Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014–2017 using a hierarchical Bayesian method and the spatial scan statistic / 한국역학회지

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.

Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014–2017 using a hierarchical Bayesian method and the spatial scan statistic / 한국역학회지

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.

Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014–2017 using a hierarchical Bayesian method and the spatial scan statistic / 한국역학회지

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.

Body mass index and composition in physical preparedness of Iranian military personnel

Military personnel are expected to maintain the highest possible level of physical fitness to do their duties. Several factors including body composition and body mass index affect the level of their physical preparedness. The current study was designed to investigate the effect of body composition and body mass index in the level of physical preparedness of Iranian military personnel. In this cross-sectional study, 255 Iranian military personnel were randomly selected. After becoming ensured of the good health of participants, their weight, height and body mass index was determined. The body composition was defined by bioelectrical impedance analysis. Maximum aerobic capacity and metabolic equivalent was estimated using the Bruce treadmill test. Maximum muscle strength in the trunk and lower extremities was measured by the back dynamometer and flexibility was measured by the sit and reach box. Maximum aerobic capacity, metabolic equivalents, maximum muscle strength adjusted for weight and flexibility had significant negative correlations with percentage of body fat [r = -.172 to -.533] and body mass index [r = -.302 to -.504]. Thus the studied factors [excluding flexibility] had more significant association with body fat. There was no significant correlation between pure maximum muscle strength [not adjusted for weight] and body mass index [r = .28, P = .658]. The percentage of body fat and body mass index affect several health-related physical fitness factors including aerobic capacity, metabolic equivalents, flexibility and maximum muscle strength adjusted for weight but not pure maximum muscle strength in military personnel.

Drinking water composition and incidence of urinary calculus: introducing a new index

We searched for a pathophysiologically based feature of major water electrolytes, which may define water quality better than the water hardness, respecting urinary calculus formation. Utilizing a multistage stratified sampling, 2310 patients were diagnosed in the imaging centers of the provincial capitals in Iran between 2007 and 2008. These were composed of 1755 patients who were settled residents of 24 provincial capitals. Data on the regional drinking water composition, obtained from an accredited registry, and their relationships with the region's incidence of urinary calculi were evaluated by metaregression models. The stone risk index [defined as the ratio of calcium to magnesium-bicarbonate product in drinking water] was used to assess the risk of calculus formation. No correlation was found between the urinary calculus incidence and the amount of calcium, bicarbonate, or the total hardness of the drinking water. In contrast, water magnesium had a marginally significant nonlinear inverse relationship with the incidence of the disease in the capitals [R2 = 26%, P = .05 for a power model]. The stone risk index was associated nonlinearly with the calculus incidence [R2 = 28.4%, P = .04]. Urinary calculus incidence was inversely related with drinking water magnesium content. We introduced a new index constructed on the foundation of a pathophysiologically based formula; the stone risk index had a strong positive association with calculus incidence. This index can have therapeutic and preventive applications, yet to be confirmed by clinical trials

Familial relations and recurrence pattern in nephrolithiasis. New words about old subjects

While medical and surgical approaches to urolithiasis are different for single and recurrent stone former [RSF], the RSF definition itself is commonly overlooked. Moreover, despite consensus on association between family history [FH] and urolithiasis, more epidemiologic evidence is required to clarify the nature of this relationship. Our purpose was to propose a more precise definition of RSF, and also to investigate how family history may affect urolithiasis. Using a multistage stratified sampling in 4 seasonal phases, 6127 subjects with imaging-proven urolithiasis were detected in 12 Iranian regions. The FH of urolithiasis and the average interval between episodes [cycles] were determined by an informed interview. Of 6127 patients with the mean age of 41.8 +/- 15.1 years, 42% had FH, and 22.2% were RSF of whom 61% were men. The patients with FH had a greater chance of recurrence [OR = 1.2, 95% Confidence Interval [CI], 1.1 to 1.4]. Furthermore, patients with positive FH had more episodes [P = .0001], comparable cycles and younger ages at the onset [P = .02] than those patients without a FH. In the RSF group, the 90[th] percentiles of the cycle were 60 months and the estimated mean stone cycle for the population was 25.34 months [99% CI, 23.0 to 27.7]. Family history seems very common in Iranian population and is a risk factor for recurrence. Moreover, RSF could be identified by the estimated average cycle in the population [25.3 months] or by the percentiles

Arterial stiffness in kidney transplant recipients an overview of methodology and applications

Cardiovascular disease is still a major cause of mortality in kidney transplant patients. This is partially attributed to the nonclassic cardiovascular risk factors including arterial stiffness, an established independent predictor of mortality in several patient populations. An extensive search was performed to review the evolution process of the method for arterial stiffness assessment and sphygmology and their applications in chronic kidney disease before and after kidney transplantation. Despite a marked change in methodology from the ancient medical practice to the current modern medicine, noninvasive assessment of arterial stiffness is still based on pulse analysis. Currently, pulse wave velocity, augmentation index, and pulse wave reflection are preferred indexes for arterial stiffness. Increased arterial stiffness has been reported in diabetes mellitus, hypertension, chronic kidney disease, cardiovascular disease, and elderly, and reduction of arterial stiffness is a key element for efficacy of the treatment and mortality reduction. Noninvasive assessment of arterial stiffness is suggested as a part of clinical assessment for kidney transplant recipients and donors and facilitates defining high-risk patients for development of cardiovascular disease. A combination of techniques is recommended for this purpose