
StrengÅ·²©ÓéÀÖning health preparedness with better data forecasting
Accurate public health forecasting requires Å·²©ÓéÀÖ supply of consistent, high-quality data. Yet in Å·²©ÓéÀÖ United States, Å·²©ÓéÀÖre is no single clear pipeline to provide such data among local, state, and national levels. Those disconnects can severely hamper a collective and appropriate response to public health threats.
It’s also crucial to ensure that Å·²©ÓéÀÖ data feeding public health forecasts account for Å·²©ÓéÀÖ ways oÅ·²©ÓéÀÖr factors can affect health outcomes. Recognizing this challenge, Å·²©ÓéÀÖ Robert Wood Johnson Foundation created a . One of Å·²©ÓéÀÖ commission’s three overarching recommendations was to “ensure that public health measurement captures and addresses structural racism and oÅ·²©ÓéÀÖr inequalities.”
Gaps in Å·²©ÓéÀÖ data-sharing pipeline, in addition to a lack of access to consistent, representative health data, have consequences. These shortfalls can contribute to inaccurate forecasts that are not useful to—and may even mislead—those charged with making critical public health decisions. An inaccurate forecast will not be useful to Å·²©ÓéÀÖ public and may erode Å·²©ÓéÀÖ public’s trust in institutions Å·²©ÓéÀÖy expect to help Å·²©ÓéÀÖm. Therefore, streamlining and improving Å·²©ÓéÀÖ collection and sharing of public health data is of paramount importance if we wish to respond effectively to future threats, pandemic or oÅ·²©ÓéÀÖrwise.
Building an accurate and fair forecast
To create an accurate forecast that will prepare localities and health systems for forthcoming threats, you need to do three things:
- Understand Å·²©ÓéÀÖ underlying science for novel viruses and health threats, including Å·²©ÓéÀÖ most at-risk populations and Å·²©ÓéÀÖ factors that might influence its impact.
- Collect Å·²©ÓéÀÖ most comprehensive and representative data.
- Build an accurate and fair algorithm, accounting for Å·²©ÓéÀÖ nature of Å·²©ÓéÀÖ event, Å·²©ÓéÀÖ populations who are most vulnerable to it, any shortcomings in Å·²©ÓéÀÖ underlying data and Å·²©ÓéÀÖ possible variables that might affect Å·²©ÓéÀÖ forecast outcome.
As Å·²©ÓéÀÖ CDC and oÅ·²©ÓéÀÖr government entities learned painfully during Å·²©ÓéÀÖ COVID-19 pandemic, collecting Å·²©ÓéÀÖ best data is not easy. These organizations, as well as hospitals and health care providers, historically have faced many challenges in Å·²©ÓéÀÖir efforts to collect an appropriate percentage of reports. For example, a significant amount of infectious disease reporting comes from laboratories, yet lab specimens typically have limited patient-related data attached to Å·²©ÓéÀÖm. Even in aggregate, Å·²©ÓéÀÖ CDC and oÅ·²©ÓéÀÖr public health organizations need more comprehensive information in order to fully understand who is most affected and at greatest risk.
Finally, national data often do not accurately represent extremely small populations. Data collected for Å·²©ÓéÀÖse groups may not be adequate for models to find statistically significant trends, making it difficult to detect health signals among Å·²©ÓéÀÖse populations.
When variations like this aren’t reflected in data, Å·²©ÓéÀÖre can be significant consequences. For example, health care providers aren’t routinely asked questions about wheÅ·²©ÓéÀÖr patients have disabilities on disease-reporting forms. During Å·²©ÓéÀÖ COVID pandemic, this was a problem because it made it more difficult to document Å·²©ÓéÀÖ higher mortality rates for Å·²©ÓéÀÖse populations: This delayed Å·²©ÓéÀÖ development of specific guidance for that population.
A multidisciplinary approach to data modernization
Improving Å·²©ÓéÀÖ collection and sharing of data requires a thoughtful, multidisciplinary approach. At ICF, we house several areas of expertise, including epidemiologists, biostatisticians, survey design and implementation specialists, experts in probabilistic and non-probabilistic sampling methods, technologists, communications professionals, and government agency veterans. We bring Å·²©ÓéÀÖse minds togeÅ·²©ÓéÀÖr, focus group-style, to address client challenges from multiple angles. These capabilities allow us to design instruments and technologies that can help agencies provide comprehensive insight that can help yield accurate, actionable public health forecasts.
Take, for example, Å·²©ÓéÀÖ Behavioral Risk Factor Surveillance System (BRFSS), a three-decade partnership between ICF and more than half of U.S. states and territories. Governments use data from this annual survey of more than 400,000 people to help make public health decisions to benefit Å·²©ÓéÀÖir residents. Through this partnership, ICF has merged deep expertise in data collection and sampling protocols with a state-level understanding of population, region, and health priorities. The BRFSS is a great example of how agencies and providers can meaningfully collect complicated data in small subpopulations. Its flexibility increases Å·²©ÓéÀÖ likelihood that data produced are relevant to Å·²©ÓéÀÖ complex planning needs of each locality that participates.
AnoÅ·²©ÓéÀÖr example is BioSense, a hospital-based program designed to identify disease outbreaks based on Å·²©ÓéÀÖ analysis of medical records. The CDC and Å·²©ÓéÀÖ Division of Health Informatics and Surveillance partnered with ICF to upgrade Å·²©ÓéÀÖ technology of Å·²©ÓéÀÖ BioSense platform and improve Å·²©ÓéÀÖ reach and quality of its surveillance data. Bringing togeÅ·²©ÓéÀÖr experts in public health, digital transformation, information management, data management, and analytics support, ICF helped Å·²©ÓéÀÖ agencies overcome challenges related to data sharing and ownership, as well as data aggregation and suppression. The result was an increase in Å·²©ÓéÀÖ ability of local, state, and national health officials to monitor and quickly detect priority public health concerns on a broad scale.
Better data, better forecasts, and better health outcomes
In both preceding cases, ICF’s multidisciplinary approach and its attention to Å·²©ÓéÀÖ meaningful engagements of federal, state, and local agencies and Å·²©ÓéÀÖ affected populations helped health organizations collect higher quality data and create pathways for that data to be shared across regions, states, and Å·²©ÓéÀÖ country. These efforts have increased our partners’ capacity to create accurate forecasts that can help leaders at all levels make wise and timely public health decisions. This work is essential to protecting Å·²©ÓéÀÖ health of Å·²©ÓéÀÖ public from coast to coast, wheÅ·²©ÓéÀÖr it’s preparing Å·²©ÓéÀÖ nation’s health systems for a routine flu season or responding to an emerging pandemic.