Tackling health inequities necessitates a different approach than in the past. This involves embracing the use of credible data to tailor decisions based on individual factors rather than generalizing health assessment. The potential influence of data cannot be disregarded as it is a key element in recognizing what determines health and care.
Using data should not only be confined to individual determinants of health but also, systemic restrictions such as policies, social, living and working environments. The majority of health systems collect data about patients, but we are all aware that health goes beyond hospital settings. It is amazing how much we can learn from data collected at community, organizational and governmental level especially on prevention of morbidities and mortalities. Undeniably, there is already a plethora of data out there that influences decision making but these data can only be useful when validated as credible. There’s a crucial need to reevaluate data activities to incorporate the entirety of health determinants. We can then use that data to steer the delivery of quality care, promote healthy living and improve outcomes for all.
Data identifies gaps in health outcomes between populations and individuals and depicts the more extensive socio-economic, policy and systemic factors that are critical to health equity. Good and credible data demonstrates patterns that can be analyzed to produce solutions against disparities and enhance personalized health and care. When such data is properly applied, it can help target and reduce inequities and realize the full health potential of populations. Health equity can progress by utilizing credible data to influence policy makers, communities and healthcare organizations towards sustainable and meaningful change.
Ensuring credible data collection and analysis
In computer language, inaccurate or poor-quality input will produce flawed output i.e. Garbage in, Garbage Out. If we collect bad data, we end up with unreliable data, poor insights and undependable solutions. The same principle applies when determining factors affecting health. Collecting credible data to analyze inequities requires an active interaction with those facing health inequities and utilizing data to find differences among population groups instead of only assessing the general population. In short, community engagement is a must to identify and examine the causes of these population variations in health.
Community engagement implies that local communities are involved in defining what and how data will be collected, organizing and conducting the data collection and analysis, and interpreting and employing the results. By working together with the community, we are ensuring that the data collected actually isolates determinants of health inequities that are relevant to the needs of that particular group. This also influences sensible policy and law changes in a collaborative manner. Additionally, it builds trusts and a sense of ownership on the solutions and decisions that come from analyzing the data.
Monitoring health inequalities is pivotal to recognizing disparities among diverse population groups and provide evidence on means to closing existing equity gaps. The local community is vital to provide valuable insights on what affects their health and what solutions could be prioritized to solve relevant disparities. By actively engaging the community, we are expanding the scope of data analysis to identify, track and improve inequities and strengthen health and care systems.
 https://www.aha.org/ahahret-guides/2011-03-01-improving-health-equity-through-data-collection-and-use-guide-hospital  Ibrahim, S. A., Charlson, M. E., & Neill, D. B. (2020). Big Data Analytics and the Struggle for Equity in Health Care: The Promise and Perils. Health equity, 4(1), 99–101. https://doi.org/10.1089/heq.2019.0112