http://repository.kemri.go.ke:8080/xmlui/handle/123456789/6 2026-08-26T03:35:35Z 2026-08-26T03:35:35Z Harrison Ochieng, Anita Musiega , Benjamin Tsofa , Jacinta Nzinga , Edwine Barasa http://repository.kemri.go.ke:8080/xmlui/handle/123456789/1847 2026-04-01T13:13:08Z 2025-07-01T00:00:00Z

Management capacity of primary healthcare facilities in low- and middle-income countries: A scoping review Harrison Ochieng, Anita Musiega , Benjamin Tsofa , Jacinta Nzinga , Edwine Barasa How health facilities are managed determines their performance and health service delivery. Management capacity of health facilities comprises the competency of managers at the individual level and the management support and work environment in their institutions. Evidence shows this management capacity influences service delivery and performance of the facility. For LMICs, there are evidence gaps as existing evidence is scarce, varied in the assessment of management capacity of PHC facilities and report a measurement gap due to the scarcity of assessment tools contextualised to the LMIC PHC setting. Our review aims to address these gaps by mapping and summarising the existing literature on management capacity of PHC facilities in LMICs, its components and performance across these components, providing evidence on what needs to be improved for better service delivery. We used Arksey and O`Malley`s scoping review methodology. We searched PubMed, Scopus, Web of Science and Google Scholar and hand-checked reference lists. We synthesized findings using a thematic approach. We included 21 articles out of the 3867 articles gotten. Individual capacity consisted of managerial competencies grouped into seven groups: (1) communication and information management, (2) financial management and planning, (3) human resource, supportive and performance management, (4) community stakeholder and engagement, (5) target setting and problem solving, (6) leadership and (7) situational analysis. Institutional capacity included functional support systems grouped into; (1) availability of resources, (2) support to undertake duties and (3) clear roles and responsibilities. Gaps were prevalent across individual and institutional capacities. There were deficiencies in the managerial competencies of the managers and the functional support systems were not adequate. These negatively affected facility service delivery and performance. There is still a scarcity of studies hence more research is needed. Furthermore, interventions such as training and supportive supervision should be considered in improving the managerial competencies of managers.

2025-07-01T00:00:00Z Tuwei, M., Karisa, J., Wanjiku, C., Kiuru, C., Ondieki, Z., Odongo, T., Mure, F., Otieno, B., Meli, P., Okoko, M., Bartilol, B., Gona, R., Constantino, L., Cole, G., Anastácio, T., Armazia, R., Alves, C., Rui, P., Ramaita, E., Rono, M., Candrinho, B., Mwangangi, J., Mbogo, C., Charlwood, D., Saute, F., Rabinovich, R., Chaccour, C., Maia, M. F. http://repository.kemri.go.ke:8080/xmlui/handle/123456789/1846 2026-04-01T13:08:00Z 2025-09-01T00:00:00Z

