Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

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

BMC Health Services ResearchBMC series ¿ open, inclusive and trusted201010:126

DOI: 10.1186/1472-6963-10-126

Received: 11 January 2010

Accepted: 17 May 2010

Published: 17 May 2010

Abstract

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.

Background

Diagnosis information is commonly used for defining morbidities and for estimating the risk of healthcare utilization. Diagnosis based comorbidity scales and risk adjustment tools, such as the Charlson Comorbidity Index[1], Elixhauser index[2], the Johns Hopkins Adjusted Clinical Group (ACG) case-mix system[3], and the Diagnostic Cost Group Hierarchical Condition Category (DCG/HCC) model[4, 5] have been verified for their effective use in adjusting healthcare costs risks [611]. Although administrative data seems to be comprehensive, efficient, low cost, and are most likely to prevent several common biases associated with primary data, the accuracy and quality of the diagnosis coding remains suspect [1215]. Previous studies found that the diagnoses identified by administrative data were highly specific but varied greatly in sensitivity and therefore recommended that all available sources of data (e.g. prescription claims database)should be included in order to overcome the potential limitations that come with a single source of data[15]. Pine et. al. also argued that risk-adjustment based entirely on administrative data is imperfect because these data do not discriminate between comorbidities and complications, and the limited numbers of secondary diagnoses within the data may not properly reflect the sickest patients [16].

Prescription claim data has several additional strengths for capturing morbidity conditions compared to diagnoses data. Healthcare purchasers (insurers) that provide a drug benefit package, claim that prescription data is often more reliable, timely, complete, and less of a gamble than diagnostic data [12, 13, 17]. In addition, for persons with a stable, well-managed chronic disease, a medication-based risk instrument may capture their health risk even without the diagnosis information reported by the providers [17]. Several medication-based morbidity measures have been developed. The Chronic Disease Score (CDS) developed by a team of physicians, pharmacists, and health services researchers at the Center for Health Studies, Group Health Cooperative of Puget Sound (GHC), is an early model for measuring morbidity conditions based on prescription data [18]. Then Clark et. al. demonstrated an approach to assign empirically derived weights for the CDS [19]. Afterwards, the CDS was revised to incorporate more drugs used for treating diseases and conditions in order to fulfil the needs to measure the health status and the risk of healthcare utilization among different types of populations [12, 17, 20, 21]. Although these medication-based risk adjustment tools have been tested, and were found to be valid in predicting future healthcare utilization, most of these tools incorporate a coding algorithm that is applied in the U.S. (i.e. required medication data contains the U.S. National Drug Codes (NDC) or the American Hospital Formulary Service (AHFS) Drug codes) , which makes studies conducted outside the U.S. operationally cumbersome.

The Johns Hopkins Adjusted Clinical Groups (ACG) system was developed to predict healthcare utilization and costs based on groupings of diagnoses [2224]. The former version of the ACG system provided the Aggregated Diagnosis Codes (ADGs; 32 diagnosis clusters) and ACGs (mutually exclusive, health status categories defined by morbidity, age, and sex) of a given population based on diagnosis data. Version 7.1 of the ACG system incorporated Rx-defined Morbidity Groups (Rx-MGs) into predictive models. Unlike earlier developed medication-based risk adjustment tools which include medication therapeutic classes to identify any limited chronic diseases or conditions, the Rx-MG algorithm first reduces nearly 90 000 U.S. NDCs to approx. 2700 units, then assigns each medication use into one of the 60 Rx-MGs based on criteria consisting of primary anatomico-physiological system, morbidity differentiation, expected duration, and severity [24, 25]. For medication data collected outside the U.S., an international mapping algorithm within the ACG system also performs the Rx-MG assignment based on the WHO Anatomical Therapeutic Chemical (ATC) classification [26]. This feature makes the ACG system stand out from the other medication-based risk adjustment tools in that it can be applied to countries where the medication data contains neither NDC nor AHFS codes.

This study aimed to verify if the Rx-MGs of the Johns Hopkins ACG system could be used for adjusting risk and for explaining the variations in healthcare cost in Taiwan. Previous researches have shown diagnosis-based ADGs to be a valid morbidity measure as well as risk adjust instrument for the NHI claim data in Taiwan [27, 28], but the application of Rx-MGs in empirical research remains absent. Although in recent studies the Rx-MGs were tested and found to be valid risk adjusters within predictive models (PMs), nevertheless, those studies are based on the limited ranking of age or populations with selected health conditions [24, 29, 30]. In the present study we compared the performance of Rx-MGs to ADGs and other diagnosis-based risk adjusters for predicting the (concurrent and prospective) total cost and the medication cost under the NHI. The performance of Rx-MGs models were tested with a sample that can represent the entire population. The fit of these models was also tested by age groups to ensure generalizability.

Methods

Risk Adjustment Instruments

Two types of risk adjusters within the Johns Hopkins ACG system were chosen for the present study: the diagnosis-based ADGs and the medication-based Rx-MGs [24]. Studies have found the Elixhauser's comorbidity index to be statistically slightly superior to the Charlson system at adjusting for comorbidity [31, 32]. Therefore, the Deyo's Charlson Comorbidity Index (CCI) [33] and the Elixhauser's Index[2] were adopted as competitors to the Rx-MGs. All of the morbidity groups or prescription groups measured by those instruments were treated as dichotomous variables in predictive models. We used the ICD codes cited by Quan et. al. to determine if each of these diagnoses were included in any of the Deyo's CCI or Elixhauser's Index [34]. Instead of using the original coding algorithms, the enhanced ICD-9-CM coding algorithms for Charlson and Elixhauser's index were adopted to solve: (1) discrepancies among coding algorithms for some conditions; (2) inconsistent defining of the 6 shared comorbidities of Deyo's and Elixhauser's original ICD-9-CM coding algorithms.

