Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: a cross-sectional study of patient electronic records in primary care

Variability in Healthcare Expenditure According to the Stratification of Adjusted Morbidity Groups in the Canary Islands (Spain)

Morbidity is the main item in the distribution of expenditure on healthcare services. The Adjusted Morbidity Group (AMG) measures comorbidity and complexity and classifies the patient into mutually exclusive clinical categories. The aim of this study is to analyse the variability of healthcare expenditure on users with similar scores classified by the AMG. Observational analytical and retrospective study. Population: 1,691,075 subjects, from Canary Islands (Spain), aged over 15 years with data from health cards, clinical history, Basic Minimum Specialised Healthcare Data Set, AMG, hospital agreements information system and Electronic Prescriptions. A descriptive, bivariant (ANOVA coefficient η²) and multivariant analysis was conducted. There is a correlation between the costs and the weight of AMG (rho = 0.678) and the prescribed active ingredients (rho = 0.689), which is smaller with age and does not exist with the other variables. As for the influence of the AMG morbidity group on the total costs of the patient, the coefficient η² (0.09) obtains a median effect in terms of the variability of expenditure, hence there is intra- and inter-group variability in the cost. In a first model created with all the variables and the cost, an explanatory power of 36.43% (R² = 0.3643) was obtained; a second model that uses solely active ingredients, AMG weight, being female and a pensioner obtained an explanatory power of 36.4%. There is room for improvement in terms of predicting the expenditure.

A systematic review of risk stratification tools internationally used in primary care settings

BACKGROUND AND AIMS

In our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub-populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance.

METHODS

We performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co-operation and Development countries published in English-language journals were included. Search engines were utilized with keywords, for example, "primary care," "risk stratification," and "model." Risk stratification tools were compared based on different measures: area under the curve (AUC) and C-statistics for dichotomous outcomes and R 2 for continuous outcomes.

RESULTS

The search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C-statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7).

CONCLUSION

Based on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes.

Predicting the Cost of Health Care Services: A Comparison of Case-mix Systems and Comorbidity Indices That Use Administrative Data

BACKGROUND

Case-mix systems and comorbidity indices aggregate clinical information about patients over time and are used to characterize need for health care services. These tools were validated for their original purpose, but those purposes are varied, and they have not been compared directly in the context of predicting costs of health care services.

OBJECTIVE

To compare predictions of next-year health care service costs across 4 tools, including: the Johns Hopkins Adjusted Clinical Groups (ACG), the Elixhauser Comorbidity Index, Charlson-Deyo Comorbidity Index, and the Canadian Institute for Health Information (CIHI) population grouper.

METHODS

British Columbia administrative data from fiscal years 2012-2013 were used to generate case-mix variables and the comorbidity indices. Outcome variables include next-year (2013-2014) total, physician, acute care, and pharmaceutical costs, Outcomes were modeled using 2-part models. Performance was compared using adjusted R, root mean squared error, and mean absolute error using the predicted and the actual next-year cost.

RESULTS

Models including the CIHI grouper (239 conditions) and ACG system had similar performance in most cost categories and slightly better fit than Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). Adding a dummy variable for nonusers in the models for CCI and ECI increased R values slightly.

CONCLUSIONS

All these systems have empirical support for use in predicting health care costs, despite in some cases being developed for other purposes. No system is particularly effective at predicting next-year acute care cost, likely because acute events are often by definition unexpected. The freely available ECI and CCI comorbidity indices implemented using the highest-performing methods developed here may be a good choice in many circumstances.

Validez de los grupos de morbilidad ajustados respecto a los clinical risk groups en el ámbito de la atención primaria

Objetivo

Comparar el rendimiento referente a la bondad de ajuste y el poder explicativo de 2 agrupadores de morbilidad en el ámbito de la atención primaria (AP): los grupos de morbilidad ajustados (GMA) y los clinical risk groups (CRG).

Diseño

Estudio transversal.

Emplazamiento

Ámbito de la AP del Instituto Catalán de la Salud (ICS), Cataluña, España.

Participantes

Población asignada a centros de AP del ICS para el año 2014.

