1
|
Shahbaz M, Harding JE, Milne B, Walters A, Underwood L, von Randow M, Xu L, Gamble GD. Comparison of outcomes of the 50-year follow-up of a randomized trial assessed by study questionnaire and by data linkage: The CONCUR study. Clin Trials 2024:17407745241259088. [PMID: 38907609 DOI: 10.1177/17407745241259088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Abstract
BACKGROUND/AIMS Self-reported questionnaires on health status after randomized trials can be time-consuming, costly, and potentially unreliable. Administrative data sets may provide cost-effective, less biased information, but it is uncertain how administrative and self-reported data compare to identify chronic conditions in a New Zealand cohort. This study aimed to determine whether record linkage could replace self-reported questionnaires to identify chronic conditions that were the outcomes of interest for trial follow-up. METHODS Participants in 50-year follow-up of a randomized trial were asked to complete a questionnaire and to consent to accessing administrative data. The proportion of participants with diabetes, pre-diabetes, hyperlipidaemia, hypertension, mental health disorders, and asthma was calculated using each data source and agreement between data sources assessed. RESULTS Participants were aged 49 years (SD = 1, n = 424, 50% male). Agreement between questionnaire and administrative data was slight for pre-diabetes (kappa = 0.10), fair for hyperlipidaemia (kappa = 0.27), substantial for diabetes (kappa = 0.65), and moderate for other conditions (all kappa >0.42). Administrative data alone identified two to three times more cases than the questionnaire for all outcomes except hypertension and mental health disorders, where the questionnaire alone identified one to two times more cases than administrative data. Combining all sources increased case detection for all outcomes. CONCLUSIONS A combination of questionnaire, pharmaceutical, and laboratory data with expert panel review were required to identify participants with chronic conditions of interest in this follow-up of a clinical trial.
Collapse
Affiliation(s)
- Mohammad Shahbaz
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Jane E Harding
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Barry Milne
- Centre of Methods and Policy Application in Social Sciences, The University of Auckland, Auckland, New Zealand
| | - Anthony Walters
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Lisa Underwood
- Centre of Methods and Policy Application in Social Sciences, The University of Auckland, Auckland, New Zealand
| | - Martin von Randow
- Centre of Methods and Policy Application in Social Sciences, The University of Auckland, Auckland, New Zealand
| | - Lois Xu
- Centre of Methods and Policy Application in Social Sciences, The University of Auckland, Auckland, New Zealand
| | - Greg D Gamble
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| |
Collapse
|
2
|
Liu H, Zhao Y, Qiao L, Yang C, Yang Y, Zhang T, Wu Q, Han J. Consistency between self-reported disease diagnosis and clinical assessment and under-reporting for chronic conditions: data from a community-based study in Xi'an, China. Front Public Health 2024; 12:1296939. [PMID: 38292908 PMCID: PMC10825002 DOI: 10.3389/fpubh.2024.1296939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 01/04/2024] [Indexed: 02/01/2024] Open
Abstract
Aims The current study aims to investigate the consistency between the surveyees' self-reported disease diagnosis and clinical assessment of eight major chronic conditions using community-based survey data collected in Xi'an, China in 2017. With a focus on under-reporting patients, we aim to explore its magnitude and associated factors, to provide an important basis for disease surveillance, health assessment and resource allocation, and public health decision-making and services. Methods Questionnaires were administered to collect self-reported chronic condition prevalence among the study participants, while physical examinations and laboratory tests were conducted for clinical assessment. For each of the eight chronic conditions, the sensitivity, specificity, under-reporting, over-reporting, and agreement were calculated. Log-binomial regression analysis was employed to identify potential factors that may influence the consistency of chronic condition reporting. Results A total of 2,272 participants were included in the analysis. Four out of the eight chronic conditions displayed under-reporting exceeding 50%. The highest under-reporting was observed for goiter [85.93, 95% confidence interval (CI): 85.25-86.62%], hyperuricemia (83.94, 95% CI: 83.22-84.66%), and thyroid nodules (72.89, 95% CI: 72.02-73.76%). Log-binomial regression analysis indicated that senior age and high BMI were potential factors associated with the under-reporting of chronic condition status in the study population. Conclusion The self-reported disease diagnosis by respondents and clinical assessment data exhibit significant inconsistency for all eight chronic conditions. Large proportions of patients with multiple chronic conditions were under-reported in Xi'an, China. Combining relevant potential factors, targeted health screenings for high-risk populations might be an effective method for identifying under-reporting patients.
Collapse
Affiliation(s)
- Haobiao Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
- Department of Occupational and Environmental Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Yanru Zhao
- Department of Endocrinology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Lichun Qiao
- Department of Occupational and Environmental Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Congying Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Ying Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Tianxiao Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
- National Anti-Drug Laboratory Shaanxi Regional Center, Xi'an, Shaanxi, China
| | - Qian Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Jing Han
- Department of Occupational and Environmental Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, Shaanxi, China
| |
Collapse
|