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Abstract Background Breast cancer represents a significant public health concern in India, accounting for 28% of all cancer diagnoses and imposing a substantial economic burden. This study introduces a novel approach to forecasting the number of breast cancer cases (based on prevalence rates) and estimating the associated economic impact in India using the autoregressive integrated moving average (ARIMA) model. Methods Data on the prevalence of breast cancer in India from 2000 to 2021 were obtained from the Global Burden of Disease (GBD) database. This dataset provided annual estimates of the number of patients with breast cancer, serving as the basis for modeling future prevalence and estimating the economic burden. The ARIMA (Auto-Regressive Integrated Moving Average) model was employed to analyze and predict breast cancer prevalence in India up to the year 2030 (time-series forecasting). Data were visualized and checked for stationarity using the Augmented Dickey-Fuller (ADF) test. Using the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots, the appropriate parameters (p, d, q) were determined. Several ARIMA configurations were tested to identify the model with the best fit. The goodness-of-fit of the model was assessed using standard metrics such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The residuals were tested using the Box-Ljung test to confirm the absence of autocorrelation and verify that they followed a white noise distribution. Using the fitted ARIMA model, prevalence rates were forecasted from 2022 to 2030, with 95% confidence intervals to capture prediction uncertainty. Direct costs were calculated based on medical expenses for breast cancer patients, such as hospital visits, diagnostic tests, treatment costs, and follow-up care. A bottom-up approach was applied, which involves aggregating individual cost components from each stage of care to estimate the total direct burden of disease. A bottom-up approach was applied, which involves aggregating individual cost components from each stage of care to estimate the total direct burden of disease. Indirect costs were estimated using the human capital approach, which assesses productivity losses due to morbidity and premature mortality. The Disability-Adjusted Life Years (DALY) associated with breast cancer were also predicted using the ARIMA model. Results The results of coefficient of determination (0.99), mean absolute percentage error (69%), mean absolute error (5229), and root mean squared error (6451.2) showed that the ARIMA (0,2,0) model fitted well. Coefficient of determination (0.99) indicated that 99% of the variance in the data was explained by the model. Akaike information criterion (411.54) and Bayesian information criterion (412.53) indicated the ARIMA (0,2,0) model was reliable when analysing our data. The result of the relative error of prediction (2.76%) also suggested that the model predicted well. The number of patients with breast cancer from 2021 to 2030 was predicted to be about 1.25 million, 1.1.29 million,, 1.34 million, 1.39 million, 1.44 million, 1.48 million, 1.53 million, 1.58 million, 1.63 million, 1.68 million, and respectively. The total economic burden of breast cancer from 2021 to 2030 was estimated to be $8 billion, $8.72 billion, $9.05 billion, $9.84 billion, $10.20 billion, $11.07 billion, $11.49 billion, $12.44 billion, $12.91 billion, $13.95 billion, respectively is estimated to rise significantly. Conclusion Breast cancer prevalence and its economic impact are projected to grow substantially in India. Between 2021 and 2030, the number of breast cancer patients is expected to increase by approximately 0.05 million annually, with an annual increase rate of about 5.6%. The associated economic burden will also rise, averaging an additional $19.55 billion per year, underscoring the need for intensified healthcare and economic strategies to manage this growing challenge.

Background: Humanitarian mine action (HMA) stakeholders have an organized presence with well-resourced medical capability in many conflict and post-conflict settings. Humanitarian mine action has the potential to positively augment local trauma care capacity for civilian casualties of explosive ordnance (EO) and explosive weapons (EWs). Yet at present, few strategies exist for coordinated engagement between HMA and the health sector to support emergency care system strengthening to improve outcomes among EO/EW casualties., Methods: A scoping literature review was conducted to identify records that described trauma care interventions pertinent to civilian casualties of EO/EW in resource-constrained settings using structured searches of indexed databases and grey literature. A 2017 World Health Organization (WHO) review on trauma systems components in low- and middle-income countries (LMICs) was updated with additional eligible reports describing trauma care interventions in LMICs or among civilian casualties of EO/EWs after 2001., Results: A total of 14,195 non-duplicative records were retrieved, of which 48 reports met eligibility criteria. Seventy-four reports from the 2017 WHO review and 16 reports identified from reference lists yielded 138 reports describing interventions in 47 countries. Intervention efficacy was assessed using heterogenous measures ranging from trainee satisfaction to patient outcomes; only 39 reported mortality differences. Interventions that could feasibly be supported by HMA stakeholders were synthesized into a bundle of opportunities for HMA engagement designated links in a Civilian Casualty Care Chain (C-CCC)., Conclusions: This review identified trauma care interventions with the potential to reduce mortality and disability among civilian EO/EW casualties that could be feasibly supported by HMA stakeholders. In partnership with local and multi-lateral health authorities, HMA can leverage their medical capabilities and expertise to strengthen emergency care capacity to improve trauma outcomes in settings affected by EO/EWs.

Introduction: Access to essential medicines is limited in developing countries mainly due to inefficiencies in health supply chain management, such as the absence of standard monitoring frameworks and poorly designed Logistics Management Information Systems (LMIS). Health supply chain managers need accurate and timely data for decision-making. However, routine health information systems suffer from poor data quality, reliance on paper-based reports, insufficient logistic formats, inadequate infrastructure, and limited human resources., Objective: This study evaluates the data quality of LMIS for health commodities in public health facilities in the Amhara National Regional State of Ethiopia., Methods: The study was conducted in Ethiopia's Amhara National Regional State. The study employed an institution-based concurrent mixed-methods design. Data collection involved 102 facilities selected through multi-stage stratified random sampling, adhering to sampling criteria set by USAID's Logistics Indicators Assessment Tool (LIAT). Data abstraction checklists were used to collect data., Results: Of the seven tracer medicines selected to evaluate data quality, there was substantial variability in inventory accuracy rates. Inventory discrepancies were significant, highlighting potential issues with manual and digital record-keeping systems, with overall mean physical and electronic inventory accuracy rates of 74.7% and 70.6%, respectively. Additionally, the Report and Requisition Form (RRF) showed trends of timely submission, with the overall mean percentage completeness for the seven tracer medicines at 90.2%. However, the data quality experienced fluctuations, with the overall average percentage of legality (authorization of LMIS reports) and the accuracy of the RRF at 77.2% and 76%, respectively., Conclusion and Recommendation: The evaluation of data quality revealed significant discrepancies in physical and electronic records, with notable fluctuations in completeness, legality, legibility, and accuracy within the health LMIS. To rectify these issues, robust data quality verification processes, clear guidelines, targeted interventions, strengthened monitoring systems, regular audits, and comprehensive training for health supply chain staff are needed., Competing Interests: All authors declared that there are no competing interests., (© 2025 Mekonen et al.)