RESUMEN
BACKGROUND: Congenital hypothyroidism (CH) is one of the most prevalent preventable causes of mental retardation. Studies show that the incidence rate of CH is very high in Iran. Disease mapping is a tool for visually expressing the frequency, incidence, or relative risk of illness. The present study aimed to model CH counts considering the effects of the neighborhood in towns and perform mapping based on the relative risk. METHODS: In this historical cohort study, data of all neonates diagnosed with CH with TSH level ≥5 mIU/L between March 21, 2017, and March 20, 2018, in health centers in Guilan, Iran were used. The number of neonates with CH was zero in most towns of Guilan Province. The Bayesian spatial zero-inflated Poisson (ZIP) regression model was employed to investigate the effect of the town's neighborhood on the relative risk of CH incidence. Then, the map of the posterior mean of the relative risk for CH incidence was provided. The analysis was performed using OpenBUGS and Arc GIS software programs. RESULTS: The relative risk of CH incidence was high in the West of Guilan. Moreover, the goodness-of-fit criterion indicated that it is more appropriate to fit the Bayesian spatial ZIP model to these data than the common model. CONCLUSIONS: Considering the high relative risk of CH in the Western towns of Guilan Province, it is better to check important risk factors in this region.
RESUMEN
BACKGROUND AND AIM: Considering the importance of non-specific low back pain (NSLBP) and its increasing spread, the need for instruments for the accurate diagnosis of back pain is evident in order to offer more effective treatment. One such instrument is the STarT Back Screening Tool (STarT) which is examined by numerous studies, while some of its psychometric dimensions still require attention. Therefore, the objective of this study was to assess the internal consistency and construct validity of this questionnaire to propose a modified version. METHOD: In this cross-sectional study, the data of 295 patients with NSLBP were analyzed. Cronbach's alpha was used to assess internal consistency, and exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed to assess construct validity. The Χ2/df, GFI, CFI, and RMSEA indices were also utilized as the goodness-of-fit criteria. Data analysis was performed in SPSS, AMOS, and EQS programs. RESULTS: Goodness-of-fit indicators were calculated for the original Persian version of the questionnaire, showing an improper fit (RMSEA = 0.162). According to the measures of sampling adequacy (MSA) of the questions, Questions 1 and 8 were deleted, resulting in an improved index (RMSEA = 0.062). All the regression coefficients in the CFA model were significant (p < 0.001for all 7 parameters). CONCLUSION: Based on the results, the modified Persian version of the STarT is simpler and more practical than the previous version, serving as a valid and reliable tool for assessing patients with low back pain. With respect to the goodness-of-fit indices, we recommend that more studies with larger samples be conducted on different populations.