Comparison of alternative risk adjustment measures for predictive modeling: high risk patient case finding using Taiwan's National Health Insurance claims

<p>Abstract</p> <p>Background</p> <p>Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.</p> <p>Methods</p> <p>A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.</p> <p>Results</p> <p>Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.</p> <p>Conclusions</p> <p>Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.</p

Comparison of Rx-defined morbidity groups and diagnosis- based risk adjusters for predicting healthcare costs in Taiwan

<p>Abstract</p> <p>Background</p> <p>Medication claims are commonly used to calculate the risk adjustment for measuring healthcare cost. The Rx-defined Morbidity Groups (Rx-MG) which combine the use of medication to indicate morbidity have been incorporated into the Adjusted Clinical Groups (ACG) Case Mix System, developed by the Johns Hopkins University. This study aims to verify that the Rx-MG can be used for adjusting risk and for explaining the variations in the healthcare cost in Taiwan.</p> <p>Methods</p> <p>The Longitudinal Health Insurance Database 2005 (LHID2005) was used in this study. The year 2006 was chosen as the baseline to predict healthcare cost (medication and total cost) in 2007. The final sample size amounted to 793 239 (81%) enrolees, and excluded any cases with discontinued enrolment. Two different kinds of models were built to predict cost: the concurrent model and the prospective model. The predictors used in the predictive models included age, gender, Aggregated Diagnosis Groups (ADG, diagnosis- defined morbidity groups), and Rx-defined Morbidity Groups. Multivariate OLS regression was used in the cost prediction modelling.</p> <p>Results</p> <p>The concurrent model adjusted for Rx-defined Morbidity Groups for total cost, and controlled for age and gender had a better predictive R-square = 0.618, compared to the model adjusted for ADGs (R²= 0.411). The model combined with Rx-MGs and ADGs performed the best for concurrently predicting total cost (R²= 0.650). For prospectively predicting total cost, the model combined Rx-MGs and ADGs (R²= 0.382) performed better than the models adjusted by Rx-MGs (R²= 0.360) or ADGs (R²= 0.252) only. Similarly, the concurrent model adjusted for Rx-MGs predicting pharmacy cost had a better performance (R-square = 0.615), than the model adjusted for ADGs (R²= 0.431). The model combined with Rx-MGs and ADGs performed the best in concurrently as well as prospectively predicting pharmacy cost (R²= 0.638 and 0.505, respectively). The prospective models showed a remarkable improvement when adjusted by prior cost.</p> <p>Conclusions</p> <p>The medication-based Rx-Defined Morbidity Groups was useful in predicting pharmacy cost as well as total cost in Taiwan. Combining the information on medication and diagnosis as adjusters could arguably be the best method for explaining variations in healthcare cost.</p

Quantifying morbidities by Adjusted Clinical Group system for a Taiwan population: A nationwide analysis