Study Populations

Taiwan launched a universal National Health Insurance (NHI) Program on March 1, 1995. As of 2007, 22.60 million of Taiwan's 22.96 million population (98.4%) were enrolled in the NHI program [35]. And, as of December 2008, 18 829 hospitals and healthcare providers (92% of all healthcare facilities in Taiwan) and 4180 pharmacies were contracted by the Bureau of National Health Insurance [36]. The NHI program features universal access to healthcare, healthcare with acceptable quality, comprehensive benefits (inpatient and ambulatory care, dental services, traditional Chinese medicine therapy, surgery, examinations, laboratory tests, prescription medications, nursing care, hospital rooms, preventive services, and certain OTC drugs). These features make the NHI claim data an appropriate source for comparing the performance of diagnosis-based as well as medication-based risk adjustment instruments.

The Longitudinal Health Insurance Database 2005 (LHID2005), which consists of one million out of 25.68 million National Health Insurance enrollees in 2005, was used in this study. The LHID2005 database was derived by the Bureau of National Health Insurance (BNHI), Department of Health and maintained by the National Health Research Institutes (NHRI) so as to make it accessible to scientists in Taiwan for research purposes. The use of the data in this study was reviewed and granted by the NHRI. The data used in this study has no unique patient identifier nor any information that could violate the privacy protection policy. All case IDs required for data linkage were encrypted before being released. There is no significant difference in the gender or age distribution, nor is there an average insured payroll-related amount between the patients in the LHID2005 and the original population [35]. This study chose 2006 as the baseline year to predict healthcare cost (medication and total cost) in 2007. The final sample size was 793 239 (81%) which excludes cases with discontinued enrolment in 2006. Because those cases which were not fully enrolled in the NHI program in 2006 had less opportunity for access to healthcare covered by the NHI, the costs of that group might be under-estimated. To test for model fit, the sample was randomly divided into the estimation (training) sample (476 558; 60%) and the validation (testing) sample (316 681; 40%).

Data Analysis

The information on the prescriptions in LHID2005 includes outpatients/clinics, inpatients, and contracted pharmacies (community pharmacies). Diagnosis data combined the diagnosis codes derived from inpatient and outpatient/clinic claims. Studies show that the truncation of healthcare expenditures in predictive models provides more stable and more robust estimates than using raw dollars [24, 37]. But, the cut-offs used for defining the outliers in those researches ranged in general from 0.5% to 20% [3843], or were set for a fixed amount by the researchers [17, 24]. In the present study, we capped pharmacy cost and the total cost at the top 1% of the cases, which are the maximums of USD 1846 and USD 7538 in 2006 as well as USD 2062 and USD 9446 in 2007, respectively.

The diagnoses derived from the National Health Insurance claim data were entered into the Johns Hopkins ACG system for ADGs assignment. The prescription codes within the claim data were first mapped to the WHO ATC codes, then entered into the Johns Hopkins ACG system for Rx-MGs assignment. For measuring the Charlson Index and the Elixhauser's index, the diagnoses for all cases were first screened by a pre-defined algorithm to improve the specificity of these codes, excluding outpatient diagnoses which were identified as with a same disease/condition but had been reported less than 3 times within the year, or it they all appeared in the same month. The exclusion criteria was not applied for the data which were input in the ACG system because the precise algorithm for assigning each single ICD code to the ADG was not disclosed by the Johns Hopkins ACG team. Another concern was that the ADG categories include acute diseases/conditions that are not included by the Charlson Index and the Elixhauser's index. Therefore, excluding those ICD codes that were reported less than 3 times may underestimate the existing acute diseases/conditions.

Multivariate OLS regression was used in the cost prediction modelling. The risk adjusters used in the predictive models included age, gender, Deyo's CCI, Elixhauser's Index, ADGs, and Rx-MGs. Because previous studies found that prior cost is a comparatively accurate predictor of true costs [44], it was also included for prospective prediction in this study. Because the relationship between prior- and current-year costs may not be strictly linear [45], we also examined a functional form that included a squared term of costs in 2006. There were five alternative models for the concurrent prediction and seven models for the prospective prediction fitted in this study. For concurrent prediction, the first model controlled for age and gender only, and was followed by models including Deyo's CCI, Elixhauser's index, ADGs, and Rx-MGs. The fifth model combined both ADGs and Rx-MGs for comparing models that included only one of these indexes. For prospective prediction, the alternative models included the five for concurrent prediction, as well as added models that were adjusted by prior cost and the square term of prior cost. The coefficients of each morbidity group within the selected indices were estimated from the estimation sample. Then the coefficients, excluding those which were statistically non-significant in each alternative model (see appendix), were applied in the validation sample. The performance of each alternative model was compared by its predictive R-square and mean of absolute prediction error (MAPE) estimated by the validation sample. Another indicator was also provided in which the MAPE is divided by the mean of cost, so that the MAPEs could be compared across the models with different means of cost. The fit of the selected models was also tested by age groups (< 18, 18-64, > = 65) for sensitivity analysis. The pharmacy cost and total cost of each group were capped at the top 1% of the cases.

Results

Patient characteristics

As shown in Table 1, the estimation and the validation sample have the same distribution of age, gender, number of Rx-MGs, and healthcare utilizations. There were 11% of cases with zero Rx-MGs in the estimation samples as well as in the validation sample. The average numbers of Rx-MGs for both samples are 7.19 and 7.20. Also, 29% of cases were with more than 10 Rx-MGs. Compared to the year 2006, the mean of the total cost increased by 12% and the mean of the total cost increased by about 10% in 2007.
Table 1

Characteristics of estimation and validation samples

 

Estimation (n = 476 558) 60%

Validation (n = 316 681) 40%

p-value

Characteristics

(n)

(%)

(n)

(%)

 

Age

    

0.641

   0-17

105 430

22.1%

70 449

22.2%

 

   18-44

193 270

40.6%

128 267

40.5%

 

   45-64

125 831

26.4%

83 385

26.3%

 

   > = 65

52 027

10.9%

34 580

10.9%

 

   Mean age (S. D.)

37.3

(21.0)

37.3

(21.0)

 

Gender

    

0.284

   Female

241 477

50.7%

160 854

50.8%

 

Rx_MGs

    

0.368

   0

53 395

11.2%

35 099

11.1%

 

   < = 3

63 060

13.2%

41 706

13.2%

 

   < = 6

108 881

22.8%

72 648

22.9%

 

   < = 9

113 026

23.7%

75 358

23.8%

 

   > = 10

138 196

29.0%

91 870

29.0%

 

   Mean number of Rx_MGs (S. D.)

