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Preplanned Studies: Epidemiological Characteristics of Osteoarthritis in Yichang City — Hubei Province, China, 2017–2018

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  • Summary

    What is already known about this topic?

    OA has been listed as the fastest increasing major public health problem and ranked second as a cause of disability by World Health Organization (WHO). With population aging, osteoarthritis (OA) is causing an increased economic burden for individuals and society and is attracting an increasing amount of scientific attention.

    What is added by this report?

    This research used healthcare data to analyze the epidemiological characteristics of OA in Yichang City, Hubei Province, China. The exact number and distribution of patients were obtained, and a descriptive analysis of OA visits by age, gender, and season was performed. Knee joint disease among women was most common, followed by other joint diseases. Knee osteoarthritis was the most common OA diagnosis between 60 and 69 years of age.

    What are the implications for public health practice?

    In China, the prevalence of OA among middle-age and elderly people was high. With increases in the size of the elderly population, the burden of disease caused by OA may increase. It is necessary to strengthen publicity to improve people’s awareness of self-health care of bone and joint. Interventions and preventive strategies targeting high-risk groups are urgently needed in order to improve healthy bones and healthy life quality among middle-age and elderly people.

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  • [1] Sen R, Hurley JA. Osteoarthritis StatPearls. Treasure Island: Stat Pearls Publishing Copyright. 2020.
    [2] Long HB, Zeng XY, Liu Q, Wang HD, Vos T, Hou YF, et al. Burden of osteoarthritis in China, 1990-2017: findings from the Global Burden of Disease Study 2017. Lancet Rheumatol 2020;2(3):e164 − 72. http://dx.doi.org/10.1016/S2665-9913(19)30145-6CrossRef
    [3] Wei J. High prevalence and burden of osteoarthritis in China. Lancet Rheumatol 2020;2(3):e127 − 8. http://dx.doi.org/10.1016/S2665-9913(20)30034-5CrossRef
    [4] Lawrence RC, Felson DT, Helmick CG, Arnold LM, Choi H, Deyo RA, et al. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States: part II. Arthritis Rheum 2008;58(1):26 − 35. http://dx.doi.org/10.1002/art.23176CrossRef
    [5] Mei YF, Zhang ZY. Epidemiological study of osteoarthritis in China: still a long way to go. Chin J Rheumatol 2019;23(2):73 − 5. http://dx.doi.org/10.3760/cma.j.issn.1007-7480.2019.02.001 (In Chinese). CrossRef
    [6] Hu HR, Xie XT, Zhang CQ. Research progress of chondrocyte autophagy in osteoarthritis. Chin J Joint Surg (Electron Ed) 2018;12(6):68 − 70. http://dx.doi.org/10.3877/cma.j.issn.1674-134X.2018.06.015 (In Chinese). CrossRef
    [7] Lu FF, Xu CZ, Zhang P, Xu Y, Liu JH. Construction and implementation of big data in healthcare in Yichang city, Hubei province. China CDC Wkly 2021;3(1):14 − 7. http://dx.doi.org/10.46234/ccdcw2020.254CrossRef
    [8] Wang Swan. The management of bone and joint health urgently needs to be broken. Health News, 2020-10-30. http://szb.jkb.com.cn/jkbpaper/html/2020-10/30/content_290323.htm. [2021-4-1].http://szb.jkb.com.cn/jkbpaper/html/2020-10/30/content_290323.htm
    [9] Neogi T, Zhang YQ. Epidemiology of osteoarthritis. Rheum Dis Clin North Am 2013;39(1):1 − 19. http://dx.doi.org/10.1016/j.rdc.2012.10.004CrossRef
    [10] Vina ER, Kwoh CK. Epidemiology of osteoarthritis: literature update. Curr Opin Rheumatol 2018;30(2):160 − 7. http://dx.doi.org/10.1097/BOR.0000000000000479CrossRef
    [11] Glyn-Jones S, Palmer AJR, Agricola R, Price AJ, Vincent TL, Weinans PH, et al. Osteoarthritis. Lancet 2015;386(9991):376 − 87. http://dx.doi.org/10.1016/S0140-6736(14)60802-3CrossRef
    [12] Timmermans EJ, van der Pas S, Dennison EM, Maggi S, Peter R, Castell MV, et al. EPOSA research group. The influence of weather conditions on outdoor physical activity among older people with and without osteoarthritis in 6 European countries. J Phys Act Health 2016;13(12):1385 − 95. http://dx.doi.org/10.1123/jpah.2016-0040CrossRef
    [13] Sun XS, Zhen XM, Hu XQ, Li YY, Gu SY, Gu YX, et al. Osteoarthritis in the middle-aged and elderly in China: prevalence and influencing factors. Int J Environ Res Public Health 2019;16(23):4701. http://dx.doi.org/10.3390/ijerph16234701CrossRef
  • FIGURE 1.  Distribution of osteoarthritis (OA) by gender and year from 2017 to 2018, Yichang City, Hubei province, China.

