Browsing by Author "Shisanya, Morris Senghor"

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Assessing the application of adapted theory of planned behaviour in predicting postpartum family planning intentions in a pragmatic randomized control trial in Western Kenya
(PLoS One, 2025-02-06) Shisanya, Morris Senghor; Kipmerewo, Mary; Everlyne Morema; Ouma, Collins
Introduction In developing countries like Kenya, addressing the high population growth rate necessitates a focus on early Postpartum Family Planning (PPFP) use. Despite the critical need for PPFP, few researchers explore the application of health behaviour change theories to enhance FP use among postpartum women. This study assesses the application of adapted Theory of Planned Behaviour (TPB) in predicting intention for early PPFP in postpartum women in Western Kenya. Methods This randomized control trial included pregnant women aged 15 to 49 attending Antenatal Care (ANC) clinics, randomly assigned to the "Nurses’ arm," "Community arm," or "Control arm." The intervention provided family planning (FP) counseling. Trained nurses and Community Health Workers (CHW) delivered counseling in their respective arms, while the control arm received routine care. Adapted TPB was integrated into client exit interviews to identify constructs influencing early PPFP intentions. Structural equation modeling (SEM) was used to predict the intention for early PPFP in the adapted TPB. Results The SEM was optimized with the removal of client knowledge on early PPFP. The final model retained satisfaction with PPFP counseling, perceived normative beliefs, attitude towards PPFP, behaviour control of PPFP choice, and perceived risk of early postpartum pregnancy. Only satisfaction with counseling (P = 0.001), perceived normative beliefs (P<0.0001), attitude towards PPFP (P<0.0001), and behaviour control of PPFP choice (P = 0.018) significantly influenced early PPFP intention. Conclusion The study demonstrates a viable application of the adapted TPB model in predicting early PPFP intention in an interventional study.
Influence of Antenatal Family Planning Counselling on Attitude Towards Early Postpartum Family Planning: A Randomised Controlled Trial in Kenya
(East Africa Health Research Journal, 2025-06-09) Shisanya, Morris Senghor; Kipmerewo, Mary; Morema, Everlyne Nyanchera; Ouma, Collins
to understand the diverse aspects influencing women’s attitudes towards early PPFP to address them effectively. There is a lack of comparative studies on the effectiveness of interventions to improve attitudes towards early PPFP. Bridging this gap is vital for evidence-based FP promotion and better maternal and child health outcomes. This study, therefore, compared attitudes toward early PPFP across nurse-led, community-based, and routine ANC groups. Methods: The study was a randomised control trial conducted in Kisumu County among pregnant women. Three arms were established: nurses’ and community interventions and a control. Sample size was determined based on expected differences in contraceptive use postpartum. Multistage sampling involving purposive, cluster, and simple random sampling was used. The intervention involved providing antenatal information on postpartum family planning (PPFP) using a mobile phone-based tool. Attitudes towards PPFP were measured using Likert scales and analysed through ANOVA. The study aimed to assess the impact of interventions on attitudes towards early PPFP. Results: Most participants (96.4%) had a positive attitude towards early PPFP, though some factors were linked to reduced positivity. Higher education (OR 0.6, 95% CI, 0.4 to 0.9, P=.026), comorbidity (OR 0.2, 95% CI, 0.1 to 0.6, P=.006), and longer counselling waiting and turnaround times (OR 0.9, 95% CI, 0.8–1.0, P=.059) and (OR: 0.9, 95% CI: 0.9 to 1.0, P=.032) were associated with more negative attitudes, while good perceived health increased positivity (OR 3.1, 95% CI, 1.0 to 9.2, P=.043). There was no significant difference in attitude between study arms (F (2,243) =3.0, P=.053). Conclusion: The study found a generally positive attitude towards early PPFP among participants, but no significant difference in attitude between intervention and control arms. Negative attitudes were associated with comorbidities, longer waiting times, and counselling turnaround times. The study recommends improvements in counselling quality by optimising waiting and turnaround times.
Predictors of Generalized Anxiety Disorder (GAD) among health care providers during the COVID–19 pandemic at a regional teaching and referral hospital in Western Kenya
(PLoS ONE, 2024-12-05) Bundi, Jared Makori; Morema, Everlyne Nyanchera; Shisanya, Morris Senghor
Corona Virus Disease of 2019 (COVID-19) is an unprecedented challenge to health care systems globally and locally. The study aimed to assess generalized anxiety disorder and associated factors among health care providers (HCP) during COVID–19 pandemic. A total of 202 health care providers participated in the study. This was a hospital-based cross-sectional study. The survey questionnaire consisted of six components: demographic factors, occupational factors, psychological factors, socioeconomic factors, and the multi-dimensional scale of perceived social support (MSPSS). The symptoms of anxiety were measured by a standardized questionnaire, a 7–item Generalized Anxiety Disorder scale (GAD—7). Chi-Square statistic was used as a selection criterion for the predictors of generalized anxiety disorder to be included in the final binary regression analysis model at α<0.05. Among 202 health care providers interviewed, the overall prevalence of anxiety symptoms was 59.9%. Some of the aspects that reduced the risk of GAD were; being a younger HCP (OR 0.11, P = 0.004), fewer years of experience (OR 0.09, P = 0.008), availability of workplace precautionary measures (OR 0.06, P = 0.004), lower income level (OR = 0.04, P = 0.014), living alone (OR = 0.02, P = 0.008) and permanent employment terms (OR = 0.0001, P<0.0001). On the other hand, insufficient state of personal protective equipment (PPEs) (OR = 10.64, P = 0.033), having a family member as a COVID-19 contact (OR = 11.24, P = 0.023) and facing COVID-19 related stigma (OR = 8.06, P = 0.001) significantly increased the odds of GAD. The study result is a call to prioritize the health care providers’ psychological well-being by putting in place measures to preserve and enhance their resilience in order to ensure they work optimally and sustain service delivery during a pandemic.
