Determinants of health literacy and its impact on glycemic control among women with gestational diabetes mellitus in a tertiary care hospital, Puducherry - A cross-sectional analytical study.

BACKGROUND: Health literacy is vital during pregnancy, as maternal health knowledge and behavior have a significant impact on the health of both mother and child. Hence, this study aimed to assess the health literacy status of pregnant women diagnosed with gestational diabetes mellitus (GDM), as well as its associated factors and impact on glycemic control. MATERIALS AND METHODS: The facility-based Cross-sectional analytical study was conducted among 200 pregnant women with GDM in a tertiary care hospital. The eligible participants were consecutively selected for the study. The study was conducted from September 2022 to March 2023. A validated semi-structured questionnaire, the Health Literacy Questionnaire (HLQ) for GDM, was used to measure health literacy status. Stata V.17 software was used for data analysis. RESULTS: Out of 200 pregnant women with GDM, the mean (SD) age of the participants is 29.5 (±5.5) years. It was observed that 164 (82%) of the participants had adequate health literacy, whereas 36 (18%) had inadequate health literacy about Gestational Diabetes. Adequate health literacy (HL) was observed among 88.5% of women with controlled blood sugar and 55.1% of women with uncontrolled blood sugar. Results of multivariate logistic regression analysis revealed that pregnant mothers' educational status (PR: 1.8; 95% CI: 1.2-2.5) and glycemic control (PR: 1.4; 95% CI (1.2-1.7) were associated with adequate HL. CONCLUSIONS: In conclusion, this study supports the association between adequate HL and glycemic control in pregnant women with GDM. Addressing this gap is essential for healthcare officials and planners to implement programs that promote women's HL during pregnancy, with a focus on low-educated groups.

Medical Management to Treat Chronic Non-healing Ulcers: A Case Series.

INTRODUCTION: Chronic non-healing leg ulcers are skin defects below the knee that resist healing for more than six weeks. They cause physical, emotional, and economic burdens to patients and society. OBJECTIVES: To introduce an innovative medical strategy that targets the chronic inflammation component in non-healing ulcers (NHUs) with rheumatic features and to evaluate its potential effectiveness in achieving complete healing. METHODS: We employed an empirical medical therapy regimen, which combined medications like deflazacort, colchicine, dapsone, hydroxychloroquine, and azathioprine. We retrospectively selected 25 patients with chronic pedal ulcers who underwent our therapy. RESULTS: The mean duration of ulcers was 7.84 years, and the time to heal was 5.97 months. Among 25 patients, 19 had atypical ulcers, four had venous ulcers, and two had diabetic neuropathy ulcers. Four patients with venous ulcers additionally underwent endovenous laser ablation. CONCLUSION: Our medical strategy showed promising results in healing chronic NHUs with rheumatic features without significant steroid-induced adverse effects.

Association between common mental disorder and glycemic control in women with gestational diabetes: A mixed-method study.

Background: Gestational diabetes mellitus (GDM) is associated with an increased risk of mental health disorders among pregnant women. Poor mental health can negatively impact glycemic control in women with GDM, leading to adverse outcomes for both the mother and the baby. Aim: To determine the prevalence of common mental disorder (CMD) in women with GDM and its association with poor glycemic control. Additionally, to explore the reasons and coping strategies for CMD in women with GDM. Methods: A sequential explanatory mixed-method study was conducted among 351 women with GDM visiting a tertiary care hospital. The prevalence of CMD was assessed using Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Perceived Stress Scale (PSS). Glycemic control was determined based on two-hour postprandial blood glucose levels. In-depth interviews were conducted with six women who screened positive for CMD and had poor glycemic control. Data were analyzed using SPSS v23 and stats v12. Chi-square test and Poisson regression were performed, and adjusted prevalence ratios (aPRs) were reported. Results: The prevalence of CMD was found to be 19.08% (95% CI: 15.32%-23.52%), with 18.2% (95% CI: 14.5%-22.6%) anxiety symptoms 8.3% (95% CI: 5.8%-11.6%) depressive symptoms and stress each. CMD was significantly related to poor glycemic control (aPR: 1.58; 95% CI: 1.23-2.03; P value <0.001). The qualitative analysis revealed individual, family, health, and facility factors influencing mental health and glycemic control. Conclusion: Common mental health disorders are prevalent in women with GDM. It has a negative association with glycemic control. Implementing a routine screening program in the ANC clinic can aid in early identification and prompt management of the CMD and its associated complications.

A multi-echelon dynamic cold chain for managing vaccine distribution.

While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities.

Convalescent plasma bank facility location-allocation problem for COVID-19.

With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers.