Abstract
The need for annual family planning program tracking data under the Family Planning 2020 (FP2020) initiative has contributed to renewed interest in family planning service statistics as a potential data source for annual estimates of the modern contraceptive prevalence rate (mCPR). We sought to assess (1) how well a set of commonly recorded data elements in routine service statistics systems could, with some fairly simple adjustments, track key population-level outcome indicators, and (2) whether some data elements performed better than others. We used data from 22 countries in Africa and Asia to analyze 3 data elements collected from service statistics: (1) number of contraceptive commodities distributed to clients, (2) number of family planning service visits, and (3) number of current contraceptive users. Data quality was assessed via analysis of mean square errors, using the United Nations Population Division World Contraceptive Use annual mCPR estimates as the "gold standard." We also examined the magnitude of several components of measurement error: (1) variance, (2) level bias, and (3) slope (or trend) bias. Our results indicate modest levels of tracking error for data on commodities to clients (7%) and service visits (10%), and somewhat higher error rates for data on current users (19%). Variance and slope bias were relatively small for all data elements. Level bias was by far the largest contributor to tracking error. Paired comparisons of data elements in countries that collected at least 2 of the 3 data elements indicated a modest advantage of data on commodities to clients. None of the data elements considered was sufficiently accurate to be used to produce reliable stand-alone annual estimates of mCPR. However, the relatively low levels of variance and slope bias indicate that trends calculated from these 3 data elements can be productively used in conjunction with the Family Planning Estimation Tool (FPET) currently used to produce annual mCPR tracking estimates for FP2020.
Highlights
Global Health: Science and Practice 2018 | Volume 6 | Number 1 errors, facility underreporting, duplicate reporting of clients who visit more than 1 service delivery point during a given reporting period, reporting delays, and deliberate padding of numbers) and (2) generally do not measure population-level indicators well, due in part to the above errors and in part to limited coverage of the contributions of the private sector (i.e., NGO and commercial providers of family planning).In response to the limitations of service statistics data and vital statistics systems, and to the challenging and often lengthy processes required to reform them,[3] a shift toward greater reliance on data from large-scale surveys was well underway by the early 1970s
The first row shows the root mean square error. Note (RMSE) for the 3 family planning service statistics considered in the study, while the bottom 3 rows show the median figures for variance, level bias, and slope bias errors, respectively
RMSEs were modest for all commodities distributed to clients and service visits data (7% and 10%, respectively), but somewhat higher for current contraceptive users data (19%)
Summary
In response to the limitations of service statistics data and vital statistics systems, and to the challenging and often lengthy processes required to reform them,[3] a shift toward greater reliance on data from large-scale surveys was well underway by the early 1970s. This shift was led by global survey programs such as the World Fertility Survey (WFS) and the Contraceptive Prevalence Surveys (CPS) in the 1980s, followed by the Demographic and Health Surveys (DHS) and most recently the Performance Monitoring and Account-. One reason is that the global Family Planning 2020 (FP2020)
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