To explore administrators’ and clinicians’ views on the factors that influence their use and adoption of a machine learning clinical decision support system (ML-CDSS) to predict patients’ risk of hepatic and renal deterioration during chemotherapy.

This was a qualitative study that used purposive sampling. 18 participants with administration and clinical backgrounds working in cancer care in England were recruited. Qualitative data were collected by conducting semi-structured interviews and a focus group. Data were analysed thematically using the framework method to identify key themes.

Participants acknowledged that monitoring blood chemistry is a core component of chemotherapy as it helps clinicians assess patient fitness and treatment response. The ML-CDSS was perceived as a potentially valuable tool for identifying patients at increased risk of hepatic and renal deterioration, supporting clinical decision-making and enhancing care efficiency. However, several concerns were raised regarding its potential implementation in practice. Participants questioned clinicians’ willingness and capacity to integrate the tool into their existing workflows. Participants also believed it was important to demonstrate the ML-CDSS’s sensitivity, specificity and validity in accurately predicting patients’ risk to build clinicians’ trust in the tool, demonstrating evidence of its efficacy and effectiveness in practice.

Administrators and clinicians recognised the potential benefits of the ML-CDSS to enhance the delivery of chemotherapy by identifying patients at risk for hepatic and renal deterioration. Successful adoption in practice depends on building trust with the tool by being transparent in its development, its effectiveness and impact. Future work should demonstrate the ML-CDSS being used in practice to generate real-world evidence.

Mycoplasma genitalium, Chlamydia trachomatis, Neisseria gonorrhoeae and Trichomonas vaginalis are sexually transmitted pathogens that are highly prevalent in developing countries and are strongly associated with pregnancy complications. In Chad, screening for these sexually transmitted infections (STIs) in pregnant women is based solely on patient-reported symptoms, even though these infections are frequently asymptomatic. This study aims to determine the prevalence of M. genitalium, C. trachomatis, N. gonorrhoeae and T. vaginalis infections, as well as their associated risk factors.

In this cross-sectional study, we recruited pregnant women attending antenatal clinics at seven hospitals in N’Djamena. Endocervical swabs were collected, and DNA was extracted. Infections were diagnosed using PCR. Risk factors were identified using a structured questionnaire, and associations were assessed using logistic regression.

A total of 525 pregnant women were enrolled, of whom 78.5% resided in urban areas, with a mean age of 25.16±5.54 years. Overall, 23.99% of the study population were diagnosed with at least one STI. The individual prevalence of M. genitalium, N. gonorrhoeae, C. trachomatis and T. vaginalis infections was 13.33%, 5.14%, 0.95% and 4.57%, respectively. Coinfections were low, with M. genitalium-T. vaginalis at 0.95%, M. genitalium-N. gonorrhoeae at 0.38% and other combinations at 0.19% each. Women residing in rural areas had nearly two times the odds of M. genitalium infection compared with urban residents (OR=1.98), indicating a higher risk. AgeM. genitalium infection (OR=1.71) were also associated with significantly increased risk.

This study demonstrates a high prevalence of STIs among pregnant women in Chad, underscoring the need for systematic screening rather than solely relying on syndromic management.

This study aimed to estimate the prevalence of depression and anxiety and associated risk factors among non-communicable diseases (NCD) clinic attendees in rural Rwanda.

Cross-sectional.

44 health centres in three rural districts in Rwanda.

Adults aged 18 years and older with a clinical diagnosis of diabetes, hypertension and/or asthma, who were attending a follow-up appointment during the study period (n=595).

Primary outcome measures were depression (measured by Patient Health Questionnaire-9) and anxiety (measured by Generalised Anxiety Disorder-7). Explanatory measures included sociodemographic and behavioural risk factors associated with depression and anxiety.

