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HOME > J Prev Med Public Health > Volume 56(4); 2023 > Article
Special Article
Updating Korean Disability Weights for Causes of Disease: Adopting an Add-on Study Method
Dasom Im1orcid, Noor Afif Mahmudah1,2orcid, Seok-Jun Yoon3orcid, Young-Eun Kim4orcid, Don-Hyung Lee5orcid, Yeon-hee Kim5orcid, Yoon-Sun Jung6orcid, Minsu Ock1,7orcid
Journal of Preventive Medicine and Public Health 2023;56(4):291-302.
DOI: https://doi.org/10.3961/jpmph.23.192
Published online: June 26, 2023
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1Department of Preventive Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea

2Department of Family and Community Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia

3Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea

4Big Data Department, National Health Insurance Service, Wonju, Korea

5Research & Statistics Team, Korean Health Promotion Institute, Seoul, Korea

6Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea

7Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, Korea

Corresponding author: Minsu Ock, Department of Preventive Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, 25 Daehagbyeongwon-ro, Dong-gu, Ulsan 44033, Korea, E-mail: ohohoms@naver.com
• Received: April 19, 2023   • Accepted: June 13, 2023

Copyright © 2023 The Korean Society for Preventive Medicine

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

  • Objectives
    Disability weights require regular updates, as they are influenced by both diseases and societal perceptions. Consequently, it is necessary to develop an up-to-date list of the causes of diseases and establish a survey panel for estimating disability weights. Accordingly, this study was conducted to calculate, assess, modify, and validate disability weights suitable for Korea, accounting for its cultural and social characteristics.
  • Methods
    The 380 causes of disease used in the survey were derived from the 2019 Global Burden of Disease Collaborative Network and from 2019 and 2020 Korean studies on disability weights for causes of disease. Disability weights were reanalyzed by integrating the findings of an earlier survey on disability weights in Korea with those of the additional survey conducted in this study. The responses were transformed into paired comparisons and analyzed using probit regression analysis. Coefficients for the causes of disease were converted into predicted probabilities, and disability weights in 2 models (model 1 and 2) were rescaled using a normal distribution and the natural logarithm, respectively.
  • Results
    The mean values for the 380 causes of disease in models 1 and 2 were 0.488 and 0.369, respectively. Both models exhibited the same order of disability weights. The disability weights for the 300 causes of disease present in both the current and 2019 studies demonstrated a Pearson correlation coefficient of 0.994 (p=0.001 for both models). This study presents a detailed add-on approach for calculating disability weights.
  • Conclusions
    This method can be employed in other countries to obtain timely disability weight estimations.
A disability weight (DW) is a measure that represents the severity of specific health states or causes of disease, in contrast to a utility weight. DW values range from 0, indicating full health, to 1, representing a disability equivalent to death [1]. DW is a critical component in calculating disability-adjusted life years (DALYs) and disability-adjusted life expectancy (DALE), which are used to determine summary measures of population health [2,3]. Therefore, it is essential to ensure the validity of DW, as it impacts the validity of the DALY and DALE calculations. An overestimated DW for a particular disease would lead to an overestimation of the burden of that disease, while the opposite would occur if the DW were underestimated.
DW validity cannot be guaranteed by a single instance of validity verification. The emergence of new diseases, as well as changes in conditions, treatments, or societal perceptions, may render previously valid DWs inapplicable in the current era. Therefore, it is necessary to assess the validity of past DWs and determine whether modifications are required. The Global Burden of Disease Study has been updating DWs to reflect their current relevance [46]. The emergence of coronavirus disease 2019 (COVID-19) has further underscored the importance of revising DWs. While some studies have temporarily replaced COVID-19 with another disease for DW measurement [7,8], COVID-19 should be incorporated into DW evaluations given the disease’s expected persistence.
Two DW measurement approaches are used according to the DALY calculation method [3]. One is an incidence-based approach involving the cause of disease, while the other is a prevalence-based approach focused on the health state. After determining which strategy to use, it is necessary to categorize the cause of disease or health state in order to construct a survey panel and to modify or create a health state description if needed [1]. Subsequently, a survey must be initiated, taking into account the valuation method and time presentation, as its results will be incorporated into DW calculations and validated. Due to cultural and social differences across countries, care should be taken when applying DWs to other nations. The 2010 Global Burden of Disease Report demonstrated relatively few discrepancies in health state preferences among countries [4]; however, it is challenging to assert that no differences were present in cultural perceptions of health states or diseases, as the 2010 survey exhibited bias in the number of participating countries and cultures. Therefore, DWs must be appropriately measured for the Korea, with their validity evaluated and their calculations adjusted as needed [1].
In this study, we estimated DWs using an incidence-based approach with an updated list of disease causes. The validity of these DWs was assessed in consideration of Korea’s cultural and social characteristics. In particular, the DW survey results from previous studies employing an incidence-based approach were utilized through an add-on study method to refine the DWs [9,10].
Study Design and Participants
A web-based self-administered survey was conducted, drawing from a previous study [10]. The survey took place between August 1, 2022, and August 30, 2022. Eligible participants were restricted to medically licensed physicians in Korea, with nurses and Korean medical doctors excluded due to improper response patterns observed in previous studies [10]. Furthermore, since the survey did not offer detailed descriptions of diseases, medical expertise was necessary to determine the cause of each disease based on its apparent value and severity level. Participants were recruited through advertisements on medical institution-related web boards, word of mouth, and snowball sampling, with participants who had completed the survey recommending other qualified individuals to join the study.
Valuation Method and Causes of Disease
