Abstract:

Long-acting injectable (LAI) antipsychotics can be an optimal solution for sustained treatment of severe mental illnesses, but the evidence on their use and accessibility in Nigeria is limited. The study aimed to undertake a systematic review of available literature on the use of LAI, its outcomes, acceptability, and barriers in treating severe mental illnesses in Nigeria. The research was conducted using databases, including PubMed, Google Scholar, Bielefeld Academic Search Engine, and CORE. The inclusion criteria were studies that assessed the use of LAI in treatment, their outcomes, acceptability, and barriers in Nigeria. Three articles were considered in the research, of which one study met the inclusion criteria, involving 128 psychiatrists and psychiatry trainees. According to the findings, the attitudes towards LAI were generally positive, with an estimated number of patients with psychotic disorders who actually received LAI treatment being 41.7%. The most commonly administered long-acting antipsychotics are fluphenazine and flupenthixol decanoate. However, there was no evidence on the benefits of LAI at the individual level, including reducing the risk of relapse, readmission, and the likelihood of adherence to treatment, and recovery process. Moreover, the study identified barriers to LAI treatment, including the cost of medication, availability, accessibility due to transportation and clinic visits, injection administration process, and a burden on patients’ families. In conclusion, the results demonstrated that LAI treatment is used and accepted among medical professionals in Nigeria. However, the evidence based on their effectiveness and cost-benefit is insufficient, which requires further high-quality multi-center studies.

References:

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Abstract:

Identifying independent determinants of under-five mortality (U5M) in Cameroon requires survival methods that properly account for the complex sampling design of DHS data. This study compares survey-weighted Cox proportional hazards regression and a discrete-time hazard model, applied to the same pooled dataset of five DHS waves (1991–2018): 35,257 live births, 2,968 under-five deaths, and 1,821 primary sampling units. Cox models were built sequentially across four covariate blocks, with model discrimination tracked at each step. As a robustness check, a discrete-time logit model with a natural cubic spline for time was fitted to 922,995 person-months. The proportional hazards assumption held globally and for every covariate. In the final Cox model (concordance=0.655), female sex (HR = 0.915, p = 0.033), maternal education (HR = 0.867 per level, p < 0.001), maternal age (HR = 0.956 per year, p < 0.001), and residence in the West region (HR = 0.675, p = 0.001) were significantly protective, while parity was the strongest risk factor (HR = 1.167 per child, p < 0.001), two other regions carried significant excess hazard, and the 2004 survey wave showed an independently elevated hazard (HR = 1.341, p < 0.001) unattenuated by covariate adjustment. The discrete-time model reproduced every Cox finding within 1%, with full concordance across all eighteen coefficients, and five sensitivity analyses left every finding unchanged (concordance 94–100%). These results demonstrate that Cox and discrete-time hazard models yield equivalent, highly robust conclusions for Cameroonian U5M determinants, and that parity reduction, maternal education, and later maternal age represent the most consistent protective targets for child survival policy, alongside the persistently protective profile of the West region.

References:

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Abstract:

Child malnutrition and suboptimal infant and young child feeding (IYCF) practices remain major public health challenges in Niger. This study assessed the nutritional status and IYCF practices of children aged 6–23 months in the Maradi Region and examined geographic disparities and factors associated with complementary feeding. A cross-sectional study based on the SMART methodology was conducted between September and October 2025. Using a two-stage cluster sampling design, 1,353 children aged 6–23 months were included. Nutritional status was assessed using WHO growth standards, while complementary feeding practices were evaluated using minimum dietary diversity (MDD), minimum meal frequency (MMF), and minimum acceptable diet (MAD). Global acute malnutrition affected 21.9% of children, including 6.5% severe acute malnutrition. Stunting affected 59.1%, underweight 49.4%, and anemia 39.0% of children. Significant geographic disparities were observed across the nine study units. Only 18.7% of children met MDD and 17.1% met MAD, although 84.6% achieved MMF. Multivariable analyses showed that maternal education was positively associated with MDD and MAD, while associations varied across feeding indicators. No significant association was found between anemia and the assessed IYCF indicators. These findings highlight a substantial burden of malnutrition and inadequate complementary feeding practices among young children in Maradi, with marked geographic inequalities. Targeted nutrition interventions that promote dietary diversity and maternal education are needed to improve child nutrition outcomes in the region.

References:

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[4].   Woldesenbet B, Tolcha A, Tsegaye B. Water, hygiene and sanitation practices are associated with stunting among children of age 24-59 months in Lemo district, South Ethiopia, in 2021: community based cross sectional study. BMC Nutr. 2023;9:16.

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[14].  Belay DG, Aragaw FM, Teklu RE, Fetene SM, Negash WD, Asmamaw DB, et al. Determinants of inadequate minimum dietary diversity intake among children aged 6-23 months in Sub-Saharan Africa: pooled prevalence and multilevel analysis of Demographic and Health Survey in 33 sub-Saharan African countries. Front Nutr. 2022;9:894552.

[15].  Ba DM, Ssentongo P, Gao X, Chinchilli VM, Richie JP Jr, Maiga M, Muscat JE. Prevalence and determinants of meeting minimum dietary diversity among children aged 6-23 months in three sub-Saharan African countries: the Demographic and Health Surveys, 2019-2020. Front Public Health. 2022;10:846049.

[16].  Stevens GA, Paciorek CJ, Flores-Urrutia MC, Borghi E, Namaste S, Wirth JP, et al. National, regional, and global estimates of anaemia by severity in women and children for 2000-19: a pooled analysis of population-representative data. Lancet Glob Health. 2022;10(5):e627-e639.

[17].  Gedfie S, Getawa S, Melku M. Prevalence and associated factors of iron deficiency and iron deficiency anemia among under-5 children: a systematic review and meta-analysis. SAGE Open Med. 2022;10:2333794X221110860.

[18].  Christian AK, Afful-Dadzie E, Marquis GS. Infant and young child feeding practices are associated with childhood anaemia and stunting in sub-Saharan Africa. BMC Nutrition. 2023;9(1):1-13.

