Health outcomes evaluation seeks to compare a new treatment or novel programme with the current standard of care, or to identify variation of outcomes across different healthcare providers. In the real world, it is not always possible to conduct randomised controlled trials to address the issue of comparator groups being different with respect to baseline risk factors for the outcomes. Therefore, risk adjustment is required to address patient factors that may lead to biases in estimates of treatment effects. It is essential when conducting outcomes evaluation of more than trivial significance. Risk adjustment begins by asking 4 questions: what outcome, what time frame, what population, and what purpose. Next, design issues are considered. This involves choosing the data source, planning data collection, defining the sample required, and selecting the variables carefully. Finally, analytical issues are considered. Regression modelling is central to every analytic strategy. Other methods that may augment regression include restriction, stratification, propensity scores, instrumental variables, and difference-in-differences. The construction of risk adjustment models is an iterative process requiring both art and science. Derived models should be validated. Limitations of risk adjustment include reliance on data availability and quality, imperfect method, ineffectiveness when comparators are very different, and sensitivity to different methods used. Thoughtful application of risk adjustment can improve the validity of comparisons between different treatments, programmes and providers. The extent of risk adjustment should be guided by its purpose. Finally, its methodology should be made explicit, so that informed readers can judge the robustness of results obtained.
Health Services Research ; Outcome Assessment (Health Care) ; Regression Analysis ; Risk Adjustment ; standards

INTRODUCTIONThe objective of this study was to determine factors, other than the Diagnostic Related Grouping (DRG), that can explain the variation in the cost of hospitalisation and length of hospital stay (LOS) in older patients.

MATERIALS AND METHODSThis was a prospective, observational cohort study involving 397 patients, aged 65 years and above. Data collected include demographic information, admission functional and cognitive status, overall illness severity score, number of referral to therapists, referral to medical social worker, cost of hospitalisation, actual LOS, discharge DRG codes and their corresponding trimmed average length of stay (ALOS).

RESULTSThe mean age of the cohort was 80.2 years. The DRG's trimmed ALOS alone explained 21% of the variation in the cost of hospitalisation and actual LOS. Incorporation of an illness severity score, number of referral to therapists and referral to medical social worker into the trimmed ALOS explained 30% and 31% of the variation in the cost and actual LOS, respectively.

CONCLUSIONThe DRG model is able to explain 21% of the variation in the cost of hospitalisation and actual LOS in older patients. Other factors that determined the variation in the cost of hospitalisation and LOS include the degree of illness severity, the number of referral to therapists and referral to medical social worker.

Age Factors ; Aged ; Confidence Intervals ; Diagnosis-Related Groups ; Female ; Frail Elderly ; statistics & numerical data ; Health Resources ; economics ; statistics & numerical data ; Health Status Indicators ; Hospitalization ; economics ; statistics & numerical data ; Humans ; Length of Stay ; statistics & numerical data ; trends ; Linear Models ; Male ; Prospective Studies ; Psychometrics ; Referral and Consultation ; Reproducibility of Results ; Severity of Illness Index ; Singapore ; Statistics, Nonparametric

INTRODUCTIONThe subacute care unit in Tan Tock Seng Hospital (TTSH) was set up in May 2009. We examined its impact on the transitions at the nexus between hospital and community sectors, patients' discharge destination and functional performance.

MATERIALS AND METHODSWe studied patients admitted during the initial 6-month period (May to October 2009). Differences in demographics, length of stay (LOS), comorbidity and severity of illness measures, functional outcomes (modified Barthel Index (MBI)) according to discharge destinations were obtained. We also studied the impact of LOS on the geriatric department and the bill size over the pre- and post-subacute implementation periods.

RESULTSMajority of the subacute patients' hospital stay was in subacute care. Of these patients, 44.9% were discharged home, 24.2% to a slow stream rehabilitation (SSR) setting and 29.2% to nursing homes. 16.9% consisted of a subgroup of dementia patients requiring further behavioural and functional interventions, of which 50% managed to be discharged home. Functional gains were seen during subacute stay; with greatest gains observed in the SSR group. There were no differences in overall LOS nor total bill size (DRG-adjusted) for the geriatric medicine department during the first 6 months of operating this new subacute model compared with the prior 4-month period.

CONCLUSIONWe propose this subacute model of geriatric care, which allows right-siting of care and improved functional outcomes. It fulfills the role easing transitions between acute hospital and community sectors. In particular, it provides specialised care to a subgroup of dementia patients with challenging behaviours and is fiscally sound from the wider hospital perspective.

Aged ; Aged, 80 and over ; Analysis of Variance ; Female ; Frail Elderly ; Geriatric Assessment ; methods ; Health Services for the Aged ; Health Status Indicators ; Humans ; Length of Stay ; Male ; Models, Organizational ; Retrospective Studies ; Singapore ; Treatment Outcome

Biomedical Research ; Frailty ; Geriatrics ; Health Policy ; Health Services ; Humans ; Practice Guidelines as Topic ; Singapore

INTRODUCTION: The rising prevalence of multiple chronic diseases is an important public health issue as it is associated with increased healthcare utilisation. This paper aimed to explore the annual per capita healthcare cost in primary care for patients with multiple chronic diseases (multimorbidity). METHODS: This was a retrospective cohort study conducted in a cluster of public primary care clinics in Singapore. De-identified data from electronic medical records were extracted from July 2015 to June 2017. Only patients with at least 1 chronic disease were included in the study. Basic demographic data and healthcare cost were extracted. A list of 20 chronic diseases was considered for multimorbidity. RESULTS: There were 254,377 patients in our study population, of whom 52.8% were female. The prevalence of multimorbidity was 62.4%. The median annual healthcare cost per capita for patients with multimorbidity was about twice the amount compared to those without multimorbidity (SGD683 versus SGD344). The greatest percentage increment in cost was when the number of chronic diseases increased from 2 to 3 (43.0%). CONCLUSION Multimorbidity is associated with higher healthcare cost in primary care. Since evidence for the optimal management of multimorbidity is still elusive, prevention or delay in the onset of multimorbidity in the general population is paramount.
Chronic Disease ; Comorbidity ; Cross-Sectional Studies ; Female ; Health Care Costs ; Humans ; Prevalence ; Primary Health Care ; Retrospective Studies ; Singapore/epidemiology*