The association of the number of comorbidities and complications with length of stay, hospital mortality and LOS high outlier, based on administrative data

  • Kazuaki Kuwabara
  • Yuichi Imanaka
  • Shinya Matsuda
  • Kiyohide Fushimi
  • Hideki Hashimoto
  • Koichi B. Ishikawa
  • Hiromasa Horiguchi
  • Kenshi Hayashida
  • Kenji Fujimori
Regular Article

Abstract

Objectives

With greater concern for efficient resource allocation and profiling of medical care, a case-mix classification was applied for the per-diem payment system in Japan. Many questions remain, one of which is the role of comorbidity and complication (CC) in grouping logic. We examined the association of the number of CC with the length of hospital stay (LOS) and hospital mortality as well as the proportion of LOS high outliers in 19 major diagnostic categories (MDCs).

Methods

This study was a secondary data analysis embedded in a government research project, including anonymous claims and clinical data during a 4-month period from July 2002. Every 19 MDC, LOS, hospital mortality or proportion of LOS high outliers was compared by the number of CC and presence of any procedures.

Results

From 82 special function hospitals, 241,268 patients were enrolled in this study. Among all patients, 50.5% were identified without any CCs, 32.4% with one or two, 13.4% with three or four, and 3.7% with over five CCs. The overall mean LOS was 22.15 days and hospital mortality 26.05 cases per 1,000 admissions. In any MDC, LOS and the proportion of outliers increased as the number of CC rose. The mortality rate increased prominently in the respiratory system and the hematology system.

Conclusions

This study demonstrated that the occurrence of more CC caused longer LOS and higher mortality in some major disease categories. Further study will clarify the association of the weighted CC with resource use through controlling procedures specific for MDC.

Keywords

Comorbidity Complication Resource use Mortality Case mix 

Introduction

The role of comorbidities and complications (CC) in inpatient medicine has been an intense area of investigation, due to their impact on resource use, mortality, functional status, quality of life, and delivery of health care in Western societies [1, 2, 3, 4, 5, 6]. Research on the impact of CC on healthcare utilization and quality of medical care is needed, as this is important to hospital payment systems and medical outcome studies [7, 8]. Many health-related problems increase with age, especially with respect to the number of chronic conditions [1]. As the proportion of chronic conditions increases across populations, practice behaviors will change that will impact hospital costs. Meanwhile, innovative procedures have been advocated for the elderly [9, 10]. Under these circumstances, the impact of CC or age on hospital resource utilization has been examined for diseases such as diabetes mellitus or for hospitalizations of trauma patients [11, 12].

In response to increasing costs associated with the rapid evolution of healthcare technology, cost containment policies and case-mix classification systems have advanced worldwide over the past 20 years [7, 8]. However, problems have emerged in situations where some patients are under-reimbursed if they have more CCs and have consumed more hospital resources than those with fewer CCs. Munoz et al. [13] reported major inequities in Diagnosis-Related Group (DRG) prospective payment systems for pediatric patients. Jencks et al. [14] showed that the number of recorded diagnoses was no higher for patients who died than for those who survived. These studies focused on selected conditions, such as pediatric illnesses, cardiovascular disease, and pneumonia.

In this study, our goal was to demonstrate the association of CCs with resource use and outcome by major diagnostic category (MDC) and treatment group (medical or surgical) using a large administrative database developed for the Japanese case-mix classification system (Diagnosis Procedure Combination; DPC). This type of systematic research has never been performed on this scale, and the results may have some important policy implications, in which MDC CCs may be considered in prior risk adjustment for payment or outcomes, such as resource use or mortality. The aims of this study were to generate descriptive statistics of CCs and to profile the association of length of stay (LOS) and hospital mortality with the number of CCs through the stratification of MDCs and treatments. Furthermore, we identified the relationship between the proportion of LOS high outliers and the number of CCs for each MDC.