[19].  Paulo HA, Andrew J, Luoga P, Chombo S, Mbishi JV, Addo IY, et al. Minimum dietary diversity behaviour among children aged 6 to 24 months and their determinants: insights from 31 Sub-Saharan African countries. BMC Nutr. 2024;10:160.

[20].  Janmohamed A, Baker MM, Doledec D, Ndiaye F, Konan ACL, Leonce A, Kouadio KL, Beye M, Yattara ML, Dissieka R. Dietary diversity and associated factors among infants and young children in three West African countries. Front Public Health. 2024;12:1386664.

[21].  Mekonen EG. Minimum dietary diversity and its associated factors among children aged 6-23 months in Senegal: Evidence from the demographic and health survey, 2023. PLoS One. 2025;20(9):e0332215.

[22].  Asmelash D, Agegnehu W, Fenta W, Asmelash Y, Debebe S, Asres A. The burden of asymptomatic malaria infection in children in Sub-Saharan Africa: a systematic review and meta-analysis exploring barriers to elimination and prevention. J Epidemiol Glob Health. 2025;15(1):17.

Abstract:

Cardiovascular diseases (CVD) remain the leading cause of death worldwide and are a major public health concern, particularly in low- and middle-income countries. The persistence of unhealthy lifestyles and inadequate preventive practices contributes significantly to the increase in the CVD burden. This study aimed to assess cardiovascular risk factors and prevention strategies among patients visiting the cardiology departments of Logbaba, Nylon, and Deido district hospitals in Douala, Cameroon. A quantitative cross-sectional descriptive study was conducted from January to June 2025 among 400 selected respondents in the target hospitals. The data were collected using a structured questionnaire and analyzed with Microsoft Excel, Epi Info and RStudio software. Frequencies and percentages were calculated and presented in tables and figures. Results revealed that 72% of respondents consumed alcohol, while 55% regularly consumed foods high in saturated fats and added sugars. In addition, 97% of participants reported high levels of stress. As for physical activity, 41% of the respondents were physically active only a few times a month. Unmodifiable risk factors were also predominant, with 73% of respondents having a family history of cardiovascular disease, with stroke being one of the most frequently reported condition. Although 77% of respondents consumed enough fruits and vegetables, and 88% took treatment for cardiovascular conditions, 51% had never received education or information on the prevention of cardiovascular disease from health professionals. The study highlights the urgent need to strengthen preventive education, promote healthy lifestyles, and improve screening for cardiovascular risks in health facilities.

References:

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Abstract:

Digital health applications are increasingly integrated into maternal and infant healthcare in Andhra Pradesh, India; yet, evidence on how healthcare providers experience this digital health integration into routine workflows, and the extent to which Artificial Intelligence (AI) enabled functionalities are used in their everyday practice remains limited. This qualitative study examines healthcare providers’ perceptions of digital health and AI integration, perceived benefits, implementation barriers, and implications for workflow and clinical decision making. Data were collected through semi-structured interviews with 36 healthcare providers, including Auxiliary Nurse Midwives (ANM), Community Health Officers (CHOs) /Mid-Level Health Providers (MLHP), and Medical Officers (MOs), across six purposively selected urban, rural, and tribal districts. Interviews were analysed using directed qualitative content analysis employing a deductive-inductive coding approach. Thematic domains identified in the doctoral research proposal were taken as the initial deductive structure while additional codes were developed inductively from the empirical data. Healthcare providers reported routine use of digital applications for registration, tracking, reporting, risk identification, referral, teleconsultation, and follow-up. Perceived benefits included improved continuity of care, programme monitoring, and timely follow-up. Poor network connectivity, repeated OTP and biometric authentication for registration or ID generation, and limited offline functionality, made regular use challenging, especially in remote areas and tribal areas. The effective integration depends on factors, such as infrastructure, usability, provider capacity, and the particular context of local implementation.

References:

[1].   Knop MR, Nagashima-Hayashi M, Lin R, Saing CH, Ung M, Oy S, et al. Impact of mHealth interventions on maternal, newborn, and child health from conception to 24 months postpartum in low- and middle-income countries: a systematic review. BMC Med. 2024;22(1):196. Available from: https://doi.org/10.1186/s12916-024-03417-9

[2].   Cockburn N, Osborne C, Withana S, Elsmore A, Nanjappa R, South M, et al. Clinical decision support systems for maternity care: a systematic review and meta-analysis. eClinicalMedicine. 2024; 76:102822. Available from: https://doi.org/10.1016/j.eclinm.2024.102822

[3].   Murthy N, Chandrasekharan S, Prakash MP, Ganju A, Peter J, Kaonga N, et al. Effects of an mHealth voice message service (mMitra) on maternal health knowledge and practices of low-income women in India: findings from a pseudo-randomized controlled trial. BMC Public Health. 2020;20(1):820. Available from: https://doi.org/10.1186/s12889-020-08965-2

[4].   Nagraj S, Kennedy S, Jha V, Norton R, Hinton L, Billot L, et al. A mobile clinical decision support system for high-risk pregnant women in rural India (SMARThealth Pregnancy): pilot cluster randomized controlled trial. JMIR Form Res. 2023;7:e44362. Available from: https://doi.org/10.2196/44362

[5].   Mohamed H, Ismail A, Sutan R, Rahman RA, Juval K. A scoping review of digital technologies in antenatal care: recent progress and applications of digital technologies. BMC Pregnancy Childbirth. 2025;25(1):153. Available from: https://doi.org/10.1186/s12884-025-07209-8

[6].   World Health Organization. WHO guideline: recommendations on digital interventions for health system strengthening. Geneva: World Health Organization; 2019. Available from: https://www.who.int/publications/i/item/9789241550505

[7].   World Health Organization. Global strategy on digital health 2020-2025. Geneva: World Health Organization; 2021. Available from: https://www.who.int/publications/i/item/9789240020924

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[9].   Ministry of Health and Family Welfare, Government of India. Measures taken by the government to use AI in the public health system. 2025 [cited 2026 Mar 30]. Available from: https://www.pib.gov.in/PressReleasePage.aspx?PRID=2113683

[10].  Mishra US, Yadav S, Joe W. The Ayushman Bharat Digital Mission of India: an assessment. Health System Reform. 2024;10(2):2392290. Available from: https://doi.org/10.1080/23288604.2024.2392290

