Objective: To explore correclational relationship between the risk factors and medical resources consumption in cardiovascular disease patients. Methods: A database containing 44940 cardiovascular disease patients in a southern Taiwan Medical center from year 2003 to 2005 was chosen as studying sample. By applying Charlson Comorbidity Index as one of the major risk factors to these cardiovascular disease patients, then using liner regression to analyze the data for their relationship with medical resources consumption. Results: Medical resources consumption increases as the cardiovascular disease patients become older. The higher the comorbidity index weight with the patient, the more the patient consumes medical resources. A patient¡¦s comorbidity index weight has great influence to the length of hospital stay of the patient and the frequency of clinic visit of the patient. The higher the comorbidity index weight a patient he or she, is likely to stay in hospital longer or to see doctors more often. The variance inflation factor (VIF) of each independent variable is ranged between 1 and 1.343, which means there are no high correlations between independent variables in the modes. In other words, there is no correlative influence effect that would invalidate the assumption. Conclusion: The findings of this study can be a good reference to hospital disease management. In addition, they can be applied to predicting the risk factor in medical resources consumption. Also they can be adapted into medical insurance payment system, thereby improving medical resource distribution.

Research Objectives: Osteoporosis has become a significant public health problem in recent years, especially with the growth of the elderly population. Osteoporotic fractures exact a terrible toll on the population with respect to morbidity, cost, and to a lesser extent mortality. These effects can lead to psychological problems, social consequences, functional limitations, and poor quality of life. Thus, knowledge regarding osteoporotic fractures is needed to evaluate the impact of osteoporotic fractures on society, to identify high-risk populations, and to help policymakers to allocate resources accordingly. This study aims to investigate the influence factors of hospital readmissions among osteoporotic fractures patients in Taiwan, and the study results are expected to increase our understanding of the magnitude of the elderly population suffering from osteoporotic fractures and to urge policymakers to develop effective national prevention strategies. Study Design: Using Taiwan¡¦s National Health Insurance database, we identified elderly patients with a hospitalization for osteoporotic fractures between 2001 and 2007. We divided readmissions into different groups (14-day, 30-day, 180-day and over 180-day) and evaluated each group¡¦s demographic, hospital characteristics, and Charlson Comorbidity Index. The claims data are also used to calculate the health services utilization of osteoporotic fractures among those elderly patients with or without readmission of osteoporotic fractures. The data analyses were carried out by Chi-square test, t test, multiple linear regression and multivariate logistic regression. Population Studied: Patients aged 50 or older with osteoporotic fractures were identified based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Principle Findings: Among 5483 osteoporotic fractures patients, 6.9% of them were readmitted within 14 days, 34.7% were readmitted within 30 days and 13.9% were readmitted within 180 days. The medical resource utilizations were significantly higher in patients with readmissions than those without readmission. Age and Charlson Comorbidity Index were significantly affected the probabilities of readmissions. Conclusion: From the perspective of health policy, the issue of osteoporotic fractures will become increasingly important in the future. This national study will help raise awareness of osteoporotic fractures and hopefully motivate public health policy makers to develop effective national prevention strategies against osteoporosis to prevent osteoporotic fractures.