<p>Abstract</p> <p>Background</p> <p>The Adjusted Clinical Group (ACG) system has been used in measuring an individual's and a population's morbidities. Although all required inputs for running the ACG system are readily available, patients' morbidities and their associations to health care utilizations have been rarely studied in Taiwan. Therefore, the objective of this study was using the ACG system to quantify morbidities for Taiwanese population and to examine their relationship to ambulatory utilizations and costs.</p> <p>Methods</p> <p>This secondary analysis examined claims data for ambulatory services provided to 2.71 million representative Taiwanese in 2002 and 2003. People were grouped by the ACG system according to age, gender, and all ambulatory diagnosis codes in a given year. The software collapses the full set of ACGs into six morbidity categories (Non-users, Healthy, Low-morbidity, Moderate-, High- and Very-high) termed Resource Utilization Bands (RUBs). Each ACG was assigned a relative weight (RW), which was calculated as the ratio of mean ambulatory cost for each ACG to that for the overall. The distribution of morbidities was compared between years 2002 and 2003. The consistency of the distributions of visits, costs, and RWs of each ACG were examined for a two-year period. The relationship between people's morbidities and their ambulatory utilizations and costs was assessed.</p> <p>Results</p> <p>Ninety-eight percent of the subjects were correctly assigned to ACGs. Except for non-users (7.9 ~ 8.3%), most subjects were assigned to ACGs of acute and minor diseases and ACGs of moderate-to-high-morbid chronic diseases. The distributions of ACG-based morbidities were highly consistent (r = 0.949, <it>p < 0.001</it>) between 2002 and 2003. The ACG-specific visits (r = 0.955, <it>p < 0.001</it>), costs (r = 0.966, <it>p < 0.001</it>) and RWs (r = 0.991, <it>p < 0.001</it>) were correlated across two years. People grouped to the high-morbid ACGs had more visits and costs than those grouped to the low-morbid ACGs. Forty-six percent of the total ambulatory costs were spent by eighteen percent of the population, who were grouped to the High- and Very-high-morbidity RUBs.</p> <p>Conclusion</p> <p>This study demonstrated the feasibility, validity, and reliability of using the ACG system to measure morbidities in a Taiwan population and to explain their associations with ambulatory utilizations and costs for the whole country.</p

Relationship between efficiency and clinical effectiveness indicators in an adjusted model of resource consumption : a cross-sectional study

Background: Adjusted clinical groups (ACG®) have been widely used to adjust resource distribution; however, the relationship with effectiveness has been questioned. The purpose of the study was to measure the relationship between efficiency assessed by ACG® and a clinical effectiveness indicator in adults attended in Primary Health Care Centres (PHCs). Methods: Research design: cross-sectional study. Subjects: 196, 593 patients aged >14 years in 13 PHCs in Catalonia (Spain). Measures: Age, sex, PHC, basic care team (BCT), visits, episodes (diagnoses), and total direct costs of PHC care and co-morbidity as measured by ACG® indicators: Efficiency indices for costs, visits, and episodes (costs EI, visits EI, episodes EI); a complexity or risk index (RI); and effectiveness measured by a general synthetic index (SI). The relationship between EI, RI, and SI in each PHC and BCT was measured by multiple correlation coefficients (r). Results: In total, 56 of the 106 defined ACG® were present in the study population, with five corresponding to 44.5% of the patients, 11 to 68.0% of patients, and 30 present in less than 0.5% of the sample. The RI in each PHC ranged from 0.9 to 1.1. Costs, visits, and episodes had similar trends for efficiency in six PHCs. There was moderate correlation between costs EI and visits EI (r = 0.59). SI correlation with episodes EI and costs EI was moderate (r = 0.48 and r = −0.34, respectively) and was r = −0.14 for visits EI. Correlation between RI and SI was r = 0.29. Conclusions: The Efficiency and Effectiveness ACG® indicators permit a comparison of primary care processes between PHCs. Acceptable correlation exists between effectiveness and indicators of efficiency in episodes and costs

Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: The impact of a local calibration

<p>Abstract</p> <p>Background</p> <p>In the financing of a national health system, where pharmaceutical spending is one of the main cost containment targets, predicting pharmacy costs for individuals and populations is essential for budget planning and care management. Although most efforts have focused on risk adjustment applying diagnostic data, the reliability of this information source has been questioned in the primary care setting. We sought to assess the usefulness of incorporating pharmacy data into claims-based predictive models (PMs). Developed primarily for the U.S. health care setting, a secondary objective was to evaluate the benefit of a local calibration in order to adapt the PMs to the Spanish health care system.</p> <p>Methods</p> <p>The population was drawn from patients within the primary care setting of Aragon, Spain (n = 84,152). Diagnostic, medication and prior cost data were used to develop PMs based on the Johns Hopkins ACG methodology. Model performance was assessed through r-squared statistics and predictive ratios. The capacity to identify future high-cost patients was examined through c-statistic, sensitivity and specificity parameters.</p> <p>Results</p> <p>The PMs based on pharmacy data had a higher capacity to predict future pharmacy expenses and to identify potential high-cost patients than the models based on diagnostic data alone and a capacity almost as high as that of the combined diagnosis-pharmacy-based PM. PMs provided considerably better predictions when calibrated to Spanish data.</p> <p>Conclusion</p> <p>Understandably, pharmacy spending is more predictable using pharmacy-based risk markers compared with diagnosis-based risk markers. Pharmacy-based PMs can assist plan administrators and medical directors in planning the health budget and identifying high-cost-risk patients amenable to care management programs.</p

