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

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

Childhood mortality and associated factors in Migori County, Kenya: evidence from a cross-sectional survey

Objectives The under-five mortality (U5M) rate in Kenya (41 per 1000 live births) remains significantly above international goals (25 per 1000 live births). This is further exacerbated by regional inequalities in mortality. We aimed to describe U5M in Migori County, Kenya, and identify associated factors that can serve as programming targets.Design Cross-sectional observational survey.Setting Areas served by the Lwala Community Alliance and control areas in Migori County, Kenya.Participants This study included 15 199 children born to respondents during the 18 years preceding the survey.Primary and secondary outcome measures The primary outcome was mortality in the first 5 years of life. The survey was powered to detect a 10% change in various health metrics over time with 80% power.Results A total of 15 199 children were included in the primary analyses, and 230 (1.5%) were deceased before the fifth birthday. The U5M rate from 2016 to 2021 was 32.2 per 1000 live births. Factors associated with U5M included year of birth (HR 0.926, p<0.001), female sex (HR 0.702, p=0.01), parental marriage (HR 0.642, p=0.036), multiple gestation pregnancy (HR 2.776, p<0.001), birth spacing less than 18 months (HR 1.894, p=0.005), indoor smoke exposure (HR 1.916, p=0.027) and previous familial contribution to the National Hospital Insurance Fund (HR 0.553, p=0.009). The most common cause of death was malaria.Conclusions We describe factors associated with childhood mortality in a Kenyan community using survival analyses of complete birth histories. Mortality rates will serve as the baseline for future programme evaluation as a part of a 10-year study design. This provides both the hyperlocal information needed to improve programming and generalisable conclusions for other organisations working in similar environments