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Laura-Arias E, Villar-Guevara M, Millones-Liza DY. Servant leadership, brand love, and work ethic: important predictors of general health in workers in the education sector. Front Psychol 2024; 15:1274965. [PMID: 38646112 PMCID: PMC11026670 DOI: 10.3389/fpsyg.2024.1274965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 03/01/2024] [Indexed: 04/23/2024] Open
Abstract
Background Building a path aimed at the wellbeing of workers in the education sector is the fundamental basis to encourage quality education. To fill the gap in knowledge and address this aspect by understanding the behavior of the study population, it was proposed as with the objective of determining if servant leadership, brand love and work ethic predict the general health in workers. Methods A non-probability sampling was applied for convenience. For this purpose, a sample of 509 workers from Peru was submitted to study, who completed a questionnaire consisting of: scale of servant leadership, work ethic, GHQ-12 and brand love. By applying a quantitative method using a structural equation modeling partial least squares approach. Results The present study demonstrated that the three constructs (servant leadership, brand love, and work ethic) predict the general health of workers in a positive and significant way, in a sample of Peruvian workers in the education sector. Furthermore, the results suggest that these factors can be used to improve the health of employees in educational institutions in Peru and possibly in other contexts as well. Conclusion Given these results and after knowing the solidity of the predictions, the importance of promoting general health in workers in the education sector.
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Affiliation(s)
- Elena Laura-Arias
- UPG de Ciencias Empresariales, Escuela de Posgrado, Universidad Peruana Unión, Lima, Perú
| | - Miluska Villar-Guevara
- UPG de Ciencias Empresariales, Escuela de Posgrado, Universidad Peruana Unión, Lima, Perú
- EP de Administración, Facultad de Ciencias Empresariales, Universidad Peruana Unión, Juliaca, Perú
| | - Dany Yudet Millones-Liza
- UPG de Ciencias Empresariales, Escuela de Posgrado, Universidad Peruana Unión, Lima, Perú
- EP de Administración, Facultad de Ciencias Empresariales, Universidad Peruana Unión, Lima, Perú
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Aga F, Dunbar SB, Kebede T, Higgins MK, Gary R. Sociodemographic and clinical correlates of diabetes self-efficacy in adults with type 2 diabetes and comorbid heart failure. Res Nurs Health 2019; 43:79-89. [PMID: 31773764 DOI: 10.1002/nur.21999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Accepted: 11/11/2019] [Indexed: 11/08/2022]
Abstract
Heart failure (HF) is a comorbidity that complicates type 2 diabetes mellitus (T2D) management and increases the chance of death. However, little is known concerning factors related to diabetes self-efficacy in comorbid HF. This secondary data analysis was aimed at describing sociodemographic and clinical correlates of diabetes self-efficacy in adults with T2D and comorbid HF. A correlational design was used to analyze cross-sectional baseline data from a randomized study of 180 participants that tested a 6-month integrated self-care intervention targeting adults with concomitant HF and T2D. Participants were enrolled from one of four large urban-tertiary hospitals in Atlanta, GA, during 2010-2013. Data were collected from medical records and self-report. We used stepwise multiple linear regressions to examine variables associated with diabetes self-efficacy. The participants' mean age was 58.1 ± 10.7 years and the majority were male (n = 118; 65.6%) and African American (n = 119; 66.1%). Good self-rated health and presence of implantable cardioverter-defibrillator (ICD) had significant positive relationships with diabetes self-efficacy, while taking both oral antiglycemic medication and insulin, history of depression, cardiac pacemaker, and taking digitalis were negatively related. These variables collectively explained 22.4% of the variation in diabetes self-efficacy. One study implication is that using self-rated health provides a quick, patient-centered assessment to evaluate patient health status. Further studies are warranted to ascertain the pathways linking ICD, pacemaker, and digitalis treatment with diabetes self-efficacy.
