Evaluation of a flexible piecewise linear mixed-effects model in the analysis of randomized cross-over trials.

Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment. They have received a lot of attention, particularly in connection with regulatory requirements for new drugs. The main advantage of using cross-over designs over conventional parallel designs is increased precision, thanks to within-subject comparisons. In the statistical literature, more recent developments are discussed in the analysis of cross-over trials, in particular regarding repeated measures. A piecewise linear model within the framework of mixed effects has been proposed in the analysis of cross-over trials. In this article, we report on a simulation study comparing performance of a piecewise linear mixed-effects (PLME) model against two commonly cited models-Grizzle's mixed-effects (GME) and Jones & Kenward's mixed-effects (JKME) models-used in the analysis of cross-over trials. Our simulation study tried to mirror real-life situation by deriving true underlying parameters from empirical data. The findings from real-life data confirmed the original hypothesis that high-dose iodine salt have significantly lowering effect on diastolic blood pressure (DBP). We further sought to evaluate the performance of PLME model against GME and JKME models, within univariate modeling framework through a simulation study mimicking a 2 × 2 cross-over design. The fixed-effects, random-effects and residual error parameters used in the simulation process were estimated from DBP data, using a PLME model. The initial results with full specification of random intercept and slope(s), showed that the univariate PLME model performed better than the GME and JKME models in estimation of variance-covariance matrix (G) governing the random effects, allowing satisfactory model convergence during estimation. When a hierarchical view-point is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive-definite. The PLME model is preferred especially in modeling an increased number of random effects, compared to the GME and JKME models that work equally well with random intercepts only. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters.

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 × $\times$ 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi et al. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a 2 × 2 $2\times 2$ crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modeling framework.

"For a man to go to hospital, then that would be his last option": A qualitative study exploring men's experiences, perceptions and healthcare needs in the implementation of Universal Health Coverage in Kenya.

The achievement of Universal Health Coverage (UHC) requires equitable access and utilization of healthcare services across all population groups, including men. However, men often face unique barriers that impede their engagement with health systems which are influenced by a myriad of socio-cultural, economic, and systemic factors. Therefore, understanding men's perspectives and experiences is crucial to identifying barriers and facilitators to their healthcare-seeking behaviour under UHC initiatives. This qualitative study sought to explore men's perceptions, experiences, healthcare needs and potential strategies to inform an impartial implementation of Universal Health Coverage (UHC) in Kenya. The study employed a qualitative research design to investigate men's healthcare experiences in 12 counties across Kenya. Thirty focus group discussions involving 296 male participants were conducted. Men were purposively selected and mobilized through the support of health facility-in-charges, public health officers, and community health extension workers. Data was coded according to emergent views and further categorized thematically into three main domains (1) Perspectives and experiences of healthcare access (2) Socio-cultural beliefs and societal expectations (3) Desires and expectations of health systems. Findings revealed complex sociocultural, economic, and health system factors that influenced men's healthcare experiences and needs which included: masculinity norms and gender roles, financial constraints and perceived unaffordability of services, lack of male-friendly and gender-responsive healthcare services, confidentiality concerns, and limited health literacy and awareness about available UHC services. Our study has revealed a disconnect between men's needs and the current healthcare system. The expectations concerning masculinity further exacerbate the problem and exclude men further hindering men's ability to receive appropriate care. This data provides important considerations for the development of comprehensive and gender-transformative approaches challenging harmful masculine norms, pushing for financial risk protection mechanisms and gender-responsive healthcare delivery attuned to the unique needs and preferences of men.

Health systems' capacity in availability of human resource for health towards implementation of Universal Health Coverage in Kenya.

INTRODUCTION: Kenya faces significant challenges related to health worker shortages, low retention rates, and the equitable distribution of Human Resource for Health (HRH). The Ministry of Health (MOH) in Kenya has established HRH norms and standards that define the minimum requirements for healthcare providers and infrastructure at various levels of the health system. The study assessed on the progress of Universal Health Coverage (UHC) piloting on Human Resource for Health in the country. METHODS: The study utilized a Convergent-Parallel-Mixed-Methods design, incorporating both quantitative and qualitative approaches. The study sampled diverse population groups and randomly selected health facilities. Four UHC pilot counties are paired with two non-UHC pilot counties, one neighboring county and the second county with a geographically distant and does not share a border with any UHC pilot counties. Stratification based on ownership and level was performed, and the required number of facilities per stratum was determined using the square root allocation method. Data on the availability of human resources for health was collected using a customized Kenya Service Availability and Readiness Assessment Mapping (SARAM) tool facilitated by KoBo ToolKitTM open-source software. Data quality checks and validation were conducted, and the HRH general service availability index was measured on availability of Nurses, Clinician, Nutritionist, Laboratory technologist and Pharmacist which is a minimum requirement across all levels of health facilities. Statistical analyses were performed using IBM SPSS version 27 and comparisons between UHC pilot counties and non-UHC counties where significance threshold was established at p < 0.05. Qualitative data collected using focus group discussions and in-depth interview guides. Ethical approval and research permits were obtained, and written informed consent was obtained from all participants. RESULTS: The study assessed 746 health facilities with a response rate of 94.3%. Public health facilities accounted for 75% of the sample. The overall healthcare professional availability index score was 17.2%. There was no significant difference in health workers' availability between UHC pilot counties and non-UHC pilot counties at P = 0.834. Public health facilities had a lower index score of 14.7% compared to non-public facilities at 27.0%. Rural areas had the highest staffing shortages, with only 11.1% meeting staffing norms, compared to 31.8% in urban areas and 30.4% in peri-urban areas. Availability of health workers increased with the advancement of The Kenya Essential Package for Health (KEPH Level), with all Level 2 facilities across counties failing to meet MOH staffing norms (0.0%) except Taita Taveta at 8.3%. Among specific cadres, nursing had the highest availability index at 93.2%, followed by clinical officers at 52.3% and laboratory professionals at 55.2%. The least available professions were nutritionists at 21.6% and pharmacist personnel at 33.0%. This result is corroborated by qualitative verbatim. CONCLUSION: The study findings highlight crucial challenges in healthcare professional availability and distribution in Kenya. The UHC pilot program has not effectively enhanced healthcare facilities to meet the standards for staffing, calling for additional interventions. Rural areas face a pronounced shortage of healthcare workers, necessitating efforts to attract and retain professionals in these regions. Public facilities have lower availability compared to private facilities, raising concerns about accessibility and quality of care provided. Primary healthcare facilities have lower availability than secondary facilities, emphasizing the need to address shortages at the community level. Disparities in the availability of different healthcare cadres must be addressed to meet diverse healthcare needs. Overall, comprehensive interventions are urgently needed to improve access to quality healthcare services and address workforce challenges.