Mediciones principales

Se analizan 3 indicadores de interés, como son el ingreso urgente, el número de visitas y el gasto en farmacia. Se aplica un análisis estratificado por centros ajustando modelos lineales generalizados a partir de las variables edad, sexo y agrupador de morbilidad para explicar cada una de las 3 variables de interés. Las medidas estadísticas para analizar el rendimiento de los distintos modelos aplicados son el índice de Akaike, el índice de Bayes y la seudovariabilidad explicada mediante cambio de deviance.

Resultados

Los resultados muestran que en el ámbito de la AP del ICS el poder explicativo de los GMA es superior al ofrecido por los CRG, especialmente para el caso de las visitas y el gasto en farmacia.

Conclusiones

El rendimiento de los GMA en el ámbito de la AP del ICS es superior al mostrado por los CRG.

Factors influencing the variation in GMS prescribing expenditure in Ireland

BACKGROUND

Pharmaceutical expenditure growth is a familiar feature in many Western health systems and is a real concern for policymakers. A state funded General Medical Services (GMS) scheme in Ireland experienced an increase in prescription expenditure of 414 % between 1998 and 2012. This paper seeks to explore the rationale for this growth by investigating the composition (Anatomical Therapeutic Chemical (ATC) Group level 1 & 5) and drivers of GMS drug expenditure in Ireland in 2012.

METHODS

A cross-sectional study was carried out on the Health Service Executive-Primary Care Reimbursement Service (HSE-PCRS) population prescribing database (n = 1,630,775). Three models were applied to test the association between annual expenditure per claimant whilst controlling for age, sex, region, and the pharmacology of the drugs as represented by the main ATC groups.

RESULTS

The mean annual cost per claimant was €751 (median = €211; SD = €1323.10; range = €3.27-€298,670). Age, sex, and regions were all significant contributory factors of expenditure, with gender having the greatest impact (β = 0.107). Those aged over 75 (β =1.195) were the greatest contributors to annual GMS prescribing costs. As regards regions, the South has the greatest cost increasing impact. When the ATC groups were included the impact of gender is diluted by the pharmacology of the products, with cardiovascular prescribing (ATC 'C') most influential (β = 1.229) and the explanatory power of the model increased from 40 % to 60 %.

CONCLUSION

Whilst policies aimed at cost containment (co-payment charges; generic substitution; reference pricing; adjustments to GMS eligibility) can be used to curtail expenditure, health promotional programs and educational interventions should be given equal emphasis. Also policies intended to affect physicians' prescribing behaviour include guidelines, information (about price and less expensive alternatives) and feedback, and the use of budgetary restrictions could yield savings in Ireland and can be easily translated to the international context.

The Direct Economic Burden of Gout in an Elderly Canadian Population

Objective.

To estimate the direct healthcare cost and resource use from the public payer perspective between patients with incident gout and matched gout-free patients in Ontario.

Methods.

Patients with incident gout aged ≥ 66 with uninterrupted Ontario Health Insurance Plan (OHIP) coverage in the 1-year baseline period were included in the study. Patients with gout were indexed at first gout diagnosis or prescription over the study period April 1, 2008, to March 31, 2014. Gout-free patients with no gout diagnosis within history were matched (up to 5:1) to each patient with gout. Linked medical records were analyzed until end of study, death, or OHIP ineligibility. Bang and Tsiatis adjusted healthcare costs and resource use were compared using bootstrap p-values and 95% CI.

Results.

A total of 29,894 patients with gout and 148,231 gout-free patients were included in the study. Patients were 56% male, had a median Adjusted Clinical Group healthcare resource use band of moderate morbidity, and had a median age of 75–79 years. Baseline comorbidities were similar between groups except for renal disease. Analyzing 5-year total healthcare costs, patients with gout ($44,297) incurred a significantly higher average healthcare cost compared to gout-free patients ($33,965), for an incremental cost of $10,332 (95% CI $9617–$11,039; p < 0.01). Similar trends were observed in all individual healthcare component cost and use metrics.

Conclusion.

Following onset of gout, patients in Ontario incur significantly greater healthcare costs and resource use compared to matched gout-free patients. Alternative gout management strategies should be investigated to reduce the incremental burden of gout borne by the Ontario healthcare system.