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Affiliation(s)
- Fekadu Aga
- Department of Nursing, School of Nursing and Midwifery, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Sandra B Dunbar
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia
| | - Tedla Kebede
- Diabetes and Endocrinology Unit, Department of Internal Medicine, Tikur Anbessa Specialized Hospital, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | | | - Rebecca Gary
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia
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3
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Myers-Wright N, Cheng B, Tafreshi SN, Lamster IB. A simple self-report health assessment questionnaire to identify oral diseases. Int Dent J 2018; 68:428-432. [PMID: 29696638 PMCID: PMC9379000 DOI: 10.1111/idj.12398] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND One approach to addressing oral health disparities for at-risk populations has been to increase discussion of oral health by non-dental healthcare providers. This study examined the accuracy of a simple instrument to detect individuals with a history of dental disease, which would then allow referral for an oral health evaluation. MATERIALS AND METHODS A two-question instrument was evaluated for the relationship to oral diseases, periodontal disease, and decayed, missing and filled teeth in 391 individuals seen in a dental school clinic for non-emergent dental care over a 3-month period. Clinical dental findings were used as outcome variables. The oral health parameters were dichotomised, using different levels of disease severity. The criteria were increased and decreased in an effort to test the robustness of our method. RESULTS While the sensitivity outcomes with one question alone showed significant ability to predict oral disease (59-71%), the addition of a second self-assessment question increased the sensitivity (76-91%) for all oral health parameters studied. As the criteria for oral disease increased so did the sensitivity of this instrument. CONCLUSION The results presented here offer evidence that a simple two-item questionnaire is an efficient and effective method of detecting populations at-risk for oral diseases.
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Affiliation(s)
- Noreen Myers-Wright
- Department of Health Policy & Management, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Bin Cheng
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Sima N. Tafreshi
- North Shore Manhasset Dental Clinic, Northwell Health System, Great Neck, NY, USA
| | - Ira B. Lamster
- School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
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4
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Willadsen TG, Siersma V, Nielsen ABS, Køster-Rasmussen R, Guassora AD, Jarbøl DE, Eusebi P, Malterud K, Reventlow S, de Fine Olivarius N. The effect of structured personal care on diabetes symptoms and self-rated health over 14 years after diabetes diagnosis. Prim Care Diabetes 2018; 12:354-363. [PMID: 29705674 DOI: 10.1016/j.pcd.2018.03.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 03/23/2018] [Accepted: 03/29/2018] [Indexed: 10/17/2022]
Abstract
AIMS To explore the effect of structured personal care on diabetes symptoms and self-rated health over 14 years after diabetes diagnosis while patients are gradually diagnosed with other chronic conditions (multimorbidity). METHODS Post hoc analysis of the Danish randomized controlled trial Diabetes Care in General Practice including 1381 patients newly diagnosed with type 2 diabetes. The effect of structured personal care compared with routine care on diabetes symptoms and self-rated health was analysed 6 and 14 years after diagnosis with a generalized multilevel Rasch model. RESULTS Structured personal care reduced the overall likelihood of reporting diabetes symptoms at the end of the intervention (OR 0.79; 95% CI: 0.64-0.97), but this effect was not explained by glycaemic control or multimorbidity. There was no effect of the intervention on diabetes symptoms after 14 years or on self-rated health after 6 years or 14 years. CONCLUSIONS Structured personal care had a beneficial effect on diabetes symptoms 6 years after diagnosis, but not on self-rated health at either follow up point. To optimally manage patients over time it is important to supplement clinical information by information provided by the patients.
