Farhang Hooshmand; Vahid Rahmanian; Mohammad Shojaei; Karamatollah Rahmanian
Abstract
Background: The overall prevalence of metabolically unhealthy (MU) phenotype in Iranian adults is a matter of debate. This study aimed to estimate the prevalence and determinants of metabolically unhealthystate in people over 30 years old in the general population in Southern Iran.Methods: In this cross-sectional ...
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Background: The overall prevalence of metabolically unhealthy (MU) phenotype in Iranian adults is a matter of debate. This study aimed to estimate the prevalence and determinants of metabolically unhealthystate in people over 30 years old in the general population in Southern Iran.Methods: In this cross-sectional population-based study, 891 participants aged ≥30 were selected using a multi-stage cluster sampling method. The study examined age, sex, education, marital status, smoking behavior, weight, height, blood pressure, fasting blood sugar, and lipid profiles. MU was defined as the existence of at least two of four constituents of metabolic abnormalities based on ATP III criteria. Data analysis was carried out in Stata version 14. Finally, a logistic regression was performed to identify the risk factors for MU prevalence.Results: The overall prevalence of MU was 49.4%, corresponding to 37.5%, 55.6%, and 60.2% of normal weight, overweight, and obese participants, respectively. MU prevalence significantly increased from 30.6% in participants aged 30-39 years to 69.7% in participants aged 60 years or older. The results of multivariate logistic regression showed that dyslipidemia (OR=2.98, CI95%:2.13-4.16), high LDL (OR=2.73, CI95%:1.77-4.20), obesity (OR=2.83, CI95%:1.84-4.36), overweight (OR=2.13, CI95%:1.53- 2.98), and higher age (OR=1.04, CI95%:1.03-1.05) was positively associated with the MU state.Conclusion: Metabolically unhealthy state is a public health problem in the study area. In terms of public health, screening for obesity and other metabolic disorders should be regularly performed in clinical practice to take appropriate preventive measures.
Vahid Rahmanian; Mohammad Jokar; Elham Mansoorian
Abstract
Background: Through the fifth wave of the Covid-19 outbreak in Jahrom, the fatality and incidence of the virus increased. The quick spread of infection is one of the causes of this dreadful situation. Therefore, recognizing the future epidemic trend can be a useful instrument to decrease mortality and ...
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Background: Through the fifth wave of the Covid-19 outbreak in Jahrom, the fatality and incidence of the virus increased. The quick spread of infection is one of the causes of this dreadful situation. Therefore, recognizing the future epidemic trend can be a useful instrument to decrease mortality and morbidity. This study aimed to determine the time trends and select the best model to predict the sixth wave of the COVID-19 outbreak using ARIMA models.Methods: We used daily data of 9533 hospital cases (Suspected and PCR-confirmed COVID-19 cases) between 4th March 2020 and 31st December 2021. Nine different ARIMA models were fitted to our data. Autocorrelation functions (ACF) and partial autocorrelation (PACF) plots were used to determine model parameters. Likelihood-ratio test for comparison of the reduced and full model was used. In addition, Akaike Information Criteria (AIC) was also used to choose the final model. Data were analyzed by STATA 14 software with a significant level of 0.05.Results: The ARIMA (3, 0, 3) model was selected among the potential models, with lower AIC (999) and MAPE (3.18%) values. This model showed that the daily number of hospitalized patients may increase from 5.85 (2.16-15.79) to 8.55 (1.47-49.48) in two months. By March 01, 2022, the predictable daily hospitalized cases could reach 468.36 (03.79-2209.88).Conclusion: Time series models is a useful tool for predictingthe hospitals’ admission trend during an epidemic. Thus, they can be used as early warning models in the readiness of hospital systems during epidemics.
Vahid Rahmanian; Saied Bokaie; Aliakbar Haghdoost; Mohsen Barouni
Abstract
Background: Visceral leishmaniasis (VL) is a neglected infection currently occurring in some regions of Europe, Asia, Africa, and America. This study was an attempt to determine the temporal patterns of VL from January 2000 to December 2019 in the Ardabil Province of north-western Iran using the Markov ...
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Background: Visceral leishmaniasis (VL) is a neglected infection currently occurring in some regions of Europe, Asia, Africa, and America. This study was an attempt to determine the temporal patterns of VL from January 2000 to December 2019 in the Ardabil Province of north-western Iran using the Markov Switching Models (MSM).Methods: This descriptive study used monthly data of 602 VL cases during the study period. The data were provided by the Leishmaniasis National Surveillance System (LNSS), the Iran Meteorological Organization (IMO), and Space Agency (SA), and two states were considered for such modelling. Given the Akaike and Bayesian information criterion, the two-state MSM with a five-month lag is an appropriate model.Results: The MSM showed that the probability of staying in the non-epidemic state is 67%, (P11), while that of staying in an epidemic state is 93% (P22). The mean absolute percentage error (MAPE) was 31.63%, and the portmanteau test (Q=19.03, P=0.66) for the residuals of the selected model revealed that the data were completely modelled. The total VL cases in the next 24 months forecasted 14 cases.Conclusion: The MSM has a relatively acceptable predictive power and is useful in planning future interventions with more information about different stages of the epidemic it provides to policymakers for early warning of epidemics.
Vahid Rahmanian; Farhang Hooshmand; Razieh Zahedi; Narges Rahmanian; Seyede Somayeh Hoseini; Zeynab Sahraian; Maryam Chegeni
Abstract
Background: Currently, COVID-19 is a global public health problem. This study aimed to estimate the seroprevalence of antibodies related to Covid-19 in the general population in southern Iran.Methods: This cross-sectional population-based study of the seroepidemiological type investigated the serological ...
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Background: Currently, COVID-19 is a global public health problem. This study aimed to estimate the seroprevalence of antibodies related to Covid-19 in the general population in southern Iran.Methods: This cross-sectional population-based study of the seroepidemiological type investigated the serological prevalence of COVID-19 from October to December 2020 in Jahrom, Fars province, Iran. A total of 612 participants were selected using multistage cluster random sampling regardless of age or gender. The dataset in the study included the participants’ demographic information, the history of exposure to COVID-19 patients, the history of PCR tests, and the history of COVID-19 symptoms in previous months. In addition, this study examined the raw and survey weight adjusted estimates with Stata version 14. Finally, logistic regression was performed to identify risk factors for serum prevalence.Results: The participants’ mean age was 38.88±13.91 and the majority were 30 to 49 years (51.4%), with a female preponderance (58.7%). The estimated adjusted seroprevalence was 32.66 (95%CI: 28.93-36.63), with 207 positive cases for either IgG or IgM. The results of multivariable logistic regression showed that seropositivity in the participants was 4.95 times more likely associated with a history of positive PCR test (OR: 4.95, 95%CI: 2.46-10.90) and 2.14 times in patients with a history of muscle pain in previous months (OR: 2.14, 95%CI: 1.03-4.47).Conclusion: The actual number of patients with COVID-19 is significantly higher than the number of cases confirmed by the disease monitoring system based on PCR tests. Therefore, tracking individuals’ contact with confirmed patients using extensive testing and segregation of asymptomatic patients can help control the epidemic.