BACKGROUND: Morbidity, defined as disease history, is an important and well-known confounder in epidemiologic studies. Numerous methods have been developed over the last 30 years to measure morbidity via valid and reliable processes. OBJECTIVE: The goal of the current study was to evaluate, via comparative predictive validity assessment, the Centers for Medicaid and Medicare Studies Hierarchical Condition Category (CMS-HCC) comorbidity model for its ability to improve the prediction of 12-month all-cause mortality among a Medicare population compared to previously published comorbidity index models. There were three specific aims: (1) challenge the current state of risk adjustment among aged populations via an evaluation of the comparative predictive validity of one novel and four existing models to predict all-cause mortality within 12 months among a heterogeneous population of Medicare beneficiaries; (2) Investigate the comparative predictive validity of the five models to predict all-cause mortality within 12 months among two homogenous populations diagnosed with ischemic heart disease and selected cancers, including prostate cancer, lung cancer, colorectal cancer, breast cancer, pancreas cancer, and endometrial cancer; and (3) measure each comorbidity model's ability to control for a known example of confounding by indication. METHODS: A retrospective cohort design was used for all specific aims. Study 1 included 257,641 Medicare beneficiaries enrolled in three Medicare Advantage prescription drug health plans in Alabama, Florida, or Ohio in 2010 and 2011. Study 2 limited analysis to 14,260 and 66,440 beneficiaries with administrative evidence of selected cancers or ischemic heart disease in 2010, respectively. Study 3 limited analysis to the beneficiaries with ischemic heart disease. For each participant, comorbidity risk scores for the following five models were generated using administrative data from 2010: an age/sex model, the Romano adaption of the Charlson Comorbidity Index (CCI) model, the Putnam adaptation of the Chronic Disease Score Model (CDS), the CMS version of the Hierarchical Condition Category (CMS-HCC) model, and the Agency for Healthcare Research and Quality (AHRQ) adaptation of the Elixhauser model. The prospective predictive validity of the models to predict all-cause mortality during 2011 was compared via the c statistic test. Participants with ischemic heart disease were randomly allocated retrospectively to either 1) a group that had "received" a hypothetical "Drug A" in 2010 or 2) a group that had "received" a hypothetical "Drug B" in 2010. In order to evaluate the impact of confounding by indication, a weighting factor was applied to the randomization process in order to force the 33,220 participants randomized to "Drug A" to have a 2.736 times higher likelihood of having at least one acute inpatient hospitalization in 2010. Each comorbidity model's ability to control for the contrived confounding by indication was evaluated via relative risk of death. RESULTS: The CMS-HCC model had statistically significant higher c-statistic values than all four existing comorbidity indices among the heterogeneous Medicare Advantage population (N=257,641) and the homogeneous populations with breast cancer (N=4,160) and prostate cancer (N=6,594). The CMS-HCC model displayed similar performance for lung cancer (N=1,384), colorectal cancer (N=1,738), endometrial cancer (N=232), and ischemic heart disease (N=66,640) and statistically significant lower performance for pancreas cancer (N=152). The log-transformed CMS-HCC model was the only model to generate a non-significant association between exposure to "Drug A" and subsequent mortality. CONCLUSION: In general, the CMS-HCC model is the preferred comorbidity measure due to its predictive performance. However, other comorbidity models may be optimal for diseases with low prevalence and/or high mortality. Researchers should carefully and thoughtfully select a comorbidity model to assess the existence and direction of confounding. The CMS-HCC model should be log-transformed when used as a dependent variable since the score is a ratio level measurement that displays a normal distribution when log transformed. The resulting score is less likely to violate the assumptions (i.e. violations of normality) of common statistical models due to extreme values. The national availability of CMS-HCC scores for all Medicare beneficiaries provides researchers with access to a new tool to measure co-morbidity among older Americans using an empirically weighted, single score. In terms of policy, it is recommended that CMS produce CMS-HCC scores for all Medicare beneficiaries on a rolling 12 month basis for each month during the year. The availability of monthly scores would increase the ease of use of the score, as well as help facilitate more rapid adoption of the tool.

Objective: The objective of this study was to compare the use of different comorbidity measures to predict future healthcare utilization and expenditures for diabetic patients. Methods: This was a retrospective study that included 8,704 diabetic patients enrolled continuously for three years in the Department of Defense TRICARE program. Administrative claims data were used to calculate six comorbidity measures: number of distinct medications, index-year healthcare expenditures, two versions of the Charlson Comorbidity Index (CCI), and two versions of the Chronic Disease Score (CDS). Linear regression models were used to estimate three health outcomes for one- and two-year post-index periods: healthcare expenditures (COST), number of hospitalizations (HOS), and number of emergency department visits (ED). Logistic regression models were used to estimate binary outcomes (above or below the 90th percentile of COST; [greater than or equal to] 1 HOS or none; [greater than or equal to] 1 ED or none). Comparisons were based on adjusted R², areas under the receiver-operator-curve (c statistics), and the Hosmer-Lemeshow goodness-of-fit tests. Results: The study population had a mean age of 51.0 years (SD = 10.5), and 46.3 percent were male. After adjusting for age and sex, the updated CCI was the best predictor of one-year and two-year HOS (adjusted R² = 8.1%, 9.3%), the number of distinct medications was superior in predicting one-year and two-year ED (adjusted R² = 9.9%, 12.4%), and the index-year healthcare expenditures explained the most variance in one-year and two-year COST (adjusted R² = 35.6%, 31.6%). In logistic regressions, the number of distinct medications was the best predictor of one-year and two-year risks of emergency department use (c = 0.653, 0.654), but the index-year healthcare expenditures performed the best in predicting one-year and two-year risks of hospitalizations (c = 0.684, 0.676) and high-expenditure cases (c = 0.810, 0.823). The updated CCI consistently outperformed the original CCI in predicting the outcomes of interest. Conclusions: In a diabetic population under age 65, the number of distinct medications and baseline healthcare expenditures appeared to have superior or similar powers compared to the CCI or CDS for the prediction of future healthcare utilization and expenditures. The updated CCI was a better predictor than the original CCI in this population. / text

No description available.

