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To assess sex disparities in tuberculosis in Vietnam, we conducted a nested, case-control study based on a 2017 tuberculosis prevalence survey. We defined the case group as all survey participants with laboratory-confirmed tuberculosis and the control group as a randomly selected group of participants with no tuberculosis. We used structural equation modeling to describe pathways from sex to tuberculosis according to an a priori conceptual framework. Our analysis included 1,319 participants, of whom 250 were case-patients. We found that sex was directly associated with tuberculosis prevalence (adjusted odds ratio for men compared with women 3.0 [95% CI 1.7-5.0]) and indirectly associated through other domains. The strong sex difference in tuberculosis prevalence is explained by a complex interplay of factors relating to behavioral and environmental risks, access to healthcare, and clinical manifestations. However, after controlling for all those factors, a direct sex effect remains that might be caused by biological factors.

Background Despite adherence to WHO guidelines, inpatient mortality among sick children admitted to hospital with complicated severe acute malnutrition (SAM) remains unacceptably high. Several studies have examined risk factors present at admission for mortality. However, risks may evolve during admission with medical and nutritional treatment or deterioration. Currently, no specific guidance exists for assessing daily treatment response. This study aimed to determine the prognostic value of monitoring clinical signs on a daily basis for assessing mortality risk during hospitalization in children with SAM. Methods This is a secondary analysis of data from a randomized trial (NCT02246296) among 843 hospitalized children with SAM. Daily clinical signs were prospectively collected during ward rounds. Multivariable extended Cox regression using backward feature selection was performed to identify daily clinical warning signs (CWS) associated with time to death within the first 21 days of hospitalization. Predictive models were subsequently developed, and their prognostic performance evaluated using Harrell’s concordance index (C-index) and time-dependent area under the curve (tAUC). Results Inpatient case fatality ratio was 16.3% ( n =127). The presence of the following CWS during daily assessment were found to be independent predictors of inpatient mortality: symptomatic hypoglycemia, reduced consciousness, chest indrawing, not able to complete feeds, nutritional edema, diarrhea, and fever. Daily risk scores computed using these 7 CWS together with MUAC<10.5cm at admission as additional CWS predict survival outcome of children with SAM with a C-index of 0.81 (95% CI 0.77–0.86). Moreover, counting signs among the top 5 CWS (reduced consciousness, symptomatic hypoglycemia, chest indrawing, not able to complete foods, and MUAC<10.5cm) provided a simpler tool with similar prognostic performance (C-index of 0.79; 95% CI 0.74–0.84). Having 1 or 2 of these CWS on any day during hospitalization was associated with a 3 or 11-fold increased mortality risk compared with no signs, respectively. Conclusions This study provides evidence for structured monitoring of daily CWS as recommended clinical practice as it improves prediction of inpatient mortality among sick children with complicated SAM. We propose a simple counting-tool to guide healthcare workers to assess treatment response for these children. Trial registration NCT02246296

Abstract In sub-Saharan Africa, accessibility to affordable quality care is often poor and health expenditures are mostly paid out of pocket. Health insurance, protecting individuals from out-of-pocket health expenses, has been put forward as a means of enhancing universal health coverage. We explored the utilization of different types of healthcare providers and the factors associated with provider choice by insurance status in rural Nigeria. We analysed year-long weekly health diaries on illnesses and injuries (health episodes) for a sample of 920 individuals with access to a private subsidized health insurance programme. The weekly diaries capture not only catastrophic events but also less severe events that are likely underreported in surveys with longer recall periods. Individuals had insurance coverage during 34% of the 1761 reported health episodes, and they consulted a healthcare provider in 90% of the episodes. Multivariable multinomial logistic regression analyses showed that insurance coverage was associated with significantly higher utilization of formal health care: individuals consulted upgraded insurance programme facilities in 20% of insured episodes compared with 3% of uninsured episodes. Nonetheless, regardless of insurance status, most consultations involved an informal provider visit, with informal providers encompassing 73 and 78% of all consultations among insured and uninsured episodes, respectively, and individuals spending 54% of total annual out-of-pocket health expenditures at such providers. Given the high frequency at which individuals consult informal providers, their position within both the primary healthcare system and health insurance schemes should be reconsidered to reach universal health coverage.

