83 research outputs found
PRISMA-P checklist for "Methodological rigor of prognostic models for predicting in-hospital paediatric mortality in low- and middle-income countries: a systematic review protocol"
No full textThis is the PRISMA-P checklist for the systematic review protocol
Correction:An exploration of mortality risk factors in non-severe pneumonia in children using clinical data from Kenya. [BMC Med. 15, (2017) (201)] DOI: 10.1186/s12916-017-0963-9
No full textThe original article [1] contains an omission in the Acknowledgements sub-section of the Declarations. The authors would like to acknowledge the work of the following members of the Clinical Information Network Author Group: David Githanga, Fred Were, Philip Ayieko, Grace Irimu, Sam Akech, Samuel Ng'arng'ar, Barnabas Kigen, Rachel Inginia, Nick Aduro, Grace Ochieng, Beatrice Mutai, Francis Kanyingi, Lydia Thuranira, Sam Otido, Magdalene Kuria, Peris Njiiri, Kigondu Rutha, Charles Nzioki, Martin Chabi, Supa Tonje, Joan Ondere, Caren Emadau, Cecelia Mutiso, Loice Mutai, Christine Manyasi, David Kimutai, Celia Muturi, Agnes Mithamo, Anne Kamunya, Alice Kariuki, Grace Wachira, Melab Musabi, Sande Charo, Naomi Muinga, Mercy Chepkirui, Wycliffe Nyachiro, Boniface Makone, Thomas Julius, George Mbevi, Morris Ogero, Susan Gachau, and James Wafula.</p
Developing pediatric prognostic model using finite mixture models
No full textThesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Statistical Sciences (MSc.SS) at Strathmore UniversityBackground: World Health Organization (WHO) guidelines recommend early identification of patients who have emergency features for early medical intervention with the aim of reducing child mortality and morbidity. Prognostic models have been developed to be used in clinical setups, but their performance in external validations has been dismal. These poor performances have been attributed to suboptimal statistical methods used for derivation of these scores. Methods: The Bayesian finite mixture model was used to succinctly identify subpopulations in a population of 47,596 patients from different geographical regions. Mixed models were used to derive a final prognostic model taking into account subgroups of the population. Clinically relevant yet routinely available prognostic factors were used in model development. Results: Amongst the 23 risk factors used, the AVPU scale which measures unconsciousness was the strongest predictor of mortality with odds of (AOR=2.94, 95% CI= 2.57 - 3.36). Oedema (AOR= 2.66, 95% CI= 2.18 - 3.24), pallor (AOR=2.09, 95% CI= 1.86 - 2.36) and the presence of >= 3 severe comorbidities (AOR=2.19, 95% CI= 1.73 - 2.74) were also associated with an increased risk of death. Conclusion: Given that patient are not alike, a statistical methodology that clusters patients into homogeneous subpopulations should be used to account for the inherent variability in the medical patients. Computational methodology such as mixture models should be used to identify inherent subpopulations that underlie the population of medical patients under study. Limitation: The use of diagnostic episodes as one of predictors in the model was based on the clinician’s impression (not a laboratory test) thus the possibility of false positives could not be ruled out
Examining which clinicians provide admission hospital care in a high mortality setting and their adherence to guidelines: An observational study in 13 hospitals
No full textBackground:
We explored who actually provides most admission care in hospitals offering supervised experiential training to graduating clinicians in a high mortality setting where practices deviate from guideline recommendations.
Methods:
We used a large observational data set from 13 Kenyan county hospitals from November 2015 through November 2018 where patients were linked to admitting clinicians. We explored guideline adherence after creating a cumulative correctness of Paediatric Admission Quality of Care (cPAQC) score on a 5-point scale (0–4) in which points represent correct, sequential progress in providing care perfectly adherent to guidelines comprising admission assessment, diagnosis and treatment. At the point where guideline adherence declined the most we dichotomised the cPAQC score and used multilevel logistic regression models to explore whether clinician and patient-level factors influence adherence.