[11].  Ministry of Health and Family Welfare, Government of India. Union Minister of Health and Family Welfare Shri Jagat Prakash Nadda launches SAHI and BODH initiatives to strengthen responsible Health AI ecosystem at the India AI Impact Summit 2026. 2026 [cited 2026 Mar 22]. Available from: https://www.pib.gov.in/PressReleasePage.aspx?PRID=2229226

[12].  World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. Available from: https://iris.who.int/server/api/core/bitstreams/f780d926-4ae3-42ce-a6d6-e898a5562621/content

[13].  Indian Council of Medical Research. Ethical guidelines for application of artificial intelligence in biomedical research and healthcare. New Delhi: Indian Council of Medical Research; 2023 [cited 2026 Mar 30]. Available from: https://www.icmr.gov.in/icmrobject/custom_data/pdf/Ethical-guidelines/Ethical_Guidelines_AI_Healthcare_2023.pdf

[14].  Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob Health. 2018;3(4):e000798. Available from: https://doi.org/10.1136/bmjgh-2018-000798

[15].  Gore MN, Olawade DB. Harnessing AI for public health: India’s roadmap. Front Public Health. 2024; 12:1417568. Available from: https://doi.org/10.3389/fpubh.2024.1417568

[16].  Joo JY, Liu MF, Ho MH. Nurses' perceptions of artificial intelligence adoption in healthcare: a qualitative systematic review. Nurse Educ Pract. 2025; 88:104542. Available from: https://doi.org/10.1016/j.nepr.2025.104542

[17].  Department of Health, Medical and Family Welfare, Government of Andhra Pradesh. CRS annual report 2024. Andhra Pradesh: Government of Andhra Pradesh; 2024 [cited 2026 Jun 8]. Available from: https://hmfw.ap.gov.in/docs/DPHFW/CRS%20Annual%20Report-2024.pdf

[18].  Department of School Education, Government of Andhra Pradesh. Educational statistics 2022-23. Andhra Pradesh: Directorate of School Education; 2022-23 [cited 2026 Jun 7]. Available from: https://cse.ap.gov.in/MISdownload/MIS_REPORT_NOTIFICATION/Educational%20Statistics%202022-23.pdf

[19].  Ministry of Tribal Affairs, Government of India. Integrated Tribal Development Agency (ITDA)/Integrated Tribal Development Project (ITDP) areas. New Delhi: Ministry of Tribal Affairs; 2016 [cited 2026 Jun 8]. Available from: https://tribal.nic.in/ST/1-ITDAArea.pdf

[20].  Ose SO. Using Excel and Word to structure qualitative data. J Appl Soc Sci. 2016;10(2):147-62. Available from: https://doi.org/10.1177/1936724416664948

[21].  Arora S, Huda RK, Verma S, Khetan M, Sangwan RK. Challenges, barriers, and facilitators in telemedicine implementation in India: a scoping review. Cureus. 2024;16(8):e67388. Available from: https://doi.org/10.7759/cureus.67388

[22].  Saha S, Quazi ZS. Does digitally enabling frontline health workers improve coverage and quality of maternal and child health services? Findings from a mixed methods evaluation of TECHO+ in Gujarat. Front Public Health. 2022; 10:856561. Available from: https://doi.org/10.3389/fpubh.2022.856561

[23].  Ranjan A, Singh G, Singh H, Singh M. Adoption, digital health literacy, and patient satisfaction of Ayushman Bharat Digital Mission: an analytical cross-sectional study among outpatient department attendees. BMC Health Serv Res. 2026;26(1):512. Available from: https://doi.org/10.1186/s12913-026-14314-7

[24].  Gandhi PA, Kathirvel S, Chakraborty S. Rural community health workers' readiness for mobile-phone based telemedicine uptake in India. J Rural Med. 2022;17(3):166-70. Available from: https://doi.org/10.2185/jrm.2021-044

Abstract:

Road transport crashes contribute to significant morbidity and mortality in Nigeria. Psychoactive substance abuse among drivers contribute to the crash. However, screening for substance use among drivers are inconsistent. To determine the available screening methods for identifying psychoactive substance use among drivers in Nigeria, a systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Four studies were identified and 50% of them met the inclusion criteria. Urine toxicology testing and breathalyzer testing were the main screening methods identified. The toxicology assays can detect many substances while breathalyzers provide rapid roadside detection of alcohol. A unified screening protocol for substance use among drivers in Nigeria is lacking. Developing and implementing a unified comprehensive screening will improve road safety in Nigeria

References:

[1].   World Health Organization. Global status report on road safety 2023. Geneva: World Health Organization; 2023.

[2].   Federal Road Safety Corps. Annual crash report and driver demographics. Abuja: Federal Road Safety Corps; 2025.

[3].   Alavi SS, Mohammadi MR, Souri H, et al. Personality, driving behavior and mental disorders factors as predictors of road traffic accidents based on logistic regression. Iran J Med Sci. 2017;42(1):24-31.

[4].   Adejoh SO, Osazuwa P, Busari-Akinbode S, et al. Insights into the experiences of persons with substance use disorders during COVID-19 lockdown in Lagos, Nigeria: a qualitative investigation. Subst Use Res Treat. 2024; 18:29768357241307752.

[5].   Rudisill TM, Smith GS. Risk factors associated with driving under the influence of drugs in the USA. Inj Prev. 2021;27(6):514-20.

[6].   Akande RO, Akande JO, Babatunde OA. Psychoactive substance abuse among commercial bus drivers in Umuahia, Abia State, South-Eastern Nigeria: an uncontrolled "epidemic" with attendant road traffic crashes. BMC Public Health. 2023;23(1):250.

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[8].   Akande RO, Akande JO, Babatunde OA, Ajayi AO, Ajayi AA, Ige RO, et al. Psychoactive substance abuse among commercial bus drivers in Umuahia, Abia State, South-Eastern Nigeria: an uncontrolled "epidemic" with attendant road traffic crashes. BMC Public Health. 2023;23(1):250.