The questionnaire consisted of 4 socio-demographic characteristics—age, sex, specialty, and occupation—as well as 20 questions regarding the ranking of causes of disease, in line with methods used in previous studies [9,10]. Of 378 causes of disease (excluding “full health” and “death”), 5 causes of disease were randomly selected for each question. Participants ranked these causes of disease in order of good health based on severity and face value [11]. “Full health” and “death” served as valuation anchor points to ensure the validity of survey responses and the participants’ full comprehension of the survey content. Specifically, “full health” was the first fixed alternative for questions 5, 10, 15, and 20, while “death” was the first fixed alternative for questions 9–12. Questions 9–12 were designed to include at least 1 of the new causes of disease as the second alternative for each question, increasing the likelihood of newly added causes of disease appearing in the questionnaire. An example of a ranking method question can be found in Supplemental Material 1.
The list of causes of diseases used in the survey required modification, as previously measured DWs needed to be reviewed and validated to ensure their relevance to the current era considering new disease outbreaks such as COVID-19, changes in treatment, and shifts in social judgment regarding specific diseases [46]. A total of 380 causes of disease were identified for the study in the following manner. First, the list of causes of disease used in the 2019 Global Burden of Disease Collaborative Network was evaluated to incorporate global DW trends. In total, 281 causes of disease were selected, excluding those that could be further classified by severity. Next, 99 additional causes of disease were included after examining the causes of disease lists from the 2016, 2019, and 2020 Korean DW measurements to reflect the cultural characteristics of Korea [9,10,12]. Specifically, common causes of diseases from those 3 years, such as “hemorrhagic and other non-ischemic strokes” and “Asperger syndrome and other autistic spectrum disorders,” were added. Other common causes of diseases from 2016 and 2019, such as “influenza and intestinal infection,” and from 2019 and 2020, such as “stomach cancer” and “breast cancer stages 1–4,” were also included. From the 2020 list, “unintentional suffocation,” “COVID-19” with severity classification, “ischemic heart disease,” and others were added. The researchers and a DW expert reviewed each selection process and the English-to-Korean translation of disease terminology.
Statistical Analysis
During the data cleaning process, incomplete surveys and incorrect anchor-point answer data related to “full health” were removed. To enhance the validity of the results, raw data for measuring DW by the cause of disease from the 2019 [9] and 2020 [10] studies were utilized. Specifically, raw data from 430 participants in the 2019 study [9] and 685 participants in the 2020 study [10] were included after eliminating any data that did not meet the present participation eligibility criteria and any data derived from a set of questions with at least 1 cause of disease that did not overlap with the present list of causes of disease. For instance, in the 2019 study [9], 71 participants who did not complete the survey were initially removed from the data, followed by the exclusion of 175 participants who were either medical students, nurses, or Korean medical doctors; data derived from a set of questions that included 38 causes of diseases were also removed. Additionally, “human immunodeficiency virus disease resulting in mycobacterial infection” and “typhoid and paratyphoid fevers” were excluded, as they did not match the current list and were subdivided into 2 separate entities, respectively. In the 2020 study [10], 95 participants who did not complete the survey were initially removed, and 157 participants who did not meet the eligibility criteria were excluded; data resulting from any questions that included 11 disease causes were also withdrawn. Chronic kidney disease due to diabetes mellitus was excluded due to its subdivision in the current list. Subsequently, the labeling of the extracted data with cause of disease numbers was converted to match the present labeling system.
First, a descriptive analysis of the participants’ socio-demographic characteristics was conducted using the complete dataset. The 5 alternatively ranked datasets were then transformed into a paired comparison format to adapt a precedent method [9,10]. Specifically, if a participant responded to a ranking questionnaire in the order of C1-C2-C3-C4-C5, this was transformed into C1–C5, C1–C4, C1–C3, C1–C2, C2–C5, C2–C4, C2–C3, C3–C5, C3–C4, and C4–C5. Probit regression analysis was performed using 2 models, in line with previous studies [12,13]. The cause of disease was treated as a dummy variable and set as an independent variable, while preference was set as a dependent variable. The regression coefficient for each cause of disease was then converted to a predicted probability. The value was rescaled based on the estimated DW of “death” (1), with this rescaled value considered the DW. The DWs were rescaled using a normal distribution and natural logarithm in models 1 and 2, respectively.
Stata version 13.1 (StataCorp., College Station, TX, USA) was utilized for all statistical analyses. A p-value <0.05 was considered to indicate statistical significance.
Ethics Statement
The Korea University Institutional Review Board (IRB No. KUIRB-2022-0221-02) granted approval for this study. Prior to the survey, participants were informed about the study’s objectives and procedures. Only those who consented to the terms participated in the study and were given coffee coupons valued at 10 000 Korean won upon completion of the survey.
For this study, 211 participants began and completed the survey. Of these, 205 participants who correctly ranked “full health” first for the anchor point questions 5, 10, 15, and 20 were selected for analysis. As an add-on, 685 and 430 participants were chosen from the 2020 and 2019 studies [9,10], respectively. Table 1 displays the socio-demographic characteristics of the participants across the 3 years. Most participants during this period were male specialists in their 30s. In the current study and the 2019 study, most participants’ specialties were neither medical nor surgical, while participants from the 2020 study primarily specialized in medicine.
Table 2 displays the DWs for the 2 models. The mean DWs for model 1 and model 2 were 0.488 and 0.369, respectively. Both models identified the same disease as having the highest DW: “trachea, bronchus, and lung cancers (stage 4),” with DWs of 0.922 in model 1 and 0.696 in model 2. Similarly, the lowest DW in both models was attributed to the same disease: “acne vulgaris,” with DWs of 0.055 in model 1 and 0.223 in model 2. The 2 models also shared the same ranking of diseases by DW.
Following “trachea, bronchus, and lung cancers (stage 4),” the diseases with the highest DWs in descending order were “pancreatic cancer,” “kidney cancer (stage 4),” and “liver cancer secondary to alcohol use (stage 4).” Conversely, the diseases with the lowest DWs in ascending order were “acne vulgaris,” “caries of deciduous teeth,” “allergic rhinitis,” and “urticaria.”
Figure 1 illustrates the DW distributions for models 1 and 2. Model 1 displays a relatively normal distribution, whereas model 2 exhibits a left-skewed distribution. In model 1, the highest number of causes of disease, 61, was found in the DW range of 0.3 to 0.4. Meanwhile, in model 2, no causes of disease were identified with DW values less than 0.2 or greater than 0.7.