MALDI-TOF MS for identification of Afro-tropical secondary malaria vectors Tuwei, M., Karisa, J., Wanjiku, C., Kiuru, C., Ondieki, Z., Odongo, T., Mure, F., Otieno, B., Meli, P., Okoko, M., Bartilol, B., Gona, R., Constantino, L., Cole, G., Anastácio, T., Armazia, R., Alves, C., Rui, P., Ramaita, E., Rono, M., Candrinho, B., Mwangangi, J., Mbogo, C., Charlwood, D., Saute, F., Rabinovich, R., Chaccour, C., Maia, M. F. Background: Characterizing malaria epidemiology at the local level requires understanding the diverse malaria vector species driving transmission, including both primary and secondary vectors. Effective mosquito surveillance and accurate species identification are critical; however, due to the associated cost and complexity, most surveillance strategies mainly focus on the primary malaria vectors. There is a need for cost-effective methods that can reliably identify both primary and secondary vectors as their role in transmission becomes increasingly important while reaching towards elimination. This study aimed to evaluate the use of MALDI-TOF MS as a sustainable tool for identifying secondary malaria vector. Methods: Adult mosquitoes were collected in Kenya and Mozambique and morphologically identified. Secondary malaria vectors were considered as any Anopheline that did not pertain to Anopheles gambiae sensu lato (s.l.). or Anopheles funestus sensu lato (s.l.). At KEMRI Wellcome Trust Research Programme, MALDI TOF MS spectra were obtained from individual cephalothoraxes. Library creation and querying were guided by confirmatory species identification using Sanger sequencing of a subset of mosquitoes, targeting the Internal Transcribed Spacer 2 (ITS2) region of nuclear ribosomal DNA and the mitochondrial Cytochrome c Oxidase Subunit I (COI) gene. The libraries were then applied for the identification of other secondary malaria vectors. Results: Species identification of secondary malaria vectors using MALDI-TOF MS showed high concordance with Sanger sequencing with an overall accuracy of 91% and a kappa value of 0.87. The technique demonstrated a sensitivity and specificity of 100% for most species, except for distinguishing between Anopheles cf. coustani 2 NFL-2015 and Anopheles ziemanni. In Kenya, the Anopheles species identified were Anopheles cf. coustani 2 NFL-2015 (19), Anopheles pretoriensis (6), Anopheles rufipes (8), Anopheles ziemanni (8), Anopheles coustani (2), and Anopheles pharoensis (1). In Mozambique, the identified species comprised: An. cf. coustani 2 NFL-2015 (10), An. pretoriensis (2), An. ziemanni (7), An. coustani (28), and An. pharoensis (4). Conclusion: The results provide evidence that MALDI-TOF can identify secondary malaria vectors from Eastern and Southeastern African regions. This technique was as efficient as DNA sequencing in identifying mosquito species. Indeed, except for An. cf coustani 2NFL-2015 and An. ziemanni, an exact species identification was obtained for all individual mosquitoes. These findings highlight the potential of MALDI-TOF MS for monitoring malaria vectors.

2025-09-01T00:00:00Z Karisa, J., Tuwei, M., Ominde, K., Bartilol, B., Ondieki, Z., Musani, H., Rono, M., Mbogo, C., Bejon, P., Mwangangi, J., Wanjiku, C., Maia, M. http://repository.kemri.go.ke:8080/xmlui/handle/123456789/1845 2026-04-01T13:03:05Z 2025-08-01T00:00:00Z

Making MALDI-TOF MS for entomological parameters accessible: A practical guide for in house library creation Karisa, J., Tuwei, M., Ominde, K., Bartilol, B., Ondieki, Z., Musani, H., Rono, M., Mbogo, C., Bejon, P., Mwangangi, J., Wanjiku, C., Maia, M. Matrix-assisted laser desorption-ionisation time of flight mass spectrometry (MALDI-TOF MS) is a powerful analytical method that has been used extensively to identify sample ions of complex mixtures, and biological samples such as proteins, tissues and microorganisms. MALDI-TOF MS has revolutionised clinical microbiology with accurate, rapid, and inexpensive species-level identification of microbes. MALDI-TOF MS technology generates spectral signatures and matches them to a library of similar organisms using bioinformatics pattern matching. The use of MALDI-TOF MS for entomological samples has been explored by multiple groups with proven efficacy at differentiating between closely related species, as well as detecting pathogens in different vectors. The low cost per sample processing, rapid turnaround and robustness are attractive for surveillance of vector control programs. Libraries are built in-house for institutional usage, although a multi-user platform with sharing of spectra and data would be attractive. Only a few studies have strived to make their libraries publicly available. Here, we outline a stepwise approach for creating an in-house MALDI-TOF MS library and subsequent query, using malaria vector species identification as a case study for entomological samples. A protocol and video of the methodology are also shared. Moreover, the libraries related to this publication have been deposited in public repository (https://doi.org/10.7910/DVN/VYQFNO37) for anyone with MALDI-TOF MS equipment to adapt.