7.19

(4.98)

7.20

(4.97)

0.247

Mean of total cost (USDa) (S. D.)

     

   Y2006

598

(2063.4)

600

(2410.4)

0.626

   Y2007

668

(2339.7)

672

(2934.7)

0.575

Mean of pharmacy cost (USDa) (S. D.)

     

   Y2006

155

(752.2)

158

(1497.9)

0.298

   Y2007

169

(808.5)

174

(1916.1)

0.172

a. USD 1. = NTD 32.5

The distribution of each Rx-MG was similar in both samples (see Table 2). A few Rx-MGs had cases less than 1%, and the number of cases for 'Immune disorders' (ALLx040) and 'Cystic fibrosis' (RESx030) were less than 100. Prevalence of several acute diseases/conditions, identified by Rx-MGs, was above 50% among the two samples: 'Allergy/immunology, acute minor', 'Gastrointestinal/hepatic, acute minor', 'Pain and inflammation', 'Infectious, acute minor', and 'Respiratory, acute minor'. The prevalence of all Rx-MGs had no significant differences among the two samples, except for 'Endocrine, Bone disorders'
Table 2

Frequency of Rx-MGs in 2006, by study sample

  

Estimation

(n = 476 558)

60%

Validation

(n = 316 681)

40%

p-value

Rx-MG label

Description

(n)a

(%)

(n)a

(%)

 
 

Allergy/immunology

     

ALLx010

   Acute minor

252 733

53.00%

168 474

53.20%

0.145

ALLx030

   Chronic inflammatory

86 509

18.20%

57 687

18.20%

0.474

ALLx040

   Immune disorders

28

< 0.1%

18

< 0.1%

0.913

ALLx050

   Transplant

437

0.10%

277

0.10%

0.538

 

Cardiovascular

     

CARx010

   Chronic medical

17 517

3.70%

11 817

3.70%

0.197

CARx020

   Congestive heart failure

14 754

3.10%

9 956

3.10%

0.229

CARx030

   High blood pressure

79 187

16.60%

52 759

16.70%

0.610

CARx040

   Hyperlipidemia

21 877

4.60%

14 572

4.60%

0.821

CARx050

   Vascular disorders

57 775

12.10%

38 503

12.20%

0.641

 

Ears-nose-throat

     

EARx010

   Acute minor

3 653

0.80%

2 424

0.80%

0.956

 

Endocrine

     

ENDx010

   Bone disorders

2 088

0.40%

1 492

0.50%

0.032

ENDx020

   Chronic medical

17 059

3.60%

11 413

3.60%

0.569

ENDx030

   Diabetes with insulin

4 813

1.00%

3 167

1.00%

0.666

ENDx040

   Diabetes without insulin

21 991

4.60%

14 634

4.60%

0.892

ENDx050

   Thyroid disorders

4 050

0.80%

2 681

0.80%

0.877

 

Eye

     

EYEx010

   Acute minor: curative

103 889

21.80%

69 173

21.80%

0.648

EYEx020

   Acute minor: palliative

79 750

16.70%

52 753

16.70%

0.371

EYEx030

   Glaucoma

31 213

6.50%

20 959

6.60%

0.227

 

Female reproductive

     

FREx010

   Hormone regulation

16 101

3.40%

10 643

3.40%

0.667

FREx020

   Infertility

2 772

0.60%

1 826

0.60%

0.771

FREx030

   Pregnancy and delivery

10 894

2.30%

7 113

2.20%

0.243

 

Gastrointestinal/hepatic

     

GASx010

   Acute minor

288 597

60.60%

191 999

60.60%

0.533

GASx020

   Chronic liver disease

11 622

2.40%

7 708

2.40%

0.893

GASx030

   Chronic stable

140 349

29.50%

93 089

29.40%

0.596

GASx040

   Inflammatory bowel disease

918

0.20%

613

0.20%

0.926

GASx050

   Pancreatic disorder

33 833

7.10%

22 647

7.20%

0.379

GASx060

   Peptic disease

91 510

19.20%

61 344

19.40%

0.062

 

General signs and symptoms

     

GSIx010

   Nausea and vomiting

69 209

14.50%

46 281

14.60%

0.257

GSIx020

   Pain

108 551

22.80%

72 225

22.80%

0.765

GSIx030

   Pain and inflammation

371 310

77.90%

247 044

78.00%

0.316

 

Genitourinary

     

GURx010

   Acute minor

27 817

5.80%

18 636

5.90%

0.375

GURx020

   Chronic renal failure

1 086

0.20%

666

0.20%

0.102

 

Hematologic

     

HEMx010

   Coagulation disorders

35 047

7.40%

23 460

7.40%

0.369

 

Infections

     

INFx010

   Acute major

26 329

5.50%

17 191

5.40%

0.065

INFx020

   Acute minor

250 114

52.50%

165 757

52.30%

0.217

INFx030

   HIV/AIDS

308

0.10%

203

0.10%

0.928

INFx040

   Tuberculosis

1 350

0.30%

883

0.30%

0.714

 

Malignancies

     

MALx010

   Malignancies

3 176

0.70%

2 074

0.70%

0.535

 

Musculoskeletal

     

MUSx010

   Gout

17 046

3.60%

11 181

3.50%

0.277

MUSx020

   Inflammatory conditions

515

0.10%

342

0.10%

0.992

 

Neurologic

     

NURx010

   Alzheimer's disease

194

< 0.1%

147

< 0.1%

0.230

NURx020

   Chronic medical

56 048

11.80%

37 471

11.80%

0.334

NURx030

   Migraine headache

5 341

1.10%

3 588

1.10%

0.612

NURx040

   Parkinson's disease

11 633

2.40%

7 656

2.40%

0.506

NURx050

   Seizure disorder

8 760

1.80%

5 846

1.80%

0.799

 

Psychosocial

     

PSYx010

   Attention-deficit disorder

692

0.10%

465

0.10%

0.852

PSYx020

   Addiction

2 531

0.50%

1 614

0.50%

0.195

PSYx030

   Anxiety

91 748

19.30%

61 452

19.40%

0.091

PSYx040

   Depression

19 482

4.10%

12 951

4.10%

0.973

PSYx050

   Acute minor

32 618

6.80%

21 680

6.80%

0.979

PSYx060

   Chronic unstable

29 899

6.30%

20 190

6.40%

0.069

 

