VISIT STATE HEALTH COMPARE
Publication
Logic Models for Stakeholder Communication
“Logic Models for Stakeholder Communication” introduces the concept of logic models, describes their use in health program planning projects, and gives guidance on how to create a logic model tailored to your program’s needs.
Publication
The Impact of Outreach
“The Impact of Outreach" describe methods of tracking impact of outreach with a specific focus on health coverage programs.
Publication
SHAP Primary Care Capacity Summary
SHADAC has developed a brief analysis of primary care capacity for each SHAP grantee state.
Each two page brief includes:
- A map of the distribution of the projected Medicaid eligible population ages 19 to 64 in 2014 for the state, by county.
- A table that illustrates the characteristics of the potentially newly eligible in the state.
- And a second map that illustrates current primary care physician supply relative to population in the state, by county
- Possible sources of provider capacity data.
- High level provider capacity questions that they state might want to consider addressing through additional analysis.
Select a state from the drop-down menu below to view state-specific briefs:
Publication
Health Reform and Provider Capacity
Lynn Blewett and Elizabeth Lukanen participated in the final all-grantee meeting of the State Health Access Program (SHAP), held August 23-24, 2011, in Washington, DC. Lynn led a session on Health Reform and Provider Capacity.
Her presentation "Health Reform and Provider Capacity," focused on the importance of conducting provider capacity at a state level given the passage of health reform, key analytic steps to this type of analysis and alternative ways to monitor and assess provider capacity issues.
Download a PDF version of these slides.
Publication
Are the Current Population Survey Uninsurance Estimates Too High? An Examination of the Imputation Process
Davern M, Rodin H, Blewett LA, Call KT. Are the Current Population Survey Uninsurance Estimates Too High? An Examination of the Imputation Process. Health Serv Res. 2007;42(5):2038-2055.
Research Objective. To determine whether the imputation procedure used to replace missing data by the U.S. Census Bureau produces bias in the estimates of health insurance coverage in the Current Population Survey's (CPS) Annual Social and Economic Supplement (ASEC).
Data Source. 2004 CPS-ASEC.
Study Design. Eleven percent of the respondents to the monthly CPS do not take the ASEC supplement and the entire supplement for these respondents is imputed by the Census Bureau. We compare the health insurance coverage of these “full-supplement imputations” with those respondents answering the ASEC supplement. We then compare demographic characteristics of the two groups and model the likelihood of having insurance coverage given the data are imputed controlling for demographic characteristics. Finally, in order to gauge the impact of imputation on the uninsurance rate we remove the full-supplement imputations and reweight the data, and we also use the multivariate regression model to simulate what the uninsurance rate would be under the counter-factual simulation that no cases had the full-supplement imputation.
Principal Findings. In the 2004 CPS-ASEC, 59.3 percent of the full-supplement imputations under age 65 years had private health insurance coverage as compared with 69.1 percent of the nonfull-supplement imputations. Furthermore, full-supplement imputations have a 26.4 percent uninsurance rate while all others have an uninsurance rate of 16.6 percent. Having imputed data remains a significant predictor of health insurance coverage in multivariate models with demographic controls. Both our reweighting strategy and our counterfactual modeling show that the uninsured rate is approximately one percentage point higher than it should be for people under 65 (i.e., approximately 2.5 million more people are counted as uninsured due to this imputation bias).
Conclusions. The imputed ASEC data are coding too many people to be uninsured. The situation is complicated by the current survey items in the ASEC instrument allowing all members of a household to be assigned coverage with the single press of a button. The Census Bureau should consider altering its imputation specifications and, more importantly, altering how it collects survey data from those who respond to the supplement.
Click here for the full article.