The AFRIDIARRHEA multimodal fusion framework for Estimating the Burden of Diarrheal Diseases Among Children Under Five in Kenya, Zimbabwe, and Somaliland
(2026-06-09) Agumba, John Onyango; Namusonge, Lucy Natecho; Ogendo, Joshua Ondura; Takavarasha, Musiiwa; Hassan, Mohamad Ahmed; Shisanya, Morris Senghor; Waswa, Lydia
Background: Accurate estimation of childhood diarrheal disease burden in Africa remains challenging because of limited surveillance, incomplete mortality data, pathogen-attribution uncertainty, and complex environmental and socioeconomic drivers. This study developed the African Diarrheal Disease Integrated Risk Intelligence and Burden Estimation Architecture (AFRIDIARRHEA), a multimodal fusion framework for estimating under-five diarrheal burden in resource-constrained settings. Methods: AFRIDIARRHEA integrates Bayesian epidemiological modeling, machine learning, temporal forecasting, geospatial analytics, pathogen attribution, environmental intelligence, and uncertainty quantification within a unified framework. Synthetic datasets representing Kenya, Zimbabwe, and Somaliland were used to evaluate mortality, morbidity, hospitalization burden, pathogen-attributed mortality, and predictive performance. Results: The framework identified substantial heterogeneity in disease burden across countries, with Zimbabwe exhibiting the highest modeled mortality and morbidity burden and Somaliland the highest hospitalization burden. Rotavirus and Shigella were the dominant contributors to pathogen-attributed mortality. The multimodal fusion model outperformed the Bayesian baseline and individual component models, achieving improved predictive accuracy, robust uncertainty calibration, and strong agreement with benchmark estimates. Conclusions: AFRIDIARRHEA demonstrates the potential of multimodal fusion modeling for integrated estimation of childhood diarrheal burden, pathogen attribution, and uncertainty in African settings. The framework provides a scalable, transparent, and policy-relevant approach for supporting vaccine prioritization, WASH investments, outbreak preparedness, and child survival programs in data-limited environments.
The role of AI in reducing maternal mortality: Current impacts and future potentials: Protocol for an analytical cross-sectional study
(PLoS One, 2025-05-14) Owoche, Patrick Oduor; Shisanya, Morris Senghor; Mayeku, Betty; Namusonge, Lucy Natecho
Background Maternal and newborn mortality remains a critical public health challenge, particularly in resource-limited settings. Despite global efforts, Kenya continues to report high maternal mortality rates of over 350 deaths per 100,000 live births and a neonatal mortality rate of 21 per 1,000 live births. Artificial Intelligence (AI)-enabled maternal healthcare interventions, such as Obstetric Point-of-Care Ultrasound (OPOCUS) and AI-driven SMS intervention on Promoting Mothers through Pregnancy and Postpartum (PROMPTS), offer innovative solutions to improve early detection, diagnosis, and maternal health-seeking behaviors. However, there is limited evidence on their usability, feasibility, and impact on maternal and neonatal outcomes. Objective This study aims to assess the implementation, user experiences, and impact of OPOCUS and PROMPTS on maternal and neonatal health outcomes in Kenya. Specifically, it evaluates their effectiveness in reducing maternal complications, improving antenatal and postnatal care utilization, and enhancing clinical decision-making while identifying potential barriers to adoption and scalability. Methods This mixed-methods, cross-sectional study will be conducted in ten counties in Kenya that have integrated AI-based maternal healthcare interventions. Quantitative data will be collected from health facility records, national health databases (KHIS), and structured surveys, while qualitative data will be gathered through key informant interviews (KIIs) with healthcare providers and policymakers, as well as focus group discussions (FGDs) with maternal health service users. Statistical analyses will include comparative pre- and post-AI implementation assessments, with thematic analysis for qualitative insights. Expected outcomes The study will generate empirical evidence on the feasibility, effectiveness, and barriers to AI integration in maternal health services. Findings will inform policy recommendations, enhance AI-assisted maternal healthcare design, and support the scaling of AI-driven interventions to improve maternal and neonatal health outcomes in Kenya and other low-resource settings. Conclusion AI-based maternal health interventions hold promise for reducing maternal mortality, improving diagnostic accuracy, and enhancing health-seeking behaviors. However, their success depends on user experiences, healthcare system readiness, and policy alignment. This study will provide critical insights for evidence-based scaling and policy integration of AI in maternal healthcare.