Results:
There were 1489 clinicians who could be linked to 53 003 patients over a period of 3 years. Patients were rarely admitted by fully qualified clinicians and predominantly by preregistration medical officer interns (MOI, 46%) and diploma level clinical officer interns (COI, 41%) with a median of 28 MOI (range 11–68) and 52 COI (range 5–160) offering care per study hospital. The cPAQC scores suggest that perfect guideline adherence is found in ≤12% of children with malaria, pneumonia or diarrhoea with dehydration. MOIs were more adherent to guidelines than COI (adjusted OR 1.19 (95% CI 1.07 to 1.34)) but multimorbidity was significantly associated with lower guideline adherence.
Conclusion:
Over 85% of admissions to hospitals in high mortality settings that offer experiential training in Kenya are conducted by preregistration clinicians. Clinical assessment is good but classifying severity of illness in accordance with guideline recommendations is a challenge. Adherence by MOI with 6 years’ training is better than COI with 3 years’ training, performance does not seem to improve during their 3 months of paediatric rotations
Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study
No full textBACKGROUND AND AIMS: Prognostic models provide evidence-based predictions and estimates of future outcomes, facilitating decision-making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)-Malawi model and three other models by Lowlavaar et al. METHODS: The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in-hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in-hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. RESULTS: The RISC-Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case-fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77-0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 -1.06), and calibration intercept was 0.81 (95% CI: 0.77-0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in-hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72-0.77), the calibration slope was 0.78 (95% CI: 0.71-0.84), and the calibration intercept was 0.37 (95% CI: 0.28-0.46). All models markedly underestimated the risk of mortality. CONCLUSION: All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability
Improving nutrition outcomes through enhanced allocative efficiency of investments in 24 high risk counties in Kenya: An optima nutrition modelling study
No full textIntroduction ;Undernutrition remains a significant global challenge, severely impacting children’s development and growth. To address this, the Sustainable Development Goals target a substantial reduction in stunting and wasting by 2025. Achieving these goals requires scaling up evidence-based nutritional interventions; however, limited budgets pose challenges in funding all necessary programs. To assist policymakers in making informed decisions, the World Bank developed the Optima Nutrition Modelling tool, which optimizes the allocation of nutrition investments. Kenya, with its high prevalence of stunting, was the focus of this study. Using the Optima Nutrition model, we aimed to (1) assess the impact of scaling up evidence-based nutrition interventions and (2) determine how existing resources could be optimized to reduce stunting, wasting, and anemia in children under five and anemia in pregnant women across 24 counties with the poorest nutrition outcomes.
Methods; Utilizing the Optima Nutrition model, we analyzed demographic and intervention data to assess the impact and allocation of interventions. Scenario analyses and optimization techniques were employed to reallocate resources and evaluate their potential impact on reducing undernutrition.
Results; When scaled up to 95% coverage and maintained until 2030, across the counties the interventions resulted in median relative reductions of 14.6% in stunting prevalence, 23% in wasting prevalence, 20.6% in anaemia prevalence among children and 64.2% PLOS One | https://doi.org/10.1371/journal.pone.0323391 May 27, 2025 2 / 15 in anaemia prevalence among pregnant women. For stunting, the optimized scenarios prioritized infant and young child feeding education, vitamin A supplementation, lipid-based nutrition supplements for children, and balanced energy-protein supplementation and multiple micronutrient supplementation for pregnant women. For wasting, cash transfers was prioritized. For anaemia in children, long-lasting insecticide treated bednets and IFA fortification of maize were prioritized. For anaemia in pregnant women, multiple micronutrient supplementation, iron and folic acid supplementation and long-lasting insecticide-treated bed nets were prioritized.
Conclusion; This study provided a comprehensive assessment of the prevalence of stunting, wasting, and anemia among children under five years in 24 counties in Kenya. The Optima model suggested that scaling up nutrition-specific interventions under the same baseline budgets could lead to significant reductions in stunting, wasting, and anemia in Kenya. Additionally, the study identified interventions that should be prioritized during nutrition intervention resource allocati
Correction: Improving nutrition outcomes through enhanced allocative efficiency of investments in 24 high risk counties in Kenya: An Optima Nutrition modelling study.
No full text[This corrects the article DOI: 10.1371/journal.pone.0323391.]
Indirect health effects of the COVID-19 pandemic in Kenya: a mixed methods assessment
No full textBackground
The COVID-19 pandemic and country measures to control it can lead to negative indirect health effects. Understanding these indirect health effects is important in informing strategies to mitigate against them. This paper presents an analysis of the indirect health effects of the pandemic in Kenya.