[9].   Bebangnidong J, Norvivor FA, Umar A, et al. Road safety threats and occupational health risk associated with psychoactive substance use and dependence risk among commercial drivers: WHO ASSIST evidence from Ghana. BMC Public Health. 2026; 26:1261.

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Abstract:

Child food poverty, defined by the UNICEF framework according to the number of food groups consumed in the previous 24 hours, is an indicator of inadequate dietary diversity among young children. This study assessed the prevalence of child food poverty, its associated factors, and urban-rural disparities among children aged 6-23 months in the Maradi region of Niger. A cross-sectional survey conducted from September to October 2025 included 1,343 children with complete dietary information. Child food poverty was categorized as no food poverty (≥5 food groups), moderate food poverty (3-4 food groups), or severe food poverty (0-2 food groups). Associations were examined using chi-square tests and multinomial logistic regression, while urban-rural differences were assessed using a Blinder-Oaxaca decomposition. Overall, 95.0% of children experienced moderate or severe food poverty, including 51.4% with severe food poverty. Severe food poverty was most common among children aged 6-11 months (90.3%). Rural residence was strongly associated with both moderate (aRRR = 7.20; 95% CI: 3.50–14.82) and severe food poverty (aRRR = 14.69; 95% CI: 6.77–31.87). Children aged 18-23 months were less likely to experience severe food poverty than younger children. Although food poverty was substantially higher in rural areas than in urban areas, observed characteristics did not significantly explain this disparity. These findings underscore the urgent need for interventions targeting dietary diversity, particularly among younger children and rural populations.

References:

[1]. World Health Organization (WHO), United Nations Children's Fund (UNICEF). Indicators for Assessing Infant and Young Child Feeding Practices: Definitions and Measurement Methods. Geneva: WHO; 2021.

[2]. Krasevec J, An X, Kumapley R, Bégin F, Frongillo EA. Diet quality and feeding practices of infants and young children aged 6–23 months in low- and middle-income countries. Maternal and Child Nutrition. 2017;13(Suppl 2):e12408.

[3]. Victora CG, Christian P, Vidaletti LP, Gatica-Domínguez G, Menon P, Black RE. Revisiting maternal and child undernutrition in low-income and middle-income countries: variable progress towards an unfinished agenda. Lancet. 2021;397(10282):1388-1399.

[4]. Akombi BJ, Agho KE, Merom D, Renzaho AM, Hall JJ. Child malnutrition in Sub-Saharan Africa: a meta-analysis of demographic and health surveys (2006-2016). PLoS One. 2017;12(5):e0177338.

[5]. UNICEF, World Health Organization, World Bank Group. Levels and Trends in Child Malnutrition: UNICEF/WHO/World Bank Joint Child Malnutrition Estimates. 2024 Edition. New York: UNICEF; 2024.

[6]. Development Initiatives. 2024 Global Nutrition Report. Bristol, United Kingdom: Development Initiatives; 2024.

[7]. Belay DG, Aragaw FM, Teklu RE, Fetene SM, Negash WD, Asmamaw DB, et al. Determinants of inadequate minimum dietary diversity intake among children aged 6–23 months in Sub-Saharan Africa: pooled prevalence and multilevel analysis of Demographic and Health Survey data from 33 countries. Frontiers in Nutrition. 2022;9:894552.

[8]. Ba DM, Ssentongo P, Gao X, Chinchilli VM, Richie JP Jr, Maiga M, Muscat JE. Prevalence and determinants of meeting minimum dietary diversity among children aged 6–23 months in three Sub-Saharan African countries: Demographic and Health Surveys, 2019–2020. Frontiers in Public Health. 2022;10:846049.

[9]. Endawkie A, Tsega Y, Asmamaw DB, Kebede N, Arefaynie M, Mawugatie TW. Multidimensional disparity in inadequate minimum dietary diversity between poor and non-poor children aged 6–23 months in Sub-Saharan Africa: a multivariate decomposition analysis. Frontiers in Public Health. 2025;13:1516129.

[10].         UNICEF. Child Food Poverty: Nutrition Deprivation in Early Childhood. Child Nutrition Report, 2024. New York: UNICEF; 2024.

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[12].         Carneiro LBV, Lacerda EMA, Oliveira N, Schincaglia RM, Alves-Santos NH, Berti TL, et al. Food poverty among children aged 6–59 months in Brazil: results from the Brazilian National Survey on Child Nutrition (ENANI-2019). Public Health Nutrition. 2024;27(1):e196.

[13].         Ahmed KY, Page A, Arora A, Ogbo FA. Trends and determinants of minimum dietary diversity among children aged 6–23 months in Sub-Saharan Africa. Nutrients. 2020;12(11):3501.

[14].         Amugsi DA, Dimbuene ZT, Mberu B. Household, community and socioeconomic determinants of dietary diversity among children in Sub-Saharan Africa. BMC Public Health. 2017;17:248.

[15].         National Institute of Statistics (INS), Nutrition Directorate, Ministry of Public Health, Population and Social Affairs of Niger. Niger Nutritional and Retrospective Mortality Survey Using SMART Methodology, 2022. Niamey: INS; 2023.

[16].         Tchumbiep Ngouene LG, et al. Nutritional Profile and Infant and Young Child Feeding Practices among Children Aged 6–23 Months in Maradi Region, Niger: SMART 2025 Data. Manuscript in preparation.

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[18].         Oaxaca RL. Male-female wage differentials in urban labor markets. International Economic Review. 1973;14(3):693-709.

[19].         Ruel MT, Alderman H; Maternal and Child Nutrition Study Group. Nutrition-sensitive interventions and programmes: how can they help accelerate progress in improving maternal and child nutrition? Lancet. 2013;382(9891):536-551.

[20].         Ickes SB, Wu M, Mandel MP, Roberts AC. Associations between social support, maternal knowledge and complementary feeding practices among caregivers of young children. Public Health Nutrition. 2018;21(9):1573-1581.