    FIGURE 2.  Distribution osteoarthritis (OA) in different age groups by study year from 2017 to 2018, Yichang City, Hubei Province, China.

    FIGURE 3.  Outpatient and hospitalized attendance of osteoarthritis (OA) patients by month from 2017 to 2018, Yichang City, Hubei Province, China.

    TABLE 1.  Characteristics of different types of osteoarthritis in 2017–2018, Yichang City, Hubei Province, China.

    Feature20172018All
    MaleFemaleAllMaleFemaleAllN
    N(%)N(%)N(%)N(%)N(%)N(%)
    Polyarthropathy (M15)
    0–29 12 2.38 19 3.76 31 6.14 8 0.89 8 0.89 16 1.78 47
    30–39 8 1.58 16 3.17 24 4.75 12 1.33 26 2.89 38 4.22 62
    40–49 21 4.16 39 7.72 60 11.88 31 3.44 72 7.99103 11.43163
    50–59 48 9.50 82 16.24130 25.74 64 7.10184 20.42248 27.52378
    60–69 48 9.50 71 14.06119 23.56 77 8.55178 19.76255 28.30374
    Over 70 52 10.30 89 17.62141 27.92115 12.76126 13.98241 26.75382
    All189 37.43316 62.57505100.00307 34.07594 65.93901100.001,406
    Coxarthropathy (M16)
    0–29 5 2.23 7 3.13 12 5.36 15 4.59 10 3.06 25 7.65 37
    30–39 4 1.79 3 1.34 7 3.13 11 3.36 13 3.98 24 7.34 31
    40–49 18 8.04 20 8.93 38 16.96 27 8.26 23 7.03 50 15.29 88
    50–59 21 9.38 42 18.75 63 28.13 31 9.48 40 12.23 71 21.71134
    60–69 20 8.93 42 18.75 62 27.68 30 9.17 43 13.15 73 22.32135
    Over 70 19 8.48 23 10.27 42 18.75 32 9.79 52 15.90 84 25.69126
    All 87 38.84137 61.16224100.00146 44.65181 55.35327100.00551
    Gonarthrosis (M17)
    0–29 72 1.81 81 2.04153 3.85 66 1.16 63 1.11129 2.27282
    30–39 88 2.21109 2.74197 4.95111 1.95115 2.02226 3.98423
    40–49204 5.13284 7.14488 12.27238 4.19376 6.62614 10.811,102
    50–59282 7.09627 15.77909 22.86423 7.45865 15.231,288 22.682,197
    60–69363 9.13869 21.851,232 30.98518 9.121,322 23.271,840 32.393,072
    Over 70335 8.42663 16.67998 25.09535 9.421,048 18.451,583 27.872,581
    All1,344 33.792,633 66.213,977 100.001,891 33.293,789 66.715,680 100.009,657
    Arthrosis of first carpometacarpal joint (M18)
    0–29 1 5.56 0 0.00 1 5.56 1 4.00 0 0.00 1 4.00 2
    30–39 1 5.56 4 22.22 5 27.78 3 12.00 5 20.00 8 32.00 13
    40–49 3 16.67 0 0.00 3 16.67 1 4.00 1 4.00 2 8.00 5
    50~59 1 5.56 4 22.22 5 27.78 3 12.00 5 20.00 8 32.00 13
    60–69 1 5.56 1 5.56 2 11.11 2 8.00 5 20.00 7 28.00 9
    Over 70 2 11.11 0 0.00 2 11.11 3 12.00 3 12.00 6 24.00 8
    All 9 50.00 9 50.00 18100.00 11 44.00 14 56.00 25100.00 43
    Other arthrosis (M19)
    0–29127 3.71185 5.41312 9.12 29 1.74 27 1.62 56 3.36368
    30–39172 5.03192 5.61364 10.64 61 3.66 46 2.76107 6.42471
    40–49235 6.87383 11.19618 18.06 97 5.82134 8.04231 13.87849
    50–59266 7.77534 15.60800 23.38187 11.22256 15.37443 26.591,243
    60–69268 7.83395 11.54663 19.37176 10.56243 14.59419 25.151,082
    Over 70351 10.26314 9.18665 19.43180 10.80230 13.81410 24.611,075
    All1,419 41.472,003 58.533,422 100.00730 43.82936 56.181,666 100.005,088
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Epidemiological Characteristics of Osteoarthritis in Yichang City — Hubei Province, China, 2017–2018

View author affiliations

Summary

What is already known about this topic?