Figure 2 displays the correlation between the DWs for causes of disease in the current and previous models [10]. The causes of disease from the 2020 study [10] were compared word by word with those in the present study, and 300 causes of disease were selected for correlation analyses. Both model 1 and model 2 had a Pearson correlation coefficient of 0.994 with a p-value of 0.001. Among the 300 overlapping causes of disease, 155 DWs increased in model 1 relative to the DWs in the previous study, while 137 DWs decreased (Supplemental Material 2). Eight DWs remained the same as in the previous study: “other meningitis” (0.583), “liver cancer secondary to alcohol use (stage 1)” (0.598), “asthma” (0.409), “Alzheimer disease and other dementias” (0.660), “conduct disorder” (0.314), “pruritus” (0.104), “other skin and subcutaneous diseases” (0.135), and “drowning” (0.527). In model 2, 145 DWs increased and 148 DWs decreased relative to the DWs in the previous study. Seven DWs remained unchanged: “latent tuberculosis infection” (0.268), “breast cancer (stage 2)” (0.393), “other drug use disorders” (0.291), “glaucoma” (0.315), “age-related and other hearing loss” (0.277), “physical violence by firearms” (0.365), and “physical violence by other means” (0.274).
This study presents the findings of an add-on study approach for revising DWs. We combined the results of a present DW survey involving approximately 200 physicians with data from previous studies (430 and 685 participants in 2019 and 2020, respectively) to update the DWs for 380 causes of disease. In earlier DW studies, independent surveys were conducted to introduce new causes of disease or refine existing ones, and the outcomes were analyzed to generate DWs. The central importance of this study is its demonstration of the potential to revise DWs using an add-on study method.
The importance of an add-on study for determining DWs stems from the fact that recent DW studies have primarily employed ordinal methods, such as paired comparison and ranking methods [14]. Estimating the DW is relatively straightforward when using cardinal methods like person trade-off, time trade-off, and standard gamble, as they allow for direct comparison with an anchor point, such as death, and can easily incorporate newly added causes of diseases. However, to calculate DW using ordinal methods, a new cause of disease must be compared to an existing cause. Therefore, if additional causes of disease are introduced without a major reorganization of the disease classification system, it is feasible to use an add-on study method for calculating DWs with ordinal methods. This approach is also applicable to DW studies focusing on health states.
The strength of the add-on study method also underscores the difficulty in recruiting physicians, who frequently serve as participants for estimating DWs related to causes of disease. Numerous DW estimation studies have been conducted reflecting the preferences of the public [13,15,16]; however, this approach is limited, as this population can only evaluate the DW of health states. Consequently, if DWs are to be calculated based directly on the cause of disease, the opinions of health professionals must be incorporated. It is essential to adapt to the anticipated expertise of these professionals to ensure valid paired comparison results, which ultimately affects the sample size for analysis due to the low number of survey participants. In fact, DW studies involving experts tend to have fewer participants than those involving the public [17]. Therefore, updating DWs by merging the results of existing surveys with data from more recent ones, as in the add-on study method, is a promising approach to enhance the efficiency of the DW estimation process.
It is not uncommon for DWs to require revision, particularly when a new cause of disease emerges, such as COVID-19, which has recently led to a high global disease burden. The calculation of DWs for this cause of disease is in high demand. However, studies calculating DALYs, including those for COVID-19, have replaced DWs with existing causes of disease or health states rather than adding COVID-19 as a specific new disease [1820]. In the present study, the severity of COVID-19 was subdivided into mild, moderate, and severe. The respective DWs were calculated as 0.110, 0.642, and 0.755 for model 1 and 0.235, 0.419, and 0.493 for model 2. The DWs for COVID-19 in this study were slightly higher than those in previous studies. However, the DW obtained in this study, derived from analyzing physicians’ responses through the direct comparison of COVID-19 to other diseases, is expected to have higher validity.
When comparing the revised DWs to those found in previous research, the values and patterns observed were generally similar (Pearson correlation, 0.994). The differences between the 2 values were mainly within 0.02, although some were model-specific. Extreme changes in DW can lead to major alterations in years lived with disability, ultimately compromising the ability to obtain a valid DALE measurement. This add-on study method demonstrated the benefit of utilizing existing survey results to obtain the DWs of new causes of disease without causing abrupt changes in the current cause of disease values during DW revision. This finding suggests that DWs can be fine-tuned based on the collective opinions of multiple health professionals, rather than implementing dramatic DW revisions influenced by the preferences or judgments of a limited number of experts.
Notably, the revised leading causes of disease in the present study were more likely to have DWs segmented by severity. Previous studies typically utilized 3 or 4 levels of severity for primary diseases such as cancer or chronic obstructive pulmonary disease [11]. In the present study, DWs were measured not only for COVID-19 but also by subdividing the severity of kidney and bladder cancer. For diabetes, DWs were calculated by categorizing the severity of each complication type. Preventing chronic diseases entirely is becoming increasingly difficult. It may be more rational to manage a disease at a lower severity and reduce the burden of a specific cause of disease, such as by preventing the development of complications. The severity-disaggregated DWs and severity distributions from this study can be used to calculate years lived with disability more accurately for specific causes of disease [21]. Additionally, monitoring will enable the evaluation of the effectiveness of interventions aimed at preventing disease complications [22,23].
The limitations of this study include the inherent challenges of surveying experts and the surveyed physicians’ inability to fully represent the views of all physicians in Korea. This recent survey involved approximately 200 physicians, whose insights may have been particularly valuable in determining the DWs for additional or revised disease causes. It is crucial to involve physicians with diverse expertise in future DW surveys to identify the valid causes of disease-related DWs.
This study presents a detailed add-on methodology for revising the DWs of 380 existing causes of disease and estimating DWs of additional causes. This research approach is applicable in Korea and other countries to generate timely DWs. The DWs obtained in this study can be utilized to determine a reasonable disease burden by choosing an appropriate DALE calculation method. Additionally, these DWs can serve as a fundamental variable in calculating healthy life expectancy.
Supplemental materials are available at https://doi.org/10.3961/jpmph.23.192.
The authors would like to thank the survey respondents.

