Quantification of long-term survival, health care utilization, and costs of prolonged ventilator dependence informs patient/family decision-making, health care policy, and understanding of specialized weaning centers (SWCs) as alternate care models. Our objective was to compare survival trajectory, health care utilization, and costs of SWC survivors with a matched cohort of ≥ 21-d-stay ICU patients.This was a retrospective longitudinal (12 y) case-control study linking to health administrative databases with matching on age, sex, Charlson comorbidity index, income quintiles, and days in ICU and hospital in preceding 12 months.We matched 201 SWC subjects to 201 prolonged ICU survivors (402-subject cohort); 42% had a Charlson score of > 4. Risk of death at 12 months was lower in SWC subjects (hazard ratio [HR] 0.70 [95% CI 0.54-0.91]) adjusting for length of hospital admission (HR 1.02 [95% CI 1.00-1.04]) and number of care location transfers (HR 0.84 [95% CI 0.75-0.93]). By follow-up end, more SWC subjects died, 149 (73%) versus 127 (62%). We found no difference in discharge to home. At 12 months, acute health care utilization was comparable for the entire cohort, except hospital readmission rates (median interquartile range [IQR] 2 [1-3) vs 1 [1-2] d). Median (IQR) cost 12 months after unit discharge was CAD $68,165 ($19,894-$153,475). 12-month costs were higher in the SWC survivors (CAD $82,874 [$29,942-$224,965] vs CAD $55,574 [$6,572-$128,962], P < .001). SWC survivors had higher community health care utilization. Regression modeling demonstrated cost was associated with stay and care transfers but not SWC admission. Over 12-y follow-up, health care utilization and costs were higher in SWC survivors.SWC admission may confer some medium-term survival advantage; however, this may be influenced by selection bias associated with admission criteria.
Principal component analysis (PCA) is frequently adopted for creating socioeconomic proxies in order to investigate the independent effects of wealth on disease status. The guidelines and methods for the creation of these proxies are well described and validated. The Demographic and Health Survey, World Health Survey and the Living Standards Measurement Survey are examples of large data sets that use PCA to create wealth indices particularly in low and middle-income countries (LMIC), where quantifying wealth-disease associations is problematic due to the unavailability of reliable income and expenditure data. However, the application of this method to smaller survey data sets, especially in rural LMIC settings, is less rigorously studied.In this paper, we aimed to highlight some of these issues by investigating the association of derived wealth indices using PCA on risk of vector-borne disease infection in Tanzania focusing on malaria and key arboviruses (ie, dengue and chikungunya). We demonstrated that indices consisting of subsets of socioeconomic indicators provided the least methodologically flawed representations of household wealth compared with an index that combined all socioeconomic variables. These results suggest that the choice of the socioeconomic indicators included in a wealth proxy can influence the relative position of households in the overall wealth hierarchy, and subsequently the strength of disease associations. This can, therefore, influence future resource planning activities and should be considered among investigators who use a PCA-derived wealth index based on community-level survey data to influence programme or policy decisions in rural LMIC settings.
IntroductionIn Ontario, the top 5% of high-cost users account for 66% of health care costs. The heavy use of resources combined with perceived inefficiencies offer an imperative to target strategies to redesign care to better meet patient needs and increase value.
Objectives and ApproachAs part of a request submitted to the Applied Health Research Question (AHRQ) review team, the main objective of this study was to identify drivers of high health care use in Ontario in order to find better ways to improve the efficiency in healthcare delivery. Using data in fiscal year 2012/13, characteristics of the top 5% of high costs users were described, and further stratified by mental health status. Total spending by sector of care were also described. Data were linked including physician, hospital, medication and long term care databases for each patient.
ResultsIn the top 5% of high-cost users, there were 729,870 patients who accounted for $20,179,208,348 of total healthcare spending in 2012/13, with the highest percentage of spending observed among older adults aged 61-80 years old. Mental health high-cost patients accounted for 6.1% of these patients, of which 51.5% were female, had a low socio-economic status and an average age of 44 years. These patients had an average of 4.9 (SD=2.3) ICD chapters and used an average of 8.7 (SD=3.8) drugs. Using the health accounts methodology (ICHA), as described by the OECD and WHO, over 90% of healthcare costs among the top 5% of high-cost patients were from inpatient care, day surgery and clinic care, physician care, outpatients drugs and inpatient rehabilitation and complex/continuing care.
Conclusion/ImplicationsThis study provides a systematic description of the needs in a high cost patient group, and serves as a platform for international comparisons across healthcare systems to better understand gaps and identify targets for intervention. These cross-comparisons offer a tool to evaluate performance of healthcare systems and to prioritize policies.
