Post From Our Publications and Reports
Measurement is essential for demonstrating shared impact and the value of collaboration, informing resource allocation, and developing effective policies to achieve healthy communities. But when you’re working across silos, what is measured and how it is measured can become a major roadblock.
Our new research paper, “Exploring consensus across sectors for measuring the social determinants of health,” published in Social Science and Medicine: Population Health (v7, April 2019) by Renee Roy Elias, Douglas Jutte, and Alison Moore, is the result of two years of research to bridge collaborations toward shared metrics.
Since the Network started in 2014, we’ve seen a proliferation of tools from all kinds of sectors, for a multitude of purposes, and drawing from a vast universe of indicators, all seeking to measure impacts on the social determinants of health. As part of the Network’s foundational work cataloging cutting-edge measurement tools and sharing information on the importance of measurement, the Network maintains Measure Up, a microsite of resources and tools to help measure and describe program impact on communities. We noticed some overlap in data sources and indicators, but we wondered: how much consensus exists? How are different sectors defining social determinants of health, and how are they measuring them? Are we speaking the same language here?
Our study looked within and across 18 social determinants of health measurement tools to understand what social determinants of health (SDOH) measurement tools exist, and what indicators they used. These measurement questions continue to be salient: New data sources with increasing granularity are being released (like the CDC’s census tract-level 500 Cities health data) and the importance of cross-sector development and measurement is becoming more well-known (see the Federal Reserve’s “What Counts: Harnessing Data for America’s Communities.”) Despite these breakthroughs, our colleagues across sectors tell us that there is still a knowledge gap in terms of designing high impact projects and choosing the most effective indicators for measuring change over time. Our study incorporated vital input from members of the Network’s measurement working group in the early stages.
The study revealed several interesting points: First, measuring social determinants of health was of growing interest across multiple sectors, notably outside of the usual health and public health suspects. Some of the tools we reviewed were developed by community development organizations, including Success Measures, from NeighborWorks America; Opportunity 360, from Enterprise National Partners; and PolicyMap, from the Reinvestment Fund.
Second, while many measurement tools analyzed could be readily summarized categorically (for example, ‘housing’, or ‘employment’,) there was wide variation in the particular SDOH categories included in each tool—no one category was universal. The most common SDOH category was education, used in 17 of the 18 SDOH measurement tools.
The most common SDOH category was education, used in 17 of the 18 SDOH measurement tools.
Third, there was an astonishing level of variation among specific indicators, with most indicators used only once. Nearly 700(!) distinct indicators were identified, with a substantial majority used in only a single tool. Even the most frequently used indicators – unemployment, income inequality, poverty, and overweight/obesity – were used in just over half of SDOH measurement tools.
Finally, we discovered quite a few unique indicators that were particularly compelling and could be useful for practitioners across sectors to be aware of. Examples include: “long commute driving alone,” “feel safe alone at night”, “adult persistent sadness,” “school proximity to traffic”, and “seat belt use.”
It’s clear that more work needs to be done to share and learn from measurement strategies, particularly around the efficacy of indicators in measuring their intended outcomes. Our findings suggest that a single shared measurement system to meet cross-sectoral needs may not be possible or even practical, especially given the need for context and community input to inform the “right” measurement strategies. However, there is still much to be gained from understanding the most and least frequently used SDOH categories and indicators across sectors.
This useful baseline study can serve as a starting point for conversations about consensus building, either as part of efforts to align or share measurement strategies, or to broaden understandings of how social determinants of health are defined and implemented across different sectors and how they might address different needs.
Sectors aren’t all speaking the same language just yet. However, we believe that this comprehensive summary of SDOH measurement tools, many of which may be unfamiliar to siloed researchers or practitioners, will provide a step in the right direction in building a shared understanding of the social determinants of health and supporting cross-sectoral collaboration.