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Abstract:

Surgical safety is a major patient safety priority, especially in high-risk specialties such as cardiothoracic surgery. Newly established centers in low-resource settings may face amplified risks from communication failures, inconsistent perioperative processes, equipment readiness gaps, delayed blood product availability, and delayed recognition of postoperative deterioration. This quality improvement study evaluated the use of Failure Modes and Effects Analysis (FMEA) to improve surgical safety in a newly established cardiothoracic and vascular center in East Africa. A prospective 12-month quality improvement study was conducted at Kalkaal Hospital Cardiothoracic and Vascular Center, Mogadishu, Somalia. A multidisciplinary team mapped the perioperative pathway, identified potential failure modes, scored risks using severity, occurrence, and detection ratings, calculated Risk Priority Numbers, and prioritized corrective interventions. Baseline and post-intervention indicators included surgical safety checklist compliance, equipment-related operative delays, structured handoff compliance, postoperative deterioration escalation time, ICU readmissions, and safety culture indicators. Twenty-one potential failure modes were identified. Five high-priority risks were selected for intervention: inadequate patient verification, delayed recognition of postoperative deterioration, equipment readiness failure, communication failure during handoff, and checklist noncompliance. Interventions included a cardiothoracic-adapted checklist, equipment readiness bundle, standardized handoff tool, ICU early warning escalation algorithm, daily safety huddles, and briefing/debriefing protocols. Checklist compliance improved from 72% to 96%, equipment delay events reduced from 10 to 6 per month, structured handoff compliance improved from 61% to 92%, median escalation time improved from 42 to 18 minutes, and ICU readmissions were eliminated during observation. FMEA was feasible and useful for strengthening surgical safety in this setting.

References:

[1].   World Health Organization. Global patient safety action plan 2021–2030: towards eliminating avoidable harm in health care. Geneva: World Health Organization; 2021.

[2].   Schwendimann R, Blatter C, Dhaini S, Simon M, Ausserhofer D. The occurrence, types and consequences of adverse events in surgery. Int J Qual Health Care. 2021;33(1):mzab009. doi:10.1093/intqhc/mzab009.

[3].   Sotto KT, Burian BK, Brindle ME. Impact of the WHO Surgical Safety Checklist relative to its design and intended use: a systematic review and meta-meta-analysis. J Am Coll Surg. 2021;233(6):794–809.e8. doi:10.1016/j.jamcollsurg.2021.08.692.

[4].   Alsadoun L, Alrumaih A, Alharbi A, et al. Evaluating the impact of the World Health Organization’s Surgical Safety Checklist on clinical outcomes and implementation strategies: a systematic review. Cureus. 2024;16(9):e69453. doi:10.7759/cureus.69453.

[5].   Dirie NI, Abdi A, Mohamed H, et al. Implementation of the WHO Surgical Safety Checklist in resource-limited Somalia: a new standard in surgical safety. Patient Saf Surg. 2024;18(1):30. doi:10.1186/s13037-024-00410-2.

[6].   ASOS-2 Investigators. Enhanced postoperative surveillance versus standard of care to reduce mortality among adult surgical patients in Africa: a cluster-randomised controlled trial. Lancet Glob Health. 2021;9(10):e1391–e1401. doi:10.1016/S2214-109X(21)00291-6.

[7].   Carayon P, Wooldridge A, Hose BZ, Salwei M, Benneyan J. Challenges and opportunities for improving patient safety through human factors and systems engineering. Health Aff. 2020;39(11):1862–1869. doi:10.1377/hlthaff.2020.00093.

[8].   Pronovost PJ, Armstrong S, Demski R, et al. Advancing patient safety through systems approaches. Lancet. 2021;398(10314):2051–2060. doi:10.1016/S0140-6736(21)01763-5.

[9].   Holden RJ, Carayon P, Gurses AP, et al. SEIPS 101 and seven simple SEIPS tools. BMJ Qual Saf. 2021;30(11):901–910. doi:10.1136/bmjqs-2020-012538.

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[11].  Vincent C, Amalberti R. Safety in healthcare beyond Safety-I. BMJ Qual Saf. 2021;30(1):3–5. doi:10.1136/bmjqs-2020-011654.

[12].  Reed JE, Howe C, Doyle C, Bell D. Successful quality improvement in healthcare: principles and practice. BMJ. 2021;372:n250. doi:10.1136/bmj.n250.

[13].  Hu ZJ, Fusch G, Hu C, Wang JY, el Helou Z, Hassan MT, et al. Completeness of reporting of quality improvement studies in neonatology is inadequate: a systematic literature survey. BMJ Open Qual. 2021;10:e001273. doi:10.1136/bmjoq-2020-001273.

[14].  Kaplan HC, Provost LP, Froehle CM, Margolis PA. Measurement for improvement science. Pediatrics. 2022;149(Suppl 2):e2021052885K. doi:10.1542/peds.2021-052885K.

[15].  Institute for Healthcare Improvement. Science of improvement: how to improve. Boston: Institute for Healthcare Improvement; 2022.

[16].  Silva R, Amaral C. Healthcare FMEA applications in high-risk specialties: integrative review. Int J Health Plann Manage. 2022;37(5):2575–2592. doi:10.1002/hpm.3480.

[17].  Patel B, Ahmed S, Kumar R. Prospective risk analysis in perioperative safety using FMEA. J Patient Saf. 2022;18(6):e1438–e1446. doi:10.1097/PTS.0000000000000917.

[18].  Vecchia M, Sacchi P, Marvulli LN, Ragazzoni L, Muzzi A, Polo L, Bruno R, Salio F. Healthcare application of failure mode and effect analysis: is there room in the infectious disease setting? A scoping review. Healthcare. 2025;13(1):82. doi:10.3390/healthcare13010082.

[19].  Santos Ribeiro Cosenza HJ, Mendes Silva ND, Sanches Zeferino AC, da Rosa dos Santos TD, Teixeira da Silva MH, Valdiviezo Viera LE, et al. HFMEA-fuzzy model: an evaluation of the main risk factors occurring in 47 emergency care units. Rev Gestao Secretariado. 2024;15(3):e3576. doi:10.7769/gesec.v15i3.3576.

[20].  Alingh CW, van Wijngaarden JDH, Paauwe J, Huijsman R. Team climate and safety outcomes in operating rooms. BMC Health Serv Res. 2021;21(1):460. doi:10.1186/s12913-021-06435-8.