Materials and methods

We conducted a secondary data analysis of a government research project on DPC development. Anonymous claims information and clinical data were provided by the Ministry of Health, Labor and Welfare (MHLW) through a research contract. Clinical data and claims information, merged into a standardized electronic format, were gathered by the MHLW for 266,677 patients who were discharged from 82 academic hospitals (80 university hospitals, National Cancer Center, and National Cardiovascular Center) between 1 July 2002 and 31 October 2002. The Japanese MHLW permits up to seven CCs (four comorbidities and three complications) to be listed in the DPC dataset. From this original dataset, we selected cases with a LOS of up to 365 days and excluded patients who died within 24 h of admission. A total of 241,268 patients were enrolled in the study. We categorized patients into three groups according to the number of CCs documented (absent, 1 or 2, 3 or 4, and 5 or more), based on a study by Munoz et al. [15].

The DPC system is made up of 16 MDCs. However, the 16th MDC consists of four different clinical entities: injury, poisoning, burns and the toxic effect of drugs; mental diseases and disorders; diseases and disorders of systemic infection; and miscellaneous. We therefore divided the 16th MDC into these four groups and analyzed a total of 19 MDCs (Table 1). The DPC database also allows a maximum of five operative procedures to be listed, for which the definition and pricing are determined from a nationally uniform fee table under the standardized fee-for-service payment system. Patients undergoing at least one surgical procedure, as defined by the Japanese fee table, comprised the “surgical group,” while patients not undergoing any surgical procedure comprised the “medical group.” We then divided patients into these two treatment groups for all 19 MDCs.
Table 1

The proportion of comorbidity and complication category by presence of procedure and MDC (%)

