Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.
ABSTRACT: BACKGROUND:For population health management, it is important to have health indices that can monitor prevailing health trends in the population. Traditional health indices are generally measurable at different geographical levels with varied number of health dimensions. The aim of this work was to develop and validate a scalable and extendable multi-dimensional health index based on individual data. METHODS:We defined health to be made up of five different domains: Physical, Mental, Social, Risk, and Healthcare utilization. Item response theory was used to develop models to compute domain scores and a health index. These were normalized to represent an individual's health percentile relative to the population (0 = worst health, 100 = best health). Data for the models came from a longitudinal health survey on 1,942 participants. The health index was validated using age, frailty, post-survey one-year healthcare utilization and one-year mortality. RESULTS:The Spearman rho between the health index and age, frailty and post-survey one-year healthcare utilization were -0.571, -0.561 and -0.435, respectively, with all p<0.001. The area under the Receiver Operating Characteristic curve (AUROC) for post-survey one-year mortality was 0.930. An advantage of the health index is that it can be calculated using different sets of questions and the number of questions can be easily expanded. CONCLUSION:The health index can be used at the individual, program, local, regional or national level to track the state of health of the population. When used together with the domain scores, it can identify regions with poor health and deficiencies within each of the five health domains.
Project description:on an individual level, lower-income has been associated with disability, morbidity and death. On a population level, the relationship of economic indicators with health is unclear.the purpose of this study was to evaluate relative fitness and frailty in relation to national income and healthcare spending, and their relationship with mortality.secondary analysis of data from the Survey of Health, Ageing and Retirement in Europe (SHARE); a longitudinal population-based survey which began in 2004.a total of 36,306 community-dwelling people aged 50 and older (16,467 men; 19,839 women) from the 15 countries which participated in the SHARE comprised the study sample. A frailty index was constructed as the proportion of deficits present in relation to the 70 deficits available in SHARE. The characteristics of the frailty index examined were mean, prevalence of frailty and proportion of the fittest group.the mean value of the frailty index was lower in higher-income countries (0.16 ± 0.12) than in lower-income countries (0.20 ± 0.14); the overall mean frailty index was negatively correlated with both gross domestic product (r = -0.79; P < 0.01) and health expenditure (r = -0.63; P < 0.05). Survival in non-frail participants at 24 months was not associated with national income (P = 0.19), whereas survival in frail people was greater in higher-income countries (P < 0.05).a country's level of frailty and fitness in adults aged 50+ years is strongly correlated with national economic indicators. In higher-income countries, not only is the prevalence of frailty lower, but frail people also live longer.
Project description:The European Health Interview Survey (EHIS) is run every 5 years to examine how people experience and rank their health, how they care about their health, and to what extent they use the healthcare services. We identified the sub-population of special interest, i.e., cardiovascular disease (CVD) patients older than 65 years, in this cross-sectional study from the Serbian national survey of population health (2568 persons from a total of 15,999 subjects surveyed). We performed univariable and multivariable logistic regression analysis to assess the correlation between the healthcare system utilization and identified demographic, geographic, socio-economic, and self-rated factors. The most important factor for the utilization of the primary and the specialist healthcare services by elderly CVD patients is the region where one lives (Southern and Eastern Serbia OR = 2.44, 95% CI = 1.58-3.77/Belgrade OR = 1.75, 95% CI = 1.32-2.30). Age is another factor, where the 65 to 74 years old CVD patients utilize healthcare services the most. Higher education (OR = 1.80, 95% CI = 1.31-2.47), being a part of the highest Wealth Index group (OR = 1.62, 95% CI = 1.10-2.40), having very poor health status (OR = 3.02, 95% CI = 1.41-6.47), and presence of long-term illness (OR = 1.49, 95% CI = 1.16-1.92), play an important role in the utilization of the specialist care only.
