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To examine the association between individual social capital and depression in older adults in Iran and to test the hypothesis that higher levels of social capital are inversely associated with depressive symptoms.
Cross-sectional study using baseline data from a longitudinal cohort.
Community-based study conducted in primary care settings across urban and rural areas of Birjand County, Eastern Iran.
A total of 1348 community-dwelling individuals aged 60 years and older were recruited through multistage stratified cluster random sampling. Participants who were bedridden or had end-stage disease (life expectancy
The primary outcome was depression status, measured using the Patient Health Questionnaire 9 items, with a score≥10 indicating depression. The main explanatory variable was social capital, assessed using a validated 69-item questionnaire capturing domains such as collective activity, social trust and network structure. Univariable and multivariable logistic regression analyses were conducted to estimate adjusted ORs and 95% CIs for associations between depression and social capital dimensions. Statistical analyses were performed using Stata V.12.0
Of the total participants, 268 (19.94%) were identified as having depressive symptoms, with a significantly higher prevalence among women (27.44%) compared with men (11.88%). Depression was more prevalent among those in the lowest wealth quintile (32.09%) and individuals with low literacy levels (28.10%). Participation in collective activities was inversely associated with depression in the second (OR=0.62, 95% CI (0.42 to 0.93)), third (OR=0.45, 95% CI (0.29 to 0.71)), fourth (OR=0.59, 95% CI (0.37 to 0.93)) and fifth (OR=0.37, 95% CI (0.22 to 0.61)) quintiles. Social trust was also associated with lower odds of depression in the third (OR=0.62, 95% CI (0.39 to 0.99)) and fourth (OR=0.64, 95% CI (0.42 to 0.97)) quintiles. Furthermore, the second (OR=0.63, 95% CI (0.40 to 0.99)) and fifth (OR=0.38, 95% CI (0.23 to 0.63)) quintiles of social network structure were inversely related to depression. These findings suggest that higher levels of social capital, particularly in terms of collective participation, trust and social networks, are associated with a reduced likelihood of depressive symptoms in older adults.
Higher levels of social capital, particularly collective engagement, interpersonal trust and diverse social networks, are associated with lower odds of depression in older adults. These findings support the need for community-based interventions to strengthen social capital as a strategy for mental health promotion among the elderly in low-income and middle-income settings.
The COVID-19 outbreak at the end of 2019 severely impacted global healthcare systems, especially primary healthcare services. This paper aimed to identify the implications derived from the strengths and weaknesses observed in Iran’s primary healthcare (PHC) programmes during the pandemic.
This was a qualitative study conducted in 2021. 13 semistructured interviews were held with Iranian healthcare policymakers and executive managers, selected via snowball sampling, using the World Health Organization’s analytical framework. Finally, a thematic analysis was conducted on the interview data.
The thematic analysis of the findings yielded five major themes: revision of healthcare financing, redefining education and research in primary healthcare, redefinition of primary healthcare, development of a new model for family medicine, and community engagement.
Addressing vertical inequality in Iran’s healthcare system was delineated to be crucial. Meanwhile, multiple strategies including enhancing family physicians’ knowledge and skills, decentralising decision-making, empowering them and involving communities in healthcare planning were presented to improve PHC and family medicine. Further empirical research is needed.
by Vahid Sadeghi, Alireza Mehridehnavi, Maryam Behdad, Alireza Vard, Mina Omrani, Mohsen Sharifi, Yasaman Sanahmadi, Niloufar Teyfouri
A considerable amount of undesirable factors in the wireless capsule endoscopy (WCE) procedure hinder the proper visualization of the small bowel and take gastroenterologists more time to review. Objective quantitative assessment of different bowel preparation paradigms and saving the physician reviewing time motivated us to present an automatic low-cost statistical model for automatically segmenting of clean and contaminated regions in the WCE images. In the model construction phase, only 20 manually pixel-labeled images have been used from the normal and reduced mucosal view classes of the Kvasir capsule endoscopy dataset. In addition to calculating prior probability, two different probabilistic tri-variate Gaussian distribution models (GDMs) with unique mean vectors and covariance matrices have been fitted to the concatenated RGB color pixel intensity values of clean and contaminated regions separately. Applying the Bayes rule, the membership probability of every pixel of the input test image to each of the two classes is evaluated. The robustness has been evaluated using 5 trials; in each round, from the total number of 2000 randomly selected images, 20 and 1980 images have been used for model construction and evaluation modes, respectively. Our experimental results indicate that accuracy, precision, specificity, sensitivity, area under the receiver operating characteristic curve (AUROC), dice similarity coefficient (DSC), and intersection over union (IOU) are 0.89 ± 0.07, 0.91 ± 0.07, 0.73 ± 0.20, 0.90 ± 0.12, 0.92 ± 0.06, 0.92 ± 0.05 and 0.86 ± 0.09, respectively. The presented scheme is easy to deploy for objectively assessing small bowel cleansing score, comparing different bowel preparation paradigms, and decreasing the inspection time. The results from the SEE-AI project dataset and CECleanliness database proved that the proposed scheme has good adaptability.
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