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Exploring health literacy categories among an Iranian adult sample: a latent class analysis.

ABSTRACT: General and electronic health literacy are important factors engaging in healthy behaviors and maintaining good health. The present study explored demographic factors associated with general and electronic health literacy in the Iranian adult population. Via stratified cluster sampling, trained interviewers visited adult residents in Qazvin Province, Iran between January, and April 2022. The participants (N = 9775; mean age = 36.44 years; 6576 [67.3%] females) completed the Health Literacy Instrument for Adults (HELIA) assessing health literacy and the eHealth Literacy Scale (eHEALS) assessing electronic health literacy. Demographic data, including age, gender, educational level, marital status, and living location (city or rural), were collected. Latent class analysis (LCA) was used to classify the participants into different health literacy/electronic health literacy levels. The relationships between health literacy/electronic health literacy levels and demographic factors were examined using χ² or analysis of variance. The LCA used HELIA scores to suggest five classes of health literacy and eHEALS scores to suggest three classes of electronic health literacy. For general and electronic health literacy, similar relationships were with demographic factors: females as compared with males had better general/electronic health literacy; younger people as compared with older people had better general/electronic health literacy; higher educational level was associated with better general/electronic health literacy; and city residents as compared with rural residents had better general/electronic health literacy. In conclusion, Iranian governmental agencies may wish to target on males, older adults, people with low educational level, and rural residents to improve their health literacy.

SUBMITTER: Yekaninejad MS

PROVIDER: S-EPMC10774330 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Exploring health literacy categories among an Iranian adult sample: a latent class analysis.

Yekaninejad Mir Saeed MS Hajiheidari Ahmadreza A Alijanzadeh Mehran M Yahaghi Rafat R Karimi Zahra Z Rahmani Jalal J Yazdi Nahid N Jafari Elahe E Alijani Hashem H Zamani Narges N Fotuhi Razie R Taherkhani Elham E Buchali Zeinab Z Zarenejad Masoume M Mahmoudi Narges N Shahmahdi Najmeh N Poorzolfaghar Leila L Ahmadizade Safie S Shahbazkhania Azam A Potenza Marc N MN Lin Chung-Ying CY Pakpour Amir H AH

Scientific reports 20240108 1

General and electronic health literacy are important factors engaging in healthy behaviors and maintaining good health. The present study explored demographic factors associated with general and electronic health literacy in the Iranian adult population. Via stratified cluster sampling, trained interviewers visited adult residents in Qazvin Province, Iran between January, and April 2022. The participants (N = 9775; mean age = 36.44 years; 6576 [67.3%] females) completed the Health Literacy Instr ...[more]

PMID: 38191607

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Project description:BackgroundeHealth technologies have been found to facilitate health-promoting practices among cancer survivors with BMI in overweight or obese categories; however, little is known about their engagement with eHealth to promote weight management and facilitate patient-clinician communication.ObjectiveThe objective of this study was to determine whether eHealth use was associated with sociodemographic characteristics, as well as medical history and experiences (ie, patient-related factors) among cancer survivors with BMI in overweight or obese categories.MethodsData were analyzed from a nationally representative cross-sectional survey (National Cancer Institute's Health Information National Trends Survey). Latent class analysis was used to derive distinct classes among cancer survivors based on sociodemographic characteristics, medical attributes, and medical experiences. Logistic regression was used to examine whether class membership was associated with different eHealth practices.ResultsThree distinct classes of cancer survivors with BMI in overweight or obese categories emerged: younger with no comorbidities, younger with comorbidities, and older with comorbidities. Compared to the other classes, the younger with comorbidities class had the highest probability of identifying as female (73%) and Hispanic (46%) and feeling that clinicians did not address their concerns (75%). The older with comorbidities class was 6.5 times more likely than the younger with comorbidities class to share eHealth data with a clinician (odds ratio [OR] 6.53, 95% CI 1.08-39.43). In contrast, the younger with no comorbidities class had a higher likelihood of using a computer to look for health information (OR 1.93, 95% CI 1.10-3.38), using an electronic device to track progress toward a health-related goal (OR 2.02, 95% CI 1.08-3.79), and using the internet to watch health-related YouTube videos (OR 2.70, 95% CI 1.52-4.81) than the older with comorbidities class.ConclusionsClass membership was associated with different patterns of eHealth engagement, indicating the importance of tailored digital strategies for delivering effective care. Future eHealth weight loss interventions should investigate strategies to engage younger cancer survivors with comorbidities and address racial and ethnic disparities in eHealth use.