IntroductionDespite progress in tuberculosis (TB) treatment coverage in past years, an estimated 183 000 people with TB may not have been diagnosed in Pakistan in 2022. Therefore, there is a need to develop models which help to steer active case finding (ACF) towards populations with a high probability of having undetected TB. The aim of this study was to cross-validate TB positivity rate predictions in ACF settings of an existing Bayesian machine learning (BML) with a simpler frequentist model.MethodsWe conducted a retrospective analysis of cross-sectional data to identify predictors for detection of bacteriologically confirmed TB cases during ACF events in Pakistan. A predictive negative binomial regression (NBR) model was created, and the presence of spatial autocorrelation was examined to account for spatial dependencies in the outcome variable. The NBR and BML models were compared on their respective predictive precisions for the identification of TB hotspots, based on Root Mean Square Error values, k-fold cross-validation and tehsil-level (sub-district) prediction rankings.Results407 (1.9%) bacteriologically confirmed cases among 21 227 visitors were detected in 414 ACF events between September 2020 and January 2022. In the final NBR, the spatial lag variable explained most variation in TB positivity rates across ACF events. NBR and BML predictions were similar at tehsil level. While the BML had a slightly lower root mean squared error (1.02 vs 1.03) the NBR had a slightly better fit based on the Akaike information criterion.ConclusionsStatistical models can be effective in predicting TB hotspots for ACF planning, and the relatively simpler NBR model was nearly as effective as a more complex BML model. The predictions of different modelling approaches were similar, suggesting that predictions are more driven by covariates rather than modelling framework. The agreement between model results increases confidence in the potential utility of models to spatially target ACF activities in high need, low access areas.

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Children with severe acute malnutrition (SAM) display immature, altered gut microbiota and have a high mortality risk. Faecal volatile organic compounds (VOCs) reflect the microbiota composition and may provide insight into metabolic dysfunction that occurs in SAM. Here we determine whether analysis of faecal VOCs could identify children with SAM with increased risk of mortality. VOC profiles from children who died within six days following admission were compared to those who were discharged alive using machine learning algorithms. VOC profiles of children who died could be separated from those who were discharged with fair accuracy (AUC) = 0.71; 95% CI 0.59–0.87; P  = 0.004). We present the first study showing differences in faecal VOC profiles between children with SAM who survived and those who died. VOC analysis holds potential to help discover metabolic pathways within the intestinal microbiome with causal association with mortality and target treatments in children with SAM. Trial Registration: The F75 study is registered at clinicaltrials.gov/ct2/show/NCT02246296.

Improved causes of death (CoD) understanding in low- and middle-income countries is needed to reduce child mortality. Compared to full autopsy, minimally invasive tissue sampling (MITS), using transcutaneous needle sampling, is a feasible, socially acceptable, and validated method. We aimed to quantify the additional contribution of MITS to CoD attribution based on clinical records and inpatient research data with intensive patient characterisation. We enrolled children aged seven days to 59 months who died while on admission for acute illness and/or severe malnutrition to Queen Elizabeth Central Hospital in Blantyre, Malawi. Standard MITS procedures included histologic, immunohistochemical, and microbiologic testing. Phase 1 CoD determination was based on medical records alone, Phase 2 also included research data, and Phase 3 included all data, including from MITS. We enrolled 29 children. Based on clinical notes alone (Phase 1), we identified 60 causal and 39 contributing conditions. Of the 45 (45%) infectious conditions, pathogens were identified in 15 (33%). Only one patient's (3%) CoD was unchanged compared to including all data (Phase 3). Further, we identified 69 new (n = 43) or adjusted (n = 26) diagnoses among 28 cases (97%); the majority were undernutrition-related (n = 22, 32%) or infectious (n = 41, 59%) conditions. Overall, the majority of final Phase 3 conditions were also undernutrition-related (n = 46, 32%) or infectious (n = 61, 43%) and a pathogen was identified in 54 (89%) of the infectious conditions. Klebsiella pneumoniae was the most prevalent aetiology in both pneumonia and sepsis. The addition of MITS to clinical and inpatient research data led to almost all (97%) of cases receiving new and/or refined diagnoses, including microbe identification in infectious conditions. Pathogens not specifically addressed by current clinical guidelines, such as Klebisiella pneumoniae, were commonly identified. Our findings support the utility of MITS to understand CoD even after thorough clinical characterisation of children during hospitalisation.

