The effectiveness of the national hepatitis B vaccination program 25 years after its introduction in Iran: a historical cohort study

ABSTRACT Introduction: Hepatitis B virus (HBV) is responsible for one of the most common human viral infections. An estimated 257 million people are living with chronic HBV infection worldwide, and mortality has reached 900,000 deaths in recent years. In 2001, the World Health Organization reported a prevalence of chronic hepatitis B infection in Iran between 2-7%. Objective: To assess the effect of the national HBV mass vaccination program after 25 years. Methods: A retrospective cohort study was conducted in vaccinated and unvaccinated people according to the year of birth. Blood samples were obtained from each enrolled person and data about demographic variables, and medical and vaccination history were collected using a standardized questionnaire. Persons were considered uninfected if they were negative for both HBsAg and anti-HBc. Also, Vaccine effectiveness was measured by calculating the risk of disease among vaccinated and unvaccinated persons and defining the percentage risk reduction of infection in the vaccinated group. Results: A total of 2720 persons were interviewed. The rate of HBV breakthrough infection among the vaccinated group was significantly lower than in unvaccinated group. One hundred ninety-four cases with positive HBV markers of infection were identified. The risk ratio of HBV infection was 0.71, 95% CI: 0.54-0.94 (vaccinated/unvaccinated). The estimated vaccination effectiveness against Hepatitis B infection was 29% (95% CI: 6%-46%). Conclusions: Iran has successfully combined hepatitis B vaccination into regular immunization programs. The WHO goal of reducing HBsAg prevalence to an equivalent of 1% by 2020 has been reached. With respect to vaccination effectiveness and low prevalence of the disease in the country, catch-up hepatitis B vaccination programs for adolescents can guarantee the immunity of the population.

Determining endemic values of cutaneous leishmaniasis in Iranian Fars province by retrospectively detected clusters and receiver operating characteristic curve analysis

To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and the endemic values of cutaneous leishmaniasis were determined by retrospective clusters derived from spatiotemporal permutation modeling on a time-series design. The accuracy of the values was assessed using receiver operating characteristic (ROC) curve. SPSS version 22, ArcGIS, and ITSM 2002 software tools were used for analysis. Results: Nine statistically significant retrospective clusters (P<0.05) resulted in finding seven significant and accurate endemic values (P<0.1). These valid endemic scores were generalized to the other 18 cities based on 6 different climates in the province. Conclusions: Retrospectively detected clusters with the help of ROC curve analysis could help determine cutaneous leishmaniasis endemic values which are essential for future prediction and prevention policies in the area.

Establishment of an early warning system for cutaneous leishmaniasis in Fars province, Iran

Objective: To establish an early warning system for cutaneous leishmaniasis in Fars province, Iran in 2016. Methods: Time-series data were recorded from 29201 cutaneous leishmaniasis cases in 25 cities of Fars province from 2010 to 2015 and were used to fit and predict the cases using time-series models. Different models were compared via Akaike information criterion/Bayesian information criterion statistics, residual analysis, autocorrelation function, and partial autocorrelation function sample/model. To decide on an outbreak, four endemic scores were evaluated including mean, median, mean + 2 standard deviations, and median + interquartile range of the past five years. Patients whose symptoms of cutaneous leishmaniasis began from 1 January 2010 to 31 December 2015 were included, and there were no exclusion criteria. Results: Regarding four statistically significant endemic values, four different cutaneous leishmaniasis space-time outbreaks were detected in 2016. The accuracy of all four endemic values was statistically significant (P<0.05). Conclusions: This study presents a protocol to set early warning systems regarding time and space features of cutaneous leishmaniasis in four steps: (i) to define endemic values based on which we could verify if there is an outbreak, (ii) to set different time-series models to forecast cutaneous leishmaniasis in future, (iii) to compare the forecasts with endemic values and decide on space-time outbreaks, and (iv) to set an alarm to health managers.

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.

Spatiotemporal clustering of cutaneous leishmaniasis in Fars province,Iran

Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis (CL) in Fars province, Iran. Methods: Spatiotemporal cluster analysis was conducted retrospectively to find spatio-temporal clusters of CL cases.Time-series data were recorded from 29 201 cases in Fars province,Iran from 2010 to 2015,which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters.Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using SaTScan software. Results: This study resulted in statistically significant spatiotemporal clusters of CL (P<0.05).The most likely cluster contained 350 cases from 1 July 2010 to 30 November 2010. Besides, 5 secondary clusters were detected in different periods of time. Finally, statistically significant sub-clusters were found within the three large clusters(P<0.05). Conclusions: Transmission of CL followed spatiotemporal pattern in Fars province, Iran.This can have an important effect on future studies on prediction and prevention of CL.