CONFLICT OF INTEREST

The authors have no conflicts of interest associated with the material presented in this paper.

FUNDING

This study was supported by funds from the Korean Health Promotion Institute (KHEPI) in 2022.

AUTHOR CONTRIBUTIONS

Conceptualization: Im D, Mahmudah NA, Yoon SJ, Kim YE, Jung YS, Ock M, Lee DH, Kim Y. Data curation: Im D, Mahmudah NA, Ock M. Formal analysis: Im D, Ock M. Funding acquisition: Yoon SJ. Methodology: Im D, Ock M. Project administration: Im D, Ock M. Visualization: Im D, Ock M. Writing – original draft: Im D, Ock M. Writing – review & editing: Im D, Mahmudah NA, Yoon SJ, Kim YE, Jung YS, Ock M, Lee DH, Kim Y.

Figure 1
Distribution of disability weights in each analytical method adopted from the previous study [10]. Model 1: Based on a normal distribution.; Model 2: Based on the natural logarithm.
jpmph-23-192f1.jpg
Figure 2
Correlation of disability weights between previous [10] and present studies. Model 1: Based on a normal distribution (A). Model 2: Based on the natural logarithm (B).
jpmph-23-192f2.jpg
Table 1
Characteristics of study participants
Characteristics Present 20201 20192
Age (y)
 19–29 17 (8.3) 100 (14.6) 52 (12.1)
 30–39 126 (61.5) 569 (83.1) 374 (87.0)
 ≥40 62 (30.2) 16 (2.3) 4 (0.9)

Sex
 Male 143 (69.8) 540 (78.8) 401 (93.3)
 Female 62 (30.2) 145 (21.2) 29 (6.7)

Occupation
 General practitioner 12 (5.9) 76 (11.1) 56 (13.0)
 Resident 19 (9.3) 65 (9.5) 6 (1.4)
 Specialist 169 (82.4) 527 (76.9) 358 (83.3)
 Other 5 (2.4) 17 (2.5) 10 (2.3)

Specialty
 Medical 71 (34.6) 259 (37.8) 153 (35.6)
 Surgical 44 (21.5) 190 (27.7) 60 (14.0)
 Other 90 (43.9) 236 (34.4) 217 (50.5)
 Total 205 (100.0) 685 (100.0) 430 (100.0)

Values are presented as number (%).