OA has been listed as the fastest increasing major public health problem and ranked second as a cause of disability by World Health Organization (WHO). With population aging, osteoarthritis (OA) is causing an increased economic burden for individuals and society and is attracting an increasing amount of scientific attention.

What is added by this report?

This research used healthcare data to analyze the epidemiological characteristics of OA in Yichang City, Hubei Province, China. The exact number and distribution of patients were obtained, and a descriptive analysis of OA visits by age, gender, and season was performed. Knee joint disease among women was most common, followed by other joint diseases. Knee osteoarthritis was the most common OA diagnosis between 60 and 69 years of age.

What are the implications for public health practice?

In China, the prevalence of OA among middle-age and elderly people was high. With increases in the size of the elderly population, the burden of disease caused by OA may increase. It is necessary to strengthen publicity to improve people’s awareness of self-health care of bone and joint. Interventions and preventive strategies targeting high-risk groups are urgently needed in order to improve healthy bones and healthy life quality among middle-age and elderly people.

  • 1. Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing, China
  • 2. Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
  • 3. Zhongguancun Institute of National Health Big Data, Beijing, China
  • 4. Shandong Provincial Armed Police Corps Hospital, Jinan, Shandong, China
  • Corresponding author:

    Xudong Li, lixd@chinacdc.cn

    Online Date: April 16 2021
    doi: 10.46234/ccdcw2021.090
  • World Health Organization (WHO) has identified osteoarthritis (OA) as the fastest growing major public health problem and the second leading cause of disability globally. OA is a disease that affects entire joints, leaving no tissues unaffected. The complete pathway that leads to destruction of joints is unknown (1). According to the Department of Bone and Joint Epidemiology of Peking University People’s Hospital, the number of disability-adjusted life years lost in China per 100,000 caused by OA increased from 92.5 in 1990 to 98.8 in 2017 (2). The overall prevalence of OA among people over the age of 65 in China was 8.1% (3). OA causes huge economic burdens on patients, families, and society; it most commonly affects the hands, hips, knees, and spine, with knee joint involvement being the most common (4). For many years the seriousness of OA has been emphasized, but there are few large-sample-size epidemiological OA cohorts established in our country, as OA epidemiology is still in its infancy. Despite many investigations and studies having been repeated in a variety of areas, resulting data have not been well recognized internationally due to inconsistencies in testing methods and diagnostic standards (5). This study is based on the Big Data Information Platform of Health in Yichang City, which includes diagnostic and treatment information from OA-related visits by 817,700 permanent residents, over a 6-month period, in 5 administrative regions, 10 tertiary hospitals, and 26 community health service centers. The study could provide a basic description of Yichang’s OA situation and help form the basis for public health risk assessments and policymaking.

    Yichang Big Data Platform on Health serves 817,700 permanent residents and the outpatient records, inpatient records, prescription drug data, and physical examination records are all included. An one-person-one-card system is used to accurately track urban residents.

    The research conducted a retrospective analysis of deidentified healthcare data from the Big Data platform. The study passed ethical review; unique identification codes were recoded. Patients diagnosed with OA who had records in the Big Data platform from January 1, 2017 to December 31, 2018 were selected. Patients with ICD codes M15-M19 who met imaging OA diagnostic criteria were included in the analytic data set. The OA cases in this analysis were redefined as at least one OA out-patient or in-patient visit, and those with multiple visits were classified as one OA patient and the earliest visit time was retained. SAS software package (version 9.4, 100 SAS Campus Drive Cary, NC 27513, USA) was used for descriptive analyses by age, gender, and joint.

    There were 16,745 patients who met inclusion criteria during the study period with a total of 23,460 visits, with an average of 1.40 visits. The mean age and standard deviation were 58.69±15.18 years; there were more females than males (Figure 1). The largest proportions of patients were among 60–69 years old, followed 50–59 years old and 70–79 years old (Figure 2). Outpatient and inpatient treatment rates were highest in the spring (Figure 3).

