BMJ Global Health最新文献

Assessing elimination of malaria locally requires a surveillance system with high sensitivity and specificity to detect its presence without ambiguity. Currently, the WHO standard criteria of observing the absence of locally acquired cases for 3 consecutive years, combined with a health systems assessment, are used to justify claims of malaria elimination. However, relying on a qualitative framework to support the application of this guideline can lead to early, over-optimistic relaxation of control measures with the potential for resurgence. Overcoming this challenge requires innovative approaches to model the coupled processes of malaria transmission and its clinical observation.We propose a novel statistical framework based on a state-space model to probabilistically demonstrate the absence of malaria, using routinely collected health system data (which is extensive but inherently imperfect). By simultaneously modelling the expected malaria burden within the population and the probability of detection, we provide a robust estimate of the surveillance system's sensitivity and the corresponding probability of local elimination (probability of freedom from infection).Our study reveals a critical limitation of the traditional criterion for declaring malaria elimination, highlighting its inherent bias and potential for misinterpreting ongoing transmission. Such oversight not only misrepresents ongoing transmission but also places communities at risk for larger outbreaks. However, we demonstrate that our integrated approach to data comprehensively addresses this issue, effectively detecting ongoing transmission patterns, even when local reports might suggest otherwise.Our integrated framework has far-reaching implications for malaria control but also for infectious disease control in general. Our approach addresses the limitations of traditional criteria for declaring freedom from disease and opens the path to true optimisation of the allocation of limited resources. Our findings emphasise the urgent need to reassess existing methods to accurately confirm malaria elimination, and the importance of using comprehensive modelling techniques to continually monitor and maintain the effectiveness of current surveillance systems, enabling decisions grounded in quantitative evidence.

Introduction: Malnutrition contributes to 45% of all childhood deaths globally, but these modelled estimates lack direct measurements in countries with high malnutrition and under-5 mortality rates. We investigated malnutrition's role in infant and child deaths in the Child Health and Mortality Prevention Surveillance (CHAMPS) network.

Methods: We analysed CHAMPS data from seven sites (Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone and South Africa) collected between 2016 and 2023. An expert panel assessed each death to determine whether malnutrition was an underlying, antecedent or immediate cause or other significant condition. Malnutrition was further classified based on postmortem anthropometry using WHO growth standards for underweight (z-scores for weight-for-age <-2), stunting (length-for-age <-2), and wasting (weight-for-length or MUAC Z-scores <-2).

Results: Of 1601 infant and child deaths, malnutrition was considered a causal or significant condition in 632 (39.5%) cases, including 85 (13.4%) with HIV infection. Postmortem measurements indicated 90.1%, 61.2% and 94.1% of these cases were underweight, stunted and wasted, respectively. Most malnutrition-related deaths (n=632) had an infectious cause (89.1%). The adjusted odds of having malnutrition as causal or significant condition were 2.4 (95% CI 1.7 to 3.2) times higher for deaths involving infectious diseases compared with other causes. Common pathogens in the causal pathway for malnutrition-related deaths included Klebsiella pneumoniae (30.4%), Streptococcus pneumoniae (21.5%), Plasmodium falciparum (18.7%) and Escherichia coli/Shigella (17.2%).

Conclusion: Malnutrition was identified as a causal or significant factor in 39.5% of under-5 deaths in the CHAMPS network, often in combination with infectious diseases. These findings highlight the need for integrated interventions addressing both malnutrition and infectious diseases to effectively reduce under-5 mortality.

Introduction: China faces the dual challenge of high air pollution and an increasing burden of cardiovascular disease (CVD). We aimed to estimate the healthcare costs associated with CVD and the quality-adjusted life years (QALYs) under scenarios of improved air quality in China.

Methods: A health prediction model was developed to estimate 10-year CVD-related costs and QALY associated with PM2.5 levels in 2015, as well as two hypothetical improved air quality scenarios: (1) the China national PM2.5 target of 35 µg/m³, and (2) the World Health Organization's (WHO) PM2.5 guideline of 5 µg/m³. Population CVD risks were estimated from the 2015 China Health and Retirement Longitudinal Study. Hazard ratios from WHO risk curves were subsequently applied to baseline cardiovascular risks to predict national 10-year estimates of ischaemic stroke and coronary heart disease-related healthcare expenditures and QALYs for individuals aged 45-85 under the three air quality scenarios.