Major diagnostic category

Absent

One or two

Three or four

Over five

N

Overall

Medical

47.8

33.8

14.9

3.5

123,895

Surgical

53.4

30.9

11.8

3.9

117,373

MDC1

Nervous system

Medical

46.2

35.1

15.3

3.4

12,292

Surgical

44.7

32.2

16.0

7.1

3,810

MDC2

Eye system

Medical

65.9

25.3

7.7

1.1

1,854

Surgical

54.5

32.9

9.9

2.7

17,974

MDC3

Ear, nose, mouth, and throat system

Medical

62.4

26.8

8.5

2.3

5,881

Surgical

65.0

26.5

6.4

2.1

8,374

MDC4

Respiratory system

Medical

44.2

36.8

15.4

3.6

14,666

Surgical

47.0

33.5

13.3

6.2

3,609

MDC5

Cardiovascular system

Medical

32.0

40.2

24.4

3.4

12,264

Surgical

31.5

37.3

23.7

7.5

10,768

MDC6

Digestive tract, hepatobiliary and pancreas system

Medical

49.3

35.3

12.7

2.6

20,697

Surgical

48.2

34.2

13.4

4.2

24,383

MDC7

Muscuoskeletal and connective tissue system

Medical

51.1

31.2

13.9

3.8

7,671

Surgical

63.5

24.9

8.8

2.8

11,018

MDC8

Skin and subcutaneous tissue system

Medical

50.3

35.1

11.9

2.7

3,500

Surgical

69.6

22.8

5.2

2.4

671

MDC9

Breast system

Medical

60.0

27.3

9.9

2.9

1,144

Surgical

69.7

23.4

5.8

1.1

2,532

MDC10

Endocrine, nutrition and metabolic system

Medical

36.0

36.1

23.7

4.2

8,156

Surgical

44.0

35.1

15.7

5.2

2,784

MDC11

Kidney, urinary tract and male reproductive system

Medical

51.0

30.7

14.5

3.8

10,031

Surgical

54.2

29.2

12.3

4.3

7,359

MDC12

Pregnancy, childbirth, puerperium and female reproductive system

Medical

61.1

28.7

7.7

2.5

6,195

Surgical

59.5

29.6

8.4

2.4

10,951

MDC13

Hematology system

Medical

46.6

29.7

15.7

8.1

4,967

Surgical

37.5

32.8

17.0

12.7

613

MDC14

Neonate system

Medical

49.6

32.0

12.9

5.5

4,386

Surgical

66.0

24.0

7.3

2.8

4,202

MDC15

Pediatric system

Medical

64.0

28.8

6.4

0.8

1,916

Surgical

70.0

20.0

5.0

5.0

20

MDC16

Injury, burns, poisonings and toxic effect of drugs

Medical

53.9

34.1

10.4

1.6

3,580

Surgical

62.1

26.1

9.0

2.9

6,083

MDC17

Mental health system

Medical

47.1

31.4

16.7

4.8

1,256

Surgical

0.0

0.0

0.0

0.0

0

MDC18

Systemic infection

Medical

41.9

34.1

16.4

7.6

962

Surgical

29.2

30.3

17.8

22.7

185

MDC19

Miscellaneous

Medical

46.2

32.9

15.7

5.2

2,477

Surgical

54.9

29.7

11.3

4.1

2,037

MDC major diagnostic category

Statistical analysis

Patient characteristics were analyzed in terms of gender, age (under 15, 15–64, and 65 years or older), and number of procedures. The association between CC category and each of these patient characteristics was assessed using Fisher’s exact test. The association between these patient characteristics and MDC was also assessed.

The mean age, LOS, and mortality rate per 1,000 admissions were calculated for each CC category and compared using analysis of variance (ANOVA). The mean LOS (days) and mortality rate (per 1,000 cases) were illustrated using spider-radar charts, stratified by treatment group (surgical or medical) and MDC. ANOVA was used to compare the mean LOS and mortality rate by treatment group for each MDC.

To define the proportion of high LOS outliers, we identified the 95th percentile of LOS for each MDC and categorized patients beyond that LOS into eight groups (e.g., four CC groups by two treatment groups). We then calculated the proportion of high LOS outliers (the numerator is the number of LOS outliers and the denominator is the number of all patients in the eight groups) and demonstrated it with a broken line for each MDC. Statistical analyses were performed using SPSS version 14. All reported P values were two-tailed, and the level of significance was accepted as less than 0.05.

Results

The most frequently documented MDC was the digestive track, hepatobiliary, and pancreatic diseases (45,080 cases, 18.7% of all study cases) and the least frequently documented MDC was systemic infections (1,147 cases, 0.5%). Eye diseases were more common in the surgical group (17,974 cases, 80.7% of all eye diseases), while patients with mental disorders received no surgical treatment. When stratified by CC category, pediatric diseases had the highest proportion of no CCs (64% in the medical group and 70% in the surgical group) and cardiovascular diseases (32% in the medical group and 31.5% in the surgical group) had the lowest proportion of no CCs. On the other hand, hematological diseases (8.1% of medical patients and 12.7% of surgical patients) and systemic infections (7.6 and 22.7%, respectively) had the highest proportion of patients with five or more CCs (Table 1).

Among the 241,268 patients in the study population, 50.5% (47.8% of the medical group and 53.4% of the surgical group) had no CCs; 32.4% (33.8 and 30.9, respectively) had one or two CCs; 13.4% (14.9 and 11.8%, respectively) had three or four CCs; and 3.7% (3.5 and 3.9%, respectively) had five or more CCs. The proportion with no CCs ranged from 41.2% (38.6% of medical group and 43.8% of surgical group) in patients 65 years and older to 65.5% (60.4 and 72.8%, respectively) in patients less than 15 years. The proportion of patients with one or two CCs ranged from 25.9% for those less than 15 years to 35.4% for those 65 years or older; the proportion with three or four CCs ranged from 6.5 to 18.2%; the proportion with five or more CCs ranged from 2.1 to 5.2%. There was no gender difference in the proportion of the four CC categories. In both the medical and surgical groups, the proportion of patients with no CCs and with five or more CCs was statistically different by age and gender. There was a significant difference in the number of procedures for the four CC categories (Table 2).
Table 2

The proportion of comorbidity and complication category by age, gender, and presence of surgical procedure (%)

Category

Absent (%)

