Behavioral determinants are the factors that influence or shape a health behavior. In the context of family planning (FP), some relevant behavioral determinants include intentions (for example, motivation to use an FP method) and attitudes (satisfaction with the method).
One benefit of behavioral determinant data is its ability to illuminate the pathway to improved health indicators and behaviors. In collecting information on factors that predict behaviors, including contraceptive use and change in a particular context, programs and policies can better support FP access and uptake. Behavioral determinant data can reveal the nuanced pathways from programs and policies to FP uptake and continuation. It can also help us understand why an intervention did not work as intended.
Increased attention on behavioral determinant data reflects broader shifts in the global family planning landscape. Previously, many donors, implementers, and governments assumed that with appropriate education, awareness, and supply, contraceptive uptake would naturally increase. While these remain important enabling factors, it has become clear that data on a variety of other behavioral determinants, such as self-efficacy in rural areas or cultural norms in a community with high birth rates, are needed to better understand patterns of and changes in contraceptive use.
PMA’s approach has similarly evolved over time from tracking global FP goals (e.g., modern contraceptive prevalence rate) to also collecting behavioral determinant data for national and sub-national use.
“Collecting these data is important from a research perspective, in that it more effectively informs governments to shape family planning policies and programs. We strive to have a strong connection with the governments that are implementing programs in areas where we are collecting data.”
Yet, behavioral determinant data is not routinely collected nor available for use in programming and policy making. For example, a Breakthrough RESEARCH-led review of over 1,500 FP indicators used in a wide range of programs in Francophone West Africa found that intermediate indicators measuring behavioral determinants such as attitudes, self-efficacy, and social norms were not widely represented. In fact, of the more than 1,500 indicators reviewed, only 121 were intermediate-level behavioral determinants, and of those, the majority focused on knowledge and awareness.
Collecting behavioral determinant data starts with a strong theory of change, as it is critical to first identify the factors that might influence contraceptive use. The theory of change must consider elements such as community context, women’s empowerment, reproductive health intentions, and supply environment. Given that contraceptive use also varies greatly between adolescents and adults, it is important to consider how behavioral determinants may differ by age, as well as other characteristics such as sex and ethnicity.
Formative research can help to inform data collection by identifying the important aspects of a determinant to a community of interest. It could be as simple as starting with a single question, such as “What does ‘quality of care’ really mean to communities in rural Burkina Faso?” Or it could involve unpacking a multifaceted term such as empowerment, which includes elements of economic, sexual, and reproductive health empowerment. It is critical to understand what each determinant means in a particular social, political, and cultural context. With that information in hand, study questions can be identified and validated within quantitative surveys to ensure that they capture the intended concepts reliably.
Collecting behavioral determinant data requires rigorously testing questions while still centering the user experience.
“One of our biggest challenges is to effectively capture key elements shaping contraceptive behaviors without overburdening respondents. It’s not always easy.”
Again, the example of empowerment comes to mind. While there is growing recognition of the centrality of economic empowerment as a behavioral determinant of FP use, there is less formative research, and less agreement on how to measure its various facets, including how many survey questions robust measurement of a multi-faceted concept requires.
Another challenge in overcoming the historic supply-side perspective that has dominated family planning investments, particularly in the Francophone West Africa region, is a historic lack of behavioral determinant data. Large household surveys tend not to collect robust modules of data demonstrating demand-side factors that include behavioral determinants, and it’s hoped that more surveys will consider developing modules providing validated scales and questions to be integrated into routine surveys.
At this point, most behavioral determinant data collection is still happening at the sub-national level. But PMA is among the few projects collecting behavioral determinant data at scale, utilizing a longitudinal panel design to follow women over time in eight countries (Kenya, Burkina Faso, Nigeria, Democratic Republic of Congo, Niger, Uganda, India, and Côte d’Ivoire). These include a range of behavioral determinants such as social norms; economic, reproductive, and contraceptive empowerment; and contraception and fertility intentions.
Dougherty details an example of how behavioral determinant data can debunk assumptions and misconceptions. Breakthrough RESEARCH has conducted novel behavioral determinant data collection in Niger, where there has been a dearth of large-scale data collection on family planning use, particularly in rural areas. “When we presented new behavioral determinant data, it became apparent that what people had presumed about the context was wrong. What had been boiled down to ‘polygamist power dynamics’ was more complex than it was made out to be,” she explains. “And within the data there was a group of young, educated women with positive behavioral health determinants, even within a context where the programmatic focus is generally on child marriage and delaying pregnancy. In the absence of household survey data, people have generalized or used anecdotes to explain why things are the way they are, which can undermine the efficacy of family planning programs.”
Self-injection programs represent one critical FP area that stands to be informed by behavioral determinant data. While this is a promising method with commensurate donor investment, recent data indicates that assumptions are being made equating availability with use. PMA recently collected data in all partner countries around users’ preference for self or provider administration. Most people indicated a preference for provider administration, potentially due to ingrained distribution patterns. According to Anglewicz, “These data indicate that at the least, we need further research to examine this preference if we are to enact self-injection programs performing at the level donors desire. Here, behavioral determinant data looking at intentions and preferences for injection can meaningfully inform these critical rollouts.”
There is hope that more large-scale surveys will establish modules of behavioral determinant data for public use. Dougherty suggests that this be accompanied by capacity building for data use at the national and sub-national levels. Anglewicz agrees: “To ensure data is used in the countries where we operate, we must deliberately integrate with the government to inform the content of our surveys. By bringing in governments and programs at the onset of survey design, it helps to ensure the data will be used once collected, which is our goal.”
He adds, “Family planning is also in need of measurement innovation.” There are some concepts emerging but not yet widely measured, including ambiguity related to fertility preferences and perceived risk of pregnancy. He contends that there is a lot of potential innovation around behavioral determinant data.
And finally, there is the more fundamental issue of data equity. Harkening back to the behavioral determinant data from Niger, Dougherty reflects: “In some places, countries are rich in data, and in others, there is such low availability but high interest and need. It’s a major issue that needs to be addressed and collecting behavioral determinant data will be critical to filling those gaps.”
Breakthrough RESEARCH is USAID’s flagship SBC global research and evaluation project. It catalyzes SBC by conducting state-of-the-art research and evaluation and promoting evidence-based solutions to improve health and development programs around the world. Breakthrough RESEARCH is a consortium led by the Population Council in partnership with Avenir Health, ideas42, Institute for Reproductive Health at Georgetown University, Population Reference Bureau, and Tulane University.
The Performance Monitoring for Action (PMA) project is fueling a data revolution to guide family planning programs. PMA surveys collect actionable data on a variety of family planning topics that inform policies at national and sub-national levels. Overall direction and support of PMA is provided by the Bill & Melinda Gates Institute for Population and Reproductive Health at the Johns Hopkins Bloomberg School of Public Health, and Jhpiego, in collaboration with national partners in each project country. PMA is funded by the Bill & Melinda Gates Foundation.