Community-based progress indicators for prevention of mother-to-child transmission and mortality rates in HIV-exposed children in rural Mozambique

Paediatric healthcare in Manhiça district through a gender lens: a retrospective analysis of 17 years of morbidity and demographic surveillance data

Background

Sex and gender are important determinants of health. Gender-based health inequities in the paediatric population have been reported in various countries, but data remain limited. In Mozambique, research on this topic is very scarce. Here we aimed to explore whether boys and girls in Manhiça district, southern Mozambique, differ in access to and provision of healthcare.

Methods

This retrospective analysis includes data on all paediatric (<15 years old) visits to six outpatient clinics and admissions to one hospital in Manhiça district from 2004 to 2020, collected through the morbidity surveillance system of the Manhiça Health and Demographic Surveillance System (HDSS). We compared characteristics and outcomes between boys and girls using descriptive statistics, standardised mean differences, and logistic regression. Post-discharge events were analysed using Cox proportional hazards regression and Fine-Gray competing risk regression. Minimum community-based incidence rates of outpatient clinic visits and hospitalisations were calculated using demographic surveillance data from the Manhiça HDSS and analysed with negative binomial regression.

Results

Girls represented 49.2% (560 630 out of 1 139 962) of paediatric visits to outpatient clinics and 45.1% (18 625 out of 41 278) of hospitalisations. The girls-to-boys incidence rate ratio (IRR) for hospitalisations was 0.81 (95% confidence interval (CI) = 0.79-0.84). Both boys and girls experienced symptoms for a median duration of one day (interquartile range (IQR) = 1-2) before seeking care. Severe manifestations at presentation to an outpatient clinic or upon hospitalisation tended to be less frequent in girls (girls-to-boys odds ratios (ORs) = 0.71-1.11). Girls were less frequently referred or admitted to hospital after an outpatient clinic visit (OR = 0.82; 95% CI = 0.79-0.86 and OR = 0.85; 95% CI = 0.84-0.87, respectively). The hospital case fatality ratio was 4.1% in boys and 4.2% in girls. The median duration of hospitalisation was three days (IQR = 2-5) and did not differ between boys and girls. Revisits to outpatient clinics, hospital readmissions, and hospital post-discharge mortality were similar in both groups.

Conclusions

Girls had fewer referrals and admissions to hospital in Manhiça district, but they were also less likely to present with severe manifestations. Other studied indicators of healthcare access and provision were overall similar for boys and girls. Further research is needed to continue assessing potential gender biases and sex differences in paediatric healthcare in Mozambique.

Over a decade of HIV infection prevalence and incidence among Mozambican pregnant women: a secondary analysis of prospectively collected data

BACKGROUND

Monitoring HIV infection estimates is critical to guide health interventions and assess their impact, especially in highly vulnerable groups to the infection such as African pregnant women. This study describes the trends of HIV infection over eleven years in women attending selected antenatal care (ANC) clinics from southern Mozambique.

METHODS

We performed a secondary analysis of data registered at the ANC clinic of the Manhiça District Hospital and from the Ministry of Health's HIV National Program Registry between 2010 and 2021. HIV incidence was calculated using prevalence estimates. HIV incidence trends over time were obtained by fitting splines regression model.

RESULTS

Data from 21,810 pregnant women were included in the analysis. Overall HIV prevalence was 29.3% (95% CI: 28.7-29.9), with a reduction from 28.2% (95% CI: 25.6-30.8) in 2010 to 21.7% (95% CI: 19.8-23.6) in 2021, except for a peak in prevalence (35.3%, 95% CI: 30.1-40.8) in 2016. Over the study period, by maternal age group, the largest reduction in HIV prevalence was in the 15-20 year-old group [62% reduction, from 14.3% (95% CI 10.8-18.4) to 5.3% (95% CI: 3.6-7.5)], followed by the 20-25 year old group [43% reduction, from 29.0% (95% CI: 24.2-34.5) to 16.6% (95% CI: 13.6-19.8)] and the 25-30 year old group [13% reduction, from 36.9% (95% CI: 31.0-43.1) to 32.0% (95% CI: 27.3-37.0)] (p < 0.001). Incidence of HIV infection increased from 12.75 per 100 person-years in 2010 to 18.65 per 100 person-years in 2018, and then decreased to 11.48 per 100 person-years in 2021.

CONCLUSIONS

The prevalence of HIV decreased while the overall incidence stayed similar in Mozambican pregnant women, during 2010 to 2021. However, both estimates remain unacceptably high, which indicates the need to revise current preventive policies and implement effective ones to improve HIV control among pregnant women.

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis

BACKGROUND

Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting the mortality risk for PWH. Nevertheless, the diverse timeframes of mortality among PWH and the potential multitude of modeling variables have cast doubt on the efficacy of the current predictive model for HIV-related deaths. To address this, we undertook a systematic review and meta-analysis, aiming to comprehensively assess the utilization of machine learning in the early prediction of HIV-related deaths and furnish evidence-based support for the advancement of artificial intelligence in this domain.

METHODS

We systematically combed through the PubMed, Cochrane, Embase, and Web of Science databases on November 25, 2023. To evaluate the bias risk in the original studies included, we employed the Predictive Model Bias Risk Assessment Tool (PROBAST). During the meta-analysis, we conducted subgroup analysis based on survival and non-survival models. Additionally, we utilized meta-regression to explore the influence of death time on the predictive value of the model for HIV-related deaths.

