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

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

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

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.

Background: Establishing cost-effective informal care services for lonely older adults living at home in developing countries can be an innovative approach for improving their well-being. This study investigated the effectiveness of an informal home care support intervention program (HoSIP) reducing the loneliness and improving quality of life of lonely community-dwelling older adults. Methods: This quasi-experimental pre-post study employed a non-randomized control group design with a 12-week intervention period and three follow-up points at the end of the HoSIP. Questionnaires were used to measure feelings of loneliness (20-item UCLA Loneliness Scale), quality of life (Control, Autonomy, Self‐Realization and Pleasure Scale), general health (12-item General Health Questionnaire), social network (six-item Lubben Social Network Scale), social support (12-item Multidimensional Scale of Perceived Social Support), and self-care ability (17-item Self-care Ability Scale for the Elderly). Repeated-measures analysis of variance was used to gauge the effect of the intervention program over time and in comparison to the control group. Data analyses were performed using the IBM SPSS Statistics software (IBM Corp., USA). Results: We found a significant relationship between the outcome variables, including feelings of loneliness (P<0.001) and quality of life (P<0.001), at different stages of measurement. Despite the positive feasibility results, the implementation of the HoSIP faced challenges due to a lack of facilities (e.g., place restriction for holding educational classes, educational facilities like computers, video projector, and whiteboard at daycare center) and the absence of supporting organizations. Conclusion Utilizing the existing capabilities of older adults to provide online and face-to-face care services can be a cost-effective way to improve their quality of life and reduce loneliness. The process of facilitating such informal care services for lonely older adults should be managed by either governmental or non-governmental organizations to reduce the rate of social isolation among this vulnerable population.

Background: Establishing cost-effective informal care services for lonely older adults living at home in developing countries can be an innovative approach for improving their well-being. This study investigated the effectiveness of an informal home care support intervention program (HoSIP) reducing the loneliness and improving quality of life of lonely community-dwelling older adults. Methods: This quasi-experimental pre-post study employed a non-randomized control group design with a 12-week intervention period and three follow-up points at the end of the HoSIP. Questionnaires were used to measure feelings of loneliness (20-item UCLA Loneliness Scale), quality of life (Control, Autonomy, Self‐Realization and Pleasure Scale), general health (12-item General Health Questionnaire), social network (six-item Lubben Social Network Scale), social support (12-item Multidimensional Scale of Perceived Social Support), and self-care ability (17-item Self-care Ability Scale for the Elderly). Repeated-measures analysis of variance was used to gauge the effect of the intervention program over time and in comparison to the control group. Data analyses were performed using the IBM SPSS Statistics software (IBM Corp., USA). Results: We found a significant relationship between the outcome variables, including feelings of loneliness (P<0.001) and quality of life (P<0.001), at different stages of measurement. Despite the positive feasibility results, the implementation of the HoSIP faced challenges due to a lack of facilities (e.g., place restriction for holding educational classes, educational facilities like computers, video projector, and whiteboard at daycare center) and the absence of supporting organizations. Conclusion Utilizing the existing capabilities of older adults to provide online and face-to-face care services can be a cost-effective way to improve their quality of life and reduce loneliness. The process of facilitating such informal care services for lonely older adults should be managed by either governmental or non-governmental organizations to reduce the rate of social isolation among this vulnerable population.

Background: Establishing cost-effective informal care services for lonely older adults living at home in developing countries can be an innovative approach for improving their well-being. This study investigated the effectiveness of an informal home care support intervention program (HoSIP) reducing the loneliness and improving quality of life of lonely community-dwelling older adults. Methods: This quasi-experimental pre-post study employed a non-randomized control group design with a 12-week intervention period and three follow-up points at the end of the HoSIP. Questionnaires were used to measure feelings of loneliness (20-item UCLA Loneliness Scale), quality of life (Control, Autonomy, Self‐Realization and Pleasure Scale), general health (12-item General Health Questionnaire), social network (six-item Lubben Social Network Scale), social support (12-item Multidimensional Scale of Perceived Social Support), and self-care ability (17-item Self-care Ability Scale for the Elderly). Repeated-measures analysis of variance was used to gauge the effect of the intervention program over time and in comparison to the control group. Data analyses were performed using the IBM SPSS Statistics software (IBM Corp., USA). Results: We found a significant relationship between the outcome variables, including feelings of loneliness (P<0.001) and quality of life (P<0.001), at different stages of measurement. Despite the positive feasibility results, the implementation of the HoSIP faced challenges due to a lack of facilities (e.g., place restriction for holding educational classes, educational facilities like computers, video projector, and whiteboard at daycare center) and the absence of supporting organizations. Conclusion Utilizing the existing capabilities of older adults to provide online and face-to-face care services can be a cost-effective way to improve their quality of life and reduce loneliness. The process of facilitating such informal care services for lonely older adults should be managed by either governmental or non-governmental organizations to reduce the rate of social isolation among this vulnerable population.

Background: Establishing cost-effective informal care services for lonely older adults living at home in developing countries can be an innovative approach for improving their well-being. This study investigated the effectiveness of an informal home care support intervention program (HoSIP) reducing the loneliness and improving quality of life of lonely community-dwelling older adults. Methods: This quasi-experimental pre-post study employed a non-randomized control group design with a 12-week intervention period and three follow-up points at the end of the HoSIP. Questionnaires were used to measure feelings of loneliness (20-item UCLA Loneliness Scale), quality of life (Control, Autonomy, Self‐Realization and Pleasure Scale), general health (12-item General Health Questionnaire), social network (six-item Lubben Social Network Scale), social support (12-item Multidimensional Scale of Perceived Social Support), and self-care ability (17-item Self-care Ability Scale for the Elderly). Repeated-measures analysis of variance was used to gauge the effect of the intervention program over time and in comparison to the control group. Data analyses were performed using the IBM SPSS Statistics software (IBM Corp., USA). Results: We found a significant relationship between the outcome variables, including feelings of loneliness (P<0.001) and quality of life (P<0.001), at different stages of measurement. Despite the positive feasibility results, the implementation of the HoSIP faced challenges due to a lack of facilities (e.g., place restriction for holding educational classes, educational facilities like computers, video projector, and whiteboard at daycare center) and the absence of supporting organizations. Conclusion Utilizing the existing capabilities of older adults to provide online and face-to-face care services can be a cost-effective way to improve their quality of life and reduce loneliness. The process of facilitating such informal care services for lonely older adults should be managed by either governmental or non-governmental organizations to reduce the rate of social isolation among this vulnerable population.