Project description:BackgroundGiven the high comorbidity and shared risk factors between depression and anxiety, whether they represent theoretically distinct disease entities or are just characteristics of a common negative affect dimension remains debated. Employing a data-driven and person-centered approach, the present study aims to identify meaningful and discrete symptom patterns of the occurrence of depression and anxiety.MethodsUsing data from an adult sample from the Japanese general population (n = 403, including 184 females, age = 42.28 ± 11.87 years), we applied latent class analysis to identify distinct symptom patterns of depression (PHQ-9) and anxiety (STAI Y1). To empirically validate the derived class memberships, we tested the association between the derived classes and personal profiles including childhood experiences, life events, and personality traits.ResultsThe best-fitting solution had four distinct symptom patterns or classes. Whereas both Class 1 and 2 had high depression, Class 1 showed high anxiety due to high anxiety-present symptoms (e.g., "I feel nervous") while Class 2 showed moderate anxiety due to few anxiety-absent symptoms (e.g., "I feel calm"). Class 3 manifested mild anxiety symptoms due to lacking responses on anxiety-absent items. Class 4 manifested the least depressive and anxiety-present symptoms as well as the most anxiety-absent symptoms. Importantly, whereas both Class 1 and 2 had higher childhood neglect and reduced reward responsiveness, etc. compared to Class 4 (i.e., the most healthy class), only Class 1 had greater negative affect and reported more negative life events.ConclusionsTo our knowledge, this is the first latent class analysis that examined the symptom patterns of depression and anxiety in Asian subjects. The classes we identified have distinct features that confirm their unique patterns of symptom endorsement. Our findings may provide insights into the etiology of depression, anxiety, and their comorbidity.

Project description:BackgroundAdult-onset Still's disease (AOSD) is a rare systemic autoinflammatory disease which encompasses patients with heterogenous presentation and a wide range of clinical courses. In this study, we aimed to identify potential subgroups of AOSD and reveal risk factors for relapse.MethodsWe included a total of 216 AOSD patients who received treatment in nine hospitals between 2000 and 2019. All patients fulfilled the Yamaguchi classification criteria. We retrospectively collected information about baseline characteristics, laboratory tests, treatment, relapse, and death. We performed latent class analysis and time-to-event analysis for relapse using the Cox proportional hazard model.ResultsThe median age at disease onset was 51.6 years. The median follow-up period was 36.8 months. At disease onset, 22.3% of the patients had macrophage activation syndrome. The median white blood cell count was 12,600/μL, and the median serum ferritin level was 7230 ng/mL. Systemic corticosteroids were administered in all but three patients (98.6%) and the median initial dosage of prednisolone was 40mg/day. Ninety-six patients (44.4%) were treated with concomitant immunosuppressants, and 22 (10.2%) were treated with biologics. Latent class analysis revealed that AOSD patients were divided into two subgroups: the typical group (Class 1: 71.8%) and the elderly-onset group (Class 2: 28.2%). During the follow-up period, 13 of 216 patients (6.0%) died (12 infections and one senility), and 76 of 216 patients (35.1%) experienced relapses. Overall and relapse-free survival rates at 5 years were 94.9% and 57.3%, respectively, and those rates were not significantly different between Class 1 and 2 (p=0.30 and p=0.19). Time-to-event analysis suggested higher neutrophil count, lower hemoglobin, and age ≥65 years at disease onset as risk factors for death and age ≥65 years at disease onset as a risk factor for relapse.ConclusionsAOSD patients were divided into two subgroups: the typical group and the elderly-onset group. Although the survival of patients with AOSD was generally good, the patients often experienced relapses. Age ≥65 years at disease onset was the risk factor for relapse.

Dataset Information

Exploring health literacy categories among an Iranian adult sample: a latent class analysis.

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Exploring health literacy categories among an Iranian adult sample: a latent class analysis.

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