County-Based Incidence Rates Illuminate Community-Specific Diseases and Conditions to Understand Population Health Outcomes
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By: Saparja Nag Nov. 01, 2017
County-based incidence rates are calculated as the proportion of Medicare discharges within the entire population of Medicare enrollees within a specific region. Using the Center for Medicare and Medicaid Services (CMS) Medicare enrollment data, the relationships between incidence rate, cost, and enrollment metrics can be visualized and understood. Access to and usage of healthcare varies widely among different geographies, such that analysis of this enrollment data allows for more complex insights into healthcare management.
The data used in Dexur analysis is sourced from CMS Medicare claims, categorized by source of admissions, diagnosis related groups (DRGs), and International Classification of Diseases (ICD) codes. These data were collected between January 2013 and December 2016 and will continue to be updated. Medicare enrollment data was obtained from CMS in July 2016. By analyzing this data by county, health inequalities with respect to specific diseases and conditions can be determined and addressed. The table below explains and defines metrics used in county-based incidence rate analysis.
| Data Field / Metric | What It Means |
|---|---|
| Category / Sub-Category / DRG Definition | Categories & sub-categories defined based on DRG Ex. DRG 470 is categorized in Orthopedics & sub-categorized as Joint Replacement |
| Total Medicare Inpatient Discharges | Total discharges/hospitalizations within a hospital with a defined admission & discharge date |
| Total Medicare Enrollment | Total number of individuals eligible for Medicare approved to submit claims for Medicare-covered services and supplies |
| Incidence Rate | Proportion of total Medicare inpatient discharges among total Medicare enrollment |
| Total Medicare Payments | Total payments made by Medicare across all discharges for that relevant DRG, category & sub-category |
| Total Medicare Payments per Enrollments | Average amount of Medicare payments per individual enrolled in Medicare within a specific region |
| Population Health Management | Aggregation of patient data across different health information technologies and data analysis to improve patients’ clinical and financial outcomes |
County-based incidence rate data is utilized in population health management to predict and analyze health outcomes in geographic regions and communities. Monitoring the incidence of specific conditions such as COPD, asthma and obesity based on genetic and environmental factors, has already been used in studies to determine if correlations exist between preventative measures with incidence of disease and mortality rates. Quantitative analyses of community-specific diseases and conditions drive population health management by uncovering financial and clinical implications of differing health outcomes.
Examination of county-level factors with respect to diseases and conditions show how geographies can be significant explanatory variables in health outcomes. For example, a study was conducted on the COPD rates in Texas residents, Geographic disparity in COPD hospitalization rates among the Texas population. COPD is highly susceptible to environmental influences yet geographic fluctuations have rarely been studied in this context. This report compared COPD patients by sex, race, and age in county populations. They found that females, non-Hispanic whites, and patients 65 and older had the highest rates of hospitalization for each respective category. Nonmetropolitan areas had higher hospitalization rates than metropolitan areas. The difference between race and age category was significant in nonmetropolitan counties while the effects of gender, race, and age category were significant in stratified analyses of metropolitan counties.1
Another condition that is similarly affected by geographies is asthma as shown in the study Individual and county level predictors of asthma related emergency department visits among children on Medicaid: A multilevel approach. By analyzing individual and community factors that lead to asthma outcomes, interventional approaches can be targeted to specific populations. The population studied was Medicaid-enrolled children with asthma throughout 29 states in 2009. The study found that although controller medications were the primary risk factor affecting patients, 6% of asthma emergency department visit risk was due to county-level factors. These factors included racial segregation and access to pulmonary physicians.
As the healthcare industry undergoes widespread reform and restructuring, it is critical to understand how a community’s overall health affects population based health outcomes. By analyzing this data from CMS claims data, the emphasis of the health industry moves towards value of care rather than volume of care. Statistical analyses, like that examined in this article, are central to the interpretation of county-based incidence rates.
References
Jackson BE, Suzuki S, Lo K, et al. “Geographic disparity in COPD hospitalization rates among the Texas population.” Respiratory medicine. 2011;105(5):734-739. doi:10.1016/j.rmed.2010.12.019.
Baltrus P, Xu J, Immergluck L, et al. “Individual and county level predictors of asthma related emergency department visits among children on Medicaid: A multilevel approach.” Journal of Asthma. 2017; 54(1): 53-61. doi: 10.1080/02770903.2016.1196367.
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