One or two (%)

Three or four (%)

Five or more (%)

N

Overall*

50.5

32.4

13.4

3.7

241,268

 

Medical

47.8

33.8

14.9

3.5

123,895

 

Surgical

53.4

30.9

11.8

3.9

117,373

Age*

 Under 15 years

 

65.5

25.9

6.5

2.1

25,969

 

Medical

60.4

29.0

8.2

2.4

15,282

 

Surgical

72.8

21.5

4.1

1.6

10,687

 15-64 years

 

53.9

31.6

11.5

3.0

126,982

 

Medical

51.1

33.2

12.8

2.9

64,097

 

Surgical

56.8

30.0

10.1

3.0

62,885

 65 years or more

 

41.2

35.4

18.2

5.2

88,317

 

Medical

38.6

36.4

20.2

4.8

44,516

 

Surgical

43.8

34.5

16.1

5.7

43,801

Gender*

 Female

 

52.3

31.9

12.4

3.4

112,243

 

Medical

48.5

33.9

14.2

3.4

56,229

 

Surgical

56.1

29.9

10.6

3.4

56,014

 Male

 

48.9

32.8

14.3

4.0

129,025

 

Medical

47.1

33.8

15.5

3.6

67,666

 

Surgical

51.0

31.8

12.9

4.3

61,359

Number of procedures*

 

1

59.3

27.9

10.0

2.8

78,456

 

2

46.1

35.1

13.7

5.1

25,108

 

3

31.1

44.0

17.7

7.2

7,354

 

4

41.7

34.3

17.2

6.7

4,698

 

5

19.9

39.8

25.4

14.9

1,757

* Statistically significance at P < 0.001

Overall, the mean age was 51.23 years and the mean LOS was 22.15 days. For patients with no CCs, one or two CCs, three or four CCs, and five or more CCs, the mean ages were 47.09, 53.83, 59.05, and 59.26 years, respectively, and the mean LOS were 17.38, 23.44, 30.60, and 45.47 days, respectively. The overall mortality rate per 1,000 admissions was 26.05 cases (13.51, 26.94, 46.94, and 104.09, respectively). There were significant differences in age, LOS, and hospital mortality rate among the four CC groups (Table 3). As the number of CCs increased, the mean age, mean LOS, and mortality rate also increased. The mean age and LOS were higher in the surgical group than in the medical group, although mortality was higher in the medical group.
Table 3

Descriptive characteristics of study variables by comorbidity or complication category and proportion of comorbidity and complication category by mean age, LOS, and mortality, stratified by treatment group

  

Overall

Comorbidity and complication category

Absent

One or two

Three or four

Five or more

Mean age* (SD)

 

51.23 (23.39)

47.09 (24.15)

53.83 (22.34)

59.05 (20.02)

59.26 (20.72)

Medical

50.51 (0.07)

46.15 (0.10)

52.56 (0.11)

58.19 (0.16)

57.53 (0.34)

Surgical

52.18 (0.07)

47.98 (0.09)

55.29 (0.11)

60.20 (0.16)

60.91 (0.28)

Mean LOS* (SE)

 

22.15 (0.051)

17.38 (0.06)

23.44 (0.09)

30.60 (0.16)

45.47 (0.40)

Medical

20.39 (0.07)

16.02 (0.09)

21.12 (0.12)

27.50 (0.20)

42.52 (0.56)

Surgical

24.02 (0.07)

18.67 (0.08)

26.12 (0.14)

34.74 (0.27)

48.30 (0.56)

Mean mortality per 1,000 cases* (SE)

 

26.05 (0.32)

13.51 (0.33)

26.94 (0.58)

49.69 (1.21)

104.09 (3.24)

Medical

36.92 (0.54)

21.90 (0.60)

37.22 (0.92)

62.29 (1.78)

130.63 (5.11)

Surgical

14.58 (0.35)

5.58 (0.30)

15.06 (0.64)

32.91 (1.52)

78.76 (3.99)

