ABSTRACT
OBJECTIVES@#Spatial information makes a crucial contribution to enhancing and monitoring the brucellosis surveillance system by facilitating the timely diagnosis and treatment of brucellosis. @*METHODS@#An exponential scan statistic model was used to formalize the spatial distribution of the adjusted delay in the diagnosis time of brucellosis (time between onset and diagnosis of the disease) in Kurdistan Province, Iran. Logistic regression analysis was used to compare variables of interest between the clustered and non-clustered areas. @*RESULTS@#The spatial distribution of clusters of human brucellosis cases with delayed diagnoses was not random in Kurdistan Province. The mean survival time (i.e., time between symptom onset and diagnosis) was 4.02 months for the short spatial cluster, which was centered around the city of Baneh, and was 4.21 months for spatiotemporal clusters centered around the cities of Baneh and Qorveh. Similarly, the mean survival time for the long spatial and spatiotemporal clusters was 6.56 months and 15.69 months, respectively. The spatial distribution of the cases inside and outside of clusters differed in terms of livestock vaccination, residence, sex, and occupational variables. @*CONCLUSIONS@#The cluster pattern of brucellosis cases with delayed diagnoses indicated poor performance of the surveillance system in Kurdistan Province. Accordingly, targeted and multi-faceted approaches should be implemented to improve the brucellosis surveillance system and to reduce the number of lost days caused by delays in the diagnosis of brucellosis, which can lead to long-term and serious complications in patients.
ABSTRACT
OBJECTIVES@#Spatial information makes a crucial contribution to enhancing and monitoring the brucellosis surveillance system by facilitating the timely diagnosis and treatment of brucellosis. @*METHODS@#An exponential scan statistic model was used to formalize the spatial distribution of the adjusted delay in the diagnosis time of brucellosis (time between onset and diagnosis of the disease) in Kurdistan Province, Iran. Logistic regression analysis was used to compare variables of interest between the clustered and non-clustered areas. @*RESULTS@#The spatial distribution of clusters of human brucellosis cases with delayed diagnoses was not random in Kurdistan Province. The mean survival time (i.e., time between symptom onset and diagnosis) was 4.02 months for the short spatial cluster, which was centered around the city of Baneh, and was 4.21 months for spatiotemporal clusters centered around the cities of Baneh and Qorveh. Similarly, the mean survival time for the long spatial and spatiotemporal clusters was 6.56 months and 15.69 months, respectively. The spatial distribution of the cases inside and outside of clusters differed in terms of livestock vaccination, residence, sex, and occupational variables. @*CONCLUSIONS@#The cluster pattern of brucellosis cases with delayed diagnoses indicated poor performance of the surveillance system in Kurdistan Province. Accordingly, targeted and multi-faceted approaches should be implemented to improve the brucellosis surveillance system and to reduce the number of lost days caused by delays in the diagnosis of brucellosis, which can lead to long-term and serious complications in patients.
ABSTRACT
Background: This study was conducted to detect clusters of pulmonary TB cases in Hamadan Province, west of Iran
Methods: All patients with pulmonary tuberculosis recorded in the surveillance system from 2005 to 2013 were studied. The spatial scan statistic was used to detect significant clusters in status of unadjusted and adjusted for age, sex and location residence variables
Results: Clusters with high rate for both purely spatial and space-time analyses were seen in the same geographical areas composed of four city of Asadabad, Bahar, Toyserkan and Nahavand. Adjustment for mentioned variables did not change location of detected clusters with high rates
Conclusions: Findings revealed evidence of significant clusters in Hamadan Province. Study results may help the health system to develop effective public health interventions and extend preventive interventions. However more study are needed to better explain of detected clusters due to limited access to effecting factors