[21].  Er OS, Gul I. The speaking up climate of nurses for patient safety concerns and unprofessional behaviors: the effects of teamwork and safety climate. J Perianesth Nurs. 2024;39(5):782–788. doi:10.1016/j.jopan.2023.12.008.

[22].  Salwei M, Carayon P. Human factors approaches to resilient surgical systems. Appl Ergon. 2023;108:103930. doi:10.1016/j.apergo.2022.103930.

[23].  Lee S, Park J, Kim H. Digital monitoring systems for early postoperative deterioration detection. J Clin Monit Comput. 2024;38(2):233–245. doi:10.1007/s10877-023-01021-4.

[24].  Hassani H, Silva ES, TajMazinani M, Mac Feely S. Artificial intelligence and patient safety prediction. Healthcare Analytics. 2023;3:100145. doi:10.1016/j.health.2023.100145.

[25].  World Health Organization. Patient safety incident reporting and learning systems: technical report and guidance. Geneva: World Health Organization; 2020.

[26].  Organisation for Economic Co-operation and Development. The economics of patient safety: strengthening a value-based approach to reducing patient harm at national level. Paris: OECD Publishing; 2024.

[27].  Malone S, Newland J, Kudchadkar SR, Prewitt K, McKay V, Prusaczyk B, et al. Sustainability in pediatric hospitals: an exploration at the intersection of quality improvement and implementation science. Front Health Serv. 2022;2:1005802. doi:10.3389/frhs.2022.1005802.

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Abstract:

Artificial intelligence (AI) is rapidly transforming healthcare delivery globally, offering significant potential to address critical health system challenges. The study employed a narrative literature review design guided by the narrative synthesis framework. with literature sourced from peer-reviewed databases and grey literature published between 2019 and 2026. A total of 68 publications met the inclusion criteria and were retained for synthesis. The review revealed that AI has been applied across diagnostic imaging, clinical decision support, public health surveillance, and telemedicine. Zambian studies have evaluated AI for tuberculosis diagnosis, breast cancer detection, and occupational lung disease. Implementation strategies emphasised governance, infrastructure, partnerships, and capacity building. Zambia has made notable progress through its National AI Strategy, SmartCare Pro initiative consolidating over 12 million patient records, Digital Health Data Governance Framework partnership with Finland, and Health AI Global Regulatory Network membership. However, significant challenges persist including unreliable infrastructure, nascent regulatory frameworks, workforce gaps, algorithmic bias, and financial constraints. Despite persistent challenges, substantial opportunities exist for AI to strengthen health service delivery, expand access to quality care, enhance efficiency, strengthen surveillance, and foster local innovation in Zambia. The National Telemedicine and AI Health Diagnostics Pilot and local innovations such as Larry RX AI demonstrate AI's potential. Recommendations include accelerating National AI Strategy operationalisation, developing a comprehensive regulatory framework, investing in resilient infrastructure, scaling workforce development, establishing sustainable financing, and aligning with continental frameworks.

References:

[1].   Okeibunor JC, Jaca A, Iwu-Jaja CJ, Idemili-Aronu N, Ba H, Zantsi ZP, et al. The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review. Front Public Health. 2023;11:1102185. Available from: https://doi.org/10.3389/fpubh.2023.1102185

[2].   World Health Organization. Digital Health Strategy 2022-2026: Zambia. Geneva: World Health Organization; 2022.

[3].   World Health Organization. Ethics and governance of artificial intelligence for health: guidance on large multi-modal models. Geneva: World Health Organization; 2024.

[4].   Kabata F, Thaldar D. Human in the loop requirement and AI healthcare applications in low-resource settings: a narrative review. S Afr J Bioeth Law. 2024;17(2):89-121. Available from: https://doi.org/10.7196/SAJBL.2024.v17i2.1975

[5].   Ciecierski-Holmes T, Singh R, Axt M, Brenner S, Barteit S. Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review. NPJ Digit Med. 2022;5:162. Available from: https://doi.org/10.1038/s41746-022-00700-y

[6].   Baethge C, Goldbeck-Wood S, Mertens S. SANRA, a scale for the quality assessment of narrative review articles. Res Integr Peer Rev. 2019;4(1):5. Available from: https://doi.org/10.1186/s41073-019-0064-8

[7].   Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Artificial intelligence in healthcare: a comprehensive review of its applications, challenges, and future prospects. Cureus. 2024;16(3):e56789. Available from: https://doi.org/10.7759/cureus.56789

[8].   Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M, et al. Guidance on the conduct of narrative synthesis in systematic reviews: a product from the ESRC Methods Programme. Lancaster: Lancaster University; 2006.

[9].   Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. 2017. Available from: https://www.researchgate.net/publication/321095969_CheXNet_Radiologist-Level_Pneumonia_Detection_on_Chest_X-Rays_with_Deep_Learning

[10].  Somwe P, Maimbolwa M, Chiyenu K, Lumpa M, Kagujje M, Muyoyeta M. Evaluating InferVision's computer-aided detection (CAD) algorithm for tuberculosis (TB) screening, Lusaka, Zambia. PLOS Glob Public Health. 2025;5(6):e0003955. Available from: https://doi.org/10.1371/journal.pgph.0003955

[11].  Muyoyeta M, Muzazu M, Seke NM, Maimbolwa MM, Kagujje M, Somwe P, et al. The accuracy of computer-aided diagnosis (CAD) for TB diagnosis among children in Zambia. SSRN. 2025. Available from: https://ssrn.com/abstract=5256835

[12].  Mwale M, Nteeni MS, Mwaba P, Chipamp MN. Prospective blinded evaluation of Thermalytix, an artificial intelligence-enhanced breast thermal imaging software, correlated with radiologist-interpreted mammograms: results of an exploratory study in Zambia. medRxiv. 2025. Available from: https://doi.org/10.1101/2025.01.12.25320093

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Abstract:

The global population of octogenarians is rising rapidly, posing significant challenges for geriatric care. This study applies a neuropsychosocial perspective, integrating geriatric psychiatry, neurobiology, social psychology, and elder law, to review literature on emotional vitality, grief, sexuality, and boundary concerns in octogenarians. Findings show that companionship, intimacy, and family engagement remain central to well-being but are often disrupted by bereavement, loneliness, and social isolation. Contrary to common assumptions, aging does not eliminate sexual expression. However, this creates a dual reality: in which certain older adults face risks of exploitation, whereas others, especially older men, may transgress relational boundaries. Current care frameworks focus mainly on preventing neglect and abuse, yet inadequately address sexuality and relational well-being. A shift toward holistic geriatric care is needed, emphasizing dignity, emotional vitality, structured grief counseling, intimacy-focused interventions, caregiver training, and family education to reduce psychological distress and improve quality of life among octogenarians.