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Affiliation(s)
- Tora Grauers Willadsen
- The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
| | - Volkert Siersma
- The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Anni Brit Sternhagen Nielsen
- The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Køster-Rasmussen
- The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ann Dorrit Guassora
- The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Dorte Ejg Jarbøl
- Research Unit of General Practice, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Paolo Eusebi
- Section of Neurology, Department of Medicine, University of Perugia, Perugia, Italy; Health Planning Service, Department of Epidemiology, Regional Health Authority of Umbria, Perugia, Italy
| | - Kirsti Malterud
- The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Research Unit for General Practice, Uni Research Health, Bergen, Norway; Department of Global Public Health and Primary Care, University of Bergen, Norway
| | - Susanne Reventlow
- The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Niels de Fine Olivarius
- The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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5
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Liu X, Chen Z, Fine JP, Liu L, Wang A, Guo J, Tao L, Mahara G, Yang K, Zhang J, Tian S, Li H, Liu K, Luo Y, Zhang F, Tang Z, Guo X. A competing-risk-based score for predicting twenty-year risk of incident diabetes: the Beijing Longitudinal Study of Ageing study. Sci Rep 2016; 6:37248. [PMID: 27849048 PMCID: PMC5110955 DOI: 10.1038/srep37248] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/26/2016] [Indexed: 11/09/2022] Open
Abstract
Few risk tools have been proposed to quantify the long-term risk of diabetes among middle-aged and elderly individuals in China. The present study aimed to develop a risk tool to estimate the 20-year risk of developing diabetes while incorporating competing risks. A three-stage stratification random-clustering sampling procedure was conducted to ensure the representativeness of the Beijing elderly. We prospectively followed 1857 community residents aged 55 years and above who were free of diabetes at baseline examination. Sub-distribution hazards models were used to adjust for the competing risks of non-diabetes death. The cumulative incidence function of twenty-year diabetes event rates was 11.60% after adjusting for the competing risks of non-diabetes death. Age, body mass index, fasting plasma glucose, health status, and physical activity were selected to form the score. The area under the ROC curve (AUC) was 0.76 (95% Confidence Interval: 0.72-0.80), and the optimism-corrected AUC was 0.78 (95% Confidence Interval: 0.69-0.87) after internal validation by bootstrapping. The calibration plot showed that the actual diabetes risk was similar to the predicted risk. The cut-off value of the risk score was 19 points, marking mark the difference between low-risk and high-risk patients, which exhibited a sensitivity of 0.74 and specificity of 0.65.
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Affiliation(s)
- Xiangtong Liu
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Zhenghong Chen
- Beijing Neurosurgical Institute, Capital Medical University, 6, Tiantanxili, Beijing, 100050, China
| | - Jason Peter Fine
- Department of Biostatistics, University of North Carolina, Chapel Hill, 46200, NC, U.S.A.,Department of Statistics &Operations Research, University of North Carolina, Chapel Hill, 319200, NC, U.S.A
| | - Long Liu
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Anxin Wang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Jin Guo
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Lixin Tao
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Gehendra Mahara
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Kun Yang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Jie Zhang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Sijia Tian
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Haibin Li
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Kuo Liu
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Yanxia Luo
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Feng Zhang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Zhe Tang
- Beijing Geriatric Clinical and Research Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
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Murray E, Hekler EB, Andersson G, Collins LM, Doherty A, Hollis C, Rivera DE, West R, Wyatt JC. Evaluating Digital Health Interventions: Key Questions and Approaches. Am J Prev Med 2016; 51:843-851. [PMID: 27745684 PMCID: PMC5324832 DOI: 10.1016/j.amepre.2016.06.008] [Citation(s) in RCA: 388] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 06/13/2016] [Accepted: 06/13/2016] [Indexed: 12/16/2022]
Abstract
Digital health interventions have enormous potential as scalable tools to improve health and healthcare delivery by improving effectiveness, efficiency, accessibility, safety, and personalization. Achieving these improvements requires a cumulative knowledge base to inform development and deployment of digital health interventions. However, evaluations of digital health interventions present special challenges. This paper aims to examine these challenges and outline an evaluation strategy in terms of the research questions needed to appraise such interventions. As they are at the intersection of biomedical, behavioral, computing, and engineering research, methods drawn from all of these disciplines are required. Relevant research questions include defining the problem and the likely benefit of the digital health intervention, which in turn requires establishing the likely reach and uptake of the intervention, the causal model describing how the intervention will achieve its intended benefit, key components, and how they interact with one another, and estimating overall benefit in terms of effectiveness, cost effectiveness, and harms. Although RCTs are important for evaluation of effectiveness and cost effectiveness, they are best undertaken only when: (1) the intervention and its delivery package are stable; (2) these can be implemented with high fidelity; and (3) there is a reasonable likelihood that the overall benefits will be clinically meaningful (improved outcomes or equivalent outcomes at lower cost). Broadening the portfolio of research questions and evaluation methods will help with developing the necessary knowledge base to inform decisions on policy, practice, and research.
