Background Current WHO recommendations for monitoring treatment response in adult pulmonary tuberculosis (TB) are sputum smear microscopy and/or culture conversion at the end of the intensive phase of treatment. These methods either have sub-optimal accuracy or a long turn-around time. There is a need to identify alternative biomarkers to monitor TB treatment response. Methods We conducted a systematic review of active pulmonary TB treatment monitoring biomarkers. We screened 9,739 articles published between January 1 st 2008 and December 31 st 2020, of which 77 met the inclusion criteria. When studies quantitatively reported biomarker levels, we meta-analyzed the average fold change in biomarkers from pre-treatment to week 8 of treatment. We also performed a meta-analysis pooling the fold-change since previous time point collected. Results A total of 81 biomarkers were identified from 77 studies. Overall, these studies exhibited extensive heterogeneity with regard to TB treatment monitoring study design and data reporting. Among the biomarkers identified, CRP, IL-6, IP-10 and TNF-α had sufficient data to analyze fold-changes. All four biomarker levels decreased during the first 8 weeks of treatment relative to baseline and relative to previous time points collected. Conclusion Based on limited data available, CRP, IL-6, IP-10 and TNF-α have been identified as biomarkers that should be further explored in the context of TB treatment monitoring. The extensive heterogeneity in TB treatment monitoring study design and reporting is a major barrier to evaluating the performance of novel biomarkers and tools for this use case. Guidance for designing and reporting treatment monitoring studies are urgently needed.
BackgroundChildhood stunting is the most common manifestation of chronic malnutrition. A growing body of literature indicates that stunting can have negative repercussions on physical and cognitive development. There are increasing concerns that low- and middle-income countries (LMICs) are particularly susceptible to adverse consequences of stunting on economic development. The aim of this review is to synthesize current evidence on interventions and policies that have had success in reducing stunting and explore the impact of successes on economic indicators.MethodsThis review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles were searched through MEDLINE via PubMed and Ovid, Cochrane Library, Web of Science and ProQuest. Only articles that addressed the effects of nutrition and cash-based interventions and/or policies on stunting and reported effects on childhood mortality and/or human capital indicators were included. Two reviewers independently abstracted data and assessed quality.ResultsSeventeen studies from Africa (47%), South America (41%), and South Asia (12%) met the eligibility criteria: 8 cohort studies, 4 case studies, 4 Randomized Control Trials (RCTs) and 1 quasi-trial. Three types of interventions/policies were evaluated: multisectoral policies, nutritional supplementations and cash-based interventions (CCT). Overall, 76% of the included studies were successful in reducing stunting and 65% of interventions/policies reported successes on stunting reductions and economic successes. Five of the 11 successful studies reported on nutritional supplementation, 4 reported on multisectoral policies, and 2 reported on CCT interventions. Average Annual Rate of Reduction (AARR) was calculated to assess the impact of multisectoral policies on childhood mortality. AARR for under 5 mortality ranged from 5.2 to 6.2% and all countries aligned with the global target of 4.4% AARR. Quality assessment yielded positive results, with the biggest concerns being attrition bias for cohort studies, blinding for trials and generalizability of results for case studies.ConclusionsEvidence suggests that investment in fighting chronic malnutrition through multisectoral policies, multi-year nutritional supplementation (protein or multiple micronutrient supplementation) and possibly CCTs can have a long-term impact on economic development of LMICs. More evidence is needed to inform practices in non-represented regions while prioritizing standardization of economic indicators in the literature.
With the Covid-19 pandemic and the introduction of the WHO’s Essential Diagnostics List (EDL), increasing global attention is focused on the crucial role of diagnostics in achieving universal health coverage. To create national EDLs and to aid health system planning, it is vital to understand the most common conditions with which people present at primary care health facilities. We undertook a systematic review of the most common reasons for primary care visits in low- and middle-income countries. Six databases were searched for articles published between January 2009 and December 2019, with the search updated on MEDLINE to January 2021. Data on the most common patient reasons for encounter (RFEs) and provider diagnoses were collected. 17 of 22,279 screened articles were included. Most studies used unvalidated diagnostic classification systems or presented provider diagnosis data grouped by organ system, rather than presenting specific diagnoses. No studies included data from low-income countries. Only four studies (from Brazil, India, Nigeria and South Africa) using the ICPC-2 classification system contained RFE and provider diagnosis data and could be pooled. The top five RFEs from the four studies were headache, fever, back or low back symptom, cough and pain general/multiple sites. The top five diagnoses were uncomplicated hypertension, upper respiratory tract infection, type 2 diabetes, malaria and health maintenance/prevention. No psychological symptoms were among the top 10 pooled RFEs. There was more variation in top diagnoses between studies than top RFEs, showing the importance of creating location-specific lists of essential diagnostics for primary care. Future studies should aim to sample primary care facilities from across their country of study and use ICPC-3 to report both patient RFEs and provider diagnoses.
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