    Figure 1.  Distribution of osteoarthritis (OA) by gender and year from 2017 to 2018, Yichang City, Hubei province, China.

    Figure 2.  Distribution osteoarthritis (OA) in different age groups by study year from 2017 to 2018, Yichang City, Hubei Province, China.

    Figure 3.  Outpatient and hospitalized attendance of osteoarthritis (OA) patients by month from 2017 to 2018, Yichang City, Hubei Province, China.

    ICD-10 codes M15–M19 were polyarthropathy (M15), coxarthropathy (M16), knee OA (M17), arthrosis of first carpometacarpal joint (M18), and other joint diseases (M19) (Table 1). Knee OA was the most common diagnosis, with women more affected than men. Among 50–59 years old, M16, M18, and M19 were the most prevalent diagnostic codes. In 2017, the highest number of M15 OA patients were 50–59 years old; while in 2018, the highest number of M15 OA patients were 60–69 years old. In both 2017 and 2018, M17 OA patients, aged 60 to 69 years, represented the highest number of cases.

    Feature20172018All
    MaleFemaleAllMaleFemaleAllN
    N(%)N(%)N(%)N(%)N(%)N(%)
    Polyarthropathy (M15)
    0–29 12 2.38 19 3.76 31 6.14 8 0.89 8 0.89 16 1.78 47
    30–39 8 1.58 16 3.17 24 4.75 12 1.33 26 2.89 38 4.22 62
    40–49 21 4.16 39 7.72 60 11.88 31 3.44 72 7.99103 11.43163
    50–59 48 9.50 82 16.24130 25.74 64 7.10184 20.42248 27.52378
    60–69 48 9.50 71 14.06119 23.56 77 8.55178 19.76255 28.30374
    Over 70 52 10.30 89 17.62141 27.92115 12.76126 13.98241 26.75382
    All189 37.43316 62.57505100.00307 34.07594 65.93901100.001,406
    Coxarthropathy (M16)
    0–29 5 2.23 7 3.13 12 5.36 15 4.59 10 3.06 25 7.65 37
    30–39 4 1.79 3 1.34 7 3.13 11 3.36 13 3.98 24 7.34 31
    40–49 18 8.04 20 8.93 38 16.96 27 8.26 23 7.03 50 15.29 88
    50–59 21 9.38 42 18.75 63 28.13 31 9.48 40 12.23 71 21.71134
    60–69 20 8.93 42 18.75 62 27.68 30 9.17 43 13.15 73 22.32135
    Over 70 19 8.48 23 10.27 42 18.75 32 9.79 52 15.90 84 25.69126
    All 87 38.84137 61.16224100.00146 44.65181 55.35327100.00551
    Gonarthrosis (M17)
    0–29 72 1.81 81 2.04153 3.85 66 1.16 63 1.11129 2.27282
    30–39 88 2.21109 2.74197 4.95111 1.95115 2.02226 3.98423
    40–49204 5.13284 7.14488 12.27238 4.19376 6.62614 10.811,102
    50–59282 7.09627 15.77909 22.86423 7.45865 15.231,288 22.682,197
    60–69363 9.13869 21.851,232 30.98518 9.121,322 23.271,840 32.393,072
    Over 70335 8.42663 16.67998 25.09535 9.421,048 18.451,583 27.872,581
    All1,344 33.792,633 66.213,977 100.001,891 33.293,789 66.715,680 100.009,657
    Arthrosis of first carpometacarpal joint (M18)
    0–29 1 5.56 0 0.00 1 5.56 1 4.00 0 0.00 1 4.00 2
    30–39 1 5.56 4 22.22 5 27.78 3 12.00 5 20.00 8 32.00 13
    40–49 3 16.67 0 0.00 3 16.67 1 4.00 1 4.00 2 8.00 5
    50~59 1 5.56 4 22.22 5 27.78 3 12.00 5 20.00 8 32.00 13
    60–69 1 5.56 1 5.56 2 11.11 2 8.00 5 20.00 7 28.00 9
    Over 70 2 11.11 0 0.00 2 11.11 3 12.00 3 12.00 6 24.00 8
    All 9 50.00 9 50.00 18100.00 11 44.00 14 56.00 25100.00 43
    Other arthrosis (M19)
    0–29127 3.71185 5.41312 9.12 29 1.74 27 1.62 56 3.36368
    30–39172 5.03192 5.61364 10.64 61 3.66 46 2.76107 6.42471
    40–49235 6.87383 11.19618 18.06 97 5.82134 8.04231 13.87849
    50–59266 7.77534 15.60800 23.38187 11.22256 15.37443 26.591,243
    60–69268 7.83395 11.54663 19.37176 10.56243 14.59419 25.151,082
    Over 70351 10.26314 9.18665 19.43180 10.80230 13.81410 24.611,075
    All1,419 41.472,003 58.533,422 100.00730 43.82936 56.181,666 100.005,088

    Table 1.  Characteristics of different types of osteoarthritis in 2017–2018, Yichang City, Hubei Province, China.