A Review on Methods of Risk Adjustment and their Use in Integrated Healthcare Systems

INTRODUCTION

Effective risk adjustment is an aspect that is more and more given weight on the background of competitive health insurance systems and vital healthcare systems. The objective of this review was to obtain an overview of existing models of risk adjustment as well as on crucial weights in risk adjustment. Moreover, the predictive performance of selected methods in international healthcare systems should be analysed.

THEORY AND METHODS

A comprehensive, systematic literature review on methods of risk adjustment was conducted in terms of an encompassing, interdisciplinary examination of the related disciplines.

RESULTS

In general, several distinctions can be made: in terms of risk horizons, in terms of risk factors or in terms of the combination of indicators included. Within these, another differentiation by three levels seems reasonable: methods based on mortality risks, methods based on morbidity risks as well as those based on information on (self-reported) health status.

CONCLUSIONS AND DISCUSSION

After the final examination of different methods of risk adjustment it was shown that the methodology used to adjust risks varies. The models differ greatly in terms of their included morbidity indicators. The findings of this review can be used in the evaluation of integrated healthcare delivery systems and can be integrated into quality- and patient-oriented reimbursement of care providers in the design of healthcare contracts.

Resource allocation and the burden of co-morbidities among patients diagnosed with chronic obstructive pulmonary disease: an observational cohort study from Danish general practice

Background

Chronic obstructive pulmonary disease is a leading cause of mortality, and associated with increased healthcare utilization and healthcare expenditure. In several countries, morbidity-based systems have changed the way resources are allocated in general practice. In primary care, fee-for-services tariffs are often based on political negotiation rather than costing systems. The potential for comprehensive measures of patient morbidity to explain variation in negotiated expenditures for patients with chronic obstructive pulmonary disease has not previously been examined. The aim of this study is to analyze fee-for-service expenditure of patients diagnosed with chronic obstructive pulmonary disease visiting Danish general practice clinics and further to assess what proportion of fee-for-service expenditure variation was explained by patient morbidity and general practice clinic characteristics, respectively.

Methods

We used patient morbidity characteristics such as diagnostic markers and multi-morbidity adjustment based on adjusted clinical groups (ACGs) and fee-for-service expenditure for a sample of primary care patients for the year 2010. Our sample included 3,973 patients in 59 general practices. We used a multi-level approach.

Results

The average annual fee-for-service expenditure of caring for patients diagnosed with chronic obstructive pulmonary disease in general practice was about EUR 400 per patient. Variation in the expenditures was driven by multimorbidity characteristics up to 28 % where as characteristics such as age and gender only explained 5 %. Expenditures increased progressively with the degree of multimorbidity. In addition, expenditures were higher for patients who had diagnostic markers based on ICPC-2 (body systems and/or components such as infections and symptoms). Nevertheless, 9.8–15.4 % of the variation in expenditure was related to the clinic in which the patient was cared for.

Conclusion

Patient morbidity and general practice clinic characteristics are significant patient-related fee-for-service expenditure drivers in chronic obstructive pulmonary disease care.

Predictors of primary health care pharmaceutical expenditure by districts in Uganda and implications for budget setting and allocation

Background

There is need for the Uganda Ministry of Health to understand predictors of primary health care pharmaceutical expenditure among districts in order to guide budget setting and to improve efficiency in allocation of the set budget among districts.

Methods

Cross sectional, retrospective observational study using secondary data. The value of pharmaceuticals procured by primary health care facilities in 87 randomly selected districts for the Financial Year 2011/2012 was collected. Various specifications of the dependent variable (pharmaceutical expenditure) were used: total pharmaceutical expenditure, Per capita district pharmaceutical expenditure, pharmaceutical expenditure per district health facility and pharmaceutical expenditure per outpatient department visit. Andersen’s behaviour model of health services utilisation was used as conceptual framework to identify independent variables likely to influence health care utilisation and hence pharmaceutical expenditure. Econometric analysis was conducted to estimate parameters of various regression models.