Abstract Background: Access to quality obstetric care is considered essential to reducing maternal and new-born mortality. We evaluated the effect of the introduction of a multifaceted voluntary health insurance programme on hospital deliveries in rural Nigeria. Methods: We used an interrupted time-series design, including a control group. The intervention consisted of providing voluntary health insurance covering primary and secondary healthcare, including antenatal and obstetric care, combined with improving the quality of healthcare facilities. We compared changes in hospital deliveries from 1 May 2005 to 30 April 2013 between the programme area and control area in a difference-in-differences analysis with multiple time periods, adjusting for observed confounders. Data were collected through household surveys. Eligible households (n = 1500) were selected from a stratified probability sample of enumeration areas. All deliveries during the 4-year baseline period (n = 460) and 4-year follow-up period (n = 380) were included. Findings: Insurance coverage increased from 0% before the insurance was introduced to 70.2% in April 2013 in the programme area. In the control area insurance coverage remained 0% between May 2005 and April 2013. Although hospital deliveries followed a common stable trend over the 4 pre-programme years (P = 0.89), the increase in hospital deliveries during the 4-year follow-up period in the programme area was 29.3 percentage points (95% CI: 16.1 to 42.6; P < 0.001) greater than the change in the control area (intention-to-treat impact), corresponding to a relative increase in hospital deliveries of 62%. Women who did not enroll in health insurance but who could make use of the upgraded care delivered significantly more often in a hospital during the follow-up period than women living in the control area (P = 0.04). Conclusions: Voluntary health insurance combined with quality healthcare services is highly effective in increasing hospital deliveries in rural Nigeria, by improving access to healthcare for insured and uninsured women in the programme area.

IntroductionTreatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routine hospital data, we evaluated the ability of multiple ML models to predict inpatient mortality in a paediatric population in a low/middle-income country.MethodWe retrospectively analysed hospital record data from 0-59 months old children admitted to the ICU of Dhaka hospital of International Centre for Diarrhoeal Disease Research, Bangladesh. Five commonly used ML models- logistic regression, least absolute shrinkage and selection operator, elastic net, gradient boosting trees (GBT) and random forest (RF), were evaluated using the area under the receiver operating characteristic curve (AUROC). Top predictors were selected using RF mean decrease Gini scores as the feature importance values.ResultsData from 5669 children was used and was reduced to 3505 patients (10% death, 90% survived) following missing data removal. The mean patient age was 10.8 months (SD=10.5). The top performing models based on the validation performance measured by mean 10-fold cross-validation AUROC on the training data set were RF and GBT. Hyperparameters were selected using cross-validation and then tested in an unseen test set. The models developed used demographic, anthropometric, clinical, biochemistry and haematological data for mortality prediction. We found RF consistently outperformed GBT and predicted the mortality with AUROC of ≥0.87 in the test set when three or more laboratory measurements were included. However, after the inclusion of a fourth laboratory measurement, very minor predictive gains (AUROC 0.87 vs 0.88) resulted. The best predictors were the biochemistry and haematological measurements, with the top predictors being total CO2, potassium, creatinine and total calcium.ConclusionsMortality in children admitted to ICU can be predicted with high accuracy using RF ML models in a real-life data set using multiple laboratory measurements with the most important features primarily coming from patient biochemistry and haematology.

RationaleSince the first documentation of skin changes in malnutrition in the early 18th century, various hair and skin changes have been reported in severely malnourished children globally. We aimed to describe the frequency and types of skin conditions in children admitted with acute illness to Queen Elizabeth Central Hospital, Blantyre, Malawi across a spectrum of nutritional status and validate an existing skin assessment tool.MethodsChildren between 1 week and 23 months of age with acute illness were enrolled and stratified by anthropometry. Standardised photographs were taken, and three dermatologists assessed skin changes and scored each child according to the SCORDoK tool.ResultsAmong 103 children, median age of 12 months, 31 (30%) had severe wasting, 11 (11%) kwashiorkor (nutritional oedema), 20 (19%) had moderate wasting, 41 (40%) had no nutritional wasting and 18 (17%) a positive HIV antibody test. Six (5.8%) of the included patients died. 51 (50%) of children presented with at least one skin change. Pigmentary changes were the most common, observed in 35 (34%), with hair loss and bullae, erosions and desquamation the second most prevalent skin condition. Common diagnoses were congenital dermal melanocytosis, diaper dermatitis, eczema and postinflammatory hyperpigmentation. Severe skin changes like flaky paint dermatosis were rarely identified. Inter-rater variability calculations showed only fair agreement (overall Fleiss’ kappa 0.25) while intrarater variability had a fair-moderate agreement (Cohen’s kappa score of 0.47–0.58).DiscussionSkin changes in hospitalised children with an acute illness and stratified according to nutritional status were not as prevalent as historically reported. Dermatological assessment by means of the SKORDoK tool using photographs is less reliable than expected.