  • OA is a clinically common chronic degenerative disease and is the leading cause of impaired mobility and dysfunction in middle-aged and elderly people (6). Yichang is located in the southwest of Hubei Province. By 2020, the city had jurisdiction over 5 municipal districts, 3 county-level cities, 3 counties, and 2 autonomous counties, with a total area of 21,000 square kilometers, a permanent population of 4.1 million, and a registered population of 3.9 million. Its Big Data Platform for Health Management covers all 13 counties and urban districts of the city, as well as the 4.1 million permanent residents; it collects more than 1.8 million pieces of data every day, and stores more than 4 billion pieces of data. Interconnection maturity has reached Grade 4 A. Since its inception in 2014, data quality has been confirmed by published studies(7). Using the big data of Yichang, we analyzed the epidemiological characteristics of osteoarthritis by gender, age, and disease type to provide a foundation for overall management and clinical treatment of OA.

    In 2017, China had 61.2 million OA patients, with an age-standardized prevalence of 3.1% (2). Based on number of the permanent residents in the Yichang urban area (817,700), we estimate that there are about 25,349 OA patients in the city in 2017. Among OA patients, 32.14% went to see a doctor, which reflects the low rate of doctor visits among patients with OA. This may be due to the fact that many patients are not fully aware of OA and consider it a normal phenomenon of aging and are ignored (8). As the population ages, so will the disease burden of OA. The proportion of OA patients in the study population was the highest between the ages of 60 and 69, with women higher than men, which is consistent with most epidemiological studies. The average age of onset for OA is around 20 years, and the majority of cases are asymptomatic. As people get older, the incidence rises; OA becomes most common after middle age (9), affecting women more than men (10). Although the effect of estrogen on OA has been extensively studied, its mechanism of action remains unknown (11).

    The spring season has the highest number of OA hospitalizations. It is not clear if this is due to changing weather. People suffering from OA often believe that changes in the weather exacerbate pain (12). More research is needed to understand the relation between OA and climate change, if any. In our study, the proportion of patients with knee OA aged 60–69 years was higher in women than men from 2017 to 2018, followed by other joint diseases (M19), polyarthropathy (M15), coxarthropathy (M16), and arthrosis of first carpometacarpal joint (M18). According to Sun X et al., the prevalence of knee OA in middle-aged and elderly people in China was 21.51%; women are more likely to develop knee OA than men (13).

    This study was subject to some limitations. First, we only analyzed OA patients who went to outpatient clinics or were hospitalized for diagnosis and treatment. The number of patients with OA was slightly underestimated due to exclusion of patients who did not seek medical attention. Second, because our study only covers the urban population of Yichang City, this exploratory research may have underestimated the number of OA patients. Data on the incidence and prevalence of OA in rural areas were not included. The incidence and prevalence of OA should be able to be analyzed in greater detail as the Big Data on Health Platform matures.

    In China, the prevalence of OA among middle-age and elderly people was high. With increases in the size of the elderly population, the burden of disease caused by OA may increase. It is necessary to strengthen publicity to improve people's awareness of self-health care of bone and joint; advocate early detection, early diagnosis, and early treatment; and increase the rate of diagnosis of OA to reduce the burden of disease brought by OA. Interventions and preventive strategies targeting high-risk groups are urgently needed in order to improve healthy bones and healthy life quality among middle-age and elderly people.

    Acknowledgements: Yong Xu, Pei Zhang, Chengzhong Xu, and Fangfang Lu from Yichang Center for Disease Control and Prevention, Hubei Province; Feng Tan, Shicheng Yu, Dapeng Yin, Lance Rodewald, and Peter Hao from Chinese Center for Disease Control and Prevention.

    Conflicts of interest: The authors who have taken part in this study declared that they did not have any other potential conflicts of interest.

    Funding: National Science and Technology Major Project of China (2012CB955500, 2012CB955504), National Key R&D Program of China (2018YFC1315305).

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