SD standard deviation, SE standard error

* Statistically significance at P < 0.001

The LOS for all MDCs, except for pediatric diseases, increased as the number of CCs increased. In particular, the mean LOS across the four CC categories was longer for hematological diseases, whereas for eye diseases, it was shortest and increased relatively slowly across the CC categories. There was a significant difference in mean LOS among the four CC categories within every MDC, except for the pediatric surgical group (P = 0.084) (Fig. 1).
Fig. 1

LOS (days) and number of comorbidities and complications stratified by treatment group (medical or surgical) and major diagnostic category

Across the four CC categories, the mortality rate was higher for respiratory disease, digestive tract, hepatobiliary and pancreatic diseases, and hematological diseases than in other MDCs. The mortality rate was approximately zero for eye diseases, skin and soft tissue diseases, mental disorders, and in the pediatric surgical group. Apart from these MDCs, the surgical group had a lower mortality rate than the medical group for respiratory diseases, as well as for digestive tract, hepatobiliary and pancreatic diseases. There were significant differences in hospital mortality among the CC categories within every MDC, except for eye diseases (medical: P = 0.793, surgical P = 0.928), skin and soft tissue diseases (P = 0.171), endocrine, nutritional, and metabolic diseases (medical: P = 0.920, surgical P = 0.416), and mental disorders (P = 0.223) (Fig. 2).
Fig. 2

Hospital mortality (per 1,000 cases) and number of comorbidities and complications, stratified by treatment group (medical or surgery) and major diagnostic category

The percentage of LOS high outliers increased as the number of CCs increased for all MDCs. The percentage of patients with five or more CCs was identified as 40–50% in MDC14 and 15 (Fig. 3).
Fig. 3

Proportion (%) of high LOS outliers by comorbidity and complication category stratified by major diagnostic category

Discussion

This study describes the characteristics of patients with CC and the association of LOS and mortality rate with the number of CCs by analyzing a large administrative dataset from Japan. To our knowledge, this is the first study to provide a profile of LOS, hospital mortality, and proportion of LOS high outliers across several diseases from a large database. In all MDCs, both the LOS and the proportion of outliers increased as the number of CCs increased, and the increase was particularly prominent for neonatal and pediatric diseases. Mortality was particularly high among the higher CC categories for respiratory diseases, digestive tract, hepatobiliary and pancreatic diseases, and hematological diseases. Therefore, the number of CCs should be taken into consideration in risk adjustment for mortality. However, the mortality rate did not increase with higher CC categories for eye diseases, skin and soft tissue diseases, mental disorders, or in the pediatric surgical group.

There were several limitations to this study. First, we gathered information from patients who were discharged during only a 4-month period in 2002. Claims data, including some clinical variables, are now being collected throughout the year, so that it will soon be possible to produce this type of study with a larger database. Second, coding accuracy and quality were not taken into consideration. At the start of this study, comorbidity was defined by the MHLW as an associated disease or disorder at admission, regardless of whether that condition was acute or chronic and stable; complication was defined as events occurring unexpectedly or owing to a planned procedure after admission. Both of these were listed separately in the dataset. Although there may be no case-mix classification system in the world where the quality of coding can be assured without chart review, coding guidelines and coder training have been promoted by the Japanese Society of Medical Record Administration. The peer review organization system for coding behavior and DPC creep will be in demand, just like in other countries that are already utilizing those kinds of case-mix classification systems [16, 17, 18, 19]. Third, there was a limitation of coding slots in the DPC dataset, whereby only seven secondary diagnoses (four comorbidities and three complications) could be listed. However, the dates and the amount of life-support care or pharmaceutical agents, which may serve as proxy data for some comorbidities or complications, were electronically collected in this DPC database [20]. A more detailed and promising analysis of this data is expected in the near future.

