Abstract
OBJECTIVESSpatial information makes a crucial contribution to enhancing and monitoring the brucellosis surveillance system by facilitating the timely diagnosis and treatment of brucellosis.METHODSAn 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.RESULTSThe 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.CONCLUSIONSThe 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.
Highlights
Brucellosis is an endemic zoonotic disease in Iran [1] and many other countries, and is among the most important challenges impeding the economic development of many of these counties
Given the debilitating consequences of this disease, which can cause serious harm and even death, it should be diagnosed in a timely manner, since timely diagnoses approaches are an effective strategy for the prevention [6] and treatment of diseases [7] such as brucellosis
In Kurdistan Province, most brucellosis patients have a history of contact with livestock [2]; having an understanding of the type of infected livestock, livestock vaccination coverage, and the type of bacterial strains (Brucella melitensis and B. abortus are the most common strains in Iran) in the region can assist in making more rational decisions to combat the disease
Summary
Brucellosis is an endemic zoonotic disease in Iran [1] and many other countries, and is among the most important challenges impeding the economic development of many of these counties. The lack of typical clinical manifestations can lead to misdiagnosis and/or a delayed diagnosis [6]; the consequent delays in treatment result in a chronic course of disease with long-term complications and difficult management. Spatial analysis can be used to assess the accessibility of healthcare services [10], an important factor in health-seeking behaviors of individuals with brucellosis, with potential impacts on delays in diagnosis. In this regard, an exponential scan statistic model for the clustering of brucellosis cases with delayed diagnoses was used to provide auxiliary information in order to improve the brucellosis surveillance system in Kurdistan Province
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