Of 595 participants, 265 (44.5%) had depression (95% CI: 40.5% to 48.6%) and 202 (33.9%) had anxiety (95% CI: 30.1% to 37.9%). Comorbidity of depression and anxiety was found in 137 participants (23%). Participants with no formal education had significantly higher odds of reporting depression and anxiety compared with those with primary and secondary/higher education (adjusted OR (aOR)=2.08; 95% CI=1.27 to 3.33, p=0.004, aOR=5.00; 95% CI=1.12 to 25.00, p=0.035, respectively). In addition, participants who were unemployed were more likely to report depression and anxiety (aOR=3.03; 95% CI=1.62 to 5.67, p

The overall prevalence of depression and anxiety was found to be significantly high among the study participants. The risk factors that were associated with depression and anxiety included level of education, district of residence, employment status and past trauma exposure. The findings emphasise the need for integrating mental health screening into NCD care, district-specific interventions, employment support services and trauma-focused care.

Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to funding limitations. The recent suspension of the US Agency for International Development funding exacerbates these issues, highlighting the need for accurate birth forecasting to guide policy and resource allocation. This study applied time-series and advanced machine-learning models to forecast future birth trends in Ethiopia.

Secondary data from the Ethiopian Demographic and Health Survey from 2000 to 2019 were used. After data preprocessing steps, including data conversion, filtering, aggregation and transformation, stationarity was checked using the Augmented Dickey-Fuller (ADF) test. Time-series decomposition was then performed, followed by time-series splitting. Seven forecasting models, including Autoregressive Integrated Moving Average, Prophet, Generalised Linear Models with Elastic Net Regularisation (GLMNET), Random Forest and Prophet-XGBoost, were built and compared. The models’ performance was evaluated using key metrics such as root mean square error (RMSE), mean absolute error (MAE) and R-squared value.

GLMNET emerged as the best model, explaining 77% of the variance with an RMSE of 119.01. Prophet-XGBoost performed reasonably well but struggled to capture the full complexity of the data, with a lower R-squared value of 0.32 and an RMSE of 146.87. Forecasts were made for both average monthly births and average births per woman over a 10-year horizon (2025–2034). The forecast for average monthly births indicated a gradual decline over the projection period. Meanwhile, the average births per woman showed an increasing trend but fluctuated over time, influenced by demographic shifts such as changes in fertility preferences, age structure and migration patterns.

This study demonstrates the effectiveness of combining time-series models and machine learning, with GLMNET and Prophet XGBoost emerging as the most effective. While average monthly births are expected to decline due to demographic transitions and migration, the average births per woman will remain high, reflecting persistent fertility preferences within certain subpopulations. These findings underscore the need for policies addressing both population trends and sociocultural factors.

This study aimed to assess high-risk human papillomavirus (HPV) infection (HPV 16/18) and its determinants among women in East Gojjam Zone, Northwest Ethiopia.

An institutional-based cross-sectional study.

The study was conducted among 337 women screened for cervical cancer in two hospitals in East Gojjam Zone from February to April 2021 gregoriean calander.

The prevalence of HPV infection was 14.2% (95% CI: 10.7% to 18.1%). The mean age of the respondents was 36.7±9.1 years. Women in the age group of 55–65 years (adjusted OR (AOR)=7.91, 95% CI: 1.95 to 32.09), early initiation of sexual intercourse (AOR=5.36, 95% CI: 1.58 to 18.13), history of sexually transmitted infection (STI) (AOR=3.52, 95% CI: 1.27 to 9.72), HIV positive status (AOR=6.8, 95% CI: 1.99 to 23.54) and number of lifetime sexual partners (AOR=4.37, 95% CI: 1.15 to 17.3) were important independent factors associated with the presence of oncogenic HPV infection.

We found a relatively low prevalence of high-risk HPV infection. Age, early initiation of sexual intercourse at less than 18 years, history of STI, being HIV seropositive and multiple sexual partners were important factors for high-risk HPV infection. Women aged >46 years, women with early initiation of sex, a history of STI, being HIV positive and a history of multiple sexual partners should be encouraged to be screened and vaccinated for HPV infection. Wider-ranging studies are also needed in HPV-infected women in association with the cervical lesion.