1.Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014–2017 using a hierarchical Bayesian method and the spatial scan statistic
Erfan AYUBI ; Mohammad BARATI ; Arasb DABBAGH MOGHADDAM ; Ali Reza KHOSHDEL
Epidemiology and Health 2018;40(1):2018032-
OBJECTIVES: This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters.METHODS: This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely high- and low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing.RESULTS: A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p < 0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p < 0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p < 0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p < 0.001).CONCLUSIONS: This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.
Bayes Theorem
;
Humans
;
Incidence
;
Iran
;
Leishmaniasis, Cutaneous
;
Military Personnel
;
Public Health
;
Spatial Analysis
2.A cholera outbreak in Alborz Province, Iran: a matched case-control study.
Ghobad MORADI ; Mohammad Aziz RASOULI ; Parvin MOHAMMADI ; Elham ELAHI ; Hojatollah BARATI
Epidemiology and Health 2016;38(1):e2016018-
OBJECTIVES: A total of 229 confirmed cholera cases were reported in Alborz Province during an outbreak that lasted from June 2011 to August 2011. This study aimed to identify potential sources of transmission in order to determine suitable interventions in similar outbreaks. In other words, the lessons learned from this retrospective study can be utilized to manage future similar outbreaks. METHODS: An age-matched and sex-matched case-control study was conducted during the outbreak. For each case, two control subjects were selected from the neighborhood. A case of cholera was defined as a bacteriologically confirmed case with signs and symptoms of cholera. This study was conducted from June 14, 2011 through August 23, 2011. The data were analyzed by calculating odds ratios (ORs) using the logistic regression method. RESULTS: In this outbreak, 229 confirmed cholera cases were diagnosed. The following risk factors were found to be associated with cholera: consumption of unrefrigerated leftover food (OR, 3.05; 95% confidence interval [CI], 1.72 to 5.41), consumption of vegetables and fruits in the previous three days (OR, 2.75; 95% CI, 1.95 to 3.89), and a history of traveling in the previous five days (OR, 5.31; 95% CI, 2.21 to 9.72). CONCLUSIONS: Consumption of vegetables and fruits has remained an unresolved risk factor in cholera outbreaks in Iran in recent years. In order to reduce the risk of cholera, sanitary standards for fruits and vegetables should be observed at all points from production to consumption, the population should be educated regarding hygienic food storage during outbreaks, and sanitary standards should be maintained when traveling during cholera outbreaks.
Case-Control Studies*
;
Cholera*
;
Disease Outbreaks
;
Food Storage
;
Fruit
;
Iran*
;
Logistic Models
;
Methods
;
Odds Ratio
;
Residence Characteristics
;
Retrospective Studies
;
Risk Factors
;
Vegetables
3.Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014–2017 using a hierarchical Bayesian method and the spatial scan statistic.
Erfan AYUBI ; Mohammad BARATI ; Arasb DABBAGH MOGHADDAM ; Ali Reza KHOSHDEL
Epidemiology and Health 2018;40(1):e2018032-
OBJECTIVES: This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters. METHODS: This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely high- and low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing. RESULTS: A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p < 0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p < 0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p < 0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p < 0.001). CONCLUSIONS: This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.
Bayes Theorem*
;
Humans
;
Incidence
;
Iran
;
Leishmaniasis, Cutaneous*
;
Military Personnel
;
Public Health
;
Spatial Analysis
4.Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014–2017 using a hierarchical Bayesian method and the spatial scan statistic
Erfan AYUBI ; Mohammad BARATI ; Arasb DABBAGH MOGHADDAM ; Ali Reza KHOSHDEL
Epidemiology and Health 2018;40():e2018032-
OBJECTIVES:
This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters.
METHODS:
This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely high- and low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing.
RESULTS:
A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p < 0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p < 0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p < 0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p < 0.001).
CONCLUSIONS
This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.