Respiratory

     

RESx010

   Acute minor

301 188

63.20%

200 630

63.40%

0.166

RESx020

   Chronic medical

29 992

6.30%

19 890

6.30%

0.820

RESx030

   Cystic fibrosis

77

< 0.1%

47

< 0.1%

0.646

RESx040

   Airway hyper-reactivity

164 532

34.50%

109 696

34.60%

0.295

 

Skin

     

SKNx010

   Acne

23 862

5.00%

15 892

5.00%

0.824

SKNx020

   Acute and recurrent

179 162

37.60%

119 167

37.60%

0.753

SKNx030

   Chronic medical

3 276

0.70%

2 100

0.70%

0.196

 

Toxic effects/adverse effects

     

TOXx010

   Acute major

574

0.10%

381

0.10%

0.986

ZZZx000

   Other and nonspecific medications

157 388

33.00%

104 784

33.10%

0.564

a Represents the number of cases which has the selected prescriptions contributed to the Rx-MG (multiple counting, since cases are not mutually exclusive in each Rx-MG).

Performance comparisons among predictive models

The predictive R-squares of five models predicting total cost concurrently ranged from 0.089 to 0.650 (see Table 3). For those models with cost adjusted by diagnosis-based morbidity measures, the ADGs model performed better than others. The Rx-MGs model has a predictive R-square 0.618, which explains the 21% more variance than the ADGs model. The model that combined ADGs and Rx-MGs had the highest predictive R-square (0.650) as well as the lowest MAPE rate (54.6%) among all models. The prospective prediction models had lower predictive R-squares than the concurrent prediction models. All of the seven models explained less than 50% of the variations in the total cost for 2007. Similar to the concurrent prediction models, the prospective prediction model which combined ADGs and Rx-MGs had a predictive R-square (0.382) that was higher than those using either ADGs or Rx-MGs. The MAPE rate was the lowest (75.9%) among all models except for those that included prior cost. The model which included prior cost increased 0.08 in R-square. The model with the square term for prior cost had no considerable improvement in predictive R-square.
Table 3

Predictive models for total cost

Morbidity Index

Source of morbidity

Predictors

Model performance - prediction of total cost

   

Concurrent (year 2006)

Prospective (year 2007)

   

R2

MAPE

MAPE*(%)

R2

MAPE

MAPE*(%)

(none)

 

Age + gender

0.089

505.9

99.5

0.092

582.9

102.9

Deyo's CCI

Diagnosis

Age + gender + CCIs

0.345

409.0

80.5

0.273

496.4

87.6

Elixhauser's Index

Diagnosis

Age + gender + E. Index

0.373

390.8

76.9

0.294

480.3

84.8

ADG

Diagnosis

Age + gender + ADGs

0.411

360.0

70.8

0.252

486.1

85.8

Rx-MG

Medication

Age + gender + Rx-MGs

0.618

297.5

58.5

0.360

448.5

79.1

ADG + Rx-MG

Diagnosis & medication

Age + gender + ADGs + Rx-MGs

0.650

277.4

54.6

0.382

430.4

75.9

ADG + Rx-MG

Diagnosis & medication

Age + gender + ADGs + Rx-MGs + prior total cost

   

0.465

386.1

68.1

ADG + Rx-MG

Diagnosis & medication

Age + gender + ADGs + Rx-MGs + prior total cost + (prior total cost)2

   

0.465

389.2

68.7

MAPE, mean absolute prediction error; MAPE*, MAPE divided by the mean of cost

As shown in Table 4, the Rx-MGs models also performed better than the diagnosis-based models for predicting medication cost concurrently and prospectively. But, unlike the results of the total cost prediction models, the ADGs models had a lower predictive R-squares and a higher MAPE rate than the model adjusted by Elixhauser's index for predicting medication cost. The models which combined ADGs and Rx-MGs also improved slightly over the model adjusted by Rx-MGs only. The ADGs and Rx-MGs combined model had a remarkable improvement in predictive R-square after adding the predictor of prior medication cost. The predictive R-square seemed to have only a negligible improvement if the square term of prior medication cost was added.
Table 4

Predictive models for medication cost

Morbidity Index

Source of morbidity

Predictors

Model performance - prediction of medication cost

   

Concurrent (year 2006)

Prospective (year 2007)

   

R2

MAPE

MAPE* (%)

R2

MAPE

MAPE* (%)

(none)

 

Age + gender

0.151

155.2

118.9

0.153

166.0

119.1

Deyo's CCI

Diagnosis

Age + gender + CCIs

0.426

113.5

87.0

0.366

129.6

93.0

Elixhauser's Index

Diagnosis

Age + gender + E. Index

0.514

97.4

74.7

0.434

115.5

82.9

ADG

Diagnosis

Age + gender + ADGs

0.431

114.4

87.7

0.360

131.2

94.2

Rx-MG

Medication

Age + gender + Rx-MGs

0.615

89.6

68.6

0.485

110.3

79.1

ADG + Rx-MG

Diagnosis & medication

Age + gender + ADGs + Rx-MGs

0.638

85.6

65.6

0.505

106.2

76.2

ADG + Rx-MG

Diagnosis & medication

Age + gender + ADGs + Rx-MGs + prior medication cost

   

0.684

73.8

53.0

ADG + Rx-MG

Diagnosis & medication

Age + gender + ADGs + Rx-MGs + prior medication cost + (prior medication cost)2

   

0.684

73.7

52.9

MAPE, mean absolute prediction error; MAPE*, MAPE divided by the mean of cost

Comparing model performance across age groups

The performance of three alternative models was compared across three age groups: < 18, 18-64, > = 65. After being capped at the 99-percentile of costs for all age groups, the result showed that models that applied to all age ranks had the highest predictive R-squares of all other sub-samples (see Table 5). The 18-64 year old age group had the highest predictive R-squares for all alternative models compared to the other two age groups. For all three sub-samples, the performance of the predictive models was similar to the whole sample: the models that were adjusted for prior cost performed the best. The result showed that R-squares for the 'under 18' age group were the lowest among all three sub-samples, implying that the predictive models are not well explained variations of costs within the sample.
Table 5