1 Source from: Kim YE, et al. J Korean Med Sci 2020;35(27):e219 [10].

2 Source from: Ock M, et al. J Korean Med Sci 2019;34(Suppl 1):e60 [9].

Table 2
Disability weights for each analysis model by cause of disease
No. Cause of disease Model 11 Model 22
1 HIV/AIDS - Drug-susceptible tuberculosis 0.724 0.470
2 HIV/AIDS - Multidrug-resistant tuberculosis without extensive drug resistance 0.787 0.519
3 HIV/AIDS - Extensively drug-resistant tuberculosis 0.806 0.537
4 HIV/AIDS resulting in other diseases 0.752 0.491
5 Syphilis 0.432 0.326
6 Chlamydial infection 0.327 0.292
7 Gonococcal infection 0.315 0.289
8 Trichomoniasis 0.330 0.293
9 Genital herpes 0.255 0.271
10 Other sexually transmitted disease 0.356 0.301
11 Latent tuberculosis infection 0.241 0.268
12 Drug-susceptible tuberculosis 0.388 0.312
13 Multidrug-resistant tuberculosis without extensive drug resistance 0.657 0.427
14 Extensively drug-resistant tuberculosis 0.677 0.439
15 Upper respiratory infections 0.207 0.259
16 Lower respiratory infections 0.329 0.293
17 Otitis media 0.191 0.255
18 Influenza 0.220 0.262
19 Pneumococcal pneumonia 0.418 0.322
20 H influenza type B pneumonia 0.442 0.330
21 Respiratory syncytial virus pneumonia 0.331 0.293
22 COVID-19 (mild) 0.110 0.235
23 COVID-19 (moderate) 0.642 0.419
24 COVID-19 (severe) 0.755 0.493
25 Diarrhoeal diseases 0.176 0.251
26 Typhoid fever 0.315 0.289
27 Paratyphoid fever 0.388 0.311
28 Invasive non-typhoidal Salmonella 0.389 0.312
29 Other intestinal infectious diseases 0.273 0.276
30 Cholera 0.415 0.321
31 Other Salmonella infections 0.318 0.290
32 Shigellosis 0.372 0.306
33 Enteropathogenic E. coli infection 0.323 0.291
34 Enterotoxigenic E. coli infection 0.306 0.286
35 Campylobacter enteritis 0.299 0.284
36 Amoebiasis 0.422 0.323
37 Cryptosporidiosis 0.531 0.365
38 Rotaviral enteritis 0.211 0.260
39 Intestinal infection 0.262 0.273
40 Malaria 0.436 0.328
41 Chagas disease 0.548 0.373
42 Visceral leishmaniasis 0.416 0.321
43 Cutaneous and mucocutaneous leishmaniasis 0.394 0.313
44 African trypanosomiasis 0.475 0.342
45 Schistosomiasis 0.391 0.313
46 Cysticercosis 0.393 0.313
47 Cystic echinococcosis 0.395 0.314
48 Lymphatic filariasis 0.480 0.344
49 Onchocerciasis 0.322 0.291
50 Trachoma 0.387 0.311
51 Dengue 0.427 0.325
52 Yellow fever 0.505 0.354
53 Rabies 0.696 0.451
54 Ascariasis 0.225 0.263
55 Trichuriasis 0.326 0.292
56 Hookworm disease 0.234 0.266
57 Food-borne trematodiases 0.313 0.288
58 Leprosy 0.601 0.398
59 Tsutsugamushi fever 0.450 0.333
60 Typhus fever 0.435 0.328
61 Hantaan virus disease 0.539 0.369
62 Ebola virus disease 0.764 0.500
63 Zika virus disease 0.509 0.356
64 Guinea worm disease 0.352 0.300
65 Other neglected tropical diseases 0.383 0.310
66 Pneumococcal meningitis 0.600 0.397
67 H influenzae type B meningitis 0.607 0.401
68 Meningococcal infection 0.546 0.372
69 Other meningitis 0.583 0.389
70 Encephalitis 0.699 0.453
71 Diphtheria 0.362 0.303
72 Whooping cough 0.319 0.290
73 Tetanus 0.539 0.369
74 Measles 0.305 0.286
75 Varicella and herpes zoster 0.276 0.277
76 Legionnaire disease 0.366 0.304
77 Leptospirosis 0.424 0.324
78 Rubella 0.362 0.303
79 Mumps 0.245 0.269
80 Acute hepatitis A 0.365 0.304
81 Acute hepatitis B 0.433 0.327
82 Acute hepatitis C 0.529 0.364
83 Acute hepatitis E 0.501 0.353
84 Other unspecified infectious diseases 0.267 0.275
85 Maternal hemorrhage 0.576 0.386
86 Maternal sepsis and other maternal infections 0.678 0.440
87 Maternal hypertensive disorders 0.414 0.320
88 Maternal obstructed labor and uterine rupture 0.654 0.426
89 Maternal abortion and miscarriage 0.365 0.304
90 Ectopic pregnancy 0.395 0.314
91 Indirect maternal deaths 0.780 0.513
92 Late maternal deaths 0.834 0.566
93 Maternal deaths aggravated by HIV/AIDS 0.904 0.663
94 Other maternal disorders 0.365 0.304
95 Neonatal preterm birth complications 0.581 0.388
96 Neonatal encephalopathy due to birth asphyxia and trauma 0.818 0.549
97 Neonatal sepsis and other neonatal infections 0.708 0.459
98 Hemolytic disease and other neonatal jaundice 0.491 0.349
99 Other neonatal disorders 0.520 0.360
100 Protein-energy malnutrition 0.421 0.323
101 Iodine deficiency 0.225 0.263
102 Vitamin A deficiency 0.194 0.255
103 Iron-deficiency anemia 0.181 0.252
104 Other nutritional deficiencies 0.225 0.263
105 Lip and oral cavity cancer 0.774 0.509
106 Nasopharynx cancer 0.818 0.549
