CountryCountry CodeDHS Collection DatesPrevious DHSContraceptive Logistics Data SourceLogistics Data Dates
    BangladeshBD7/2011–12/20112007PPMR6/2011–12/2011
    BoliviaBO8/2003–1/20041998PipeLine7/2003–2/2004
    Burkina FasoBF5/2010–12/20102003PipeLine4/2010–1/2011
    CameroonCM2/2004–9/20041998PipeLine1/2004–10/2004
    Côte d'IvoireCI12/2011–5/20121998–99PPMR11/2011–6/2012
    EthiopiaET4/2011–9/20112005PPMR1/2011–1/2012
    GhanaGH9/2008–11/20082003PipeLine8/2008–12/2008
    GuineaGN2/2005–6/20051999PipeLine1/2005–7/2005
    HaitiHT1/2012–6/20122007PPMR3/2011–9/2012
    HondurasHN10/2005–5/2006‐‐PipeLine9/2005–6/2006
    JordanJO7/2002–10/20021997PipeLine6/2002–11/2002
    KenyaKE11/2008–3/20092003PPMR10/2008–5/2009
    LiberiaLR12/2006–4/2007‐‐PipeLine11/2006–5/2007
    MadagascarMD11/2008–7/20092005PipeLine10/2008–8/2009
    MalawiMW6/2010–10/20102006PPMR5/2010–11/2010
    MaliML4/2006–12/20062001PipeLine3/2006–12/2006
    MozambiqueMZ5/2011–12/20112005PPMR4/2011–6/2011
    NepalNP1/2011–6/20112006PPMR1/2011–7/2011
    NicaraguaNC9/2001–12/20011998–99PipeLine8/2001–1/2002
    NigerNI2/2012–7/20122006PPMR6/2013
    NigeriaNG6/2008–11/20082003PipeLine7/2008–12/2008
    PakistanPK10/2012–4/20132006–07PPMR12/2012–3/2012
    PhilippinesPH6/2003–9/20031998PipeLine2/2003–11/2003
    RwandaRW9/2010–4/20112009PPMR6/2010–5/2011
    SenegalSN10/2010–5/20112005PPMR7/2010–7/2011
    TanzaniaTZ12/2009–5/20102006PPMR9/2009–6/2010
    TogoTG2/1998–5/19981988PipeLine1/1998–6/1998
    UgandaUG6/2011–12/20112006PPMR1/2011–12/2011
    ZambiaZM4/2007–10/20072003PipeLine3/2007–11/2007
    ZimbabweZW9/2010–3/20112007PPMR6/2010–6/2011
    • Abbreviations: DHS, Demographic and Health Surveys; PipeLine, Pipeline Monitoring and Procurement Planning System; PPMR, Procurement Planning and Monitoring Report.

    • View popup
    TABLE 2 Modern Contraceptive Prevalence Rate (mCPR), Prevalence Rates of Short‐Acting Methods, and Public‐Sector Market Share, by Country, From DHS
    Prevalence (%)Public‐Sector Prevalence (%)Public‐Sector Market Share (%)
    CountrymCPR (%)OCICMCOCICMCOCICMC
    Bangladesh52.127.211.25.512.27.40.945.066.516.8
    Bolivia23.72.55.33.10.84.00.231.574.57.5
    Burkina Faso14.32.85.13.12.35.00.383.497.38.9
    Cameroon13.51.31.19.70.60.80.649.375.06.4
    Côte d'Ivoire13.96.11.95.01.41.70.223.489.24.2
    Ethiopia18.71.514.00.31.012.10.067.386.311.7
    Ghana13.53.64.23.60.53.70.112.887.02.7
    Guinea6.81.61.12.50.70.90.242.086.27.5
    Haiti21.61.711.75.80.45.50.722.446.611.2
    Honduras37.77.18.62.32.06.20.628.472.224.2
    Jordan41.27.50.93.42.70.41.336.546.737.3
    Kenya28.04.714.82.62.09.70.542.665.320.5
    Liberia11.73.83.73.52.22.61.456.869.140.9
    Madagascar23.04.814.11.02.811.70.057.382.94.9
    Malawi32.61.919.22.71.616.21.281.884.446.1
    Mali6.22.62.20.51.01.70.036.876.73.7
    Mozambique12.14.34.32.93.74.11.086.295.434.8
    Nepal33.23.27.03.31.64.81.150.96932.3
    Nicaragua43.99.09.12.25.36.80.859.474.335.8
    Niger11.05.01.90.14.11.80.182.994.469.2
    Nigeria11.11.62.04.70.31.10.219.054.74.0
    Pakistan26.11.62.88.80.81.61.647.756.517.8
    Philippines23.58.42.01.24.81.90.356.692.527.4
    Rwanda25.23.914.61.83.714.20.994.297.151.4
    Senegal8.92.93.70.62.43.50.182.494.820.7
    Tanzania23.65.18.54.23.76.80.773.580.017.0
    Togo7.91.11.73.40.41.60.539.591.615.0
    Uganda20.72.110.73.21.04.20.945.739.128.6
    Zambia24.67.46.25.04.55.72.661.392.151.7
    Zimbabwe40.527.36.13.520.25.41.673.888.445.9
    • Abbreviations: CPR, contraceptive prevalence rate; DHS, Demographic and Health Surveys; IC, injectable contraceptives; MC, male condoms; mCPR, CPR for modern methods; OC, oral contraceptives.