Total cost predictive models for specific age groups

Predictors

Variation explained by model (R2)

 

Total cost

Medication cost

 

(all)

Age < 18

Age 18-64

Age > = 65

(all)

Age < 18

Age 18-64

Age > = 65

Concurrent prediction (year 2006)

        

   Age + gender + Rx-MGs

0.618

0.471

0.589

0.552

0.615

0.399

0.571

0.528

   Age + gender + ADGs + Rx-MGs

0.650

0.602

0.629

0.570

0.638

0.520

0.600

0.543

Prospective prediction (year 2007)

        

   Age + gender + Rx-MGs

0.360

0.220

0.333

0.268

0.485

0.244

0.446

0.287

   Age + gender + ADGs + Rx-MGs

0.382

0.281

0.359

0.278

0.505

0.308

0.472

0.300

   Age + gender + ADGs + Rx-MGs + prior cost

0.465

0.359

0.451

0.376

0.684

0.477

0.677

0.442

Discussion

This study has demonstrated that the Rx-Defined Morbidity Groups are applicable for predicting the total cost and the medication cost in a universal health insurance system. Although a few articles attempted to predict or explain variations of medication use by applying the Johns Hopkins ACG case-mix system, these analytical models are mainly based on diagnosis-based risk adjusters (i.e. the EDCs, ADGs, or ACGs) within the ACG system [10, 11, 46]. Two recent articles reported studies that had applied the Johns Hopkins ACG system for identifying high-risk patients and predicting healthcare utilization. However, the authors chose predictive models embedded within the ACG system (i.e. the Dx-PM, Rx-PM, and DxRx-PM) instead of adjusting risks by original morbidity groups (i.e. the ADGs or Rx-MGs) [24, 47]. Therefore, we believe that the present article is the first one to describe an empirical study using Rx-MGs for healthcare cost prediction as well as comparing the model performance with other diagnosis-based predictive models.

In this study, the model adjusted by Rx-MGs could explain over 60% of the variations for total cost and medication cost in the concurrent year. Clark et. al. used two versions of the Chronic Disease Score to explain variations of total cost, the R-squares for concurrent prediction were 0.09 and 0.19 [19]. Fishman et. al. used the Rx-Risk model to predict healthcare cost, and the validation R-square of that model was 0.0874. They also took sensitivity analyses for cases with patients younger than 18 or older than 18. The R-squares for these two sub-samples were 0.083 and 0.077, respectively [17]. Sales et. al. used Rx-Risk-V, a modification from Rx-Risk for the veteran population, to predict cost. The R-square of the concurrent prediction was 0.202 [48]. Compared to former researches using medication-based morbidity measures to predict cost, the performance of the Rx-MGs model is relatively better than others. This study also found that the Rx-MGs model is applicable to all the different age groups, although the performance varied among these groups. The Rx-MGs model also performed better than other diagnosis-based alternative models in this study. This finding is consistent with other studies which found that prescription data are superior for predicting pharmacy cost [6, 24]. However, our study also found that the Rx-MGs model is superior for predicting total cost. One possible explanation for the superior performance of the Rx-MGs model compared to other medication based morbidity measures reported by previous studies is that the NHI pays for almost all prescription drugs, except for those that are very new in the market, expensive, and not yet approved by the Department of Health. Furthermore, this study aggregated prescriptions from outpatients/clinics, inpatients, and community pharmacies. This comprehensive data was intended to help capture all prescription-related morbidities for each case, something that was not done in similar studies. In addition, the Rx-MGs consisted of not only the chronic diseases or conditions, but they also included several acute diseases or syndromes. This feature makes the Rx-MGs stand out from other chronic disease focused instruments (e.g. the Chronic Disease Score) by capturing all possible risks for healthcare utilization. In addition, although the ADGs do capture the diagnoses of acute diseases or syndromes, the number of ADG categories is smaller than that of the Rx-MGs, which might explain why the performance of Rx-MGs models are superior to the ADGs models. Another possible explanation is that the annual medication cost is merely one fourth of the annual total healthcare cost in NHI. Therefore the model that can explain more variations of medication cost is expected to have a better performance for predicting total cost. However, the real cause for the gap in performance between ADGs and Rx-MGs models needs further investigation.

The predictive R-squares of the ADGs models in this study are larger than those reported by two other similar studies which also used Taiwan NHI data [27, 38]. These two earlier studies did not enforce the 'full enrolment' criteria as applied in our study. Therefore the disease burden of those cases selected in these two earlier studies may not be equally accessed. Second, we capped the cost at the 99-percentile, which might be the most critical point to explain the improvement in model performance. We conducted another analysis using original cost (without capping the cost) for the prediction models. The result of that analysis showed that age/sex also adjusted for 4% to 5% of the variances, which is quite similar to Lee and Huang's findings[28]. Chang and Weiner also found that after truncating the cost at top 0.5%, the performance of the models improved significantly[38]. After adjusting for prior healthcare utilization, our proposed model combined with Rx-MGs and ADGs out-performed others models for predicting future medication cost, which could explain over 68% of the variations for future medication cost. The findings of this study are similar to the findings of Forrest, et al.'s study which showed that the Combined Diagnostic/Medication Predictive Models (DxRx-PMs) had the highest R-squares for explaining variations of pharmacy charges and total healthcare charges [24]. Other studies have shown that adding diagnosis-based morbidity measures to medication-based models could improve the prediction of total healthcare utilization [6, 19, 49, 50]. However, those findings supported combining those two types of measure to improve cost prediction. On the other hand, Schneeweiss et.al. compared the performance of four diagnosis-based and two medication-based comorbidity scores to predict mortality. They found that while diagnoses-based scores performed better than medication-based scores in predicting future mortality, combining diagnoses and medication-based scores showed an improvement in predicting mortality [49]. The strength of employing all available diagnosis and prescription data is that some potential risk factors may not be captured in a single morbidity measurement, and each morbidity measurement captures different risks. Therefore, combining different morbidity measures in a given predictive model can be more informative than employing just one. Although using more than one morbidity measurement in a single model may raise the concern of multicollinearity, an empirical study showed that there is only a low correlation between different measures [51].