RESULTS

After our comprehensive review, we analyzed a total of 24 pieces of literature, encompassing data from 401,389 individuals diagnosed with HIV. Within this dataset, 23 articles specifically delved into deaths during long-term follow-ups outside hospital settings. The machine learning models applied for predicting these deaths comprised survival models (COX regression) and other non-survival models. The outcomes of the meta-analysis unveiled that within the training set, the c-index for predicting deaths among people with HIV (PWH) using predictive models stands at 0.83 (95% CI: 0.75-0.91). In the validation set, the c-index is slightly lower at 0.81 (95% CI: 0.78-0.85). Notably, the meta-regression analysis demonstrated that neither follow-up time nor the occurrence of death events significantly impacted the performance of the machine learning models.

CONCLUSIONS

The study suggests that machine learning is a viable approach for developing non-time-based predictions regarding HIV deaths. Nevertheless, the limited inclusion of original studies necessitates additional multicenter studies for thorough validation.

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study

Objective

The study aimed to use supervised machine learning models to predict the length and risk of prolonged hospitalization in PLWHs to help physicians timely clinical intervention and avoid waste of health resources.

Methods

Regression models were established based on RF, KNN, SVM, and XGB to predict the length of hospital stay using RMSE, MAE, MAPE, and R2, while classification models were established based on RF, KNN, SVM, NN, and XGB to predict risk of prolonged hospital stay using accuracy, PPV, NPV, specificity, sensitivity, and kappa, and visualization evaluation based on AUROC, AUPRC, calibration curves and decision curves of all models were used for internally validation.

Results

In regression models, XGB model performed best in the internal validation (RMSE = 16.81, MAE = 10.39, MAPE = 0.98, R2 = 0.47) to predict the length of hospital stay, while in classification models, NN model presented good fitting and stable features and performed best in testing sets, with excellent accuracy (0.7623), PPV (0.7853), NPV (0.7092), sensitivity (0.8754), specificity (0.5882), and kappa (0.4672), and further visualization evaluation indicated that the largest AUROC (0.9779), AUPRC (0.773) and well-performed calibration curve and decision curve in the internal validation.

Conclusion

This study showed that XGB model was effective in predicting the length of hospital stay, while NN model was effective in predicting the risk of prolonged hospitalization in PLWH. Based on predictive models, an intelligent medical prediction system may be developed to effectively predict the length of stay and risk of HIV patients according to their medical records, which helped reduce the waste of healthcare resources.

Laboratory services in the context of prevention of mother-to-child transmission of HIV testing requirements in Copperbelt Province, Zambia: a qualitative inquiry

INTRODUCTION

Reliable and timely laboratory results are crucial for monitoring the Prevention of the Mother-to-Child Transmission (PMTCT) cascade, particularly to enable early HIV diagnosis and early intervention. We sought to explore whether and how laboratory services have been prepared to absorb new testing requirements following PMTCT Test-and-Treat policy changes in three districts of Zambia.

METHOD

We employed in-depth interviews and thematic data analysis, informed by the health system dynamic framework. Twenty-Six health workers were purposively selected and a document review of laboratory services in the context of PMTCT was undertaken. All face-to-face interviews were conducted in three local government areas in the Copperbelt Province (one urban and two rural) between February 2019 and July 2020. We extracted notes and markings from the transcripts for coding. Different codes were sorted into potential themes and the data extracted were put within the identified themes. Trustworthiness was confirmed by keeping records of all data field notes, transcripts, and reflexive journals.

RESULTS

The findings revealed that the health system inputs (infrastructure and supplies, human resources, knowledge, and information and finance) and service delivery were unequal between the rural and urban sites, and this affected the ability of health facilities to apply the new testing requirements, especially, in the rural-based health facilities. The major barriers identified include gaps in the capacity of the existing laboratory system to perform crucial PMTCT clinical and surveillance functions in a coordinated manner and insufficient skilled human resources to absorb the increased testing demands. The centralized laboratory system for HIV testing of mothers and exposed neonates meant facilities had to send specimens to other facilities and districts which resulted in high turnaround time and hence delayed HIV diagnosis.

CONCLUSION

New guidelines implemented without sufficient capacitation of health system laboratory capacity severely limited the effectiveness of PMTCT program implementation. This study documented the areas relating to health system inputs and laboratory service delivery where greater support to enable the absorption of the new testing requirements is needed.

Molecular Detection of Soil-Transmitted Helminths and Enteric Protozoa Infection in Children and Its Association with Household Water and Sanitation in Manhiça District, Southern Mozambique

Intestinal parasite infections can have detrimental health consequences in children. In Mozambique, soil-transmitted helminth (STH) infections are controlled through mass drug administration since 2011, but no specific control program exists for enteric protozoa. This study evaluates STH and protozoan infections in children attending healthcare in Manhiça district, Southern Mozambique, and its association with water and sanitation conditions. We conducted a cross-sectional study in children between 2 and 10 years old in two health centers (n = 405). A stool sample and metadata were collected from each child. Samples were analyzed by multi-parallel real-time quantitative PCR (qPCR). We fitted logistic regression-adjusted models to assess the association between STH or protozoan infection with household water and sanitation use. Nineteen percent were infected with at least one STH and 77.5% with at least one enteric protozoon. qPCR detected 18.8% of participants with intestinal polyparasitism. Protected or unprotected water well use showed a higher risk for at least one protozoan infection in children (OR: 2.59, CI: 1.01-6.65, p-value = 0.010; OR: 5.21, CI: 1.56-17.46, p-value = 0.010, respectively) compared to household piped water. A high proportion of children had enteric protozoan infections. Well consumable water displayed high risk for that.