    • View popup
    TABLE 3 Association Between Referent Public‐Sector Prevalence Rates and Average Monthly or Quarterly Logistics Distribution Data, by Contraceptive Type and Model Type
    Model and Contraceptive TypeNβ0β1β2R2‐adj
    Bivariate Model
     Injectable contraceptives30−4.110.72***NA0.90
     Oral contraceptivesa27−4.460.45***NA0.48
     Male condoms28−6.490.44***NA0.28
    Multivariate Model
     Injectable contraceptives28−4.210.62***5.70.91
     Oral contraceptivesa25−4.970.23*34.93***0.72
     Male condoms26−6.660.19171.93***0.48
    • ↵a​ The analysis was restricted to countries with <20 average monthly distribution per 100 women of reproductive age.

    • ↵* P<.05, ** P<.01, *** P<.001.

    • View popup
    TABLE 4 Evaluation of Model Accuracy and Precision
    Difference Between Model Estimates and DHS Referent ValuesProportion of Model‐Estimated Values Within 1, 2, and 5 Percentage Points of the DHS Value
    Model     Maximum Absolute Error (%)Mean Absolute Error (MAE) (%)Median Absolute Error (%)1 Percentage Point (%)2 Percentage Points (%)5 Percentage Points (%)
    Injectables
     Multivariate3.81.00.65789100
     Bivariate7.01.10.7579097
     CYP8.61.40.8548693
    Oral Contraceptives
     Multivariate2.90.60.48492100
     Bivariate3.00.90.66789100
     CYP3.41.00.86092100
    Condoms
     Multivariate1.30.30.292100100
     Bivariate1.90.40.393100100
     CYP14.42.40.6627785
    All Short-Acting Methods
     Multivariate4.61.41.33574100
     Bivariate7.81.91.2406488
     CYP17.03.41.5436178

Additional Files

  • Figures
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  • GHSP-D-15-00116 Supplementary Material

    Levine et al. doi: 10.9745/GHSP-D-15-00116

    • Supplementary Material - Cunningham et al. doi: 10.9745/GHSP-D-15-00116
    • Supplementary Material - Cunningham et al. doi: 10.9745/GHSP-D-15-00116
    • Supplementary Material - Cunningham et al. doi: 10.9745/GHSP-D-15-00116
    • Supplementary Material - Cunningham et al. doi: 10.9745/GHSP-D-15-00116
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Global Health: Science and Practice: 3 (3)
Global Health: Science and Practice
Vol. 3, No. 3
September 10, 2015
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Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries
Marc Cunningham, Ariella Bock, Niquelle Brown, Suzy Sacher, Benjamin Hatch, Andrew Inglis, Dana Aronovich
Global Health: Science and Practice Sep 2015, 3 (3) 462-481; DOI: 10.9745/GHSP-D-15-00116

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Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries
Marc Cunningham, Ariella Bock, Niquelle Brown, Suzy Sacher, Benjamin Hatch, Andrew Inglis, Dana Aronovich
Global Health: Science and Practice Sep 2015, 3 (3) 462-481; DOI: 10.9745/GHSP-D-15-00116
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