Previous studies have shown that combined prior costs and morbidity measures are important in determining future high cost patients [24, 30, 41]. Hsu et. al. found that incorporating information of the previous year's drug use or cost into the risk adjustment approach would greatly improve the accuracy of the prediction. They pointed out that drug costs tend to be stable from year to year and are more predictable than other types of medical costs. Therefore, ignoring past costs may result in preventable misallocation of resources and creates a strong incentives for reverse patient selection [45]. The data of our study also support that predictive models combined with Rx-MGs, ADGs, and prior cost performed the best in predicting future cost. However, investigators have argued that this could provide incentives to increase utilization or to favor a specific style of practicing medicine in addition to medical needs. Thus, payment models that include utilization measures among the predictor variables must proceed with caution [41, 52].

Compared to other diagnosis-based predictive models, this study has demonstrated that the Rx-MGs model out-performs all other diagnosis-based models in explaining or predicting healthcare utilization. In future applications, the Rx-MGs could be applied for describing and comparing disease patterns among populations. The models which use Rx-MGs alone or combined with ADGs could also be applied for helping local health authorities or case managers to identify high risk populations for disease management programs [24, 29, 53]. A comprehensive and integrated care delivery system could be provided to those who have a high utilization of healthcare but have a low severity of illness, instead of delivering fragmented acute care to them. The Rx-MGs or other predictive models within the ACG system could also be tested for their efficiency and appropriateness in allocating healthcare resources or setting payment rates by future researchers or policy makers.

There are several limitations to this study. First, we used ADGs and Rx-MGs as risk adjusters for comparing them with two other commonly used morbidity measures. However, the Johns Hopkins ACG system provides prediction models (PMs) which include disease or frailty markers other than ADGs or Rx-MGs, and they have a better performance than the ADGs or other diagnosis-based measures. The PMs were not included as competing models in this study because the 'risk scores' provided by the Dx-PM or Rx-PM as the summary measures of disease burden were provided by the ACG system [24]. Although the more efficient risk adjusters included in the prediction models could be expected to provide the better performance in predicting cost, the performance of those models is somehow hard to compare with other models that are wholly based on morbidity measures (e.g. the Charlson Comorbidity Index). Second, we excluded those cases with discontinued enrolment in 2006 to ensure equality accessibility for healthcare covered by NHI. However, the reasons for the discontinued enrolment in NHI might be very diverse. Thus these cases that were excluded by our study might be high-risk users (e.g. cancer patients at the end-of-life year) or healthy users (e.g. young students studying abroad). Hence the analytical strategy used in this study could limit its generalizability. Another limitation is the approach to treat outliers in this study. Although we capped at the top 1% of costs, those cases with capped costs generally accounted for approximately 25% of the healthcare expenditure. That implies that the predictive models applied to real data cannot perform as well as in this study. Another analysis also found that when applying the predictive models to those high-risk users with actual cost data, the performance of the models declines significantly. This finding seems to suggest that in order to address this issue it might be best to identify and manage those cases by using the risk adjustment instruments, instead of "predicting" their future healthcare utilization [24, 29]. The fourth potential limitation in this study is that we failed to incorporate socio-economic status indicators into the predictive models. However, in a recent article the authors argued that adding socioeconomic patient characteristics improves the predictive model only slightly [54]. The information on socio-economic status is quite limited in the NHI database. We carried out another analysis to incorporate household income into the predictive models. The results showed that as a proxy of the socio-economic status it did not have a statistically significant impact on costs.

Conclusions

This study demonstrated that compared to other diagnosis-based predictive models, the Rx-MGs model out-performs all other models in explaining variations of cost and predicting future healthcare utilization. For countries or regions that routinely collect prescription claim data, the Rx-MGs within the Johns Hopkins ACG case-mix system could be applied to predict future healthcare utilization as well as allocate resources for healthcare.

Declarations

Acknowledgements

This study was supported by grants from the Department of Health, Taiwan (96Z4149; DOH098-TD-D-113-098016) and from the 'Aiming for the top, university and elite research center development plan' (MoEATU, 99RH0021). The authors would like to thank Karen Kinder Siemens and Chad Abrams for their technical support. The authors also thank Roger Haesevoets for proofreading the manuscript for English.

Authors’ Affiliations

(1)
Institute of Health Care Organization Administration, College of Public Health, National Taiwan University
(2)
Institute of Preventive Medicine, College of Public Health, National Taiwan University
(3)
Center for Health Insurance Research, College of Public Health, National Taiwan University