Project description:Background:Frailty is a key determinant of health status and outcomes of health care interventions in older adults that is not readily measured in Medicare data. This study aimed to develop and validate a claims-based frailty index (CFI). Methods:We used data from Medicare Current Beneficiary Survey 2006 (development sample: n = 5,593) and 2011 (validation sample: n = 4,424). A CFI was developed using the 2006 claims data to approximate a survey-based frailty index (SFI) calculated from the 2006 survey data as a reference standard. We compared CFI to combined comorbidity index (CCI) in the ability to predict death, disability, recurrent falls, and health care utilization in 2007. As validation, we calculated a CFI using the 2011 claims data to predict these outcomes in 2012. Results:The CFI was correlated with SFI (correlation coefficient: 0.60). In the development sample, CFI was similar to CCI in predicting mortality (C statistic: 0.77 vs. 0.78), but better than CCI for disability, mobility impairment, and recurrent falls (C statistic: 0.62-0.66 vs. 0.56-0.60). Although both indices similarly explained the variation in hospital days, CFI outperformed CCI in explaining the variation in skilled nursing facility days. Adding CFI to age, sex, and CCI improved prediction. In the validation sample, CFI and CCI performed similarly for mortality (C statistic: 0.71 vs. 0.72). Other results were comparable to those from the development sample. Conclusion:A novel frailty index can measure the risk for adverse health outcomes that is not otherwise quantified using demographic characteristics and traditional comorbidity measures in Medicare data.
Project description:Frail patients may have heightened risk of dysphagia, a potentially modifiable health factor. Our aim is to examine whether the relationship between dysphagia and adverse health outcomes differs by frailty conditions among inpatients???50 years of age. Medical or surgical hospitalizations among patients???50 years of age in the Healthcare Cost and Utilization Project's National Inpatient Sample from 2014 through the first three quarters of 2015 were included. Adverse outcomes included length of stay (LOS), hospital costs, in-hospital mortality, discharge status, and medical complications. Dysphagia was determined by ICD-9-CM codes. Frailty was defined as (a)???1 condition in the10-item Johns Hopkins Adjusted Clinical Groups (ACG) frailty measure and a frailty index for the (b) ACG and (c) a 19-item Frailty Risk Score (FRS) categorized as non-frail, pre-frail, and frail. Weighted generalized linear models for complex survey designs using generalized estimating equations were performed. Of 6,230,114 unweighted hospitalizations, 4.0% had a dysphagia diagnosis. Dysphagia presented in 3.1% and 11.0% of non-frail and frail hospitalizations using the binary ACG (p?<?0.001) and in 2.9%, 7.9%, and 16.0% of non-frail, pre-frail, and frail hospitalizations using the indexed FRS (p?<?0.001). Dysphagia was associated with greater LOS, higher total costs, increased non-routine discharges, and more medical complications among both frail and non-frail patients using the three frailty definitions. Dysphagia was associated with adverse outcomes in both frail and non-frail medical or surgical hospitalizations. Dysphagia management is an important consideration for providers seeking to reduce risk in vulnerable populations.
Project description:Although critical care physicians view obesity as an independent poor prognostic marker, growing evidence suggests that obesity is, instead, associated with improved mortality following ICU admission. However, this prior empirical work may be biased by preferential admission of obese patients to ICUs, and little is known about other patient-centered outcomes following critical illness. We sought to determine whether 1-year mortality, healthcare utilization, and functional outcomes following a severe sepsis hospitalization differ by body mass index.Observational cohort study.U.S. hospitals.We analyzed 1,404 severe sepsis hospitalizations (1999-2005) among Medicare beneficiaries enrolled in the nationally representative Health and Retirement Study, of which 597 (42.5%) were normal weight, 473 (33.7%) were overweight, and 334 (23.8%) were obese or severely obese, as assessed at their survey prior to acute illness. Underweight patients were excluded a priori.None.Using Medicare claims, we identified severe sepsis hospitalizations and measured inpatient healthcare facility use and calculated total and itemized Medicare spending in the year following hospital discharge. Using the National Death Index, we determined mortality. We ascertained pre- and postmorbid functional status from survey data. Patients with greater body mass indexes experienced lower 1-year mortality compared with nonobese patients, and there was a dose-response relationship such that obese (odds ratio = 0.59; 95% CI, 0.39-0.88) and severely obese patients (odds ratio = 0.46; 95% CI, 0.26-0.80) had the lowest mortality. Total days in a healthcare facility and Medicare expenditures were greater for obese patients (p < 0.01 for both comparisons), but average daily utilization (p = 0.44) and Medicare spending were similar (p = 0.65) among normal, overweight, and obese survivors. Total function limitations following severe sepsis did not differ by body mass index category (p = 0.64).Obesity is associated with improved mortality among severe sepsis patients. Due to longer survival, obese sepsis survivors use more healthcare and result in higher Medicare spending in the year following hospitalization. Median daily healthcare utilization was similar across body mass index categories.