Does the pharmacy expenditure of patients always correspond with their morbidity burden? Exploring new approaches in the interpretation of pharmacy expenditure

<p>Abstract</p> <p>Background</p> <p>The computerisation of primary health care (PHC) records offers the opportunity to focus on pharmacy expenditure from the perspective of the morbidity of individuals. The objective of the present study was to analyse the behaviour of pharmacy expenditure within different morbidity groups. We paid special attention to the identification of individuals who had higher values of pharmacy expenditure than their morbidity would otherwise suggest (i.e. outliers).</p> <p>Methods</p> <p>Observational study consisting of 75,574 patients seen at PHC centres in Zaragoza, Spain, at least once in 2005. Demographic and disease variables were analysed (ACG^®8.1), together with a response variable that we termed 'total pharmacy expenditure per patient'. Outlier patients were identified based on boxplot methods, adjusted boxplot for asymmetric distributions, and by analysing standardised residuals of tobit regression models.</p> <p>Results</p> <p>The pharmacy expenditure of up to 7% of attendees in the studied PHC centres during one year exceeded expectations given their morbidity burden. This group of patients was responsible for up to 24% of the total annual pharmacy expenditure. There was a significantly higher number of outlier patients within the low-morbidity band which matched up with the higher variation coefficient observed in this group (3.2 vs. 2.0 and 1.3 in the moderate- and high-morbidity bands, respectively).</p> <p>Conclusions</p> <p>With appropriate validation, the methodologies of the present study could be incorporated in the routine monitoring of the prescribing profile of general practitioners. This could not only enable evaluation of their performance, but also target groups of outlier patients and foster analyses of the causes of unusually high pharmacy expenditures among them. This interpretation of pharmacy expenditure gives new clues for the efficiency in utilisation of healthcare resources, and could be complementary to management interventions focused on individuals with a high morbidity burden.</p

Social media for health promotion and weight management: A critical debate

© 2018 The Author(s). Background: In 2016 an estimated 1.9 billion adults world-wide were either overweight or obese. The health consequences of obesity are responsible for 2.8 million preventable deaths per year. The WHO now considers obesity as a global epidemic and recommends population-wide health promotion strategies to address this issue. Weight gain is caused by increased energy intake and physical inactivity, so treatment should focus on changes to behaviour regarding diet and physical activity. Discussion: The WHO has also recognised the importance of social resources as a valuable agent for behaviour change in health promotion. Social resources are translated at the community level as support provided by significant others such as family, partners and peers, in the form of information, material aid and encouragement. Social support has been shown to improve health and well-being, whereas social isolation has been shown to have a negative impact on health outcomes. Social support provided by peers has been shown to be a useful strategy to employ in weight management programmes. The documented increased use of ICT and social media has presented health promoters with a potentially useful medium to increase social support for weight management. Conclusion: While the use of social media for health promotion is an emerging field of investigation, preliminary research suggests that it increases participant engagement, and may provide a cost-effective tool to provide social support for individuals participating in weight management programmes. With stringent privacy protocols in place, social media may be a useful, cost-effective accompaniment to multifactorial weight management programmes. However more research is needed to identify how to make the best use of social media as health promotion tool

Protein docking prediction using predicted protein-protein interface

<p>Abstract</p> <p>Background</p> <p>Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.</p> <p>Results</p> <p>We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.</p> <p>Conclusion</p> <p>We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.</p