How Modeling Can Inform Strategies to Improve Population Health Workshop Summary (2016) / Chapter Skim
Currently Skimming:
6 Opportunities for the Future
6 Opportunities for the Future
Pages 65-78
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 65... ...
At CMMI, he said, intelligent predictive modeling is at a premium because he and his colleagues have 1Thissection is based on the presentation by Darshak Sanghavi, Director of the Population and Preventive Health Models Group at the Center for Medicare & Medicaid Innovation, and the statements are not endorsed or verified by the Institute of Medicine.
|
From page 66... ...
• The public health sector looks at and acts on community-level data on condi tions, while the health care sector looks at and acts on individual-level data on clinical outcomes. Models for prevention and public policy could benefit from population health information systems that integrate clinical outcomes collected routinely by health care systems with individual-level social, economic, and environmental factors.
|
From page 67... ...
Similarly, the real goal may be for a hyperactive first-grader to be able to hold down a good job as an adult, but performance is judged based on improved scores on a symptom checklist, or the real goal is to prevent hip fractures in an elderly woman, but performance is judged according to bone density scans in perimenopausal women. Sanghavi and his colleagues are trying to determine if judging performance on the basis of surrogate endpoints is a good strategy, given that the relation of a surrogate endpoint to the actual desired outcome may be weak or nonexistent and that an intervention can improve the surrogate outcome but also have adverse side effects.
|
From page 68... ...
He also provided a link to an interactive and predictive analytical model that evaluates the spread of HIV and cost per HIV infection averted for various interventions and public health strategies in two settings, New York City and East Africa.2 LESSONS FROM MODELS FOR POPULATION HEALTH3 In the workshop's final presentation, Rajiv Bhatia discussed some of the experiences that he has had over the past 15 years of conducting health impact assessments (HIAs) , which he described as a trans disciplinary decision-support practice that anticipates the health effect of 2Seehttp://torchresearch.org.
|
From page 69... ...
They images two use conceptual models to explain the nexus between policy andeachhealth, and they use quantitative models to raster uneditable quantify the effects of decision on health determinants and outcomes. As an example of a model used in HIAs, Bhatia showed a map of areas of San Francisco with an unacceptable air pollution health risk based on the levels of airborne particulate matter found there -- levels that are known to be associated with an unacceptable mortality risk -- and also on the levels of airborne carcinogens, which are associated with a separate
|
From page 70... ...
70 HOW MODELING CAN INFORM STRATEGIES FIGURE 6-2 Information for patients showing the benefit of statin therapy on the absolute risk of having a heart attack. SOURCES: Mayo Clinic, 2015; Sanghavi presentation, April 9, 2015.
|
From page 71... ...
The problem that this HIA was trying to address was that in San Francisco, as in most cities, there is one air pollution monitoring station that is located far from any significant air pollution sources and that therefore cannot identify pollution hot spots. Using a model that had been developed to identify areas in San Francisco that exceeded federal and state pollution levels, Bhatia and his collaborators helped the city's planning office justify and craft a policy to prevent poor indoor air quality in new housing.
|
From page 72... ...
The object of this modeling exercise was to examine how the patterns of pedestrian injuries related to the design and operation of city streets. Using a binomial regression model that took as its inputs vehicle and pedestrian volumes, traffic speed, road and intersection characteristics, and area population characteristics, the model explained about three-quarters of the variance in pedestrian injury density among city census tracts and produced risk information to inform city planning decisions.
|
From page 73... ...
He did have to find these models, but after that it was a matter of getting local data to apply to the model. For example, a prototype for the pedestrian injury model had been developed and validated by the Federal Highway Administration, and Bhatia and his colleagues took that best practice model and replicated it in San Francisco.
|
From page 74... ...
In his opinion, too few epidemiological studies examine public policies as risk factors directly, which limits the evidence available for modeling how policy affects health. The public health sector looks at and acts on community-level data on conditions, while the health care sector looks at and acts on individual-level data on clinical outcomes, which creates a disconnect among sources of evidence.
|
From page 75... ...
In contrast, Pascual said, every government website that he has accessed data flunks this data format test. Bhatia agreed that there are many encumbrances to accessing the data needed to understand population health dynamics and that more needs to be done to link datasets representing determinants and outcomes.
|
From page 76... ...
He cited the Apple Research Kit as an example of the willingness of individuals to contribute health data for the public good, and he said it is now up to the public health community to decide what kind of information it wants from this willing population of individuals. George Isham asked Bhatia if he could comment further on the idea of collecting data on the social determinants of health at the individual level.
|
From page 77... ...
He suggested that payers and insurance plans could lead such an effort. He added that Medicaid in particular would benefit from collecting nonclinical risk factors identified from epidemiological social determinants research at enrollment and using this data to shed light on what the differential cost and utilization of these risk factors are.
|
From page 78... ...
' That is where we hope innovation can occur." A workshop participant asked if it would be possible to develop riskadjustment measures for social determinants in the Medicaid payment model, and Sanghavi answered that this is an issue that CMS struggles with because it means that if some Medicaid agencies pay more for complex patients, they might choose to pay less for some other group of patients. He added that CMMI can build models to address complexity, but such efforts are multifaceted.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.