Jorjani Biomedicine Journal

Background & Objective: Inability to measure exact exposure in epidemiological studies is a common problem in many studies, especially cross-sectional studies. Depending on the extent of misclassification, results may be affected. Existing methods for solving this problem require a lot of time and money and it is not practical for some of the exposures. Recently, new methods have been proposed in 1:1 matched case–control studies that have solved these problems to some extent. In the present study we have aimed to extend the existing Bayesian method to adjust for misclassification in matched case–control Studies with 1:2 matching.

Methods: Here, the standard Dirichlet prior distribution for a multinomial model was extended to allow the data of exposure–disease (OR) parameter to be imported into the model excluding other parameters. Information that exist in literature about association between exposure and disease were used as prior information about OR. In order to correct the misclassification Sensitivity Analysis was accomplished and the results were obtained under three Bayesian Methods.

Results: The results of naïve Bayesian model were similar to the classic model. The second Bayesian model by employing prior information about the OR, was heavily affected by these information.

The third proposed model provides maximum bias adjustment for the risk of heavy metals, smoking and drug abuse. This model showed that heavy metals are not an important risk factor although raw model (logistic regression Classic) detected this exposure as an influencing factor on the incidence of lung cancer. Sensitivity analysis showed that third model is robust regarding to different levels of Sensitivity and Specificity.

Conclusion: The present study showed that although in most of exposures the results of the second and third model were similar but the proposed model would be able to correct the misclassification to some extent.

Background and Objective: One of the important and common applications of telemedicine is the diagnosis and treatment of skin diseases. However, teledermatology requires further commitments to provide standard services and appropriate electronic data transfer for reliable diagnostics to comply with technical standards and legal requirements. The present study aimed to determine the factors affecting the establishment of Teledermatology in the qualitative and quantitative sections .
Material and Methods: A mixed-method study (including quantitative and qualitative sections) was conducted in 2018. In the qualitative section, experts were selected by purposive sampling and the data were collected through 2 focus group discussions and 6 semi-structured interviews. The content of sessions and interviews were transcribed and then analyzed by the content analysis method. In the quantitative section, 384 participants were selected randomly. The data were collected using a researcher-made questionnaire whose validity and reliability were confirmed. Data analyses were performed in SPSS.
Results: A total of 8 categories and 58 subcategories were extracted. The 8 categories of the factors affecting the establishment of teledermatology in Iran included service provider, service recipient, structural, technological, economic, policymaking, legal, and cultural. Cultural factors (3.95±0.77) and policymaking factors (3.22±0.84), respectively, had the highest and the lowest effects on the establishment of teledermatology in Iran.
Conclusion: Factors affecting the establishment of Teledermatology are diverse and extensive, which the most effective factors were cultural factors, technological factors and factors related to service providers. These findings can help the policymakers and managers interested in the establishment of teledermatology in Iran.