107 Other pharynx cancer 0.797 0.528
108 Esophageal cancer 0.885 0.632
109 Stomach cancer (stage 1) 0.457 0.336
110 Stomach cancer (stage 2) 0.615 0.405
111 Stomach cancer (stage 3) 0.794 0.526
112 Stomach cancer (stage 4) 0.905 0.664
113 Colon and rectum cancers (stage 1) 0.489 0.348
114 Colon and rectum cancers (stage 2) 0.646 0.421
115 Colon and rectum cancers (stage 3) 0.814 0.545
116 Colon and rectum cancers (stage 4) 0.888 0.637
117 Liver cancer secondary to hepatitis B 0.759 0.496
118 Liver cancer secondary to hepatitis C 0.786 0.519
119 Liver cancer secondary to alcohol use (stage 1) 0.598 0.396
120 Liver cancer secondary to alcohol use (stage 2) 0.722 0.468
121 Liver cancer secondary to alcohol use (stage 3) 0.815 0.546
122 Liver cancer secondary to alcohol use (stage 4) 0.911 0.675
123 Liver cancer due to NASH 0.774 0.508
124 Liver cancer due to other causes 0.786 0.519
125 Gallbladder and biliary tract cancer 0.830 0.562
126 Pancreatic cancer 0.919 0.690
127 Larynx cancer 0.868 0.607
128 Trachea, bronchus and lung cancers (stage 1) 0.585 0.390
129 Trachea, bronchus and lung cancers (stage 2) 0.715 0.464
130 Trachea, bronchus and lung cancers (stage 3) 0.847 0.581
131 Trachea, bronchus and lung cancers (stage 4) 0.922 0.696
132 Malignant skin melanoma 0.824 0.555
133 Non-melanoma skin cancer (squamous-cell carcinoma) 0.636 0.416
134 Non-melanoma skin cancer (basal cell carcinoma) 0.656 0.427
135 Breast cancer (stage 1) 0.459 0.336
136 Breast cancer (stage 2) 0.592 0.393
137 Breast cancer (stage 3) 0.769 0.504
138 Breast cancer (stage 4) 0.880 0.625
139 Cervical cancer (stage 1) 0.419 0.322
140 Cervical cancer (stage 2) 0.592 0.393
141 Cervical cancer (stage 3) 0.764 0.500
142 Cervical cancer (stage 4) 0.880 0.624
143 Uterine cancer 0.704 0.456
144 Ovarian cancer 0.804 0.535
145 Prostate cancer (stage 1) 0.473 0.342
146 Prostate cancer (stage 2) 0.601 0.397
147 Prostate cancer (stage 3) 0.728 0.473
148 Prostate cancer (stage 4) 0.863 0.601
149 Testicular cancer 0.746 0.486
150 Kidney cancer (stage 1) 0.539 0.369
151 Kidney cancer (stage 2) 0.729 0.473
152 Kidney cancer (stage 3) 0.854 0.589
153 Kidney cancer (stage 4) 0.916 0.684
154 Bladder cancer (stage 1) 0.534 0.366
155 Bladder cancer (stage 2) 0.630 0.412
156 Bladder cancer (stage 3) 0.790 0.522
157 Bladder cancer (stage 4) 0.863 0.600
158 Other urinary organ cancers 0.737 0.479
159 Brain and nervous system cancer 0.875 0.618
160 Thyroid cancer (stage 1) 0.276 0.277
161 Thyroid cancer (stage 2) 0.467 0.339
162 Thyroid cancer (stage 3) 0.619 0.407
163 Thyroid cancer (stage 4) 0.802 0.533
164 Mesothelioma 0.785 0.518
165 Hodgkin lymphoma 0.712 0.462
166 Non-Hodgkin lymphoma 0.711 0.461
167 Multiple myeloma 0.740 0.481
168 Acute lymphoid leukemia 0.811 0.542
169 Chronic lymphoid leukemia 0.748 0.488
170 Acute myeloid leukemia 0.827 0.558
171 Chronic myeloid leukemia 0.769 0.504
172 Other leukemia 0.829 0.561
173 Bone and connective tissue cancer 0.768 0.503
174 Benign neoplasm of brain and other parts of central nervous system 0.507 0.355
175 Other malignant neoplasms 0.778 0.512
176 Myelodysplastic, myeloproliferative, and other hematopoietic neoplasms 0.751 0.490
177 Benign and in situ intestinal neoplasms 0.279 0.278
178 Benign and in situ cervical and uterine neoplasms 0.366 0.304
179 Other benign and in situ neoplasms 0.216 0.261
180 Rheumatic heart disease 0.627 0.411
181 Stable ischemic heart disease 0.521 0.361
182 Unstable angina 0.630 0.412
183 Ischemic stroke (mild) 0.527 0.363
184 Ischemic stroke (moderate) 0.782 0.515
185 Ischemic stroke (severe) 0.828 0.559
186 Hemorrhagic and other non-ischemic stroke 0.798 0.530
187 Hypertensive heart disease 0.462 0.338
188 Non-rheumatic calcific aortic valvular heart disease 0.652 0.425
189 Non-rheumatic degenerative mitral valvular heart disease 0.605 0.399
190 Other non-rheumatic valvular heart diseases 0.637 0.416
191 Myocarditis 0.649 0.423
192 Alcoholic cardiomyopathy 0.629 0.412
193 Other cardiomyopathy 0.671 0.436
194 Atrial fibrillation and flutter 0.543 0.370
195 Aortic aneurysm 0.728 0.472
196 Peripheral vascular disease 0.415 0.321
197 Endocarditis 0.684 0.444
198 Other cardiovascular and circulatory diseases 0.543 0.371
199 Hemorrhoid 0.132 0.240
200 Varicose veins of lower extremities 0.143 0.243
201 Chronic obstructive pulmonary disease (mild) 0.444 0.331
