Documentation of Cognitive Functioning Measures in the Health and Retirement Study

Ofstedal, Mary Beth; Fisher, Gwenith G.

doi:10.7826/isr-um.06.585031.001.05.0010.2005

Cited by 374 publications

(374 citation statements)

References 10 publications

(20 reference statements)

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“…35 for a review). We measured numeracy using the numeracy module of the US Health and Retirement Study (36). We found that, similar to previous reports (37), our adolescents and older adults had lower numerical skills than young or midlife adults (Fig.…”

Section: Significancesupporting

confidence: 69%

Like cognitive function, decision making across the life span shows profound age-related changes

Tymula

Belmaker²,

Ruderman³

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

242

268

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Significance Although largely unstudied, behavioral changes in decision making across the life span have implications for problems associated with poor decision making at different life stages, such as careless driving in adolescents and disadvantageous medical or financial decision making in older adults. We examine age-based differences in individual decision-making characteristics—choice consistency, rationality, and preferences for known and unknown risks—in 12- to 90-y-olds. We found that even the healthiest of elders show profoundly compromised decision making, and that risk attitudes show systematic changes across the life span that have important policy implications.

show abstract

Section: Significancesupporting

confidence: 69%

Like cognitive function, decision making across the life span shows profound age-related changes

Tymula

Belmaker²,

Ruderman³

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

242

268

View full text Add to dashboard Cite

show abstract

“…Composite scores for ADL difficulty were computed and cut-offs determined a priori to represent no limitations (0), mild/moderate limitations (1-2), and severe limitations (3 or more). The HRS tests cognitive function using well-described methods; [29][30][31][32][33] final models used a binary measure to describe patients as having no cognitive impairment versus any cognitive impairment. Two binary measures related to disability were obtained from the HRS data: whether the patient required nursing home services during the most recent HRS interview, or had used home health care services in the 2 years prior to the interview.…”

Section: Data Sources and Measures Of Disability And Social Determinamentioning

confidence: 99%

The Impact of Disability and Social Determinants of Health on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort Study

et al. 2016

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BACKGROUND: Readmission rates after pneumonia, heart failure, and acute myocardial infarction hospitalizations are risk-adjusted for age, gender, and medical comorbidities and used to penalize hospitals. OBJECTIVE: To assess the impact of disability and social d e t e r m i n an t s o f h ea l t h o n c o n d i t i o n-s p e c i f i c readmissions beyond current risk adjustment. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of Medicare patients using 1) linked Health and Retirement Study-Medicare claims data (HRS-CMS) and 2) Healthcare Cost and Utilization Project State Inpatient Databases (Florida, Washington) linked with ZIP Code-level measures from the Census American Community Survey (ACS-HCUP). Multilevel logistic regression models assessed the impact of disability and selected social determinants of health on readmission beyond current risk adjustment. M A I N M E A S U R E S : O u t c o m e s m e a s u r e d w e r e readmissions ≤30 days after hospitalizations for pneumonia, heart failure, or acute myocardial infarction. HRS-CMS models included disability measures (activities of daily living [ADL] limitations, cognitive impairment, nursing home residence, home healthcare use) and social determinants of health (spouse, children, wealth, Medicaid, race). ACS-HCUP model measures were ZIP Codepercentage of residents ≥65 years of age with ADL difficulty, spouse, income, Medicaid, and patient-level and hospital-level race. KEY RESULTS: For pneumonia, ≥3 ADL difficulties (OR 1.61, CI 1.079-2.391) and prior home healthcare needs (OR 1.68, CI 1.204-2.355) increased readmission in HRS-CMS models (N = 1631); ADL difficulties (OR 1.20, CI 1.063-1.352) and 'other' race (OR 1.14, CI 1.001-1.301) increased readmission in ACS-HCUP models (N = 27,297). For heart failure, children (OR 0.66,) and wealth (OR 0.53, CI 0.349-0.787) lowered readmission in HRS-CMS models (N = 2068), while black (OR 1.17, CI 1.056-1.292)

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“…Self-respondents were classified into cognition strata based on the full set of HRS cognitive tests (aggregate scores ranging from 0-35). Herzog, et al (1997) and Ofstedal et al (2005) provide a detailed description of these HRS cognition measures. Proxy respondents were classified based on scores ranging from 1.0 to 5.0 on the IQCODE scale (Jorm, 1994).…”

Section: B Sample Stratification Sample Allocation and Selectionmentioning

confidence: 99%

ADAMS: Sample Design, Weighting and Analysis for ADAMS

Heeringa¹,

Fisher²,

Hurd³

et al. 2009

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Documentation of Cognitive Functioning Measures in the Health and Retirement Study

Cited by 374 publications

References 10 publications

Like cognitive function, decision making across the life span shows profound age-related changes

Like cognitive function, decision making across the life span shows profound age-related changes

The Impact of Disability and Social Determinants of Health on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort Study

ADAMS: Sample Design, Weighting and Analysis for ADAMS

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