Results

All models were significant overall (P < 0.01), with explanatory power ranging from 51 to 82 %. The log linear model for total pharmaceutical expenditure explained about 80 % of the observed variation in total pharmaceutical expenditure (Adjusted R² = 0.797) and contained the following variables: Immunisation coverage, Total outpatient department attendance, Urbanisation, Total number of government health facilities and total number of Health Centre IIs. The model based on Per capita Pharmaceutical expenditure explained about 50 % of the observed variation in per capita pharmaceutical expenditure (Adjusted R² = 0.513) and was more balanced with the following variables: Outpatient per capita attendance, percentage of rural population below poverty line 2005, Male Literacy rate, Whether a district is characterised by MOH as difficult to reach or not and the Human poverty index.

Conclusions

The log-linear model based on total pharmaceutical expenditure works acceptably well and can be considered useful for predicting future total pharmaceutical expenditure following observed trends. It can be used as a simple tool for rough estimation of the potential overall national primary health pharmaceutical expenditure to guide budget setting. The model based on pharmaceutical expenditure per capita is a more balanced model containing both need and enabling factor variables. These variables would be useful in allocating any set budget to districts.

Development of MY-DRG casemix pharmacy service weights in UKM Medical Centre in Malaysia

BACKGROUND

The service weight is among several issues and challenges in the implementation of case-mix in developing countries, including Malaysia. The aim of this study is to develop the Malaysian Diagnosis Related Group (MY-DRG) case-mix pharmacy service weight in University Kebangsaan Malaysia-Medical Center (UKMMC) by identifying the actual cost of pharmacy services by MY-DRG groups in the hospital.

METHODS

All patients admitted to UKMMC in 2011 were recruited in this study. Combination of Step-down and Bottom-up costing methodology has been used in this study. The drug and supplies cost; the cost of staff; the overhead cost; and the equipment cost make up the four components of pharmacy. Direct costing approach has been employed to calculate Drugs and supplies cost from electronic-prescription system; and the inpatient pharmacy staff cost, while the overhead cost and the pharmacy equipments cost have been calculated indirectly from MY-DRG data base. The total pharmacy cost was obtained by summing the four pharmacy components' cost per each MY-DRG. The Pharmacy service weight of a MY-DRG was estimated by dividing the average pharmacy cost of the investigated MY-DRG on the average of a specified MY-DRG (which usually the average pharmacy cost of all MY-DRGs).

RESULTS

Drugs and supplies were the main component (86.0%) of pharmacy cost compared o overhead cost centers (7.3%), staff cost (6.5%) and pharmacy equipments (0.2%) respectively. Out of 789 inpatient MY-DRGs case-mix groups, 450 (57.0%) groups were utilized by the UKMMC. Pharmacy service weight has been calculated for each of these 450 MY-DRGs groups. MY-DRG case-mix group of Lymphoma & Chronic Leukemia group with severity level three (C-4-11-III) has the highest pharmacy service weight of 11.8 equivalents to average pharmacy cost of RM 5383.90. While the MY-DRG case-mix group for Circumcision with severity level one (V-1-15-I) has the lowest pharmacy service weight of 0.04 equivalents to average pharmacy cost of RM 17.83.

CONCLUSION

A mixed approach which is based partly on top-down and partly on bottom up costing methodology has been recruited to develop MY-DRG case-mix pharmacy service weight for 450 groups utilized by the UKMMC in 2011.

Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool

Background

Pharmaceutical expenditure is undergoing very high growth, and accounts for 30% of overall healthcare expenditure in Spain. In this paper we present a prediction model for primary health care pharmaceutical expenditure based on Clinical Risk Groups (CRG), a system that classifies individuals into mutually exclusive categories and assigns each person to a severity level if s/he has a chronic health condition. This model may be used to draw up budgets and control health spending.

Methods

Descriptive study, cross-sectional. The study used a database of 4,700,000 population, with the following information: age, gender, assigned CRG group, chronic conditions and pharmaceutical expenditure. The predictive model for pharmaceutical expenditure was developed using CRG with 9 core groups and estimated by means of ordinary least squares (OLS). The weights obtained in the regression model were used to establish a case mix system to assign a prospective budget to health districts.

Results

The risk adjustment tool proved to have an acceptable level of prediction (R² ≥ 0.55) to explain pharmaceutical expenditure. Significant differences were observed between the predictive budget using the model developed and real spending in some health districts. For evaluation of pharmaceutical spending of pediatricians, other models have to be established.