Project description:OBJECTIVES:To determine the association of the frailty phenotype with subsequent healthcare costs and utilization. DESIGN:Prospective cohort study (Study of Osteoporotic Fractures (SOF)). SETTING:Four U.S. sites. PARTICIPANTS:Community-dwelling women (mean age 80.2) participating in SOF Year 10 (Y10) examination linked with their Medicare claims data (N=2,150). MEASUREMENTS:At Y10, frailty phenotype defined using criteria similar to those used in the Cardiovascular Health Study frailty phenotype and categorized as robust, intermediate stage, or frail. Participant multimorbidity burden ascertained using claims data. Functional limitations assessed by asking about difficulty performing instrumental activities of daily living. Total direct healthcare costs and utilization ascertained during 12 months after Y10. RESULTS:Mean total annualized cost±standard deviation (2014 dollars) was $3,781±6,920 for robust women, $6,632±12,452 for intermediate stage women, and $10,755?±?16,589 for frail women. After adjustment for age, site, multimorbidity burden, and cognition, frail women had greater mean total (cost ratio (CR)=1.91, 95% confidence interval (CI)=1.59-2.31) and outpatient (CR=1.55, 95% CI=1.36-1.78) costs than robust women and greater odds of hospitalization (odds ratio (OR)=2.05, 95% CI=1.47-2.87) and a skilled nursing facility stay (OR=3.85, 95% CI=1.88-7.88). There were smaller but significant effects of the intermediate stage category on these outcomes. Individual frailty components (shrinking, poor energy, slowness, low physical activity) were also each associated with higher total costs. Functional limitations partially mediated the association between the frailty phenotype and total costs (CR further adjusted for self-reported limitations=1.32, 95% CI=1.07-1.63 for frail vs robust; CR=1.35, 95% CI=1.18-1.55 for intermediate stage vs robust women). CONCLUSION:Intermediate stage and frail older community-dwelling women had higher subsequent total healthcare costs and utilization after accounting for multimorbidity and functional limitations. Frailty phenotype assessment may improve identification of older adults likely to require costly, extensive care.
Project description:Frailty is an independent age-associated predictor of morbidity and mortality. Despite this, many countries lack population estimates with large heterogeneity between studies. No population-based standardised metric for frailty is available. We applied the deficit accumulation model of frailty to create a frailty index (FI) using population-level estimates from the Global Burden of Disease (GBD) 2017 study across 195 countries to create a novel GBD frailty index (GBD-FI). Standard FI criteria were applied to all GBD categories to select GBD-FI items. Content validity was assessed by comparing the GBD-FI with a selection of established FIs. Properties including the rate of deficit accumulation with age were examined to assess construct validity. Linear regression models were created to assess if mean GBD-FI scores predicted one-year incident mortality. From all 554 GBD items, 36 were selected for the GBD-FI. Face validity against established FIs was variable. Characteristic properties of a FI-higher mean score for females and a deficit accumulation rate of approximately 0.03 per year, were observed. GBD-FI items were responsible for 19% of total Disability-Adjusted Life Years for those aged ?70 years in 2017. Country-specific mean GBD-FI scores ranged from 0.14 (China) to 0.19 (Hungary) and were a better predictor of mortality from non-communicable diseases than age, gender, Healthcare Access and Quality Index or Socio-Demographic Index scores. The GBD-FI is a valid measure of frailty at population-level but further external validation is required.
