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BackgroundHepatitis B virus (HBV) and hepatitis C virus (HCV) infections are common among individuals with human immune deficiency virus (HIV) infection worldwide. In this study, we did a systematic review and meta-analysis of the published literature to estimate the global and regional prevalence of HCV, HBV and HIV coinfections among HIV-positive prisoners.MethodsWe searched PubMed via MEDLINE, Embase, the Cochrane Library, SCOPUS, and Web of science (ISI) to identify studies that reported the prevalence of HBV and HCV among prisoners living with HIV. We used an eight-item checklist for critically appraisal studies of prevalence/incidence of a health problem to assess the quality of publications in the included 48 cross-sectional and 4 cohort studies. We used random-effect models and meta-regression for the meta-analysis of the results of the included studies.ResultsThe number of the included studies were 50 for HCV-HIV, and 23 for HBV-HIV co-infections. The pooled prevalence rates of the coinfections were 12% [95% confidence interval (CI) 9.0-16.0] for HBV-HIV and 62% (95% CI 53.0-71.0) for HCV-HIV. Among HIV-positive prisoners who reported drug injection, the prevalence of HBV increased to 15% (95% CI 5.0-23.0), and the HCV prevalence increased to 78% (95% CI 51.0-100). The prevalence of HBV-HIV coinfection among prisoners ranged from 3% in the East Mediterranean region to 27% in the American region. Also, the prevalence of HCV-HIV coinfections among prisoners ranged from 6% in Europe to 98% in the East Mediterranean regions.ConclusionsOur findings suggested that the high prevalence of HBV and HCV co-infection among HIV-positive prisoners, particularly among those with a history of drug injection, varies significantly across the globe. The results of Meta-regression analysis showed a sliding increase in the prevalence of the studied co-infections among prisoners over the past decades, rising a call for better screening and treatment programs targeting this high-risk population. To prevent the above coinfections among prisoners, aimed public health services (e.g. harm reduction via access to clean needles), human rights, equity, and ethics are to be seriously delivered or practiced in prisons. Protocol registration number: CRD42018115707 (in the PROSPERO international).

The growing fetus is very sensitive to environmental conditions. There is limited and conflicting evidence about the short-term effects of exposure to air pollutants on the pregnancy outcome. In this time-stratified case-crossover study, the effect of several air pollutants (i.e. O3, CO, NO2, SO2, and PM2.5) on the occurrence of stillbirth was evaluated in Tehran (the capital of Iran) between December 2018 and March 2023. Using a quasi-Poisson regression model and distributed lag nonlinear models (DLNM), we estimated the effect of exposure to air pollutants measured as lags (0 to 7 days) and cumulative average days (0–2, 0–6, and 0–14-day lag) before delivery on stillbirth. The association was adjusted for potential confounding factors including meteorological factors. During the study period in Tehran, 5311 stillbirths were reported. In single-pollutant models, during the entire year, SO2 (lag 1 day) and NO2 (lag 2 days) were found to have a direct and significant relationship with stillbirth. In the warm seasons, we found direct and inverse relationships between NO2 (lag 2 days) and PM2.5 (lag 4 days), respectively. In cold seasons, PM 2.5 (lag 1 day) and cumulative lag (0–2 days), SO2 (lag 1 day), and cumulative lag (0–2 and 0–6 days) were found to have direct and significant relationships with stillbirth. In two-pollutant models, SO2 & CO, and SO2 & O3, direct and significant associations were observed between SO2 exposures and stillbirth for the entire year. A similar pattern was observed for PM2.5 in combinations with NO2, and O3 and for NO2 in the two-pollutant model (O3 & NO2). However, in warm seasons, inverse associations were observed between PM2.5 and stillbirth in combinations with O3, NO2, and SO2, while NO2 showed a direct association in combinations with PM2.5, CO, and O3. In the cold seasons, direct and significant associations were observed between SO2 and stillbirth in model combinations with CO, O3, and NO2. This relationship was observed for PM2.5 in combination with CO, and NO2. Also, for CO in the two-pollutant model of CO & NO2. As a result, this study showed evidence of a relationship between short-term exposure to ambient air pollution before birth, especially SO2, NO2, PM2.5, and CO with increased risk of stillbirth. [ABSTRACT FROM AUTHOR]

Abstract Background Nosocomial infection (NI) or cross-infection is a major health problem in hospitals worldwide. Aim This study aimed to report the status of NIs and to evaluate the Iranian nosocomial infection surveillance system (INISS) in a teaching hospital in the south of Iran. Methods This is a comparative historical study on the records of hospital admitted patients from 2018 to 2019. Data on patients who were diagnosed with NI was extracted from the INISS database. The database includes data on the incidence of different types of NIs in each hospital ward, the patient’s infection outcome, the agents involved, and the site of infection. Results The results indicated that the rate of NI (cases of NI/ 100 admissions) in the hospital was %2.95. The highest rate of NIs was reported from ICUs. Of the infected patients, 45.61% were female, 98.95% had underlying diseases, and 30.88% died due to nosocomial infections. The median (IQR) of the duration of hospital stay among infected patients was 13 (7–18). The most common site of infection was VAE (ventilator-associated events) (39.40%) and the most common isolated agent, irrespective of the organ involved, was Acinetobacter (spp.) (22.75%). Conclusions We reported ICU and Acinetobacter (spp.) as the most affected ward and most common agent involved in recorded NIs respectively. The rate of NI in the study hospital was exceptionally low when compared to its counterparts in a few other developed countries. The INISS needs to be further evaluated with regard to the completeness and representativeness of the surveillance system. Also, we need to evaluate the adherence to the INISS guidelines among staff and physicians in reporting the NIs.

Background: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronavirus disease 19 (COVID-19) using machine learning (ML) techniques in Shiraz, Iran. Study Design: An ecological study. Methods: In this ecological research, three main ML techniques, including decision trees, random forest, and extreme gradient boosting (XGBoost), have been applied to correlate meteorological parameters and air pollutants with infection transmission, hospitalization, and death due to COVID-19 from 1 October 2020 to 1 March 2022. These parameters and pollutants included particulate matter (PM2), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), ozone (O3), carbon monoxide (CO), temperature (T), relative humidity (RH), dew point (DP), air pressure (AP), and wind speed (WS). Results: Based on the three ML techniques, NO2 (lag 5 day), CO (lag 4), and T (lag 25) were the most important environmental features affecting the spread of COVID-19 infection. In addition, the most important features contributing to hospitalization due to COVID-19 included RH (lag 28), T (lag 11), and O3 (lag 10). After adjusting for the number of infections, the most important features affecting the number of deaths caused by COVID-19 were NO2 (lag 20), O3 (lag 22), and NO (lag 23). Conclusion: Our findings suggested that epidemics caused by COVID-19 and (possibly) similarly viral transmitted infections, including flu, air pollutants, and meteorological parameters, can be used to predict their burden on the community and health system. In addition, meteorological and air quality data should be included in preventive measures. [ABSTRACT FROM AUTHOR]