Conclusion

The model is a valid tool to implement rational measures of cost containment in pharmaceutical expenditure, though it requires specific weights to adjust and forecast budgets.

Association between fee-for-service expenditures and morbidity burden in primary care

BACKGROUND

In primary care, fee-for-services (FFS) tariffs are often based on political negotiation rather than costing systems. The potential for comprehensive measures of patient morbidity to explain variation in negotiated FFS expenditures has not previously been examined.

OBJECTIVES

To examine the relative explanatory power of morbidity measures and related general practice (GP) clinic characteristics in explaining variation in politically negotiated FFS expenditures.

METHODS

We applied a multilevel approach to consider factors that explain FFS expenditures among patients and GP clinics. We used patient morbidity characteristics such as diagnostic markers, multimorbidity casemix adjustment based on resource utilisation bands (RUB) and related GP clinic characteristics for the year 2010. Our sample included 139,527 patients visiting GP clinics.

RESULTS

Out of the individual expenditures, 31.6% were explained by age, gender and RUB, and around 18% were explained by RUB. Expenditures increased progressively with the degree of resource use (RUB0-RUB5). Adding more patient-specific morbidity measures increased the explanatory power to 44%; 3.8-9.4% of the variation in expenditures was related to the GP clinic in which the patient was treated.

CONCLUSIONS

Morbidity measures were significant patient-related FFS expenditure drivers. The association between FFS expenditure and morbidity burden appears to be at the same level as similar studies in the hospital sector, where fees are based on average costing. However, our results indicate that there may be room for improvement of the association between politically negotiated FFS expenditures and morbidity in primary care.

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.

Obtaining the mean relative weights of the cost of care in Catalonia (Spain): retrospective application of the adjusted clinical groups case-mix system in primary health care

OBJECTIVE

The study aims to obtain the mean relative weights (MRWs) of the cost of care through the retrospective application of the adjusted clinical groups (ACGs) in several primary health care (PHC) centres in Catalonia (Spain) in routine clinical practice.

METHODS

This is a retrospective study based on computerized medical records. All patients attended by 13 PHC teams in 2008 were included. The principle measurements were: demographic variables (age and sex), dependent variables (number of diagnoses and total costs), and case-mix or co-morbidity variables (International Classification of Primary Care). The costs model for each patient was established by differentiating the fix costs from the variable costs. In the bivariate analysis, the Student's t, analysis of variance, chi-squared, Pearson's linear correlation and Mann-Whitney-Wilcoxon tests were used. In order to compare the MRW of the present study with those of the United States (US), the concordance [intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC)] and the correlation (coefficient of determination: R²) were measured.

RESULTS

The total number of patients studied was 227,235, and the frequentation was 5.9 visits/habitant/year) and with a mean diagnoses number of 4.5 (3.2). The distribution of costs was €148.7 million, of which 29.1% were fixed costs. The mean total cost per patient/year was €654.2 (851.7), which was considered to be the reference MRW. Relationship between study-MRW and US-MRW: ICC was 0.40 [confidential interval (CI) 95%: 0.21-0.60] and the CCC was 0.42 (CI 95%: 0.35-0.49). The correlation between the US MRW and the MRW of the present study can be seen; the adjusted R² value is 0.691. The explanatory power of the ACG classification was 36.9% for the total costs. The R² of the total cost without considering outliers was 56.9%.

CONCLUSIONS

The methodology has been shown appropriate for promoting the calculation of the MRW for each category of the classification. The results provide a possible practical application in PHC clinical management.

Adaptive capacity of the Adjusted Clinical Groups Case-Mix System to the cost of primary healthcare in Catalonia (Spain): a observational study

OBJECTIVES

To describe the adaptive capacity of the Adjusted Clinical Groups (ACG) system to the cost of care in primary healthcare centres in Catalonia (Spain).

DESIGN

Retrospective study (multicentres) conducted using computerised medical records.

SETTING

13 primary care teams in 2008 were included.

PARTICIPANTS

All patients registered in the study centres who required care between 1 January and 31 December 2008 were finally studied. Patients not registered in the study centres during the study period were excluded.