202 Chronic obstructive pulmonary disease (moderate) 0.669 0.434
203 Chronic obstructive pulmonary disease (severe) 0.771 0.506
204 Silicosis 0.669 0.435
205 Asbestosis 0.659 0.429
206 Coal workers’ pneumoconiosis 0.671 0.435
207 Other pneumoconiosis 0.594 0.394
208 Asthma 0.409 0.318
209 Interstitial lung disease and pulmonary sarcoidosis 0.707 0.459
210 Other chronic respiratory diseases 0.503 0.354
211 Cirrhosis and other chronic liver diseases due to hepatitis B 0.674 0.437
212 Cirrhosis and other chronic liver diseases due to hepatitis C 0.680 0.441
213 Cirrhosis and other chronic liver diseases due to alcohol use (mild) 0.512 0.357
214 Cirrhosis and other chronic liver diseases due to alcohol use (moderate) 0.640 0.418
215 Cirrhosis and other chronic liver diseases due to alcohol use (severe) 0.683 0.443
216 Cirrhosis and other chronic liver diseases due to NAFLD 0.540 0.369
217 Cirrhosis and other chronic liver diseases due to other causes 0.629 0.412
218 Peptic ulcer disease 0.247 0.269
219 Gastritis and duodenitis 0.144 0.243
220 Gastroesophageal reflux disease 0.126 0.239
221 Appendicitis 0.246 0.269
222 Paralytic ileus and intestinal obstruction 0.427 0.325
223 Inguinal, femoral, and abdominal hernia 0.254 0.271
224 Inflammatory bowel disease 0.461 0.337
225 Vascular intestinal disorders 0.524 0.362
226 Gallbladder and biliary diseases 0.432 0.327
227 Pancreatitis 0.454 0.335
228 Other digestive diseases 0.198 0.256
229 Alzheimer disease and other dementias 0.660 0.429
230 Parkinson disease 0.699 0.453
231 Idiopathic epilepsy 0.613 0.403
232 Multiple sclerosis 0.674 0.437
233 Motor neuron disease 0.712 0.461
234 Migraine 0.186 0.253
235 Tension-type headache 0.180 0.252
236 Other neurological disorders 0.483 0.346
237 Schizophrenia 0.695 0.451
238 Major depressive disorder (mild) 0.312 0.288
239 Major depressive disorder (moderate) 0.544 0.371
240 Major depressive disorder (severe) 0.585 0.390
241 Dysthymia 0.239 0.267
242 Bipolar disorder 0.457 0.336
243 Anxiety disorders 0.309 0.287
244 Panic disorder 0.384 0.310
245 Obsessive-compulsive disorder 0.320 0.290
246 Post-traumatic stress disorder 0.392 0.313
247 Anorexia nervosa 0.402 0.316
248 Bulimia nervosa 0.371 0.306
249 Autism spectrum disorders 0.520 0.361
250 Asperger syndrome and other autistic spectrum disorders 0.488 0.348
251 Attention-deficit/hyperactivity disorder 0.230 0.265
252 Conduct disorder 0.314 0.288
253 Idiopathic developmental intellectual disability 0.458 0.336
254 Borderline personality disorder 0.433 0.327
255 Other mental disorders 0.486 0.347
256 Alcohol use disorders 0.428 0.325
257 Opioid use disorders 0.491 0.349
258 Cocaine use disorders 0.516 0.359
259 Amphetamine use disorders 0.483 0.346
260 Cannabis use disorders 0.384 0.310
261 Other drug use disorders 0.322 0.291
262 Diabetes mellitus type 1 without complications 0.394 0.314
263 Diabetes mellitus type 1 with complications 0.632 0.413
264 Diabetes mellitus type 2 without complications 0.322 0.291
265 Diabetes mellitus type 2 with complications 0.665 0.432
266 Metabolic syndrome 0.272 0.276
267 Chronic kidney disease due to diabetes mellitus type 1 0.692 0.448
268 Chronic kidney disease due to diabetes mellitus type 2 0.660 0.429
269 Chronic kidney disease due to hypertension 0.610 0.402
270 Chronic kidney disease due to glomerulonephritis 0.639 0.417
271 Chronic kidney disease due to other and unspecified causes 0.631 0.413
272 Acute glomerulonephritis 0.464 0.338
273 Eczema 0.134 0.241
274 Atopic dermatitis 0.224 0.263
275 Contact dermatitis 0.109 0.235
276 Seborrheic dermatitis 0.122 0.238
277 Psoriasis 0.242 0.268
278 Cellulitis 0.231 0.265
279 Pyoderma 0.320 0.290
280 Scabies 0.181 0.252
281 Fungal skin diseases 0.226 0.264
282 Viral skin diseases 0.197 0.256
283 Acne vulgaris 0.055 0.223
284 Alopecia areata 0.131 0.240
285 Pruritus 0.104 0.234
286 Urticaria 0.102 0.233
287 Decubitus ulcer 0.494 0.350
288 Other skin and subcutaneous diseases 0.135 0.241
289 Glaucoma 0.399 0.315
290 Cataract 0.281 0.279
291 Age-related macular degeneration 0.404 0.317
292 Refraction and accommodation disorders 0.207 0.259
293 Near vision loss 0.279 0.278
294 Other vision loss 0.601 0.398
295 Age-related and other hearing loss 0.274 0.277
296 Allergic rhinitis 0.084 0.229
297 Other sense organ diseases 0.323 0.291