References:

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[2].      Alias AN, Mokti K, Ibrahim MY, Saupin S, Madrim MF. Elderly abuse and neglect on population health: Literature review and interventions from selected countries. Korean J Fam Med. 2023;44(6):311-8. https://doi:10.4082/kjfm.23.0046.

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Abstract:

Anti‑N‑Methyl‑D‑Aspartate (NMDA) receptor encephalitis is a rare form of limbic encephalitis but represents the most common autoimmune encephalitis in children. Its clinical manifestations and disease course differ from adults in terms of tumor association, neurological presentation, and symptom frequency. While associations with autoimmune diseases such as Graves’ disease, autoimmune polyglandular syndrome, and type 1 diabetes have been reported in adults, a link with type 1 diabetes has not previously been described in children. We conducted a case review of a 16‑year‑old adolescent with known type 1 diabetes admitted to Al Sabah Children’s Hospital, Juba, South Sudan. Clinical features, treatment, and outcomes were documented. Literature was reviewed to contextualize findings and highlight diagnostic challenges in resource‑limited settings. The patient presented with altered consciousness, seizures, psychotic symptoms, and movement disorder. Following initiation of high‑dose corticosteroid therapy, the patient achieved complete recovery within four weeks. This case suggests a possible association between type 1 diabetes and anti‑NMDA receptor encephalitis in pediatric patients, an observation not previously documented. Anti‑NMDA receptor encephalitis should be considered in pediatric patients with type 1 diabetes presenting with neuropsychiatric symptoms. This case emphasizes the need for improved diagnostic capacity, awareness, and access to advanced neuroimaging and serological markers in South Sudan and other resource‑limited settings.

References:

[1].   Wu PY, Chi CS, Tsai CR, Yang YL, Lee HF. Long-Term Outcome of Pediatric Patients with Anti-NMDA Receptor Encephalitis in a Single Center. Children (Basel). 2023 Jan 18;10(2):182. doi: 10.3390/children10020182. PMID: 36832312; PMCID: PMC9954979.

[2].   Florance NR, Davis RL, Lam C, Szperka C, Zhou L, Ahmad S, Campen CJ, Moss H, Peter N, Gleichman AJ, Glaser CA, Lynch DR, Rosenfeld MR, Dalmau J. Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis in children and adolescents. Ann Neurol. 2009 Jul;66(1):11-8. doi: 10.1002/ana.21756. PMID: 19670433; PMCID: PMC2826225.

[3].   Zhang M, Li W, Zhou S, Zhou Y, Yang H, Yu L, Wang J, Wang Y, Zhang L. Clinical Features, Treatment, and Outcomes Among Chinese Children with Anti-methyl-D-aspartate Receptor (Anti-NMDAR) Encephalitis. Front Neurol. 2019 Jun 6;10:596. doi: 10.3389/fneur.2019.00596. PMID: 31244759; PMCID: PMC6562280.

[4].   Moen A. Diagnosing and Treating Anti N-Methyl-D-Aspartate (NMDA) Receptor Encephalitis: A Pediatric Perspective 2014, VOLUME 23, NUMBER 4

[5].   Gable MS, Sheriff H, Dalmau J, Tilley DH, Glaser CA. The frequency of autoimmune N-methyl-D-aspartate receptor encephalitis surpasses that of individual viral etiologies in young individuals enrolled in the California Encephalitis Project. Clin Infect Dis. 2012 Apr;54(7):899-904. doi: 10.1093/cid/cir1038. Epub 2012 Jan 26. PMID: 22281844; PMCID: PMC3297648.

[6].   Dubey D, Pittock SJ, Kelly CR, McKeon A, Lopez-Chiriboga AS, Lennon VA, Gadoth A, Smith CY, Bryant SC, Klein CJ, Aksamit AJ, Toledano M, Boeve BF, Tillema JM, Flanagan EP. Autoimmune encephalitis epidemiology and a comparison to infectious encephalitis. Ann Neurol. 2018 Jan;83(1):166-177. doi: 10.1002/ana.25131. PMID: 29293273; PMCID: PMC6011827.

[7].   Dalmau J, Gleichman AJ, Hughes EG, Rossi JE, Peng X, Lai M, Dessain SK, Rosenfeld MR, Balice-Gordon R, Lynch DR. Anti-NMDA-receptor encephalitis: case series and analysis of the effects of antibodies. Lancet Neurol. 2008 Dec;7(12):1091-8. doi: 10.1016/S1474-4422(08)70224-2. Epub 2008 Oct 11. PMID: 18851928; PMCID: PMC2607118.

[8].   Mann AP, Grebenciucova E, Lukas RV. Anti-N-methyl-D-aspartate-receptor encephalitis: diagnosis, optimal management, and challenges. Ther Clin Risk Manag. 2014 Jul 1;10:517-25. doi: 10.2147/TCRM.S61967. PMID: 25061311; PMCID: PMC4085332.

[9].   Agarwal R, Gupta V. Anti-NMDAR Encephalitis in Children. 2023 Jul 17. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan–. PMID: 32965889.

[10].  Sandweiss AJ, Erickson TA, Jiang Y, Kannan V, Yarimi JM, Levine JM, Fisher K, Muscal E, Demmler-Harrison G, Murray KO, Ronca SE. Infectious profiles in pediatric anti-N-methyl-d-aspartate receptor encephalitis. J Neuroimmunol. 2023 Aug 15;381:578139. doi: 10.1016/j.jneuroim.2023.578139. Epub 2023 Jun 20. PMID: 37364517.