OUTCOME MEASURES

Demographic (age and sex), dependent (cost of care) and case-mix variables were studied. The cost model for each patient was established by differentiating the fixed and variable costs. To evaluate the adaptive capacity of the ACG system, Pearson's coefficient of variation and the percentage of outliers were calculated. To evaluate the explanatory power of the ACG system, the authors used the coefficient of determination (R(2)).

RESULTS

The number of patients studied was 227 235 (frequency: 5.9 visits per person per year), with a mean of 4.5 (3.2) episodes and 8.1 (8.2) visits per patient per year. The mean total cost was €654.2. The explanatory power of the ACG system was 36.9% for costs (56.5% without outliers). 10 ACG categories accounted for 60.1% of all cases and 19 for 80.9%. 5 categories represented 71% of poor performance (N=78 887, 34.7%), particularly category 0300-Acute Minor, Age 6+ (N=26 909, 11.8%), which had a coefficient of variation =139% and 6.6% of outliers.

CONCLUSIONS

The ACG system is an appropriate manner of classifying patients in routine clinical practice in primary healthcare centres in Catalonia, although improvements to the adaptive capacity through disaggregation of some categories according to age groups and, especially, the number of acute episodes in paediatric patients would be necessary to reduce intra-group variation.

[Comparison of three methods for measuring multiple morbidity according to the use of health resources in primary healthcare]

OBJECTIVE

To compare three methods of measuring multiple morbidity according to the use of health resources (cost of care) in primary healthcare (PHC).

DESIGN

Retrospective study using computerized medical records.

SETTING

Thirteen PHC teams in Catalonia (Spain).

PARTICIPANTS

Assigned patients requiring care in 2008.

MAIN MEASUREMENTS

The socio-demographic variables were co-morbidity and costs. Methods of comparison were: a) Combined Comorbidity Index (CCI): an index itself was developed from the scores of acute and chronic episodes, b) Charlson Index (ChI), and c) Adjusted Clinical Groups case-mix: resource use bands (RUB). The cost model was constructed by differentiating between fixed (operational) and variable costs.

STATISTICAL ANALYSIS

3 multiple lineal regression models were developed to assess the explanatory power of each measurement of co-morbidity which were compared from the determination coefficient (R(2)), p< .05.

RESULTS

The study included 227,235 patients. The mean unit of cost was €654.2. The CCI explained an R(2)=50.4%, the ChI an R(2)=29.2% and BUR an R(2)=39.7% of the variability of the cost. The behaviour of the ICC is acceptable, albeit with low scores (1 to 3 points), showing inconclusive results.

CONCLUSIONS

The CCI may be a simple method of predicting PHC costs in routine clinical practice. If confirmed, these results will allow improvements in the comparison of the case-mix.

Multimorbidity and its measurement

Multimorbidity is increasing in frequency. It can be quantitatively measured and is a major correlate of high use of health services resources of all types, especially over time. The ACG System for characterizing multimorbidity is the only widely used method that is based on combinations of different TYPES of diagnoses over time, rather than the presence or absence of particular conditions or numbers of conditions. It incorporates administrative data (as from claims forms or medical records) on all types of encounters and is not limited to diagnoses captured during hospitalizations or other places of encounter. It can be employed in any one or combination of analytic models, and can incorporate medication use if desired. It is being used in clinical care, management of health services resources, in health services research to control for degree of morbidity, and in understanding morbidity patterns over time. In addition to its research uses, it is being employed in many countries in various applications as a policy to better understand health needs of populations and tailor health services resources to health needs.

[Distribution of primary care expenditure according to sex and age group: a retrospective analysis]

OBJECTIVE

To study the primary care expenditure per person and the weight of different health resources within sex and age groups.

DESIGN

Cost analysis. Retrospective descriptive study.

SETTING

14 urban primary care centers (assigned population: 313,000).

PARTICIPANTS

All patients who visited during 2008 (227,235).

STUDY PERIOD

January to December 2008.

MAIN MEASUREMENTS

Age, sex, visits, laboratory, radiology and complementary tests and referrals to specialists were obtained from patient electronic files. Pharmacy expenditure was obtained from invoices sent to the national health system by pharmacists. Fixed/semi-fixed costs were distributed among visits and a mean cost/visit was obtained. Costs were assigned for laboratory (mean application cost), for radiology and complementary tests (rate per type of test) and for referrals (adjusted rate). Descriptive analysis of data (median, interquartile range and coefficient of variance).