298 Rheumatoid arthritis 0.423 0.323
299 Osteoarthritis, hip 0.353 0.300
300 Osteoarthritis, knee 0.277 0.277
301 Osteoarthritis, hand 0.242 0.268
302 Osteoarthritis, other 0.282 0.279
303 Low back pain (mild) 0.132 0.240
304 Low back pain (moderate) 0.304 0.285
305 Low back pain (severe) 0.393 0.313
306 Neck pain 0.125 0.238
307 Gout 0.341 0.296
308 Other musculoskeletal disorders 0.194 0.255
309 Systemic lupus erythematosus 0.619 0.407
310 Neural tube defects 0.765 0.501
311 Congenital heart anomalies 0.690 0.447
312 Orofacial clefts 0.491 0.349
313 Down syndrome 0.652 0.424
314 Turner syndrome 0.563 0.379
315 Klinefelter syndrome 0.558 0.377
316 Other chromosomal abnormalities 0.650 0.423
317 Congenital musculoskeletal and limb anomalies 0.640 0.418
318 Urogenital congenital anomalies 0.520 0.360
319 Digestive congenital anomalies 0.530 0.365
320 Other congenital anomalies 0.590 0.392
321 Interstitial nephritis and urinary tract infections 0.409 0.319
322 Urolithiasis 0.261 0.273
323 Benign prostatic hyperplasia 0.208 0.259
324 Male infertility 0.296 0.283
325 Urinary incontinence 0.233 0.265
326 Other urinary diseases 0.182 0.252
327 Uterine fibroids 0.201 0.257
328 Polycystic ovarian syndrome 0.382 0.310
329 Female infertility 0.305 0.286
330 Endometriosis 0.317 0.289
331 Genital prolapse 0.379 0.308
332 Premenstrual syndrome 0.154 0.245
333 Other gynecological diseases 0.251 0.270
334 Thalassemias 0.484 0.346
335 Thalassemia trait 0.479 0.344
336 Sickle cell disorders 0.543 0.371
337 Sickle cell trait 0.496 0.351
338 G6PD deficiency 0.520 0.360
339 G6PD trait 0.526 0.363
340 Other hemoglobinopathies and hemolytic anemias 0.470 0.340
341 Endocrine, metabolic, blood, and immune disorders 0.438 0.329
342 Caries of deciduous teeth 0.067 0.225
343 Caries of permanent teeth 0.129 0.239
344 Periodontal disease 0.204 0.258
345 Edentulism and severe tooth loss 0.465 0.339
346 Other oral disorders 0.193 0.255
347 Sudden infant death syndrome 0.865 0.604
348 Pedestrian road injuries 0.453 0.334
349 Cyclist road injuries 0.290 0.281
350 Motorcyclist road injuries 0.527 0.364
351 Motor vehicle road injuries 0.508 0.356
352 Other road injuries 0.318 0.289
353 Other transport injuries 0.418 0.322
354 Falls 0.415 0.321
355 Drowning 0.527 0.363
356 Fire, heat, and hot substances 0.399 0.315
357 Poisoning by carbon monoxide 0.776 0.510
358 Poisoning by other means 0.655 0.426
359 Unintentional firearm injuries 0.469 0.340
360 Unintentional suffocation 0.686 0.445
361 Other exposure to mechanical forces 0.301 0.285
362 Adverse effects of medical treatment 0.302 0.285
363 Venomous animal contact 0.398 0.315
364 Non-venomous animal contact 0.107 0.234
365 Pulmonary aspiration and foreign body in airway 0.569 0.382
366 Foreign body in eyes 0.117 0.237
367 Foreign body in other body part 0.161 0.247
368 Environmental heat and cold exposure 0.247 0.269
369 Exposure to forces of nature 0.249 0.270
370 Other unintentional injury 0.249 0.270
371 Self-harm by firearm 0.574 0.384
372 Self-harm by other specified means 0.548 0.372
373 Physical violence by firearm 0.531 0.365
374 Physical violence by sharp object 0.276 0.277
375 Sexual violence 0.520 0.360
376 Physical violence by other means 0.265 0.274
377 Conflict and terrorism 0.516 0.359
378 Police conflict or execution 0.550 0.373
Mean 0.488 0.369

HIV/AIDS, human immunodeficiency virus/acquired immune deficiency syndrome; COVID-19, coronavirus disease 2019; NASH, nonalcoholic fatty liver disease; NAFLD, nonalcoholic fatty liver disease; G6PD, glucose 6-phosphate dehydrogenase.

1 Based on a normal distribution.

2 Based on the natural logarithm.

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Figure & Data

References

    Citations

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    • Trends in Healthy Life Expectancy (HALE) and Disparities by Income and Region in Korea (2008–2020): Analysis of a Nationwide Claims Database
      Yoon-Sun Jung, Young-Eun Kim, Minsu Ock, Seok-Jun Yoon
      Journal of Korean Medical Science.2024;[Epub]     CrossRef
    • Measuring the Burden of Disease in Korea Using Disability-Adjusted Life Years (2008–2020)
      Yoon-Sun Jung, Young-Eun Kim, Minsu Ock, Seok-Jun Yoon
      Journal of Korean Medical Science.2024;[Epub]     CrossRef

    Figure

    JPMPH : Journal of Preventive Medicine and Public Health