Dataset Information

Implementing federal health reform in the States: who is included and excluded and what are their characteristics?

ABSTRACT:

Objective

To estimate the characteristics and number of nonelderly adults eligible and ineligible for Affordable Care Act (ACA) expansions.

Data sources and settings

Two secondary data sources are used in this analysis: the 2008 Panel of the Survey of Income and Program Participation (SIPP) and the 2009 American Community Survey (ACS).

Study design

We use multiple imputation to incorporate model-based uncertainty into the prediction of immigration status into the ACS from the SIPP. Key variables include place of birth, year of entry to the U.S., and health insurance coverage.

Data collection/extracting methods

No primary data are used in this study.

Principle findings

We estimate that potentially 3.5 million nonelderly adults will be excluded from the ACA Medicaid Expansion and 2 million from the health insurance exchanges because of their immigration status. We also find significant differences in estimates of excluded nonelderly adults across states.

Conclusions

Over 15 percent of income-eligible uninsured nonelderly adults will be potentially excluded from the ACA coverage expansions due to their immigration status. Policy makers must be careful to exclude ineligible nonelderly adults before estimating the impact of the ACA on coverage rates.

SUBMITTER: Fried B

PROVIDER: S-EPMC4256553 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Publications

Implementing federal health reform in the States: who is included and excluded and what are their characteristics?

Fried Brett B Pintor Jessie Kemmick JK Graven Peter P Blewett Lynn A LA

Health services research 20140926

<h4>Objective</h4>To estimate the characteristics and number of nonelderly adults eligible and ineligible for Affordable Care Act (ACA) expansions.<h4>Data sources and settings</h4>Two secondary data sources are used in this analysis: the 2008 Panel of the Survey of Income and Program Participation (SIPP) and the 2009 American Community Survey (ACS).<h4>Study design</h4>We use multiple imputation to incorporate model-based uncertainty into the prediction of immigration status into the ACS from t ...[more]

PMID: 25255892

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Project description:BackgroundMultilevel analyses are ideally suited to assess the effects of ecological (higher level) and individual (lower level) exposure variables simultaneously. In applying such analyses to measures of ecologies in epidemiological studies, individual variables are usually aggregated into the higher level unit. Typically, the aggregated measure includes responses of every individual belonging to that group (i.e. it constitutes a self-included measure). More recently, researchers have developed an aggregate measure which excludes the response of the individual to whom the aggregate measure is linked (i.e. a self-excluded measure). In this study, we clarify the substantive and technical properties of these two measures when they are used as exposures in multilevel models.MethodsAlthough the differences between the two aggregated measures are mathematically subtle, distinguishing between them is important in terms of the specific scientific questions to be addressed. We then show how these measures can be used in two distinct types of multilevel models-self-included model and self-excluded model-and interpret the parameters in each model by imposing hypothetical interventions. The concept is tested on empirical data of workplace social capital and employees' systolic blood pressure.ResultsResearchers assume group-level interventions when using a self-included model, and individual-level interventions when using a self-excluded model. Analytical re-parameterizations of these two models highlight their differences in parameter interpretation. Cluster-mean centered self-included models enable researchers to decompose the collective effect into its within- and between-group components. The benefit of cluster-mean centering procedure is further discussed in terms of hypothetical interventions.ConclusionsWhen investigating the potential roles of aggregated variables, researchers should carefully explore which type of model-self-included or self-excluded-is suitable for a given situation, particularly when group sizes are relatively small.

Project description:ImportanceTrauma registries are the primary data mechanism in trauma systems to evaluate and improve the care of injured patients. Research has suggested that trauma registries may miss high-risk older adults, who commonly experience morbidity and mortality after injury.ObjectiveTo compare injured older adults who were included in with those excluded from trauma registries, with a focus on patients with serious injuries, requiring major surgery, or dying after injury.Design, setting, and participantsThis cohort study included all injured adults 65 years and older transported by 44 emergency medical services agencies to 51 trauma and nontrauma centers in 7 counties in Oregon and Washington from January 1, 2011, to December 31, 2011, with follow-up through December 31, 2012. Record linkage was used to match emergency medical services records with state trauma registries, state discharge databases, state death registries, and Medicare claims. Data were analyzed from August to November 2018.ExposuresInclusion in vs exclusion from a trauma registry.Main outcomes and measuresMortality up to 12 months, including time to death and causes of death.ResultsOf 8161 included patients, 5579 (68.4%) were women, and the mean (SE) age was 82.2 (0.10) years. A total of 1720 older adults (21.1%) were matched to a trauma registry record. Seriously injured patients not captured by trauma registries ranged from 18% (7 of 38 patients with abdominal-pelvic Abbreviated Injury Scale score of 3 or greater) to 80.0% (1792 of 2241 patients with extremity Abbreviated Injury Scale score of 3 or greater), while 68 of 186 patients requiring major nonorthopedic surgery (36.6%) and 1809 of 2325 patients requiring orthopedic surgery (77.8%) were not included in trauma registries. Of patients with serious injuries or undergoing major surgery missed by trauma registries (range by injury and procedure type, 36.0% to 57.1%), 36.4% (39.3% when excluding serious extremity injuries and orthopedic procedures) were treated at trauma centers, particularly level III through V hospitals. When registry and nonregistry groups were tracked over 12 months, 93 of 188 in-hospital deaths (49.5%) and 1531 of 1887 total deaths (81.1%) occurred in the nonregistry cohort.Conclusions and relevanceIn their current form, trauma registries are ineffective in capturing, tracking, and evaluating injured older adults, although mortality following injury is frequently due to noninjury causes. High-risk injured older adults are not included in registries because of care in nontrauma hospitals, restrictive registry inclusion criteria, and being missed by registries in trauma centers.

Dataset Information

Implementing federal health reform in the States: who is included and excluded and what are their characteristics?

Objective

Data sources and settings

Study design

Data collection/extracting methods

Principle findings

Conclusions

Publications

Implementing federal health reform in the States: who is included and excluded and what are their characteristics?

Similar Datasets

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