Annual case counts and clinical characteristics of pediatric and adolescent patients with diabetes in Kenyatta National Hospital, Nairobi, Kenya. A 14 year retrospective study
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Phoebe Wamalwa
,
Lucy Mungai
Abstract
Objectives
There is little data on prevalence, incidence rate and clinical characteristics on diabetes amongst the pediatric and adolescent group in sub-Saharan Africa. Therefore, this study aimed to document annual case counts, describe clinical characteristics, and assess loss to follow-up among pediatric and adolescent patients with diabetes at Kenyatta National Hospital.
Methods
This was a hospital-based retrospective, descriptive study carried out at Kenyatta National Hospital, Pediatric Endocrinology Unit, between January 2008 and December 2021 amongst diabetic patients aged 25 years and below. Data was analyzed using Statistical Package for Social Science (SPSS) version 23.0.
Results
Type 1 diabetes was the leading form of diabetes at 99.3 % (n=288). Most, 56.3 %, of cases of type 1 diabetes got diagnosed within the ages of 6–18 years, majority being 6–11 years. Most patients, 90.2 % presented in diabetic ketoacidosis (DKA) at initial diagnosis. There was a sustained increasing trend in type 1 diabetes with a notable dip in hospital visitations during covid time, the year 2020. Patients with type 1 diabetes took an average of 2.5 months and a median interval of 18 days from symptom onset to diagnosis. A third of the cases of type 1 diabetes, 31.25 %, were lost to follow up.
Conclusions
The increasing cases of type 1 diabetes with delayed diagnosis require allocation of more resources and increased awareness creation. Measures need to be put in place to manage chronic conditions during pandemics. Hospital-based tracking system is required to prevent loss to follow up cases.
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Research ethics: Approval of this study was obtained from the Ethics Committee of the University of Nairobi and Kenyatta National Hospital on 29th Mar 2022; ID P968/12/2021.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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