Analysis of associated risk factors among recurrent cutaneous leishmaniasis patients: A cross-sectional study in Khyber Pakhtunkhwa, Pakistan

Background Leishmaniasis is the second and fourth highest cause of mortality and morbidity respectively among all tropical diseases. Recurrence in the onset of leishmaniasis is a major problem that needs to be addressed to reduce the case fatality rate and ensure timely clinical intervention. Here we are investigating the association of risk factors with recurrent cutaneous leishmaniasis to address this issue. Material and methods Patients received by Nasser Ullah Khan Babar Hospital in Peshawar, Pakistan from March 2019 to July 2020 were enrolled in this study. Those patients who developed symptoms after completion of treatment were included in Group-A while those who had atypical scars like leishmaniasis but were negative for cutaneous leishmaniasis were included in the comparison group tagged as Group B. All those individuals who had completed six weeks of treatment for CL but had normal complete blood counts (CBC) were included to avoid other underlying immunological pathologies, while we excluded those participants who had co-morbidities like diabetes, liver disease, cardiac disease, and pregnant and lactating women through their history Association was tested between Group-A and Group-B with other explanatory variables through chi-square test. The regression model was proposed to determine the predictors. Result A total of 48 participants of both sexes were included in the study with a mean age of 32.2 ± 15.10. The data suggest that females are overrepresented among the patients with recurrent leishmaniasis [21(53.8 %,); p = 0.07]. Compared to patients; healthy participants had a higher proportion of adults (19–59 years) versus adolescents (13–18 years) [26(66.7 %) vs 07(17.9), p = 0.004]. Multivariate logistic regression analysis shows that females are 2.1 times more prone to infections among cases as compared to healthy individuals [unadjusted OR 2.20, 95 % confidence interval (CI) 1.5–10.6, p = 0.02; adjusted OR 2.1, 95 % CI 1.50–10.69, p = 0.02]. We propose that patients receiving intradermal were less likely to be infected as compared to those receiving intralesional injections [unadjusted OR 0.07.0, 95 % confidence interval (CI) 1.18–3.37, p = 0.03; adjusted OR 0.06, 95 % CI 1.18–3.38, p = 0.03]. Conclusion Old age (adults) and sex (females) were the strongest predictors to be associated with recurrent leishmaniasis. Similarly, the choice of intradermal as compared to intralesional injection and the prolonged treatment duration were strongly associated with greater chances of recurrence

A multiscale model to predict current absolute risk of femoral fracture in a postmenopausal population

Osteoporotic hip fractures are a major healthcare problem. Fall severity and bone strength are important risk factors of hip fracture. This study aims to obtain a mechanistic explanation for fracture risk in dependence of these risk factors. A novel modelling approach is developed that combines models at different scales to overcome the challenge of a large space–time domain of interest and considers the variability of impact forces between potential falls in a subject. The multiscale model and its component models are verified with respect to numerical approximations made therein, the propagation of measurement uncertainties of model inputs is quantified, and model predictions are validated against experimental and clinical data. The main results are model predicted absolute risk of current fracture (ARF0) that ranged from 1.93 to 81.6% (median 36.1%) for subjects in a retrospective cohort of 98 postmenopausal British women (49 fracture cases and 49 controls); ARF0 was computed up to a precision of 1.92 percentage points (pp) due to numerical approximations made in the model; ARF0 possessed an uncertainty of 4.00 pp due to uncertainties in measuring model inputs; ARF0 classified observed fracture status in the above cohort with AUC = 0.852 (95% CI 0.753–0.918), 77.6% specificity (95% CI 63.4–86.5%) and 81.6% sensitivity (95% CI 68.3–91.1%). These results demonstrate that ARF0 can be computed using the model with sufficient precision to distinguish between subjects and that the novel mechanism of fracture risk determination based on fall dynamics, hip impact and bone strength can be considered validated