Malnutrition among under-5 children is a major challenge in low-income and middle-income countries, contributing to poor growth and development. Growth monitoring and promotion (GMP) is recommended by WHO for early detection, yet its implementation remains weak due to limited resources and health system gaps. While many studies focus on utilisation, little is known about facility readiness, especially from multicountry analyses. This study aimed to assess the readiness of health facilities to provide growth monitoring (GM) service and associated factors using nationally representative data.

Secondary analysis of existing cross-sectional survey data.

Secondary data from service provision assessment surveys of Afghanistan, Bangladesh, Ethiopia, Haiti, Nepal and Tanzania was used.

We included 6595 health facilities, which have complete information. Health facilities’ readiness score was constructed as a counting score with regard to the availability of essential indicators required to provide GM services by using WHO service availability and readiness assessment.

Among 6595 health facilities, 28.5% (95% CI 27.4% to 29.6%) of the facilities reported at least 75% of the relevant items for GM service provision. The factors associated with health facilities’ readiness to provide GM service were quality assurance activities (incident rate ratio (IRR): 1.15; 95% CI 1.07 to 1.24), separate user fees (IRR: 1.10; 95% CI 1.00 to 1.20), fixed user fees (IRR: 1.20; 95% CI 1.10 to 1.31) and presence of trained health provider at facility 24 hours (IRR: 1.09; 95% CI 1.02 to 1.17).

Health facilities in six countries showed low readiness for GMP services. Strengthening facility capacity, staffing, equipment and quality assurance is essential to improve readiness.

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.

Patient engagement (PE), or a patient’s participation in their healthcare, is an important component of comprehensive healthcare delivery, yet there is not an existing, publicly available, measurement tool to assess PE capacity and behaviours. We sought to develop a survey to measure PE capacity and behaviours for use in ambulatory healthcare clinics.

Measure development and psychometric evaluation.

A total of 1180 adults in the USA from 2022 to 2024, including 1050 individuals who had indicated they had seen a healthcare provider in the prior 12 months who were recruited nationally via social media across three separate samples; 8 patient advisors and healthcare providers recruited from a large, midwestern US Academic Medical Center; and 122 patients recruited from five participating ambulatory clinics in the Midwestern USA.

An initial survey was developed based on a concept mapping approach with a Project Advisory Board composed of patients, researchers and clinicians. Social media was then used to recruit 540 participants nationally (Sample 1) to complete the initial, 101-item version of the survey to generate data for factor analysis. We conducted exploratory and confirmatory factor analyses to assess model and item fit to inform item reduction, and subsequently conducted cognitive interviews with eight additional participants (patient advisors and providers; Sample 2), who read survey items aloud, shared their thoughts and selected a response. The survey was revised and shortened based on these results. Next, a test–retest survey, also administered nationally via another round of social media recruitment, was administered two times to a separate sample (n=155; Sample 3), 2 weeks apart. We further revised the survey to remove items with low temporal stability based on these results. For clinic administration, research staff approached patients (n=122; Sample 4) in waiting rooms in one of five ambulatory clinics to complete the survey electronically or on paper to determine feasibility of in-clinic survey completion. We engaged in further item reduction based on provider feedback about survey length and fielded a final revised and shortened survey nationally via a final round of social media recruitment (n=355; Sample 5) to obtain psychometric data on this final version.

Cronbach’s alphas, intraclass correlations (ICCs), Comparative Fit Index (CFI), root mean square error of approximation (RMSEA), standardised root mean squared residual (SRMR).

The final PE Capacity Survey (PECS) includes six domains across two scales: ‘engagement behaviours’ (ie, preparing for appointments, ensuring understanding, adhering to care) and ‘engagement capacity’ (ie, healthcare navigation resources, resilience, relationship with provider). The PECS is 18 questions, can be completed during a clinic visit in less than 10 minutes, and produces scores which demonstrate acceptable internal consistency reliability (α=0.72 engagement behaviours, 0.76 engagement capacity), indicating items are measuring the same overarching construct. The scales also had high test–retest reliability (ICC=0.82 behaviours, 0.86 capacity), indicating stability of response over time, and expected dimensionality with high fit indices for the final scales (behaviours: CFI=0.97; RMSEA=0.07; SRMR=0.05; capacity: CFI=0.99; RMSEA=0.06; SRMR=0.06), indicating initial evidence of construct validity.

The PECS is the first known measure to assess patients’ capacity for engagement and represents a step toward informing interventions and care plans that acknowledge a patient’s engagement capacity and supporting engagement behaviours. Future work should be done to validate the measure in other languages and patient populations, and to assess criterion-related validity of the measure against patient outcomes.