References

  1. Charlson ME, Pompei P, Ales KL, Mackenzie CR: A New Method of Classifying Prognostic Co-Morbidity in Longitudinal-Studies - Development and Validation. Journal of Chronic Diseases. 1987, 40 (5): 373-383. 10.1016/0021-9681(87)90171-8.View ArticlePubMed
  2. Elixhauser A, Steiner C, Harris DR, Coffey RN: Comorbidity measures for use with administrative data. Medical Care. 1998, 36 (1): 8-27. 10.1097/00005650-199801000-00004.View ArticlePubMed
  3. Weiner JP, Dobson A, Maxwell SL, Coleman K, Starfield BH, Anderson GF: Risk-adjusted Medicare capitation rates using ambulatory and inpatient diagnoses. Health Care Finan Rev. 1996, 17 (3): 77-99.
  4. Ash AS, Ellis RP, Pope GC, Ayanian JZ, Bates DW, Burstin H, Iezzoni LI, MacKay E, Yu W: Using diagnoses to describe populations and predict costs. Health Care Finan Rev. 2000, 21 (3): 7-28.
  5. Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Iezzoni LI, Ingber MJ, Levy JM, Robst J: Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Finan Rev. 2004, 25 (4): 119-141.
  6. Zhao Y, Ash AS, Ellis RP, Ayanian JZ, Pope GC, Bowen B, Weyuker L: Predicting pharmacy costs and other medical costs using diagnoses and drug claims. Medical Care. 2005, 43 (1): 34-43.PubMed
  7. Charlson ME, Charlson RE, Peterson JC, Marinopoulos SS, Briggs WM, Hollenberg JP: The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. Journal of Clinical Epidemiology. 2008, 61 (12): 1234-1240. 10.1016/j.jclinepi.2008.01.006.View ArticlePubMed
  8. Perkins AJ, Kroenke K, Unutzer J, Katon W, Williams JW, Hope C, Callahan CM: Common comorbidity scales were similar in their ability to predict health care costs and mortality. Journal of Clinical Epidemiology. 2004, 57 (10): 1040-1048. 10.1016/j.jclinepi.2004.03.002.View ArticlePubMed
  9. Krop JS, Saudek CD, Weller WE, Powe NR, Shaffer T, Anderson GF: Predicting expenditures for medicare beneficiaries with diabetes - A prospective cohort study from 1994 to 1996. Diabetes Care. 1999, 22 (10): 1660-1666. 10.2337/diacare.22.10.1660.View ArticlePubMed
  10. Orueta JF, Urraca J, Berraondo I, Darpon J, Aurrekoetxea JJ: Adjusted Clinical Groups (ACGs) explain the utilization of primary care in Spain based on information registered in the medical records: A cross-sectional study. Health Policy. 2006, 76 (1): 38-48. 10.1016/j.healthpol.2005.04.005.View ArticlePubMed
  11. Aguado A, Guino E, Mukherjee B, Sicras A, Serrat J, Acedo M, Ferro JJ, Moreno V: Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: A cross-sectional study of patient electronic records in primary care. Bmc Health Services Research. 2008, 8: 10.1186/1472-6963-8-53.
  12. Gilmer T, Kronick R, Fishman P, Ganiats TG: The medicaid R-x model - Pharmacy-based risk adjustment for public programs. Medical Care. 2001, 39 (11): 1188-1202. 10.1097/00005650-200111000-00006.View ArticlePubMed
  13. Malone DC, Billups SJ, Valuck RJ, Carter BL: Development of a chronic disease indicator score using a veterans affairs medical center medication database. Journal of Clinical Epidemiology. 1999, 52 (6): 551-557. 10.1016/S0895-4356(99)00029-3.View ArticlePubMed
  14. Ghali WA, Quan H, Brant R: Risk adjustment using administrative data - Impact of a diagnosis-type indicator. J Gen Intern Med. 2001, 16 (8): 519-524. 10.1046/j.1525-1497.2001.016008519.x.PubMed CentralView ArticlePubMed
  15. Wilchesky M, Tamblyn RM, Huang A: Validation of diagnostic codes within medical services claims. Journal of Clinical Epidemiology. 2004, 57 (2): 131-141. 10.1016/S0895-4356(03)00246-4.View ArticlePubMed
  16. Pine M, Jordan HS, Elixhauser A, Fry DE, Hoaglin DC, Jones B, Meimban R, Warner D, Gonzales J: Modifying ICD-9-CM Coding of Secondary Diagnoses to Improve Risk-Adjustment of Inpatient Mortality Rates. Medical Decision Making. 2009, 29 (1): 69-81. 10.1177/0272989X08323297.View ArticlePubMed
  17. Fishman PA, Goodman MJ, Hornbrook MC, Meenan RT, Bachman DJ, Rosetti MCO: Risk adjustment using automated ambulatory pharmacy data - The RxRisk model. Medical Care. 2003, 41 (1): 84-99. 10.1097/00005650-200301000-00011.View ArticlePubMed
  18. Vonkorff M, Wagner EH, Saunders K: A Chronic Disease Score from Automated Pharmacy Data. Journal of Clinical Epidemiology. 1992, 45 (2): 197-203. 10.1016/0895-4356(92)90016-G.View Article
  19. Clark DO, Vonkorff M, Saunders K, Baluch WM, Simon GE: A Chronic Disease Score with Empirically Derived Weights. Medical Care. 1995, 33 (8): 783-795. 10.1097/00005650-199508000-00004.View ArticlePubMed
  20. Fishman PA, Shay DK: Development and estimation of a pediatric chronic disease score using automated pharmacy data. Medical Care. 1999, 37 (9): 874-883. 10.1097/00005650-199909000-00004.View ArticlePubMed
  21. Sloan KL, Sales AE, Liu CF, Fishman P, Nichol P, Suzuki NT, Sharp ND: Construction and characteristics of the RxRisk-V - A VA-adapted pharmacy-based case-mix instrument. Medical Care. 2003, 41 (6): 761-774. 10.1097/00005650-200306000-00009.PubMed
  22. Starfield B, Weiner J, Mumford L, Steinwachs D: Ambulatory Care Groups - a Categorization of Diagnoses for Research and Management. Health Serv Res. 1991, 26 (1): 53-74.PubMed CentralPubMed
  23. Tucker A, Weiner J, Abrams C: Health-Based Risk Adjustment: Application to Premium Development and Profiling. Financial strategy for managed care organizations: rate setting, risk adjustment, and competitive advantage. Edited by: Wrightson CW. 2002, Chicago, Ill Health Administration Press, 165-225.
  24. Forrest CB, Lemke KW, Bodycombe DP, Weiner JP: Medication, Diagnostic, and Cost Information as Predictors of High-Risk Patients in Need of Care Management. Am J Manag Care. 2009, 15 (1): 41-48.PubMed
  25. NEW! ACG RX Predictive Model. [http://www.acg.jhsph.edu/ACGDocuments/ACG%20Rx-PM%20Product%20Sheet.pdf]
  26. About the ATC/DDD system. [http://www.whocc.no/atcddd/]
