Trends of non-vaccination, under-vaccination and missed opportunities for vaccination (2003–2014) amongst children 0–23 months in Kenya

Bibliographic Data

ID19592562
AuthorsChristopher Ochieng’ Odero (0000-0003-2317-9405, Maseno University, corresponding author), Doreen Othero (0000-0003-1612-8458, Maseno University, corresponding author), Vincent Were (0000-0002-3554-795X, Kenya Medical Research Institute, corresponding author), Collins Ouma (0000-0001-8205-8887, Maseno University, corresponding author)
EditorsRamachandran Thiruvengadam (0000-0002-9195-2730)
Year2024
Volume4
Issue2
Pagese0002906
Publication date2024-02-06
Open AccessYes
TypeARTICLE
VenuePLOS Global Public Health (JOURNAL)
Journal identifiersISSN: 2767-3375 • E-ISSN: 2767-3375
PublisherPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pgph.0002906
PMID38319922
OpenAlexW4391578335
LanguageEN
Citations received1
References cited27

Vaccines are effective and cost-effective. Non-vaccination, under-vaccination, and missed opportunities for vaccination (MOV), have contributed to incomplete vaccination coverage in Kenya. Analyzing their trends is essential for targeting interventions and improvement strategies. This study aimed to assess trends of non-vaccination, under-vaccination, and MOV among children aged 0–23 months in Kenya using data obtained from the Kenya Demographic and Health Surveys (KDHS) conducted in 2003, 2008/09, and 2014. A two-stage, multi-stage, and stratified sampling technique was used. Weighted analysis was conducted to ensure generalizability to the full population. Using the KDHS sample size estimation process, the sample size was estimated for each indicator, with varying standard error estimates, level of coverage and estimated response rates. Final sample size was 2380 (2003), 2237 (2008/09) and 7380 (2014). To determine the level of non-vaccination, under-vaccination and MOV among children aged 0–23 months, a weighted descriptive analysis was used to estimate their prevalence, with 95% confidence intervals (CI) for each year. MOV was defined using an algorithm as a binary variable. Data coding and recoding were done using Stata (version 14; College Station, TX: StataCorp LP). Trends in proportions of non-vaccination, under-vaccination and MOV were compared between 2003, 2008/09, and 2014 using the Cochrane-Armitage trend test. All results with P ≤0.05 were considered statistically significant. Trends in proportion of non-vaccination among children aged 0–23 months in Kenya was 13.2%, 6.1% and 3.2% in 2003, 2008/09 and 2014, respectively ( P = 0.0001). Trends in proportion of under-vaccination among children aged 0–23 months in Kenya was 54.3%, 50% and 51.3% in 2003, 2008/09 and 2014, respectively ( P = 0.0109). The trends in proportion of children who experienced MOV was 22.7% in 2003, 31.9% in 2008/09 and 37.6% in 2014 ( P = 0.0001). In the study duration, non-vaccination decreased by 10%, under-vaccination remained relatively stable, and MOV increased by ~15%. There is need for the Government and partners to implement initiatives that improve vaccine access and coverage, particularly in regions with low coverage rates, and to address missed opportunities for vaccination

Confidence interval · Descriptive statistics · Environmental health · Generalizability theory · Population · Psychological intervention · Sample size determination · Statistics · Stratified sampling · Vaccination · Demography · Influenza Virus Research Studies · Mathematics · Medicine · Vaccine Coverage and Hesitancy · Viral gastroenteritis research and epidemiology · Immunology · Internal Medicine · Pediatrics

Tools

Open DOIOpen Access