JMIR Research Protocols (Jun 2022)

Morbidity Patterns in Primary Care in Hong Kong: Protocol for a Practice-Based Morbidity Survey

  • Julie Yun Chen,
  • David Chao,
  • Samuel Yeung-shan Wong,
  • Tsui Yee Emily Tse,
  • Eric Yuk Fai Wan,
  • Joyce Pui Yan Tsang,
  • Maria Kwan Wa Leung,
  • Welchie Ko,
  • Yim-chu Li,
  • Catherine Chen,
  • Wan Luk,
  • Man-Chi Dao,
  • Michelle Wong,
  • Wing Mun Leung,
  • Cindy Lo Kuen Lam

DOI
https://doi.org/10.2196/37334
Journal volume & issue
Vol. 11, no. 6
p. e37334

Abstract

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BackgroundUp-to-date and accurate information about the health problems encountered by primary care doctors is essential to understanding the morbidity pattern of the community to better inform health care policy and practice. Morbidity surveys of doctors allow documentation of actual consultations, reflecting the patient’s reason for seeking care as well as the doctor’s diagnostic interpretation of the illness and management approach. Such surveys are particularly critical in the absence of a centralized primary care electronic medical record database. ObjectiveWith the changing sociodemographic profile of the population and implementation of health care initiatives in the past 10 years, the aim of this study is to determine the morbidity and management patterns in Hong Kong primary care during a pandemic and compare the results with the last survey conducted in 2007-2008. MethodsThis will be a prospective, practice-based survey of Hong Kong primary care doctors. Participants will be recruited by convenience and targeted sampling from both public and private sectors. Participating doctors will record the health problems and corresponding management activities for consecutive patient encounters during one designated week in each season of the year. Coding of health problems will follow the International Classification of Primary Care, Second Edition. Descriptive statistics will be used to calculate the prevalence of health problems and diseases as well as the rates of management activities (referral, investigation, prescription, preventive care). Nonlinear mixed effects models will assess the differences between the private and public sectors as well as factors associated with morbidity and management patterns in primary care. ResultsThe data collection will last from March 1, 2021, to August 31, 2022. As of April 2022, 176 doctor-weeks of data have been collected. ConclusionsThe results will provide information about the health of the community and inform the planning and allocation of health care resources. Trial RegistrationClinicalTrials.gov NCT04736992; https://clinicaltrials.gov/ct2/show/NCT04736992 International Registered Report Identifier (IRRID)DERR1-10.2196/37334