Although the WHO and the Centers for Disease Control and Prevention (CDC) classify preconception health risks (PCHRs) into biomedical, behavioural and social categories, this classification remains theoretical, mainly inconsistent and lacks a scientifically robust framework. Data-driven clustering techniques may help clarify this complexity for policymakers and healthcare providers. This study aimed to assess the status of PCHRs and identify latent classes of these risks among women preparing for pregnancy.

This community-based cross-sectional study was conducted from 31 July to 16 August 2024 in Tigray, Ethiopia, among 865 married women planning to conceive within the next 6 months. Data were gathered through face-to-face interviews using a structured questionnaire. Risk factor indicators covering lifestyle behaviours, substance use, nutritional risks and related factors were developed based on guidelines from the WHO, the CDC and national recommendations. Latent class analysis (LCA) was employed to identify distinct classes of PCHRs, with the optimal number of classes determined using statistical fit indices, adequacy criteria and interpretability. The study also evaluated the overall distribution of PCHRs among participants.

The study took place in Tigray, Ethiopia, among married women intending to become pregnant within 6 months.

Burden of PCHRs and identified distinct latent classes of these risks within the participants.

All participants were exposed to at least four PCHRs, with 84.2% experiencing between 6 and 12 risk factors. The optimal LCA model identified four distinct classes of PCHRs: lifestyle behavioural risks (n=458, 52.9%), reproductive health risks and chronic medical conditions (n=106, 12.25%), nutritional risks and environmental exposure (n=149, 17.23%) and social determinants of health (n=152, 17.57%).

Our study reveals a high baseline level of PCHRs, with all participants exhibiting multiple risk factors for adverse pregnancy outcomes. The identification of four distinct risk profiles underscores the need for tailored risk-specific interventions, particularly in conflict-affected settings. Our findings point out the need for targeted preconception care and risk stratification in national health strategies to improve maternal and child health outcomes.

To develop and validate a risk prediction model for preterm premature rupture of membranes (PPROM) to enable early identification of at-risk women and support clinical decision-making in North Wollo Zone, Ethiopia.

A hospital-based retrospective cross-sectional study.

Six public hospitals in the North Wollo Zone, Northern Ethiopia.

A total of 1098 pregnant women were included in the study using systematic random sampling.

Occurrence of PPROM.

Data were collected between 20 November 2023 and 20 March 2024, using structured interviews and medical record reviews. A risk prediction model was developed using Least Absolute Shrinkage and Selection Operator and logistic regression. Model performance was assessed through area under the curve (AUC), calibration plots and the Hosmer-Lemeshow test. Internal validation was conducted via bootstrap resampling. A simplified risk score was created to categorise women into high-risk and low-risk groups, and its clinical utility was evaluated using decision curve analysis.

Among the 1098 participants (100% response rate), the mean age was 21.54 years (IQR: 18–26), with 57.2% aged 20–34 years. The prevalence of PPROM was 10.75% (95% CI 9.01% to 12.77%). Seven significant predictors were identified: maternal age

PPROM remains a significant obstetric complication in the study area. The validated risk prediction model showed moderate to good performance and can be used to support early screening and risk-based management in antenatal care (ANC). Integrating the tool into routine ANC services, along with health education and management of modifiable risk factors, may help reduce PPROM-related adverse outcomes. Further external validation is recommended.

This study aimed to assess functional disability and associated factors among people with severe mental illness attending public hospitals in Harar town, Eastern Ethiopia.

An institution-based cross-sectional study was conducted in public hospitals found in Harar town, Eastern Ethiopia, from 1 April to 10 May 2023.

A total of 342 patients with severe mental illness attending public hospitals in Harar town, Eastern Ethiopia, were included.

The main outcome of this study was functional disability, which was measured using a 12-item version of the WHO Disability Assessment Schedule.

In this study, the mean (±) score of functional disability among patients with severe mental illness was 31.8 (±9.6) (95% CI: 30.7 to 32.8). The multiple linear regression model revealed that unemployment (β: 1.93; 95% CI: 0.28 to 3.59), total illness duration (5–10 years and above 10 years) (β: 3.71; 95% CI: 1.95 to 5.48 and β: 4.51; 95% CI: 2.41 to 6.62, respectively), age at illness onset (β: –0.37; 95% CI: –0.45 to –0.28), having drug side effects (β: 3.05; 95% CI: 1.46 to 4.63), medication non-adherence (β: 6.15; 95% CI: 4.56 to 7.73) and having high perceived stigma (β: 2.27; 95% CI: 0.72 to 3.82) were significantly associated with functional disability.

Patients with severe mental illness had a high mean functional disability score. Unemployment, age at onset of mental illness, total duration of illness, medication side effects, medication non-adherence and higher perceived stigma were factors that were associated with functional disability. Given the mean functional disability score observed among respondents, a multipronged intervention strategy that prioritises early detection and management of severe mental illness, particularly targeting individuals at risk, is recommended to mitigate disability and enhance quality of life.