Predictors of mortality among older patients in the medical wards of a tertiary hospital in Nigeria
- 32 Downloads
Older people face the biggest challenges in the overburdened healthcare services in Nigeria especially when hospitalized. There is no reliable data on the predictors of mortality in this population.
To determine the predictors of mortality among older patients on admission in the medical wards of University College Hospital, Ibadan.
Using a prospective cohort design, we investigated 450 patients (> 60 years) from the day of admission to death or discharge. Variables assessed included sociodemographic, family dynamics, lifestyle habits, healthcare utilization, quality of life, frailty, anxiety, depression, cognition, functional disability and anthropometric parameters. Kaplan–Meier method and Log-rank test were used to estimate and compare survival functions, respectively. Cox proportional hazard regression analysis was used to determine the predictors of mortality.
The mean age of the subjects was 71.5 ± 8.0 years and 234 (52.0%) were females. Overall, there were 99 (22.0%) in-hospital deaths. The median survival time (MST) was 36.0 ± 3.0 days [females = 40.0 ± 3.5 days vs males = 31.0 ± 4.5 days (p < 0.001)]. There was a significant negative correlation between MST and age (r = − 0.931). Predictors of mortality on Cox’s proportional hazard regression analyses were male sex HR = 2.03 (95% CI 1.27–3.24), severe frailty HR = 2.07 (1.02–4.20), cognitive impairment HR = 1.90 (1.14–3.17) and having ≥ 5 morbidities HR = 1.94 (1.14–3.30).
There was a high mortality among older patients particularly the frail, male or those with multiple morbidities. Prompt and holistic management of morbidities and targeted interventions for cognitive impairment and frailty are needed to improve survival during hospitalization.
KeywordsMedical ward Mortality Older patients Predictors Nigeria
Compliance with ethical standards
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
The University of Ibadan/University College Hospital Institutional Ethical Review Board (UI/UCH ERB) approved this research (reference number: UI/EC/12/0092).
Informed consent was obtained from all individual participants included in this study.
- 8.Naidoo A (2009) Trends in adult medical admissions at Tambo Memorial Hospital, Gauteng, between 2005 and 2007. Dissertation, University of the WitwatersrandGoogle Scholar
- 11.National Population Commission (2010) Population Distribution by Sex, State, LGA & Senatorial District. 2006 Popul Hous Census III:1–64Google Scholar
- 18.Stafford L, Berk M, Jackson HJ (2007) Validity of the hospital anxiety and depression scale and patient health questionnaire-9 to screen for depression in patients with coronary artery disease. Gen Hosp Psychiatry 29:417–424. https://doi.org/10.1016/j.genhosppsych.2007.06.005 CrossRefPubMedGoogle Scholar
- 19.Callahan CM, Unverzagt FW, Hui SL et al (2002) Six-item screener to identify cognitive impairment among potential subjects for clinical research. Med Care 40:771–781. https://doi.org/10.1097/01.MLR.0000024610.33213.C8 CrossRefPubMedGoogle Scholar
- 24.World Health Organisation (1995) Physical status: the use and interpretation of anthropometry: report of a WHO expert committee. World Health Organ Tech Rep Ser 854:1–452Google Scholar
- 25.Walters SJ (2009) What is a Cox model? Survival (Lond):1–8. http://www.whatisseries.co.uk
- 30.Benyamini Y, Blumstein T, Lusky A et al (2003) Gender differences in the self-rated health-mortality association: is it poor self-rated health that predicts mortality or excellent self-rated health that predicts survival? Gerontologist 43:396–405. https://doi.org/10.1093/geront/43.3.396 CrossRefPubMedGoogle Scholar