Results: Under PM2.5 levels in 2015, we estimated a cumulative 10-year incidence of 35.40 million CVD events, resulting in healthcare costs of US$96.12 billion and 4.44 billion QALYs. Under the national target of 35 µg/m³, the projected 10-year CVD incidence was 31.92 million cases, resulting in cost savings of US$9.29 billion and 3.43 million QALY gains compared with 2015 levels. If PM2.5 concentration levels meet the WHO's guideline of 5 µg/m³, the projected number of CVD events would decrease to 24.18 million, translating to cost savings of approximately US$30.10 billion and gains of 11.29 million QALYs.

Conclusion: Our findings indicate that achieving the WHO recommended PM2.5 concentration level of 5 µg/m³ could lead to over threefold greater health and economic benefits than those achievable under national standards of 35 µg/m³. This underscores the potential need for stricter future national PM2.5 standards. Our findings also inform other low- and middle-income countries in establishing effective long-term PM2.5 targets.

Introduction: Academic-government partnerships are important to advance timely, responsive and relevant evidence for decision-making (policy, guideline, law and regulation) deliberations. Deliberate and strategic integrated knowledge translation (KT) approaches within such partnerships have been shown to facilitate evidence-informed decision-making (EIDM). We used Cochrane's KT Framework to map and analyse COVID-19 response activities instituted by a strategic academic-government partnership to support EIDM during the COVID-19 pandemic in South Africa.

Methods: We used Cochrane's KT Framework to map and analyse COVID-19 response activities instituted by a strategic academic-government partnership to support EIDM during the COVID-19 pandemic in South Africa. The COVID-19 response activities included coproducing rapid therapeutics reviews, engaging stakeholders with review evidence, packaging and disseminating review products, facilitating access to rapid reviews for evidence users and adapting partnership processes for rapid review production.

Results: This paper highlights the importance of (a) authentic partnerships between evidence producers and users (motivated by context-specific goals, trust and relationships); (b) intentional and systematic stakeholder engagement to promote the rapid exchange of information; (c) using tailored, responsive and relevant KT to promote the uptake of evidence and (d) monitoring and evaluating the implementation of KT to identify lessons learnt and adaptation of KT approaches.

Conclusion: In responding to future emergencies, a comprehensive KT strategy, including the expertise of KT practitioners and science communicators to make evidence and guideline recommendations accessible, should be embedded. Additionally, streamlining bureaucratic processes for approving and communicating information; identifying and addressing decision-maker capacity needs; engaging a range of stakeholders and integrating KT in usual decision-making processes, is recommended. Adequate investment by governments is needed for sustaining KT approaches that can enhance EIDM for improving public health outcomes.

Introduction: Adverse perinatal outcomes (APO) pose a significant global challenge, particularly in low- and middle-income countries (LMICs). This study aims to analyse two cohorts of high-risk pregnant women for APO to comprehend risk factors and improve prediction accuracy.

Methods: We considered an LMIC and a high-income country (HIC) population to derive XGBoost classifiers to predict low birth weight (LBW) from a comprehensive set of maternal and fetal characteristics including socio-demographic, past and current pregnancy information, fetal biometry and fetoplacental Doppler measurements. Data were sourced from the FeDoC (Fetal Doppler Collaborative) study (Pakistan, LMIC) and theIMPACT (Improving Mothers for a Better PrenAtal Care Trial) study (Spain, HIC), and included 520 and 746 pregnancies assessed from 28 weeks gestation, respectively. The models were trained on varying subsets of the mentioned characteristics to evaluate their contribution in predicting LBW cases. For external validation, and to highlight potential differential risk factors for LBW, we investigated the generalisation of these models across cohorts. Models' performance was evaluated through the area under the curve (AUC), and their interpretability was assessed using SHapley Additive exPlanations.