Given the paucity of this kind of analysis in the literature, it is useful to document the mean LOS, mortality rates, and proportions of LOS high outliers by MDC and treatment. In general, economic incentives may induce problems like creeping or changing of coding response, whereby institutions may list more CCs or truncate chronic diagnoses unrelated to resource use, outcome, or payment [21]. As a result, the number of recorded diagnoses on the medical chart may not be significantly higher for patients who die than for those who survive, and the number of CCs would not be a reliable index for predicting complications or hospital mortality. Therefore, setting aside the problem of coding accuracy, this study provides basic but instructive suggestions, as the results were free of reporting bias, due to the lack of opportunities for the up-coding of CCs before the start of the DPC-based payment system.

Overall, the mean LOS in Japan may be longer than that in Western countries. Japanese hospitals accommodate patients with both acute and sub-acute or chronic illnesses, functions that are typically performed by different facilities in Western countries [22]. Table 3 shows that the overall mean LOS was 22.15 days (20.39–42.52 days in medical DPC and 24.02–48.30 in surgical DPC), which is two to six times longer than any other country in the OECD Health Data [23]. These hospitals may not be representative of Japanese acute care hospitals, but these results may more accurately reflect mortality and resource consumption, which would not be captured in Western data that do not include data from external care facilities and other auxiliary health facilities. It might be the strength of this study.

In our study, as the number of CCs increased, resource use indices tended to increase for all MDCs, which corresponded to the results of Munoz et al. [15]. They reported that hospital cost, LOS, percentage of LOS outliers, and mortality increased as the number of CCs per patient increased, even for patients categorized into medical non-complications and comorbidity-stratified DRG groups, resulting in financial risk for hospitals without any DRG adjustments based upon CCs. In another study by Munoz et al. [13], major inequities in the DRG prospective hospital payment system were confirmed for pediatric patients, generating a financial burden for hospital management. For example, if hospital cost correlates positively with LOS, hematological diseases or systemic infections may cause more financial loss, while eye diseases may not. Such comparisons have policy implications, and further studies are needed to examine the presence of cost-profit differences in Japan’s DPC system.

The number of CCs may also be selected as a risk factor for LOS or hospital mortality for respiratory diseases, as well as for digestive track, hepatobiliary, and pancreatic diseases. The number of procedures specific for these diseases, clinical severity, or difficulty of executing a procedure in terms of experience or time consumed may affect LOS, mortality, or LOS outlier among the MDCs. Further evaluation will be needed to describe the association of these clinical variables with the number of CCs or some targeted CCs specific for every MDC.

The first key of DPC classification is principal diagnosis and types of procedures or CCs determined by the DPC group [24]. All of these determinant elements are listed in the DPC definition table where many kinds of CCs are also contained, ranging from chronic stable illnesses, such as diabetes mellitus without organ damage, to acute or critical conditions, such as cardiogenic shock [14, 15]. Each MDC includes several CCs determined by the opinion of experts from the relevant specialties. Our research team was requested to identify the CCs responsible for more resource use or higher mortality, while controlling for variables affecting those indices, such as demographics and treatment. According to the report of resource use variation in cardiovascular diseases and malignant respiratory or intestinal neoplasms, CCs had less incremental effect on the proportion of variance in LOS or total charge than other variables, such as treatment type and intensive or neo-adjuvant therapy [25]. Among these diseases, CCs of gastric or colonic neoplasms explained more variation than those of any others. Some CCs are strongly associated with other CCs, such as hypertension with atherosclerosis. Therefore, further analyses of this kind will be needed to identify CCs that consume more resources, particularly for musculoskeletal diseases and neonatal disease. Through this type of systematic investigation, we can readily answer questions such as “Is the number of CCs correlated with resource use or outcome?” or “Which factors have the greatest impact on LOS or per diem payment: the number of CCs, a specific CC, or certain treatments?” In making decisions about payments, answering these types of questions will facilitate and improve financial allocation. In addition, health policy makers could examine the extent to which the number of CCs can explain variations in resource use or outcomes, enabling a systematic comparison of healthcare performance across MDCs.

