Literature DB >> 23803394

Impact of cigarette smoking on utilization of nursing home services.

Kenneth E Warner1, Ryan J McCammon, Brant E Fries, Kenneth M Langa.   

Abstract

INTRODUCTION: Few studies have examined the effects of smoking on nursing home utilization, generally using poor data on smoking status. No previous study has distinguished utilization for recent from long-term quitters.
METHODS: Using the Health and Retirement Study, we assessed nursing home utilization by never-smokers, long-term quitters (quit >3 years), recent quitters (quit ≤3 years), and current smokers. We used logistic regression to evaluate the likelihood of a nursing home admission. For those with an admission, we used negative binomial regression on the number of nursing home nights. Finally, we employed zero-inflated negative binomial regression to estimate nights for the full sample.
RESULTS: Controlling for other variables, compared with never-smokers, long-term quitters have an odds ratio (OR) for nursing home admission of 1.18 (95% CI: 1.07-1.2), current smokers 1.39 (1.23-1.57), and recent quitters 1.55 (1.29-1.87). The probability of admission rises rapidly with age and is lower for African Americans and Hispanics, more affluent respondents, respondents with a spouse present in the home, and respondents with a living child. Given admission, smoking status is not associated with length of stay (LOS). LOS is longer for older respondents and women and shorter for more affluent respondents and those with spouses present.
CONCLUSIONS: Compared with otherwise identical never-smokers, former and current smokers have a significantly increased risk of nursing home admission. That recent quitters are at greatest risk of admission is consistent with evidence that many stop smoking because they are sick, often due to smoking.

Entities:  

Mesh:

Year:  2013        PMID: 23803394      PMCID: PMC3790633          DOI: 10.1093/ntr/ntt079

Source DB:  PubMed          Journal:  Nicotine Tob Res        ISSN: 1462-2203            Impact factor:   4.244


  13 in total

1.  Lifestyle risk factors predict healthcare costs in an aging cohort.

Authors:  J Paul Leigh; Helen B Hubert; Patrick S Romano
Journal:  Am J Prev Med       Date:  2005-12       Impact factor: 5.043

2.  State estimates of total medical expenditures attributable to cigarette smoking, 1993.

Authors:  L S Miller; X Zhang; D P Rice; W Max
Journal:  Public Health Rep       Date:  1998 Sep-Oct       Impact factor: 2.792

3.  Lifestyle-related risk factors and risk of future nursing home admission.

Authors:  Elmira Valiyeva; Louise B Russell; Jane E Miller; Monika M Safford
Journal:  Arch Intern Med       Date:  2006-05-08

4.  Self-reported health and functional status information improves prediction of inpatient admissions and costs.

Authors:  Nancy A Perrin; Matt Stiefel; David M Mosen; Alan Bauck; Elizabeth Shuster; Erin M Dirks
Journal:  Am J Manag Care       Date:  2011-12-01       Impact factor: 2.229

5.  Cigarette smoking, mortality, institutional and community-based care utilization in an adult community.

Authors:  R M Kaplan; D L Wingard; J B McPhillips; D Williams-Jones; E Barrett-Connor
Journal:  J Community Health       Date:  1992-02

6.  The economic burden of smoking in California.

Authors:  W Max; D P Rice; H-Y Sung; X Zhang; L Miller
Journal:  Tob Control       Date:  2004-09       Impact factor: 7.552

7.  Comparing self-reported health status and diagnosis-based risk adjustment to predict 1- and 2 to 5-year mortality.

Authors:  Kenneth Pietz; Laura A Petersen
Journal:  Health Serv Res       Date:  2007-04       Impact factor: 3.402

8.  The impact of smoking and quitting on health care use.

Authors:  E H Wagner; S J Curry; L Grothaus; K W Saunders; C M McBride
Journal:  Arch Intern Med       Date:  1995-09-11

9.  Smoking cessation attempts in relation to prior health care charges: the effect of antecedent smoking-related symptoms?

Authors:  Brian C Martinson; Patrick J O'Connor; Nicolaas P Pronk; Sharon J Rolnick
Journal:  Am J Health Promot       Date:  2003 Nov-Dec

10.  Risk adjustment methods for Home Care Quality Indicators (HCQIs) based on the minimum data set for home care.

Authors:  Dawn M Dalby; John P Hirdes; Brant E Fries
Journal:  BMC Health Serv Res       Date:  2005-01-18       Impact factor: 2.655

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.