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[12].  Titulaer MJ, McCracken L, Gabilondo I, Armangué T, Glaser C, Iizuka T, Honig LS, Benseler SM, Kawachi I, Martinez-Hernandez E, Aguilar E, Gresa-Arribas N, Ryan-Florance N, Torrents A, Saiz A, Rosenfeld MR, Balice-Gordon R, Graus F, Dalmau J. Treatment and prognostic factors for long-term outcome in patients with anti-NMDA receptor encephalitis: an observational cohort study. Lancet Neurol. 2013 Feb;12(2):157-65. doi: 10.1016/S1474-4422(12)70310-1. Epub 2013 Jan 3. PMID: 23290630; PMCID: PMC3563251.

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[17].  Favier M, Joubert B, Picard G, Rogemond V, Thomas L, Rheims S, Bailhache M, Villega F, Pédespan JM, Berzero G, Psimaras D, Antoine JC, Desestret V, Honnorat J. Initial clinical presentation of young children with N-methyl-d-aspartate receptor encephalitis. Eur J Paediatr Neurol. 2018 May;22(3):404-11. doi: 10.1016/j.ejpn.2017.12.014. Epub 2017 Dec 28. PMID: 29310866.

Abstract:

Childhood malnutrition remains a major public health challenge in Mali, where acute malnutrition affects nearly 9% of children under five, conventional treatment coverage remains low, and 70–80% of the population first seeks traditional medicine. This pilot study aimed to describe practices, perceptions, and interactions among traditional pediatric practitioners (TPPs), mothers, healthcare workers, and community leaders regarding childhood malnutrition in Bamako, and to identify factors enabling or hindering collaboration between traditional and conventional health systems. A descriptive qualitative study was conducted from February to April 2021 in three Bamako communes (II, IV, and V), combining semi-structured interviews and focus group discussions with 4 TPPs, staff from 3 community health centers (CHCs), and 13 community leaders, alongside anthropometric screening (mid-upper arm circumference, weight-for-height, and height-for-age) of 302 children aged 6–59 months attending traditional consultations. TPPs reported substantial daily attendance for common pediatric conditions, while malnutrition was rarely recognized by families or practitioners. Screening nevertheless identified 15.8% global acute malnutrition and 14.2% stunting. TPPs, mothers, and healthcare workers were generally receptive to strengthened collaboration, including MUAC training and bidirectional referral. Key barriers included the absence of formal referral pathways, divergent understandings of malnutrition, and limited feedback to TPPs. Involving TPPs in nutritional screening and awareness-raising appears feasible and could facilitate earlier detection among children who may otherwise remain outside the formal health system. Operational research with quantitative impact evaluation is warranted to confirm these findings.

References:

[1].   Ministère de la Santé et de l’Hygiène Publique du Mali. Plan d’Action Multisectoriel de Nutrition 2021–2025. Ministère de la Santé et de l’Hygiène Publique, Mali; 2021. https://faolex.fao.org/docs/pdf/mli211666.pdf

[2].   Degefa K, Tadesse A, Ackley C, Madrid L, Assefa N, Breines M, Sivalogan K, Maixenchs M, Blevins J. Using traditional healers to treat child malnutrition: a qualitative study of health-seeking behaviour in eastern Ethiopia. BMC Public Health. 2022;22(1):873. https://doi.org/10.1186/s12889-022-13323-5

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[5].   Dougnon OA, Charle-Cuéllar P, Toure F, Gado AG, Sanoussi A, Lazoumar RH, et al. Impact of integration of severe acute malnutrition treatment in primary health care provided by community health workers in rural Niger. Nutrients. 2021;13(11):4067. https://doi.org/10.3390/nu13114067

[6].   Tattersall N. Mali’s traditional healers unlock herbal cures. Reuters. 2006. https://www.reuters.com/article/world/malis-traditional-healers-unlock-herbal-cures-idUSL16899669/

[7].   Krah E, de Kruijf J, Ragno L. Integrating traditional healers into the health care system: challenges and opportunities in rural Northern Ghana. J Community Health. 2018;43(1):157-63. https://doi.org/10.1007/s10900-017-0398-4

[8].   Ikhoyameh M, Okete WE, Ogboye RM, Owoyemi OK, Gbadebo OS. Integrating traditional medicine into the African healthcare system post-Traditional Medicine Global Summit: challenges and recommendations. Pan Afr Med J. 2024;47:146. https://doi.org/10.11604/pamj.2024.47.146.43011

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[13].  UNICEF. Les enfants au Mali. UNICEF Mali; 2020. https://www.unicef.org/mali/les-enfants-au-mali

[14].  ONG ALIMA. Tout augmente, surtout la faim : l’urgence alimentaire s’intensifie sur le continent africain. ALIMA; 2023. https://alima.ngo/communique/urgence-alimentaire-intensifie/

[15].  Chigangu C. Analyse des capacités des tradipraticiens et des herboristes à intégrer la nutrition dans le paquet d’activités de la médecine traditionnelle. Mémoire de Master 2; 2018–2019.

[16].  Balfe A. GOAL work with traditional healers to tackle malnutrition in children in Niger. GOAL Global; 2019. https://www.goalglobal.org/stories/goal-work-with-traditional-healers-to-tackle-malnutrition-in-children-in-niger/

[17].  Menber Y, Belachew T, Fentahun N. Diagnostic accuracy of MUAC for assessment of acute malnutrition among children aged 6–59 months in Africa: systematic review and meta-analysis. Front Nutr. 2025;12:1536386. https://doi.org/10.3389/fnut.2025.1536386

[18].  Master R, Mguntha A, Baltzell K, Bandawe G, Hooft A. Understanding the perspective of traditional healers on their role within the Malawian healthcare system: a qualitative study in Thyolo District. BMC Complement Med Ther. 2026;26(1):203. https://doi.org/10.1186/s12906-026-05387-2

[19].  Vissoh ACS, Klotoé JR, Fah L, Agbodjento E, Koudokpon H, Togbe E, et al. Knowledge and practices of traditional management of child malnutrition and associated pathologies in Benin. J Ethnobiol Ethnomed. 2024;20(1):47. https://doi.org/10.1186/s13002-024-00684-x

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