In conclusion, we assessed the association of the number of CCs with LOS, hospital mortality, and the proportion of LOS high outliers. In all MDCs, LOS and the proportion of outliers increased as the number of CCs increased. This study demonstrated that the number of CCs should be taken into consideration in risk adjustment for mortality, especially for respiratory diseases, digestive tract, hepatobiliary and pancreatic diseases, and hematological diseases. Mortality rates were not associated with CCs for eye diseases, skin and soft tissue diseases, mental disorders, and in the pediatric surgical group. Further studies are needed to investigate the type of CCs that impact outcomes and resource use, as well as to assess the impact of CCs on treatment selection. Calculating resource use or mortality with weighted CCs, comparative profiling of CCs, and determining associations of CCs with financial burden across MDCs may all play important roles in policy making for an equitable payment system.

Notes

Acknowledgments

This original article is a product of our research team for developing and refining the Japanese case-mix classification (DPC) in cooperation with the Ministry of Health, Labor and Welfare. This research study was led and funded by the Ministry. None of the members have any relevant conflicts of interest.

References

  1. 1.
    Ginsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, van den Bos GA. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001;55:661–74.Google Scholar
  2. 2.
    Evans RL, Hendricks RD, Lawrence KV, Bishop DS. Identifying factors associated with health care use: a hospital-based risk screening index. Soc Sci Med. 1988;27:947–54.PubMedCrossRefGoogle Scholar
  3. 3.
    Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspective. J Clin Epidemiol. 1993;46:1075–9.PubMedCrossRefGoogle Scholar
  4. 4.
    Melfi C, Holleman E, Arthur D, Katz B. Selecting a patient characteristics index for the prediction of medical outcomes using administrative claims data. J Clin Epidemiol. 1995;48:917–26.PubMedCrossRefGoogle Scholar
  5. 5.
    Starfield B, Lemke KW, Bernbardt T, Foldes SS, Forrest CB, Weiner JP. Comorbidity: implication for the importance of primary care in ‘Case’ management. Ann Fam Med. 2003;1:8–14.PubMedCrossRefGoogle Scholar
  6. 6.
    Zhan C, Miller MR. Administrative data based patient safety research: a critical review. Qual Saf Health Care. 2003;12:58–63.CrossRefGoogle Scholar
  7. 7.
    Fetter RB. Casemix classification systems. Aust Health Rev. 1999;22:16–34.PubMedCrossRefGoogle Scholar
  8. 8.
    3M Health Information Systems. All patient diagnosis related groups (AP-DRGs) ver.12.0 definition manual. Wallingford: 3M Health Information Systems; 1994.Google Scholar
  9. 9.
    Clark MA, Bakhai A, Lacey MJ, Pelletier EM, Cohen DJ. Clinical and economic outcomes of percutaneous coronary interventions in the elderly. Circulation. 2004;110:259–64.PubMedCrossRefGoogle Scholar
  10. 10.
    Harrell AG, Lincourt AE, Novitsky YW, Rosen MJ, Kuwada TS, Kercher KW et al. Advantages of laparoscopic appendectomy in the elderly. Am Surg. 2006;72:474–80.PubMedGoogle Scholar
  11. 11.
    Struijs JN, Baan CA, Schellevis FG, Westert GP, van den Bos GA. Comorbidity in patients with diabetes mellitus: impact on medical health care utilization. BMC Health Serv Res. 2006;6:84.PubMedCrossRefGoogle Scholar
  12. 12.
    Bergeron E, Lavoie A, Moore L, Clas D, Rossignol M. Comorbidity and age are both independent predictors of length of hospitalization in trauma patients. Can J Surg. 2005;48:361–6.PubMedGoogle Scholar