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Affiliation(s)
- Elizabeth Murray
- Research Department of Primary Care and Population Health, University College London, London, United Kingdom.
| | - Eric B Hekler
- Designing Health Lab, School of Nutrition and Health Promotion, Arizona State University, Phoenix, Arizona
| | - Gerhard Andersson
- Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden; Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Linda M Collins
- The Methodology Center and Department of Human Development and Family Studies, The Pennsylvania State University, State College, Pennsylvania
| | - Aiden Doherty
- MRC Clinical Trial Service Unit Hub, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Chris Hollis
- NIHR MindTech HTC, University of Nottingham, Nottingham, United Kingdom
| | - Daniel E Rivera
- School for the Engineering of Matter, Transport, and Energy, Ira A. Fulton Schools of Engineering, Arizona State University, Phoenix, Arizona
| | - Robert West
- Research Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Jeremy C Wyatt
- Wessex Institute, University of Southampton, Southampton, United Kingdom
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7
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Liu X, Fine JP, Chen Z, Liu L, Li X, Wang A, Guo J, Tao L, Mahara G, Tang Z, Guo X. Prediction of the 20-year incidence of diabetes in older Chinese: Application of the competing risk method in a longitudinal study. Medicine (Baltimore) 2016; 95:e5057. [PMID: 27749572 PMCID: PMC5059075 DOI: 10.1097/md.0000000000005057] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 09/08/2016] [Accepted: 09/10/2016] [Indexed: 11/26/2022] Open
Abstract
The competing risk method has become more acceptable for time-to-event data analysis because of its advantage over the standard Cox model in accounting for competing events in the risk set. This study aimed to construct a prediction model for diabetes using a subdistribution hazards model.We prospectively followed 1857 community residents who were aged ≥ 55 years, free of diabetes at baseline examination from August 1992 to December 2012. Diabetes was defined as a self-reported history of diabetes diagnosis, taking antidiabetic medicine, or having fasting plasma glucose (FPG) ≥ 7.0 mmol/L. A questionnaire was used to measure diabetes risk factors, including dietary habits, lifestyle, psychological factors, cognitive function, and physical condition. Gray test and a subdistribution hazards model were used to construct a prediction algorithm for 20-year risk of diabetes. Receiver operating characteristic (ROC) curves, bootstrap cross-validated Wolber concordance index (C-index) statistics, and calibration plots were used to assess model performance.During the 20-year follow-up period, 144 cases were documented for diabetes incidence with a median follow-up of 10.9 years (interquartile range: 8.0-15.3 years). The cumulative incidence function of 20-year diabetes incidence was 11.60% after adjusting for the competing risk of nondiabetes death. Gray test showed that body mass index, FPG, self-rated heath status, and physical activity were associated with the cumulative incidence function of diabetes after adjusting for age. Finally, 5 standard risk factors (poor self-rated health status [subdistribution hazard ratio (SHR) = 1.73, P = 0.005], less physical activity [SHR = 1.39, P = 0.047], 55-65 years old [SHR = 4.37, P < 0.001], overweight [SHR = 2.15, P < 0.001] or obesity [SHR = 1.96, P = 0.003], and impaired fasting glucose [IFG] [SHR = 1.99, P < 0.001]) were significantly associated with incident diabetes. Model performance was moderate to excellent, as indicated by its bootstrap cross-validated discrimination C-index (0.74, 95% CI: 0.70-0.79) and calibration plot.Poor self-rated health, physical inactivity, being 55 to 65 years of age, overweight/obesity, and IFG were significant predictors of incident diabetes. Early prevention with a goal of achieving optimal levels of all risk factors should become a key element of diabetes prevention.