RESULTS

Median expenditure was 362 € (321 € for men and 396 € for women). For the up to 2 year-old group it was 410 €, for aged 15-44 203 € and 75 and above 1,255 €.). The up to 2 years old visits represented 81% of total cost while pharmacy was 8%, while for those aged 75 and above visits accounted for 21% of the total cost and pharmacy 63%.

CONCLUSIONS

Expenditure in primary care is higher in women, although the greatest differences were observed with age. In older than 74 years the median expenditure was six-fold higher than that for 3-44 years old group. In pediatrics the main source of expenditure was visits, representing 80% of the total in up to 2 years old. From 45 years old, pharmacy was the main source of expenditure and in those older than 74 it represented over 60% of the total.

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

Background

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.

Methods

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.

Results

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.

Conclusions

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.

The relationship between effectiveness and costs measured by a risk-adjusted case-mix system: multicentre study of Catalonian population data bases

Background

The main objective of this study is to measure the relationship between morbidity, direct health care costs and the degree of clinical effectiveness (resolution) of health centres and health professionals by the retrospective application of Adjusted Clinical Groups in a Spanish population setting. The secondary objectives are to determine the factors determining inadequate correlations and the opinion of health professionals on these instruments.

Methods/Design

We will carry out a multi-centre, retrospective study using patient records from 15 primary health care centres and population data bases. The main measurements will be: general variables (age and sex, centre, service [family medicine, paediatrics], and medical unit), dependent variables (mean number of visits, episodes and direct costs), co-morbidity (Johns Hopkins University Adjusted Clinical Groups Case-Mix System) and effectiveness.

The totality of centres/patients will be considered as the standard for comparison. The efficiency index for visits, tests (laboratory, radiology, others), referrals, pharmaceutical prescriptions and total will be calculated as the ratio: observed variables/variables expected by indirect standardization.

The model of cost/patient/year will differentiate fixed/semi-fixed (visits) costs of the variables for each patient attended/year (N = 350,000 inhabitants). The mean relative weights of the cost of care will be obtained. The effectiveness will be measured using a set of 50 indicators of process, efficiency and/or health results, and an adjusted synthetic index will be constructed (method: percentile 50).

The correlation between the efficiency (relative-weights) and synthetic (by centre and physician) indices will be established using the coefficient of determination. The opinion/degree of acceptance of physicians (N = 1,000) will be measured using a structured questionnaire including various dimensions. Statistical analysis: multiple regression analysis (procedure: enter), ANCOVA (method: Bonferroni's adjustment) and multilevel analysis will be carried out to correct models. The level of statistical significance will be p < 0.05.

[Identification of pharmacy cost outliers in primary care]

OBJECTIVE

To identify pharmacy cost outlier patients in Primary Care, describing epidemiological differences between normal users and outliers; and to study the explanatory power of risk adjustment tools based on Adjusted Clinical Groups (ACG) as regards the variability of pharmacy expenditure for both groups of patients.

DESIGN

Observational, retrospective study.

SETTING

23 health centres located in the regions of Aragon, Catalonia and the Balearic Islands.

PARTICIPANTS

The study sample consisted of 286,450 patients who were seen at least once in 2005.

MEASUREMENTS

Variables related to demographic features, pharmacy cost, and case-mix (ACG 7.1) were collected. Pharmacy cost outliers were selected according to the inter-quartile range method. A linear regression model was developed to measure the explanatory power of ACG. This same model was applied stratifying the population by variables of the physician, the health centre and the region.

RESULTS

One out of ten patients was classified as an outlier. This group was responsible for 60% of the total pharmacy expenditure. These outlier patients were 26.3 years older than normal users and had a higher comorbidity. The explanatory power of the ACG classification system was markedly lower -3% vs. 26.4% for normal users-.

CONCLUSIONS

Further research should be done on factors causing a lack of adequacy of ACG among pharmacy outlier patients. Although it could be thought that social circumstances might play a role in the clinical state of patients, it is more likely that the applied trimming method does not allow outliers with justifiable clinical reasons for higher costs to be distinguished from those without them.