Methods
We employed a mixed-methods approach, combining the analysis of secondary quantitative data obtained from the Kenya Health Information System database (from January 2019 to November 2020) and a qualitative inquiry involving key informant interviews (n = 12) and document reviews. Quantitative data were analysed using an interrupted time series analysis (using March 2020 as the intervention period). Thematic analysis approach was employed to analyse qualitative data.
Results
Quantitative findings show mixed findings, with statistically significant reduction in inpatient utilization, and increase in the number of sexual violence cases per OPD visit that could be attributed to COVID-19 and its mitigation measures. Key informants reported that while financing of essential health services and domestic supply chains were not affected, international supply chains, health workforce, health infrastructure, service provision, and patient access were disrupted. However, the negative effects were thought to be transient, with mitigation measures leading to a bounce back.
Conclusion
Finding from this study provide some insights into the effects of the pandemic and its mitigation measures in Kenya. The analysis emphasizes the value of strategies to minimize these undesired effects, and the critical role that routine health system data can play in monitoring continuity of service delivery
Recalibrating prognostic models to improve predictions of in‐hospital child mortality in resource‐limited settings
No full textBackground
In an external validation study, model recalibration is suggested once there is evidence of poor model calibration but with acceptable discriminatory abilities. We identified four models, namely RISC-Malawi (Respiratory Index of Severity in Children) developed in Malawi, and three other predictive models developed in Uganda by Lowlaavar et al. (2016). These prognostic models exhibited poor calibration performance in the recent external validation study, hence the need for recalibration.
Objective
In this study, we aim to recalibrate these models using regression coefficients updating strategy and determine how much their performances improve.
Methods
We used data collected by the Clinical Information Network from paediatric wards of 20 public county referral hospitals. Missing data were multiply imputed using chained equations. Model updating entailed adjustment of the model's calibration performance while the discriminatory ability remained unaltered. We used two strategies to adjust the model: intercept-only and the logistic recalibration method.
Results
Eligibility criteria for the RISC-Malawi model were met in 50,669 patients, split into two sets: a model-recalibrating set (n = 30,343) and a test set (n = 20,326). For the Lowlaavar models, 10,782 patients met the eligibility criteria, of whom 6175 were used to recalibrate the models and 4607 were used to test the performance of the adjusted model. The intercept of the recalibrated RISC-Malawi model was 0.12 (95% CI 0.07, 0.17), while the slope of the same model was 1.08 (95% CI 1.03, 1.13). The performance of the recalibrated models on the test set suggested that no model met the threshold of a perfectly calibrated model, which includes a calibration slope of 1 and a calibration-in-the-large/intercept of 0.
Conclusions
Even after model adjustment, the calibration performances of the 4 models did not meet the recommended threshold for perfect calibration. This finding is suggestive of models over/underestimating the predicted risk of in-hospital mortality, potentially harmful clinically. Therefore, researchers may consider other alternatives, such as ensemble techniques to combine these models into a meta-model to improve out-of-sample predictive performance
Replication Data for: Revealing the extent of the COVID-19 pandemic in Kenya based on serological and PCR-test data
No full textThis is a replication dataset for the submitted manuscript "Ojal, J., Brand, S.P., Were, V., Okiro, E.A., Kombe, I.K., Mburu, C., Aziza, R., Ogero, M., Agweyu, A., Warimwe, G.M. and Uyoga, S., 2020. Revealing the extent of the COVID-19 pandemic in Kenya based on serological and PCR-test data. medRxiv.".
The related study was conducted to estimate the SARS-CoV-2 pandemic peak and COVID-19 disease burden including reported severe cases and deaths in the major urban counties in Kenya. National surveillance PCR tests, serological surveys, and mobility data were used to develop and fit a county-specific transmission model
in the country.
These datasets presented contain the number of positive PCR-confirmed swab tests for each county by date of sample collection (21st Feb to 6th August), the number of positive and negative serological results for each county by date of sample collection (21st Feb to 6th August), number of deaths with a PCR-confirmed swab test for each county by recorded date of death (21st Feb to 6th August), the total number of swab samples collected in Mombasa county, and analyzed at Kemri-Wellcome Research Program testing center (21st Feb – 27th June), and summary data of Kenyan epidemic,
including reported total number of test performed. The datasets are used for COVID-19 forecasts.</p
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