Immunity to Hepatitis-A virus: A nationwide population-based seroprevalence study from Iran.

BACKGROUND: The mortality rate of acute Hepatitis A increases from 0.1% in the children to 1.2%, in the adults. Hepatitis A is efficiently prevented by HAV-vaccine, but the strategy for distributing this vaccine among countries is dependent on their level of immunity to HAV. This study aimed to detect the level of immunity to HAV in Iran. METHODS: In this population-based seroprevalence study, 5419 participants from 12 of provinces of Iran, including 57 urban and 120 rural areas were chosen through a multi-stage cluster random sampling. Participants were interviewed by filling checklists and 3 cc of blood sample was obtained from each of them. IBM SPSS statistics V.21 software was used for univariable and multivariable analysis of data. RESULTS: Mean of age of Interviewees was 26.4 ± 16 years, ranging from 1 to 94 years with a male to female ratio 1.02. Overall, 3603 (66.5%) of subjects were seropositive for HAV-IgG. Among the age groups, 41.1% of children by the age 15 years and 82.6% of adults around 30 years old were immune to HAV. The Mid-point age of population immunity was 21 years. Residents of the borders of the country, people who had less access to the safe water or sanitary toilet, individuals with low socioeconomic status and persons who were a member of dense families had the most probability of seropositivity. CONCLUSIONS: This study showed that Iran is among HAV low endemic countries and vaccination against HAV is recommended only in the high-risk population, including patients with chronic liver diseases, patients with coagulopathy, travelers to the high endemic areas, and homosexuals. Establishment of national HAV surveillance system, concerning of health system about the occurrence of the HAV outbreaks, implementation of harm reduction strategies, improving economic indices and sanitation and access to the safe water in the deprived regions is recommended.

Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis.

OBJECTIVES: To predict the occurrence of zoonotic cutaneous leishmaniasis (ZCL) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. METHODS: The Box-Jenkins approach was applied to fit the SARIMA model for ZCL incidence from 2004 to 2015. Then the model was used to predict the number of ZCL cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with ZCL incidence. RESULTS: SARIMA(2,0,0) (2,1,0)12 was the preferred model for predicting ZCL incidence in the east of Fars province (validation Root Mean Square Error, RMSE = 0.27). It showed that ZCL incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of SARIMA models was improved by the inclusion of rainfall at a lag of 2 months (ß = -0.02), rainy days at a lag of 2 months (ß = -0.09) and relative humidity at a lag of 8 months (ß = 0.13) as external regressors (P-values < 0.05). The latter was the best climatic variable for predicting ZCL cases (validation RMSE = 0.26). CONCLUSIONS: Time series models can be useful tools to predict the trend of ZCL in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision.

Does prospective permutation scan statistics work well with cutaneous leishmaniais as a high-frequency or malaria as a low-frequency infection in Fars province, Iran?

Objective: To determine whether permutation scan statistics was more efficient in finding prospective spatial-temporal outbreaks for cutaneous leishmaniasis (CL) or for malaria in Fars province, Iran in 2016.Methods: Using time-series data including 29177 CL cases recorded during 2010-2015 and 357 malaria cases recorded during 2010-2015, CL and malaria cases were predicted in 2016. Predicted cases were used to verify if they followed uniform distribution over time and space using space-time analysis. To testify the uniformity of distributions, permutation scan statistics was applied prospectively to detect statistically significant and non-significant outbreaks. Finally, the findings were compared to determine whether permutation scan statistics worked better for CL or for malaria in the area. Prospective permutation scan modeling was performed using SatScan software.Results: A total of 5359 CL and 23 malaria cases were predicted in 2016 using time-series models. Applied time-series models were well-fitted regarding auto correlation function, partial auto correlation function sample/model, and residual analysis criteria (Pv was set to 0.1). The results indicated two significant prospective spatial-temporal outbreaks for CL (P<0.5) including Most Likely Clusters, and one non-significant outbreak for malaria (P>0.5) in the area.Conclusions: Both CL and malaria follow a space-time trend in the area, but prospective permutation scan modeling works better for detecting CL spatial-temporal outbreaks. It is not far away from expectation since clusters are defined as accumulation of cases in specified times and places. Although this method seems to work better with finding the outbreaks of a high-frequency disease;i.e., CL, it is able to find non-significant outbreaks. This is clinically important for both high- and low-frequency infections;i.e., CL and malaria.

Nested polymerase chain reaction and sequence- based detection of leishmania infection of sand flies in recently emerged endemic focus of zoonotic cutaneous leishmaniasis, southern iran.

BACKGROUND: Geographical distribution of zoonotic cutaneous leishmaniasis (ZCL) has continuously been extended in recent years in Iran. The Beiza District is one of the newly-emerged endemic foci of ZCL in southern Iran. The main aim of the present study was to detect the vector(s) of ZCL in this area. METHODS: To detect the fauna and vectors of ZCL in this district, sand flies were caught using sticky papers. Seventy randomly selected female sand flies out of 730 were molecularly investigated for Leishmania infection using species-specific nested polymerase chain reaction (PCR) assay between April and October 2010. RESULTS: A total of 2543 sand flies were caught. The fauna was identified as 10 species (five Phlebotomus spp. and five Sergentomyia spp.). Phlebotomus papatasi was the most dominant species both indoors and outdoors (37.55% and 16.35 %, respectively). L. major was detected in 5 out of 48 investigated Phlebotomus papatasi (10.41%). Sequence-based characterization was carried out to confirm the PCR findings. The positive samples were shown to have 75-88% similarity with L. major sequences in GenBank. CONCLUSION: According to the findings of the present study, similar to the other foci of ZCL in Iran, P. papatasi is the proven and primary vector of CL. This study could be drawn upon for future strategy planning in this newly emerged endemic focus.

Spatial analysis of eco-environmental risk factors of cutaneous leishmaniasis in Southern Iran.

BACKGROUND: Despite the advances in the diagnosis and treatment of leishmaniasis, it is still considered as a severe public health problem particularly in developing countries and a great economic burden on the health resources. The present study was designed and conducted to determine the eco-environmental characteristics of the leishmaniasis disease by spatial analysis. MATERIALS AND METHODS: In an ecological study, data were collected on eco-environmental factors of Fars province in Iran and on cutaneous leishmaniasis (CL) cases from 2002 to 2009. geographic weighted regression (GWR) was used to analyse the data and compare them with ordinary least square (OLS) regression model results. Moran's Index was applied for analysis of spatial autocorrelation in residual of OLS. P value less than 0.05 was considered as significant and adjusted R(2) was used for model preferences. RESULTS: There was a significant spatial autocorrelation in the residuals of OLS model (Z=2.45, P=0.014). GWR showed that rainy days, minimum temperature, wind velocity, maximum relative humidity and population density were the most important eco-environmental risk factors and explained 0.388 of the associated factors of CL. CONCLUSION: Spatial analysis can be a good tool for detection and prediction of CL disease. In autocorrelated and non-stationary data, GWR model yields a better fitness than OLS regression model. Also, population density can be used as a surrogate variable of acquired immunity and increase the adjusted R(2).