Project description:BACKGROUND:To overcome the limitations of administrative data in adequately adjusting for differences in patients' risk of readmissions, recent studies have added supplemental data from patient surveys and other sources (e.g., electronic health records). However, judging the adequacy of enhanced risk adjustment for use in assessment of 30-day readmission as a hospital quality indicator is not straightforward. In this paper, we evaluate the adequacy of risk adjustment by comparing the one-year costs of those readmitted within 30 days to those not after excluding the costs of the readmission. METHODS:In this two-step study, we first used comprehensive administrative and survey data on a nationally representative Medicare cohort of hospitalized patients to compare patients with a medical admission who experienced a 30-day readmission to patients without a readmission in terms of their overall Medicare payments during 12 months following the index discharge. We then examined the extent to which a series of enhanced risk adjustment models incorporating code-based comorbidities, self-reported health status and prior healthcare utilization, reduced the payment differences between the admitted and not readmitted groups. RESULTS:Our analytic cohort consisted 4684 index medical hospitalization of which 842 met the 30-day readmission criteria. Those readmitted were more likely to be older, White, sicker and with higher healthcare utilization in the previous year. The unadjusted subsequent one-year Medicare spending among those readmitted ($56,856) was 60% higher than that among the non-readmitted ($35,465). Even with enhanced risk adjustment, and across a variety of sensitivity analyses, one-year Medicare spending remained substantially higher (46.6%, p < 0.01) among readmitted patients. CONCLUSIONS:Enhanced risk adjustment models combining health status indicators from administrative and survey data with previous healthcare utilization are unable to substantially reduce the cost differences between those medical admission patients readmitted within 30 days and those not. The unmeasured patient severity that these cost differences most likely reflect raises the question of the fairness of programs that place large penalties on hospitals with higher than expected readmission rates.
Project description:To examine the patterns of health care utilization by the elderly and test the influence of functional decline.We used the three regular waves of the SHARE survey to estimate the influence of frailty on health care utilization in 10 European countries. We controlled for the main correlates of frailty and unobserved individual effects.The frail elderly increase their primary and hospital care utilization before the onset of disability. Multimorbidity moderates the effect of frailty on care utilization.The prevalence of frailty is high in most countries and is expected to increase. This renders frailty prevention and remediation efforts imperative for two complementary reasons: to promote healthier aging and to reduce the burden on health systems.
Project description:BACKGROUND:A rapidly ageing population with increasing prevalence of chronic disease presents policymakers the urgent task of tailoring healthcare services to optimally meet changing needs. While healthcare needs-based segmentation is a promising approach to efficiently assessing and responding to healthcare needs at the population level, it is not clear how available schemes perform in the context of community-based surveys administered by non-medically trained personnel. The aim of this prospective cohort, community setting study is to evaluate 4 segmentation schemes in terms of practicality and predictive validity for future health outcomes and service utilization. METHODS:A cohort was identified from a cross-sectional health and social characteristics survey of Singapore public rental housing residents aged 60?years and above. Baseline survey data was used to assign individuals into segments as defined by 4 predefined population segmentation schemes developed in Singapore, Delaware, Lombardy and North-West London. From electronic data records, mortality, hospital admissions, emergency department visits, and specialist outpatient clinic visits were assessed for 180?days after baseline segment assignment and compared to segment membership for each segmentation scheme. RESULTS:Of 1324 residents contacted, 928 agreed to participate in the survey (70% response). All subjects could be assigned an exclusive segment for each segmentation scheme. Individuals in more severe segments tended to have lower quality of life as assessed by the EQ-5D Index for health utility. All population segmentation schemes were observed to exhibit an ability to differentiate different levels of mortality and healthcare utilization. CONCLUSIONS:It is practical to assign individuals to healthcare needs-based population segments through community surveys by non-medically trained personnel. The resulting segments for all 4 schemes evaluated in this way have an ability to predict health outcomes and utilization over the medium term (180?days), with significant overlap for some segments. Healthcare needs-based segmentation schemes which are designed to guide action hold particular promise for promoting efficient allocation of services to meet the needs of salient population groups. Further evaluation is needed to determine if these schemes also predict responsiveness to interventions to meet needs implied by segment membership.