The purpose of this work was to further understand nutritional status, especially body weight and composition, during colorectal cancer progression. Population-based studies of colorectal cancer patients were conducted using administrative health data (primary and co-morbid diseases, demographics), and computed tomography (CT) imaging (body composition). In cohort 1, administrative health data was used to study comorbidities and nutritional status in 574 colorectal cancer patients referred for chemotherapy. Multivariate Cox regression revealed several comorbidities, performance status and weight loss 20% predicted survival. In cohort 2, a serial CT image analysis assessed longitudinal body composition changes during the last 12 months preceding death from colorectal cancer (n=34). Body composition changes were typified by exponential increases in liver metastases with concurrent accelerations of muscle and fat loss. These results have the potential to make a difference in how colorectal cancer patients are treated and researched by dietitians, oncologists, and health services researchers. / Nutrition and Metabolism

Background/Objectives: West Nile neuroinvasive disease (WNND) is a severe neurological illness that develops in approximately 1% of individuals infected with West Nile virus (WNV). Manifesting most frequently as encephalitis (WNE), meningitis (WNM), or acute flaccid paralysis (WNP), there is no cure for WNND beyond supportive care and rehabilitation, and death or permanent disability are common outcomes. As the virus arrived in North America less than 20 years ago, determinants of severe disease progression following infection are still being explored. This project is the first to examine comorbid conditions as risk factors of WNND in Ontario using a population-based study design. As prevention is the only avenue of defence against WNND, identifying comorbid risk factors of WNND would allow for public health prevention campaigns targeted to high-risk groups. The main objectives of this thesis were to explore whether pre-existing chronic diseases were associated with the development of WNND, or any of its three manifestations (i.e., encephalitis, meningitis, acute flaccid paralysis). Methods: This was a retrospective, population-based study including all Ontario residents with a confirmed diagnosis of WNV infection between January 1, 2002 and December 31, 2012. A cohort of individuals with WNV was identified from a provincial laboratory database and individually-linked to health administrative databases. In the WNV cohort, individuals with WNND and 13 comorbid conditions were identified using algorithms based on ICD-10-CA diagnostic codes. Incidence of WNND following WNV infection was then compared among individuals with and without comorbid conditions using relative risks estimated by log binomial regression. Additionally, risk ratios were calculated for associations between specific comorbid conditions and WNND neuroinvasive manifestation (i.e., encephalitis, meningitis, acute flaccid paralysis). Finally, associations between Charlson Comorbidity Index (CCI) scoring and development of WNND was examined through calculation of relative risk using log binomial regression. Results/Potential Impact: Risk factors for WNND included male sex (aRR: 1.21; 95% CI: 1.00-1.46) in addition to the combined effect of hypertension and increasing age (5-year intervals) (aRR: 1.16; 95% CI: 1.08-1.24); WNND was also associated with increasing CCI scores; individuals in low, medium, and high categories had increased risk compared to individuals with a score of zero, but the greatest risk was in the high CCI category (aRR: 3.45; 95% CI: 2.25-4.83) Male sex (aRR: 1.32; 95% CI: 1.00-1.76), increasing age (aRR: 1.02; 95% CI: 1.02-1.03), and being immunocompromised (aRR: 2.61; 95% CI: 1.23-4.53) were associated with development of WNE. No risk factors were identified for WNM and WNP. Identification of comorbid risk factors of WNND will allow public health officials to identify high-risk groups and to develop prevention strategies targeted for vulnerable individuals.

The objective of this study was to investigate the utilization of the diagnose-based Charlson Comorbidity Index (CCI) and the medication-based Rx-Risk Comorbidity Index on Swedish administrative data. Data was collected over a ten-year period from the National Patient Register and the National Prescribed Medication Register on 3609 respondents from the national public health survey 2018, aged 16-84 and registered in Stockholm County. The overall aim was to identify comorbid conditions in the study population; and to examine if the identified comorbidities differ between indices, based on subject characteristics such as age and gender. Moreover, the specific aim was to quantify correlation between the indices, as well as within indices over look-back periods of up to ten years. Among the study population, 13 % were identified with at least one comorbid condition through CCI, and 87 % had medications indicative of at least one condition covered by Rx-Risk. Both the original Charlson weights and updated weights by Quan were used to compute the comorbidity scores for CCI. Results showed that when CCI and Quan may have scored low, the Rx-Risk picked up more conditions. The Spearman rank correlation between CCI and Quan scores resulted in relatively high correlation with a coefficient of 0.82 (p-value < 0.05) over look-back periods of 2, 5 and 10 years. Moreover, the correlation between CCI and Rx-Risk was fairly low over all look-back periods with a correlation coefficient of 0.34 (p-value < 0.05) at most. The within-correlation showed that CCI identified much of the comorbidity between the one- and two-year look-back periods, whilst Rx-Risk identified much comorbidity within the one-year look-back period. The overall implications of the presented results are that a utilization of Charlson index and Rx-Risk is likely to capture comorbid conditions in different health care settings, and thus expected correlation is to be of modest level between the two indices. The research question of interest should therefore determine which index is favorable when assessment of comorbidity is desired.