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Affiliation(s)
- Xiangtong Liu
- School of Public Health, Capital Medical University
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, P.R. China
| | - Jason Peter Fine
- Department of Biostatistics
- Department of Statistics & Operations Research, University of North Carolina, Chapel Hill, USA
| | - Zhenghong Chen
- Beijing Neurosurgical Institute, Capital Medical University, Tiantanxili, Beijing, P.R. China
| | - Long Liu
- School of Public Health, Capital Medical University
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, P.R. China
| | - Xia Li
- The Graduate Entry Medical School, University of Limerick, Limerick, Ireland
| | - Anxin Wang
- School of Public Health, Capital Medical University
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, P.R. China
| | - Jin Guo
- School of Public Health, Capital Medical University
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, P.R. China
| | - Lixin Tao
- School of Public Health, Capital Medical University
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, P.R. China
| | - Gehendra Mahara
- School of Public Health, Capital Medical University
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, P.R. China
| | - Zhe Tang
- Beijing Geriatric Clinical and Research Center, Xuanwu Hospital, Capital Medical University, Beijing, P.R. China
| | - Xiuhua Guo
- School of Public Health, Capital Medical University
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, P.R. China
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Jørgensen P, Langhammer A, Krokstad S, Forsmo S. Diagnostic labelling influences self-rated health. A prospective cohort study: the HUNT Study, Norway. Fam Pract 2015; 32:492-9. [PMID: 26240089 PMCID: PMC4576760 DOI: 10.1093/fampra/cmv065] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Studies have shown an independent association between poor self-rated health (SRH) and increased mortality. Few studies, however, have investigated any possible impact on SRH of diagnostic labelling. OBJECTIVE To test whether SRH differed in persons with known and unknown hypothyroidism, diabetes mellitus (DM) or hypertension, opposed to persons without these conditions, after 11-year follow-up. METHODS Prospective population-based cohort study in North-Trøndelag County, Norway, HUNT2 (1995-97) to HUNT3 (2006-08). All inhabitants aged 20 years and older were invited. The response rate was 69.5% in HUNT2 and 54.1% in HUNT3. In total, 34144 persons aged 20-70 years were included in the study population. The outcome was poor SRH. RESULTS Persons with known disease had an increased odds ratio (OR) to report poor SRH at follow-up; figures ranging from 1.11 (0.68-1.79) to 2.52 (1.46-4.34) (men with hypothyroidism kept out owing to too few numbers). However, in persons not reporting, but having laboratory results indicating these diseases (unknown disease), no corresponding associations with SRH were found. Contrary, the OR for poor SRH in women with unknown hypothyroidism and unknown hypertension was 0.64 (0.38-1.06) and 0.89 (0.79-1.01), respectively. CONCLUSIONS Awareness opposed to ignorance of hypothyroidism, DM and hypertension seemed to be associated with poor perceived health, suggesting that diagnostic labelling could have a negative effect on SRH. This relationship needs to be tested more thoroughly in future research but should be kept in mind regarding the benefits of early diagnosing of diseases.
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Affiliation(s)
- Pål Jørgensen
- Department of Public Health and General Practice, Norwegian University of Science and Technology, 7489 Trondheim and
| | - Arnulf Langhammer
- Department of Public Health and General Practice, HUNT Research Centre, Norwegian University of Science and Technology, 7600 Levanger, Norway
| | - Steinar Krokstad
- Department of Public Health and General Practice, HUNT Research Centre, Norwegian University of Science and Technology, 7600 Levanger, Norway
| | - Siri Forsmo
- Department of Public Health and General Practice, Norwegian University of Science and Technology, 7489 Trondheim and
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