A landscape evaluation of caffeine citrate availability and use in newborn care across five low- and middle-income countries

Apnoea of prematurity (AOP) is a common complication among preterm infants (< 37 weeks gestation), globally. However, access to caffeine citrate (CC) that is a proven safe and effective treatment in high-income countries is largely unavailable in low- and-middle income countries, where most preterm infants are born. Therefore, the overall aim of this study was to describe the demand, policies, and supply factors affecting the availability and clinical use of CC in LMICs. A mixed methods approach was used to collect data from diverse settings in LMICs including Ethiopia, Kenya, Nigeria, South Africa, and India. Qualitative semi-structured interviews and focus group discussions were conducted with 107 different health care providers, and 21 policymakers and other stakeholders from industry. Additional data was collected using standard questionnaires. A thematic framework approach was used to analyze the qualitative data and descriptive statistics were used to summarize the quantitative data. The findings indicate that there is variation in in-country policies on the use of CC in the prevention and treatment of AOP and its availability across the LMICs. As a result, the knowledge and experience of using CC also varied with clinicians in Ethiopia having no experience of using it while those in India have greater knowledge and experience of using it. This, in turn, influenced the demand, and our findings show that only 29% of eligible preterm infants are receiving CC in these countries. There is an urgent need to address the multilevel barriers to accessing CC for managing AOP in Africa. These include cost, lack of national policies, and, therefore, lack of demand stemming from its clinical equivalency with aminophylline. Practical ways to reduce the cost of CC in LMICs could potentially increase its availability and use

Cost-effectiveness analysis of Option B+ for HIV prevention and treatment of mothers and children in Malawi.

BACKGROUND: The Ministry of Health in Malawi is implementing a pragmatic and innovative approach for the management of all HIV-infected pregnant women, termed Option B+, which consists of providing life-long antiretroviral treatment, regardless of their CD4 count or clinical stage. Our objective was to determine if Option B+ represents a cost-effective option. METHODS: A decision model simulates the disease progression of a cohort of HIV-infected pregnant women receiving prophylaxis and antiretroviral therapy, and estimates the number of paediatric infections averted and maternal life years gained over a ten-year time horizon. We assess the cost-effectiveness from the Ministry of Health perspective while taking into account the practical realities of implementing ART services in Malawi. RESULTS: If implemented as recommended by the World Health Organization, options A, B and B+ are equivalent in preventing new infant infections, yielding cost effectiveness ratios between US$37 and US$ 69 per disability adjusted life year averted in children. However, when the three options are compared to the current practice, the provision of antiretroviral therapy to all mothers (Option B+) not only prevents infant infections, but also improves the ten-year survival in mothers more than four-fold. This translates into saving more than 250,000 maternal life years, as compared to mothers receiving only Option A or B, with savings of 153,000 and 172,000 life years respectively. Option B+ also yields favourable incremental cost effectiveness ratios (ICER) of US$ 455 per life year gained over the current practice. CONCLUSION: In Malawi, Option B+ represents a favorable policy option from a cost-effectiveness perspective to prevent future infant infections, save mothers' lives and reduce orphanhood. Although Option B+ would require more financial resources initially, it would save societal resources in the long-term and represents a strategic option to simplify and integrate HIV services into maternal, newborn and child health programmes

<b>Table 1.</b> ARV regimens for HIV prevention and treatment of mothers and children compared in the analysis - Current Practice 2010, WHO Option A, WHO Option B and Malawi's Option B+.

<p><b>Table 1.</b> ARV regimens for HIV prevention and treatment of mothers and children compared in the analysis - Current Practice 2010, WHO Option A, WHO Option B and Malawi's Option B+.</p

Costs and paediatric outcomes from preventing mother to child transmission programmatic interventions for 18 months of prophylaxis and treatment^* (US $ 2010).

*<p>Assumes 663,000 pregnant women, 66,500 HIV-infected pregnant women annually, and 90% (59,850) of those women reached by Option A, B and B+.</p>**<p>Assumes no needed CD4 to start ART under the Malawi Option B+ approach; however, in practice some HIV-infected pregnant women will have access to CD4 testing as part of staging and response to treatment</p>***<p>Background infections if no ARV interventions = 20,681</p

Input parameters and plausible ranges used for sensitivity analysis and relevant references for the Malawi analysis (US $ 2010).

<p>Input parameters and plausible ranges used for sensitivity analysis and relevant references for the Malawi analysis (US $ 2010).</p

Cost effectiveness of various strategies for the prevention of new pediatric infections and the treatment of HIV-infected mothers in Malawi.

<p>Current practice represents our base case scenario or the status quo in 2010. The next set of scenarios highlight the cost effectiveness of incrementally expanding program implementation and service delivery coverage, and ranges from PMTCT only to the addition of integrated ART-ANC services for eligible pregnant women, both identified immediately and at a later time. Universal coverage implies the availability of HIV services for mother and children at any point of needing treatment. Option B+ offers ART to pregnant women regardless of CD4 count.</p

Abbreviated decision tree summarizing the analytical approach, policy options and results.

<p>Abbreviated decision tree summarizing the analytical approach, policy options and results.</p

Tornado diagram for the ICER of Option B+, base case is $455 per life year gained shown with the dotted line.

<p>Tornado diagram for the ICER of Option B+, base case is $455 per life year gained shown with the dotted line.</p

Results from sensitivity analyses on input parameters affecting outcomes in HIV-infected mothers; USperlifeyeargained(comparedtothecurrentpractice)andpaediatricoutcomes;US per life year gained (compared to the current practice) and paediatric outcomes; US per DALY averted.

<p>Results from sensitivity analyses on input parameters affecting outcomes in HIV-infected mothers; US$per life year gained (compared to the current practice) and paediatric outcomes; US$ per DALY averted.</p

An Improved Framework for Detecting Thyroid Disease Using Filter-Based Feature Selection and Stacking Ensemble

In recent years, machine learning (ML) has become a pivotal tool for predicting and diagnosing thyroid disease. While many studies have explored the use of individual ML models for thyroid disease detection, the accuracy and robustness of these single-model approaches are often constrained by data imbalance and inherent model biases. This study introduces a filter-based feature selection and stacking-based ensemble ML framework, tailored specifically for thyroid disease detection. This framework capitalizes on the collective strengths of multiple base models by aggregating their predictions, aiming to surpass the predictive performance of individual models. Such an approach can also reduce screening time and costs considering few clinical attributes are used for diagnosis. Through extensive experiments conducted on a clinical thyroid disease dataset, the filter-based feature selection approach and the ensemble learning method demonstrated superior discriminative ability, reflected by improved receiver operating characteristic-area under the curve (ROC-AUC) scores of 99.9%. The proposed framework sheds light on the complementary strengths of different base models, fostering a deeper understanding of their joint predictive performance. Our findings underscore the potential of ensemble strategies to significantly improve the efficacy of ML-based detection of thyroid diseases, marking a shift from reliance on single models to more robust, collective approaches