Results: In FeDoC, Doppler variables demonstrated the highest value at predicting LBW compared with biometry and maternal clinical data (AUC_Doppler, 0.67; AUC_Clinical, 0.65; AUC_Biometry, 0.63), and its combination with maternal clinical data yielded the best prediction (AUC_{Clinical+Doppler}, 0.71). In IMPACT, fetal biometry emerged as the most predictive set (AUC_Biometry, 0.75; AUC_Doppler, 0.70; AUC_Clinical, 0.69) and its combination with Doppler and maternal clinical data achieved the highest accuracy (AUC_{Clinical+Biometry+Doppler}, 0.81). External validation consistently indicated that biometry combined with Doppler data yielded the best prediction.

Conclusions: Our findings provide new insights into the predictive role of different clinical and ultrasound descriptors in two populations at high risk for APO, highlighting that different approaches are required for different populations. However, Doppler data improves prediction capabilities in both settings, underscoring the value of standardising ultrasound data acquisition, as practiced in HIC, to enhance LBW prediction in LMIC. This alignment contributes to bridging the health equity gap.

Introduction: Chikungunya is a mosquito-borne arboviral disease posing an emerging global public health threat. Understanding the global burden of chikungunya is critical for designing effective prevention and control strategies. However, current estimates of the economic and health impact of chikungunya remain limited and are potentially underestimated. This study aims to provide a comprehensive overview of the chikungunya burden worldwide.

Methods: We analysed the global burden of chikungunya between 2011 and 2020 and calculated disability-adjusted life years (DALYs) and direct and indirect costs using a data-driven simulation model. The main outcomes were the number of cases, the total DALY burden, and the direct and indirect costs of acute and chronic chikungunya between 2011 and 2020.

Results: Our study revealed a total of 18.7 million chikungunya cases in 110 countries between 2011 and 2020, causing 1.95 million DALYs. Most of this burden was found in the Latin American and Caribbean region. The total economic burden caused by chikungunya over these 10 years was estimated at $2.8 billion in direct costs and $47.1 billion in indirect costs worldwide. Long-term chronic illness was the source of most costs and DALYs.

Conclusion: Chikungunya has a higher disease burden than was previously estimated and costs related to the disease are substantial. Especially in combination with its unpredictable nature, chikungunya could significantly impact local health systems. Insights from this study could inform decision makers on the impact of chikungunya on population health and help them to appropriately allocate resources to protect vulnerable populations from this debilitating disease.

Introduction: To ensure there is adequate investment into diagnostics, an understanding of the magnitude of impact and return on investment is necessary. We, therefore, sought to understand the health and economic impacts of the molecular diagnostic programme in South Africa, to deepen the understanding of the broad value of diagnostics and guide future healthcare investments.

Methods: We calculated the 10-year (where data were available) total cost and disability-adjusted life-years (DALYs) averted associated with molecular testing for tuberculosis diagnosis (2013-2022), HIV viral load monitoring (2013-2022), early infant diagnosis of HIV infection (2013-2022) and SARS-CoV-2 testing (2020-2022), based on the actual number of molecular tests conducted in South Africa for the respective time periods. We then calculated the economic value associated with those health gains and subsequent return on investment.

Results: Since the inception of the molecular diagnostics programme in South Africa, approximately 4.3 million DALYs (uncertainty range (UR): 2.8-5.8 million) have been averted as a direct consequence of this programme. This has generated an estimated US$28.3 billion in economic value due to these health gains (UR$18.4-UR$38.7 billion). The return on investment varied by specific diagnostic test (20.3 (UR 15.2-25.4) for tuberculosis, 7.7 (UR 1.6-13.9) for HIV viral load testing, 63.0 (UR 63.0-65.5) for early infant diagnosis of HIV and 2.5 (UR 0.7-4.6) for SARS-CoV-2), for an average of 13.9 (UR 9.0-18.9) for the entire molecular diagnostics programme or US$13.9 of value for each UR$1 invested.

Conclusions: The molecular diagnostics programme in South Africa generated a significant amount of health gains and economic value associated with these health gains. The return on investment rivals other high-impact public health interventions such as childhood vaccination. The molecular diagnostics programme in South Africa is highly impactful and will continue to be an excellent investment in South African public health expenditure.