  13. 13.
    Munoz E, Lory M, Josephson J, Goldstein J, Brewster J, Wise L. Pediatric patients, DRG hospital payment, and comorbidities. J pediatr. 1989;115:545–8.PubMedCrossRefGoogle Scholar
  14. 14.
    Jencks SF, Williams DK, Kay TL. Assessing hospital-associated deaths from discharge data. JAMA. 1988;206:2240–6.CrossRefGoogle Scholar
  15. 15.
    Munoz E, Rosner F, Friedman R, Sterman H, Goldstein J, Weis L. Financial risk, hospital cost, and complications and comorbidities in medical non-complications and comorbidity-stratified diagnosis-related groups. Am J Med. 1988;84:933–9.PubMedCrossRefGoogle Scholar
  16. 16.
    Hsia DC, Krushat WM, Fagan AB, Tebbutt JA, Kusserow RP. Accuracy of diagnostic coding for Medicare patients under the prospective-payment system. N Engl J Med. 1988;318:352–5.PubMedCrossRefGoogle Scholar
  17. 17.
    Green J, Wintfeld N. How accurate are hospital discharge data for evaluating effectiveness of care? Med Care. 1993;8:719–31.CrossRefGoogle Scholar
  18. 18.
    Lorenzoni L, Cas RD, Aparo UL. Continuous training as a key to increase the accuracy of administrative data. J Eval Clin Pract. 2000;6:371–7.PubMedCrossRefGoogle Scholar
  19. 19.
    Preyra C. Coding response to a case-mix measurement system based on multiple diagnosis. Health Serv Res. 2004;39:1027–45.PubMedCrossRefGoogle Scholar
  20. 20.
    Matsuda S. Casemix as a tool of transparency of medical services. Jpn J Soc Sec Policy. 2007;6 Suppl 1:43–53.Google Scholar
  21. 21.
    Iezzoni LI. Data sources, implications: information from medical records, patients. In: Iezzoni LI, editor. Risk adjustment for measuring healthcare outcomes. 2nd ed. Chicago: Health Administration Press; 1997. p. 243–78.Google Scholar
  22. 22.
    Ishizaki T, Imanaka Y, Hirose M, Kuwabara K, Ogawa T, Harada Y. A first look at variations in use of breast conserving surgery at five teaching hospitals in Japan. Int J Qual Health Care. 2002;5:411–8.CrossRefGoogle Scholar
  23. 23.
    OECD Health Data (2003) Show health expenditures at an all-time high. Available from URL: http://www.oecd.org/dataoecd/10/20/2789777.pdf/[Accessed 2007 Nov 14].
  24. 24.
    Matsuda S, Imanaka Y, Kuwabara K, Fushimi K, Hashomoto H, Ishikawa BK. Japan case mix project-general perspective. In: Proceeding of the 18th International Case Mix Conference PCS/E 2002, Innsburuck, Austria, p. 722–6.Google Scholar
  25. 25.
    Kuwabara K, Imanaka Y, Matsuda S, Fushimi K, Hashimoto H, Ishikawa KB. Profiling of resource use variation among six diseases treated at 82 Japanese special functioning hospitals, based on administrative database. Health Policy. 2006;78:306–18.PubMedCrossRefGoogle Scholar

Copyright information

© The Japanese Society for Hygiene 2008

Authors and Affiliations

  • Kazuaki Kuwabara
    • 1
  • Yuichi Imanaka
    • 2
  • Shinya Matsuda
    • 3
  • Kiyohide Fushimi
    • 4
  • Hideki Hashimoto
    • 5
  • Koichi B. Ishikawa
    • 6
  • Hiromasa Horiguchi
    • 5
  • Kenshi Hayashida
    • 2
  • Kenji Fujimori
    • 7
  1. 1.Department of Health Care Administration and Management, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
  2. 2.Department of Healthcare Economics and Quality Management, Graduate School of Medicine, School of Public HealthKyoto UniversityKyotoJapan
  3. 3.University of Occupational and Environmental HealthFukuokaJapan
  4. 4.Tokyo Medical and Dental UniversityTokyoJapan
  5. 5.The University of Tokyo Graduate School of MedicineTokyoJapan
  6. 6.National Cancer CenterTokyoJapan
  7. 7.Hokkaido UniversitySapporoJapan

Personalised recommendations