2025-08-01T00:00:00Z Kartavya J. Vyas, Jonathan A. Muir, Zachary J. Madewell, Priya M. Gupta, Dianna M. Blau, Shams E. Arifeen, Emily S. Gurley, Atique I. Chowdhury, Kazi M. Islam, Afruna Rahman, J. Anthony G. Scott, Nega Assefa, Lola Madrid, Yohanis A. Asefa, Yasir Y. Abdullahi, Dickens Onyango, Victor Akelo, Beth A. Tippett-Barr, George Aol, Samba O. Sow, Karen L. Kotloff, Milagritos D. Tapia, Adama M. Keita, Kiranpreet Chawla, Quique Bassat, Inacio Mandomando, Ariel Nhacolo, Charfudin Sacoor, Ikechukwu Ogbuanu, Dickens Kowuor, Babatunde Duduyemi, Andrew Moseray, James S. Squire, Shabir Madhi, Sana Mahtab, Yasmin Adam, Amy Wise, Takwanisa Machemedza, Cynthia G. Whitney http://repository.kemri.go.ke:8080/xmlui/handle/123456789/1844 2026-04-01T12:59:32Z 2025-09-01T00:00:00Z

Major Causes of Perinatal and Paediatric Mortality in Sub-Saharan Africa and South Asia: Adjustment for Selection Bias in the CHAMPS Network Kartavya J. Vyas, Jonathan A. Muir, Zachary J. Madewell, Priya M. Gupta, Dianna M. Blau, Shams E. Arifeen, Emily S. Gurley, Atique I. Chowdhury, Kazi M. Islam, Afruna Rahman, J. Anthony G. Scott, Nega Assefa, Lola Madrid, Yohanis A. Asefa, Yasir Y. Abdullahi, Dickens Onyango, Victor Akelo, Beth A. Tippett-Barr, George Aol, Samba O. Sow, Karen L. Kotloff, Milagritos D. Tapia, Adama M. Keita, Kiranpreet Chawla, Quique Bassat, Inacio Mandomando, Ariel Nhacolo, Charfudin Sacoor, Ikechukwu Ogbuanu, Dickens Kowuor, Babatunde Duduyemi, Andrew Moseray, James S. Squire, Shabir Madhi, Sana Mahtab, Yasmin Adam, Amy Wise, Takwanisa Machemedza, Cynthia G. Whitney Background Studies of child mortality that employ minimally invasive tissue sampling (MITS) produce highly accurate cause of death data; however, selection bias may render these as non-representative of their underlying populations. Objectives Estimate cause-specific mortality fractions and rates for the five most frequent causes—underlying and others in the chain of events leading to death—among stillbirths, neonatal, infant and child deaths—in Sub-Saharan Africa and South Asia, adjusted for any identified selection biases. Methods The Child Health and Mortality Prevention Surveillance (CHAMPS) Network collects standardised, population-based, longitudinal data on causes of death among stillbirths and under-five children in 12 catchments in seven countries in Sub-Saharan Africa and South Asia. Cause-specific mortality fractions and rates were calculated for the five most frequent causes among stillbirths, neonatal, infant and child deaths, and for the five most frequent maternal conditions among perinatal deaths; all estimates were subsequently adjusted for selection bias. Selection probabilities were estimated from membership in subgroups defined by factors hypothesised to affect selection. Results In 2017–2020, of 10,122 deaths ascertained, 5847 (57.8%) were enrolled in CHAMPS and 2654 (26.2%) additionally consented to MITS. Estimates were calculated for 265 and 65 site/age-specific causes of death and maternal conditions, respectively; five (1.9%) and four (6.2%) required adjustment, respectively, but they did not meaningfully change. Estimates were calculated for 34 site-specific causes of death among all stillbirths and under-five deaths combined; 28 (82.4%) required adjustment (all included age at death), and change-in-estimates demonstrated considerable variability. Conclusions Selection bias is not a concern in the CHAMPS Network. Deaths where MITS were performed accurately represent the distribution of causes of death in their respective target populations, specifically when stratified by age or adjusted accordingly. Future studies of child mortality that employ MITS should consider adjusting for age at death for their measures of frequency.

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