Patterns of multimorbidity in community health centres in Shanghai, China: a retrospective, cross-sectional study based on outpatient data from 2014 to 2018

被引:2
|
作者
Jin, Hua [1 ,2 ]
Wang, Zhaoxin [2 ,3 ,4 ]
Guo, Aizhen [1 ,2 ]
Zhang, Hanzhi [1 ,2 ]
Liu, Wei [5 ]
Zhu, Yuqin [6 ]
Hua, Ming [7 ]
Shi, Jianjun [1 ,2 ]
Shi, Jianwei [3 ]
Yu, Dehua [1 ,2 ]
机构
[1] Tongji Univ, Yangpu Hosp, Clin Res Ctr Gen Practice, Sch Med,Dept Gen Practice, Shanghai, Peoples R China
[2] Shanghai Gen Practice & Community Hlth Dev Res Ct, Shanghai, Peoples R China
[3] Shanghai Jiao Tong Univ, Sch Publ Hlth, Dept Social Med & Hlth Management, Sch Med, Shanghai, Peoples R China
[4] Hainan Med Univ, Sch Management, Haikou, Hainan, Peoples R China
[5] Huangpu Dist Dapuqiao Community Hlth Ctr, Shanghai, Peoples R China
[6] Tongji Univ, Yangpu Hosp, Sch Med, Dept Emergency, Shanghai, Peoples R China
[7] Jingan Dist Daning Community Hlth Ctr, Shanghai, Peoples R China
来源
BMJ OPEN | 2022年 / 12卷 / 10期
基金
中国国家自然科学基金;
关键词
epidemiology; primary care; public health; OLDER-ADULTS; PRIMARY-CARE; PREVALENCE; FRAILTY;
D O I
10.1136/bmjopen-2021-048727
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Objective Caring for patients with multimorbidity is an important part of primary care. It has become increasingly relevant that understanding the spectrum of multimorbidity will help general practitioners (GPs) acquire working knowledge and improve management skills. However, there was little research on characteristics of multimorbidity in primary care in China. This study aimed to identify the spectrum of frequency, proportion and ranking of multimorbidity patterns in adult patients seen at community health centres (CHCs) in Shanghai, China. Design and setting This was an observational, retrospective, cross-sectional study analysis of outpatient data of 244 CHCs in Shanghai, China. Participants Adult patients with chronic disease who visited Shanghai CHCs during 2014-2018 were selected from Shanghai CHC electronic medical records database using the International Classification of Diseases 10th Revision codes matched to the Second Version of International Classification of Primary Care codes. Primary and secondary outcome measures A number of adult patients with chronic disease were counted. Then frequency, proportion and rank of disease patterns of multimorbidity were analysed. Results Analysis of 301 651 158 electronic health records of 5 909 280 adult patients (54.2% females) found the multimorbidity proportion to be 81.2%. The prevalence of multimorbidity increased with age, which climbed from 43.7% among those aged 19-34 to 94.9% among those more than 80 years of age. The proportion of multimorbidity was higher in females (83.2%) than males (79.7%). Vascular and metabolic diseases were the most frequent diseases for patients over 45 years old. Conclusions Multimorbidity has brought huge challenges to primary care practice in Shanghai. The Shanghai government should strengthen its support for the multitargeted prevention of chronic diseases and the improvement of GPs' management capabilities.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Distribution characteristics of mental disorders in community health service center: based on outpatient data from 2014-2022 in Shanghai, China
    Qian, Jie
    Zhang, Hanzhi
    Guo, Aizhen
    Fu, Qiangqiang
    Shi, Jianwei
    Jin, Hua
    Yu, Dehua
    BMC PSYCHIATRY, 2025, 25 (01)
  • [2] Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study
    Garin, Noe
    Koyanagi, Ai
    Chatterji, Somnath
    Tyrovolas, Stefanos
    Olaya, Beatriz
    Leonardi, Matilde
    Lara, Elvira
    Koskinen, Seppo
    Tobiasz-Adamczyk, Beata
    Luis Ayuso-Mateos, Jose
    Maria Haro, Josep
    JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES, 2016, 71 (02): : 205 - 214
  • [3] Prevalence and patterns of multimorbidity in northeastern China: a cross-sectional study
    Wang, S. B.
    D'Arcy, C.
    Yu, Y. Q.
    Li, B.
    Liu, Y. W.
    Tao, Y. C.
    Wu, Y. H.
    Zhang, Q. Q.
    Xu, Z. Q.
    Fu, Y. L.
    Kou, C. G.
    PUBLIC HEALTH, 2015, 129 (11) : 1539 - 1546
  • [4] Disease patterns in high-cost individuals with multimorbidity: a retrospective cross-sectional study in primary care
    Soley-Bori, Marina
    Ashworth, Mark
    Mcgreevy, Alice
    Wang, Yanzhong
    Durbaba, Stevo
    Dodhia, Hiten
    Fox-Rushby, Julia
    BRITISH JOURNAL OF GENERAL PRACTICE, 2024, 74 (740) : 134 - 136
  • [5] Prevalence of multimorbidity among adults attending primary health care centres in Qatar: A retrospective cross-sectional study
    Mohideen, Fathima Shezoon
    Honest, Prince Christopher Rajkumar
    Syed, Mohamed Ahmed
    David, Kirubah Vasandhi
    Abdulmajeed, Jazeel
    Ramireddy, Neelima
    JOURNAL OF FAMILY MEDICINE AND PRIMARY CARE, 2021, 10 (05) : 1823 - +
  • [6] Burden of multimorbidity in relation to age, gender and immigrant status: a cross-sectional study based on administrative data
    Lenzi, Jacopo
    Avaldi, Vera Maria
    Rucci, Paola
    Pieri, Giulia
    Fantini, Maria Pia
    BMJ OPEN, 2016, 6 (12):
  • [7] Comparing the prevalence of multimorbidity using different operational definitions in primary care in Singapore based on a cross-sectional study using retrospective, large administrative data
    Lee, Yi An Janis
    Xie, Ying
    Lee, Poay Sian Sabrina
    Lee, Eng Sing
    BMJ OPEN, 2020, 10 (12):
  • [8] Prevalence and patterns of multimorbidity among the elderly in China: a cross-sectional study using national survey data
    Zhang, Ran
    Lu, Yun
    Shi, Liuyan
    Zhang, Songlin
    Chang, Feng
    BMJ OPEN, 2019, 9 (08):
  • [9] Multimorbidity patterns and functional disability in elderly Brazilians: a cross-sectional study with data from the Brazilian National Health Survey
    Schmidt, Tauana Prestes
    Pudla Wagner, Katia Jakovljevic
    Ceola Schneider, Ione Jayce
    Danielewicz, Ana Lucia
    CADERNOS DE SAUDE PUBLICA, 2020, 36 (11):
  • [10] Impact of multimorbidity and complex multimorbidity on healthcare utilisation in older Australian adults aged 45 years or more: a large population-based cross-sectional data linkage study
    Kabir, Alamgir
    Conway, Damian P.
    Ansari, Sameera
    Tran, An
    Rhee, Joel J.
    Barr, Margo
    BMJ OPEN, 2024, 14 (01):