Tags

Type your tag names separated by a space and hit enter

Factors associated with long-stay nursing home admissions among the U.S. elderly population: comparison of logistic regression and the Cox proportional hazards model with policy implications for social work.
Soc Work Health Care. 2009; 48(2):154-68.SW

Abstract

Two statistical methods were compared to identify key factors associated with long-stay nursing home (LSNH) admission among the U.S. elderly population. Social Work's interest in services to the elderly makes this research critical to the profession. Effectively transitioning the "baby boomer" population into appropriate long-term care will be a great societal challenge. It remains a challenge paramount to the practice of social work. Secondary data analyses using four waves (1995, 1998, 2000, and 2002) of the Health Retirement Study (HRS) coupled with the Assets and Health Dynamics among the Oldest Old (AHEAD) surveys were conducted. Multivariable logistic regression and Cox proportional hazards model were performed and compared. Older age, lower self-perceived health, worse instrumental activities of daily living (IADL), psychiatric problems, and living alone were found significantly associated with increased risk of LSNH admission. In contrast, being female, African American, or Hispanic; owning a home; and having lower level of cognitive impairment reduced the admission risk. Home ownership showed a significant effect in logistic regression, but a marginal effect in the Cox model. The Cox model generally provided more precise parameter estimates than logistic regression. Logistic regression, used frequently in analyses, can provide a good approximation to the Cox model in identifying factors of LSNH admission. However, the Cox model gives more information on how soon the LSNH admission may happen. Our analyses, based on two models, dually identified the factors associated with LSNH admission; therefore, results discussed confidently provide implications for both public and private long-term care policies, as well as improving the assessment capabilities of social work practitioners for development of screening programs among at-risk elderly. Given the predicted surge in this population, significant factors found from this study can be utilized in a strengths-based empowerment approach by social workers to aid in avoiding LSNH utilization.

Authors+Show Affiliations

Health Core, Inc., Wilmington, Delaware, USA.No affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

19197772

Citation

Cai, Qian, et al. "Factors Associated With Long-stay Nursing Home Admissions Among the U.S. Elderly Population: Comparison of Logistic Regression and the Cox Proportional Hazards Model With Policy Implications for Social Work." Social Work in Health Care, vol. 48, no. 2, 2009, pp. 154-68.
Cai Q, Salmon JW, Rodgers ME. Factors associated with long-stay nursing home admissions among the U.S. elderly population: comparison of logistic regression and the Cox proportional hazards model with policy implications for social work. Soc Work Health Care. 2009;48(2):154-68.
Cai, Q., Salmon, J. W., & Rodgers, M. E. (2009). Factors associated with long-stay nursing home admissions among the U.S. elderly population: comparison of logistic regression and the Cox proportional hazards model with policy implications for social work. Social Work in Health Care, 48(2), 154-68. https://doi.org/10.1080/00981380802580588
Cai Q, Salmon JW, Rodgers ME. Factors Associated With Long-stay Nursing Home Admissions Among the U.S. Elderly Population: Comparison of Logistic Regression and the Cox Proportional Hazards Model With Policy Implications for Social Work. Soc Work Health Care. 2009;48(2):154-68. PubMed PMID: 19197772.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Factors associated with long-stay nursing home admissions among the U.S. elderly population: comparison of logistic regression and the Cox proportional hazards model with policy implications for social work. AU - Cai,Qian, AU - Salmon,J Warren, AU - Rodgers,Mark E, PY - 2009/2/7/entrez PY - 2009/2/7/pubmed PY - 2009/4/1/medline SP - 154 EP - 68 JF - Social work in health care JO - Soc Work Health Care VL - 48 IS - 2 N2 - Two statistical methods were compared to identify key factors associated with long-stay nursing home (LSNH) admission among the U.S. elderly population. Social Work's interest in services to the elderly makes this research critical to the profession. Effectively transitioning the "baby boomer" population into appropriate long-term care will be a great societal challenge. It remains a challenge paramount to the practice of social work. Secondary data analyses using four waves (1995, 1998, 2000, and 2002) of the Health Retirement Study (HRS) coupled with the Assets and Health Dynamics among the Oldest Old (AHEAD) surveys were conducted. Multivariable logistic regression and Cox proportional hazards model were performed and compared. Older age, lower self-perceived health, worse instrumental activities of daily living (IADL), psychiatric problems, and living alone were found significantly associated with increased risk of LSNH admission. In contrast, being female, African American, or Hispanic; owning a home; and having lower level of cognitive impairment reduced the admission risk. Home ownership showed a significant effect in logistic regression, but a marginal effect in the Cox model. The Cox model generally provided more precise parameter estimates than logistic regression. Logistic regression, used frequently in analyses, can provide a good approximation to the Cox model in identifying factors of LSNH admission. However, the Cox model gives more information on how soon the LSNH admission may happen. Our analyses, based on two models, dually identified the factors associated with LSNH admission; therefore, results discussed confidently provide implications for both public and private long-term care policies, as well as improving the assessment capabilities of social work practitioners for development of screening programs among at-risk elderly. Given the predicted surge in this population, significant factors found from this study can be utilized in a strengths-based empowerment approach by social workers to aid in avoiding LSNH utilization. SN - 0098-1389 UR - https://www.unboundmedicine.com/medline/citation/19197772/Factors_associated_with_long_stay_nursing_home_admissions_among_the_U_S__elderly_population:_comparison_of_logistic_regression_and_the_Cox_proportional_hazards_model_with_policy_implications_for_social_work_ L2 - http://www.tandfonline.com/doi/full/10.1080/00981380802580588 DB - PRIME DP - Unbound Medicine ER -