UNFPA SDG indicator custodianship
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1 UNFPA SDG indicator custodianship Presentation to the Expert meeting of the IAEG-SDG Working Group on Geospatial Information Session 5 7 December 2017 Alfredo L. Fort, MD PhD Senior M&E Advisor Population and Development Branch Technical Division - UNFPA 1
2 Objectives of presentation: Introduce the indicators UNFPA is custodian of, & their geospatial elements; current status with these indicators; Issues and challenges related to the geographic location element in these indicators; Opportunities for WG to support indicator reporting using geospatial and earth observations data. 2
3 UNFPA Indicator custodianship UNFPA is custodian to 2 indicators: 5.6.1: Proportion of women aged years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care. (Tier II) 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education (Tier III) 3
4 Indicator Definition: Proportion of women aged years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care. (Tier II) Interpretation A woman is considered to have autonomy in reproductive health decision making and to be empowered to exercise her reproductive rights if she: (1) can say NO to sex with their husband/partner if they do not want to, (2) decides on use / non-use of contraception, and (3) decides on health care / reproductive health carefor herself. Only women who provide a yes answer to all three components are considered as women who make their own decisions regarding sexual relations, contraceptive use and reproductive health care.
5 Current status of Indicator upgraded: from Tier III to Tier II Data available for 45 countries (DHS) Percent of women currently married or in union and using any type of contraception who make their own decision on sexual relations, contraception and their own health care Namibia (2013) Lesotho (2014) Kenya (2014) Mozambique (2011) Uganda (2011) Tanzania (2010) Malawi (2010) DRC (2014) Ghana (2014) Gabon (2012) Sierra Leone (2013) Togo (2014) Cote d'ivoire (2012) Burkina Faso (2010) Chad (2015) Mali (2013) Kyrgyz Republic (2012) Albania (2009) Guyana (2009) Haiti (2012) Jordan (2012) Cambodia (2014) East & Southern Africa West & Central Africa Eastern Europe & Central Asia, Latin America & Caribbean, Arab States, Asia & Pacific Efforts to expand data collection with new DHS/other surveys
6 Indicator Definition:Number of countries with laws and regulations that guarantee full and equalaccess to women and menaged 15 years and older to sexual and reproductive health care, information and education Tier III indicator, refined as per above (in red) A toolhas been developed, with questions assessing policies and legal enablers and barriers on 4 main SRH domains: Pregnancy and childbirth Contraception Comprehensive sexuality education and information Sexual health and well-being
7 Current status of Indicator Survey tool and methodology being pilot-tested in 2017 (Oct Dec) Seeking additional countries (incl developed)
8 Geospatial elements of Indicators 1. For 5.6.2: None a) National-level indicator b) Potential disaggregation limited to political/admin boundaries 2. For 5.6.1: a) Disaggregation by geographic location: i. With Demographic and Health Surveys, disaggregation = one sub-national level due to sample size limitations ii. But, surveys collect household XY coordinates b) Geospatial issues: i. Any changes in admin boundaries may affect comparability of data ii. Limited GIS capacity in NSO (e.g., geocoding) iii. CO need guidance on release and display of geospatial data iv. Potential for generating Small Area Estimations (SAE): linking census geospatial and survey data 8
9 Example: Small Area Estimation Description of Small Area Estimation (SAE) 1.Regression model: coefficients for predicting probability of use of contraception by woman, from DHS data 2.Coefficients from DHS = applied to Census Data to predict probabilities of using contraception for individual women 3.The individual contraceptive use probabilities from census data are aggregated to district level Application of SAE 1.Estimate the number of women aged (married / in union) in need of contraception 2. Identify priority districts (#s or prevalence) DHS CENSUS 2. Develop a model for predicting the probability of individual contraceptive use using DHS data CENSUS 4. Aggregate the estimation of individual contraceptive use to small area administrative level and map results Major Steps: 1. Identify common variables associated with contraceptive indicators in DHS and census data DHS JJ Variable 1 Variable 2 Variable 3 3. Apply the model to census and estimate the probability of individual contraceptive use 9
10 Application: Small Area Estimation Estimating proportion of women with need for family planning satisfied with modern methods (SDG Indicator 3.7.1) at district level, -Nepal DHS Data- regional level Note: Census year is same year as DHS year or close Small Area Estimation using Census and DHS- district level
11 Thank you 11
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