  27. Lee WC, Huang TP: Explanatory ability of the ACG system regarding the utilization and expenditure of the National Health Insurance population in Taiwan - A 5-year analysis. J Chin Med Assoc. 2008, 71 (4): 191-199. 10.1016/S1726-4901(08)70103-5.View ArticlePubMed
  28. Lee WC: Quantifying morbidities by Adjusted Clinical Group system for a Taiwan population: A nationwide analysis. Bmc Health Services Research. 2008, 8: 10.1186/1472-6963-8-153.
  29. Sylvia ML, Shadmi E, Hsiao CJ, Boyd CM, Schuster AB, Boult C: Clinical features of high-risk older persons identified by predictive modeling. DIS MANAGE. 2006, 9 (1): 56-62. 10.1089/dis.2006.9.56.View Article
  30. Sylvia ML, Griswold M, Dunbar L, Boyd CM, Park M, Boult C: Guided care: cost and utilization outcomes in a pilot study. DIS MANAGE. 2008, 11 (1): 29-36. 10.1089/dis.2008.111723.View Article
  31. Southern DA, Quan H, Ghali WA: Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004, 42 (4): 355-360. 10.1097/01.mlr.0000118861.56848.ee.View ArticlePubMed
  32. Stukenborg GJ, Wagner DP, Connors AF: Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations. Med Care. 2001, 39 (7): 727-739. 10.1097/00005650-200107000-00009.View ArticlePubMed
  33. Deyo RA, Cherkin DC, Ciol MA: Adapting a Clinical Comorbidity Index for Use with Icd-9-Cm Administrative Databases. Journal of Clinical Epidemiology. 1992, 45 (6): 613-619. 10.1016/0895-4356(92)90133-8.View ArticlePubMed
  34. Quan HD, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA: Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005, 43 (11): 1130-1139. 10.1097/01.mlr.0000182534.19832.83.View ArticlePubMed
  35. Introduction to the National Health Insurance Research Database (NHIRD), Taiwan. [http://w3.nhri.org.tw/nhird/date_01.html]
  36. Universal Coverage under NHI in Taiwan. [http://www.nhi.gov.tw/english/webdata.asp?menu%20=%2011&menu_id%20=%20290&webdata_id%20=%202965]
  37. Iezzoni LI: Risk adjustment for measuring health care outcomes. 2003, Chicago: Health Administration Press, 3
  38. Chang H-Y, Weiner J: An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan. BMC Medicine. 2010, 8 (1): 7-10.1186/1741-7015-8-7.PubMed CentralView ArticlePubMed
  39. Meenan RT, Goodman MJ, Fishman PA, Hornbrook MC, O'Keeffe-Rosetti MC, Bachman DJ: Using risk-adjustment models to identify high-cost risks. Medical Care. 2003, 41 (11): 1301-1312. 10.1097/01.MLR.0000094480.13057.75.View ArticlePubMed
  40. Meenan RT, O'Keeffe-Rosetti MC, Hornbrook MC, Bachman DJ, Goodman MJ, Fishman PA, Hurtado AV: The sensitivity and specificity of forecasting high-cost users of medical care. Medical Care. 1999, 37 (8): 815-823. 10.1097/00005650-199908000-00011.View ArticlePubMed
  41. Ash AS, Zhao Y, Ellis RP, Schlein Kramer M: Finding future high-cost cases: comparing prior cost versus diagnosis-based methods. Health Services Research. 2001, 36 (6 Pt 2): 194-206.PubMed CentralPubMed
  42. LeBlanc M, Moon J, Kooperberg C: Extreme regression. Biostatistics. 2006, 7 (1): 71-84. 10.1093/biostatistics/kxi041.View ArticlePubMed
  43. Gregori D, Petrinco M, Barbati G, Bo S, Desideri A, Zanetti R, Merletti F, Pagano E: Extreme regression models for characterizing high-cost patients. J Eval Clin Pract. 2009, 15 (1): 164-171. 10.1111/j.1365-2753.2008.00976.x.View ArticlePubMed
  44. Bertsimas D, Bjarnadottir MV, Kane MA, Kryder JC, Pandey R, Vempala S, Wang G: Algorithmic Prediction of Health-Care Costs. Operations Research. 2008, 56 (6): 1382-1392. 10.1287/opre.1080.0619.View Article
  45. Hsu J, Huang J, Fung V, Price M, Brand R, Hui R, Fireman B, Dow W, Bertko J, Newhouse JP: Distributing $800 Billion: An Early Assessment Of Medicare Part D Risk Adjustment. Health Aff. 2009, 28 (1): 215-225. 10.1377/hlthaff.28.1.215.View Article
  46. Sicras-Mainar A, Navarro-Artieda R, Ruano-Ruano I, Velasco-Velasco S, Frias-Garrido X, Llopart J, Llausi-Selles R: Efficiency in drug prescription measured by the application of adjusted clinical groups in five Spanish primary care centres. Value Health. 2007, 10 (6): A364-A364.View Article
  47. Calderon-Larranaga A, Abrams C, Poblador-Plou B, Weiner JP, Prados-Torres A: Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: The impact of a local calibration. Bmc Health Services Research. 2010, 10: 10.1186/1472-6963-10-22.
  48. Sales AE, Liu CF, Sloan KL, Malkin J, Fishman PA, Rosen AK, Loveland S, Nichol WP, Suzuki NT, Perrin E, et al: Predicting costs of care using a pharmacy-based measure risk adjustment in a veteran population. Medical Care. 2003, 41 (6): 753-760. 10.1097/00005650-200306000-00008.PubMed
  49. Schneeweiss S, Seeger JD, Maclure M, Wang PS, Avorn J, Glynn RJ: Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. American Journal of Epidemiology. 2001, 154 (9): 854-864. 10.1093/aje/154.9.854.View ArticlePubMed
  50. Zhao Y, Ellis RP, Ash AS, Calabrese D, Ayanian JZ, Slaughter JP, Weyuker L, Bowen B: Measuring population health risks using inpatient diagnoses and outpatient pharmacy data. Health Services Research. 2001, 36 (6 Pt 2): 180-193.PubMed CentralPubMed
  51. Baser O, Palmer L, Stephenson J: The estimation power of alternative comorbidity indices. Value Health. 2008, 11 (5): 946-955. 10.1111/j.1524-4733.2008.00343.x.View ArticlePubMed
  52. Robst J, Levy JM, Ingber MJ: Diagnosis-based risk adjustment for medicare prescription drug plan payments. Health Care Finan Rev. 2007, 28 (4): 15-30.
  53. Rosen AK, Wang F, Montez ME, Rakovski CC, Berlowitzi DR, Lucove JC: Identifying future high-healthcare users - Exploring the value of diagnostic and prior utilization information. Disease Management & Health Outcomes. 2005, 13 (2): 117-127.View Article
  54. Hvenegaard A, Street A, Sorensen TH, Gyrd-Hansen D: Comparing hospital costs: What is gained by accounting for more than a case-mix index?. Social Science & Medicine. 2009, 69 (4): 640-647.View Article
  55. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/10/126/prepub

Copyright

© Kuo and Lai; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement