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A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population.

ABSTRACT: Socioeconomic status (SES) is an important predictor for outcomes of chronic diseases. However, it is often unavailable in clinical data. We sought to determine whether an individual housing-based SES index termed HOUSES can influence the likelihood of multiple chronic conditions (MCC) and hospitalisation in a community population.Participants were residents of Olmsted County, Minnesota, aged >18 years, who were enrolled in Mayo Clinic Biobank on 31 December 2010, with follow-up until 31 December 2011. Primary outcome was all-cause hospitalisation over 1 calendar-year. Secondary outcome was MCC determined through a Minnesota Medical Tiering score. A logistic regression model was used to assess the association of HOUSES with the Minnesota tiering score. With adjustment for age, sex and MCC, the association of HOUSES with hospitalisation risk was tested using the Cox proportional hazards model.Eligible patients totalled 6402 persons (median age, 57 years; 25th-75th quartiles, 45-68 years). The lowest quartile of HOUSES was associated with a higher Minnesota tiering score after adjustment for age and sex (OR (95% CI) 2.4 (2.0 to 3.1)) when compared with the highest HOUSES quartile. Patients in the lowest HOUSES quartile had higher risk of all-cause hospitalisation (age, sex, MCC-adjusted HR (95% CI) 1.53 (1.18 to 1.98)) compared with those in the highest quartile.Low SES, as assessed by HOUSES, was associated with increased risk of hospitalisation and greater MCC health burden. HOUSES may be a clinically useful surrogate for SES to assess risk stratification for patient care and clinical research.

SUBMITTER: Takahashi PY

PROVIDER: S-EPMC4852846 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population.

Takahashi Paul Y PY Ryu Euijung E Hathcock Matthew A MA Olson Janet E JE Bielinski Suzette J SJ Cerhan James R JR Rand-Weaver Jennifer J Juhn Young J YJ

Journal of epidemiology and community health 20151012 3

<h4>Background</h4>Socioeconomic status (SES) is an important predictor for outcomes of chronic diseases. However, it is often unavailable in clinical data. We sought to determine whether an individual housing-based SES index termed HOUSES can influence the likelihood of multiple chronic conditions (MCC) and hospitalisation in a community population.<h4>Methods</h4>Participants were residents of Olmsted County, Minnesota, aged >18 years, who were enrolled in Mayo Clinic Biobank on 31 December 20 ...[more]

PMID: 26458399

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Project description:BackgroundIndividuals of low socioeconomic status experience a disproportionate burden of chronic conditions; however it is unclear whether chronic condition burden affects survival differently across socioeconomic strata.MethodsThis retrospective cohort study used health administrative data from all residents of Ontario, Canada aged 65 to 105 with at least one of 16 chronic conditions on April 1, 2009 (n = 1,518,939). Chronic condition burden and unadjusted mortality were compared across neighborhood income quintiles. Multivariable Cox proportional hazards models were used to examine the effect of number of chronic conditions on two-year survival across income quintiles.FindingsPrevalence of five or more chronic conditions was significantly higher among older adults in the poorest neighborhoods (18.2%) than the wealthiest (14.3%) (Standardized difference > 0·1). There was also a socioeconomic gradient in unadjusted mortality over two years: 10.1% of people in the poorest neighborhoods died compared with 7.6% of people in the wealthiest neighborhoods. In adjusted analyses, having more chronic conditions was associated with a statistically significant increase in hazard of death over two years, however the magnitude of this effect was comparable across income quintiles. Individuals in the poorest neighborhoods with four chronic conditions had 2.07 times higher hazard of death (95% CI: 1.97-2.19) than those with one chronic condition, but this was comparable to the hazard associated with four chronic conditions in the wealthiest neighborhoods (HR: 2.29, 95% CI: 2.16-2.43).InterpretationAmong older adults with universal access to health care, the deleterious effect of increasing chronic condition burden on two-year hazard of death was consistent across neighborhood income quintiles once baseline differences in condition burden were accounted for. This may be partly attributable to equal access to, and utilization of, health care. Alternate explanations for these findings, including study limitations, are also discussed.

Project description:In 2006, the Centers for Medicare & Medicaid Services, which administers the Medicare program in the United States, launched the Chronic Condition Data Warehouse (CCW). The CCW contains all Medicare fee-for-service (FFS) institutional and non-institutional claims, nursing home and home health assessment data, and enrollment/eligibility information from January 1, 1999 forward for a random 5% sample of Medicare beneficiaries (and 100% of the Medicare population from 2000 forward). Twenty-one predefined chronic condition indicator variables are coded within the CCW, to facilitate research on chronic conditions. The current article describes this new data source, and the authors demonstrate the utility of the CCW in describing the extent of chronic disease among Medicare beneficiaries. Medicare claims were analyzed to determine the prevalence, utilization, and Medicare program costs for some common and high cost chronic conditions in the Medicare FFS population in 2005. Chronic conditions explored include diabetes, chronic obstructive pulmonary disease (COPD), heart failure, cancer, chronic kidney disease (CKD), and depression. Fifty percent of Medicare FFS beneficiaries were receiving care for one or more of these chronic conditions. The highest prevalence is observed for diabetes, with nearly one-fourth of the Medicare FFS study cohort receiving treatment for this condition (24.3 percent). The annual number of inpatient days during 2005 is highest for CKD (9.51 days) and COPD (8.18 days). As the number of chronic conditions increases, the average per beneficiary Medicare payment amount increases dramatically. The annual Medicare payment amounts for a beneficiary with only one of the chronic conditions is $7,172. For those with two conditions, payment jumps to $14,931, and for those with three or more conditions, the annual Medicare payments per beneficiary is $32,498. The CCW data files have tremendous value for health services research. The longitudinal data and beneficiary linkage within the CCW are features of this data source which make it ideal for further studies regarding disease prevalence and progression over time. As additional years of administrative data are accumulated in the CCW, the expanded history of beneficiary services increases the value of this already rich data source.

Project description:BackgroundReal-world information on socioeconomic differences within and between chronic conditions represents an important data source for treatments and decision-makers executing and prioritising healthcare resources.AimsThe aim of this study was to estimate the prevalence and mean of socioeconomic disparities from educational, income, and socioeconomic positions of 199 chronic conditions and disease groups, including sex and age group estimates, for use in planning of care services and prioritisation, by healthcare professionals, decision-makers and researchers.MethodsThe study population includes all Danish residents 16 years and above, alive on 1 January 2013 (n = 4,555,439). The data was established by linking seven national registers encompassing educational achievements, incomes, socioeconomic positions, hospital- and general practice services, and filled-in out-of-hospital prescriptions. The health register data were used to identify the 199+ chronic conditions. Socioeconomic differences were primarily measured as differences in educational prevalence levels from low to high educational achievements using a ratio. Furthermore, multiple binary logistic regression models were carried out to control for potential confounding and residual correlations of the crude estimates.ResultsThe prevalence of having one or more chronic conditions for patients with no educational achievement was 768 per thousand compared to 601.3 for patients with higher educational achievement (ratio 1.3). Across disease groups, the highest educational differences were found within disease group F-mental and behavioural (ratio 2.5), E-endocrine, nutritional and metabolic disease (ratio 2.4), I-diseases of the circulatory system (ratio 2.1) and, K-diseases of the digestive system (ratio 2.1). The highest educational differences among the 29 common diseases were found among schizophrenia (ratio 5.9), hyperkinetic disorders (ratio 5.2), dementia (ratio 4.9), osteoporosis (ratio 3.9), type 2 diabetes (ratio 3.8), chronic obstructive pulmonary disease COPD (ratio 3.3), heart conditions and stroke (ratios ranging from 2.3-3.1).ConclusionsA nationwide catalogue of socioeconomic disparities for 199+ chronic conditions and disease groups is catalogued and provided. The catalogue findings underline a large scope of socioeconomic disparities that exist across most chronic conditions. The data offer essential information on the socioeconomic disparities to inform future socially differentiated treatments, healthcare planning, etiological, economic, and other research areas.

Project description:ObjectivesEvolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation's healthcare costs. However, it is still not known precisely how MCC emerge and accumulate among individuals or in the general population. This study investigates major patterns of MCC transitions in a diverse population of patients and identifies the risk factors affecting the transition process.MethodsA Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major transitions of four MCC that include hypertension (HTN), depression, Post- Traumatic Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care during three or more years between 2002-2015, is used for training the proposed LRMCL algorithm.ResultsTwo major clusters of MCC transition patterns with 78% and 22% probability of membership respectively were identified. The primary cluster demonstrated the possibility of improvement when the number of MCC is small and an increase in probability of MCC accumulation as the number of co- morbidities increased. The second cluster showed stability (no change) of MCC overtime as the major pattern. Age was the most significant risk factor associated with the most probable cluster for each IAV.ConclusionsThese findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision- making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.

Project description:BACKGROUND:Approximately one-third of all adults worldwide are diagnosed with multiple chronic conditions (MCCs). The literature has identified several challenges facing providers and patients coping with managing MCCs in the community, yet few studies have considered their viewpoints in combination. A qualitative study involving healthcare providers and users was thus conducted to examine facilitators and barriers of managing patients with MCCs in the community in Singapore. METHODS:This study involves 26 semi-structured interviews with 10 physicians, 2 caregivers and 14 patients seeking treatment in the polyclinics that provide subsidised primary care services. Topic guides were developed with reference to the literature review, Chronic Care Model (CCM) and framework for patient-centred access to healthcare. RESULTS:Despite the perceived affordability and availability of the support system, some patients still encountered financial difficulties in managing care. These include inadequacy of the nation-wide medical savings scheme to cover outpatient treatment and medications. Half of healthcare users did not know where to seek help. While patients could access comprehensive services in polyclinics, those who did not visit the clinics might not receive timely care. Furthermore, patients reported long consultation waiting time. Physicians were able to propose and drive quality improvement projects to improve care quality. However, there were challenges to delivering safe and quality care with limited consultation duration due to the need to manage high patient load and waiting time, inadequate communication with specialists to coordinate care, and resource constraints in managing complex patients. Although providers could equip patients with self-management and lifestyle-related guidelines, patients' actions are influenced by multiple factors, including work requirements, beliefs and environment. CONCLUSIONS:There were barriers on care access, delivery and self-management. It is crucial to adopt a whole-of-society approach involving individuals, community, institutions and policymakers to improve and support MCC management. This study has also highlighted the importance of considering the different viewpoints of healthcare providers and users in policy formulation and community care planning.

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A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population.

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A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population.

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