Mapping of anaemia prevalence among pregnant women in Kenya (2016–2019)

Abstract

Background Reducing the burden of anaemia is a critical global health priority that could improve maternal outcomes amongst pregnant women and their neonates. As more counties in Kenya commit to universal health coverage, there is a growing need for optimal allocation of the limited resources to sustain the gains achieved with the devolution of healthcare services. This study aimed to describe the spatio-temporal patterns of maternal anaemia prevalence in Kenya from 2016 to 2019. Methods Quarterly reported sub-county level maternal anaemia cases from January 2016 – December 2019 were obtained from the Kenyan District Health Information System. A Bayesian hierarchical negative binomial spatio-temporal conditional autoregressive (CAR) model was used to estimate maternal anaemia prevalence by sub-county and quarter. Spatial and temporal correlations were considered by assuming a conditional autoregressive and a first-order autoregressive process on sub-county and seasonal specific random effects, respectively. Results The overall estimated number of pregnant women with anaemia increased by 90.1% (95% uncertainty interval [95% UI], 89.9–90.2) from 155,539 cases in 2016 to 295,642 cases 2019. Based on the WHO classification criteria, the proportion of sub-counties with normal prevalence decreased from 28.0% (95% UI, 25.4–30.7) in 2016 to 5.4% (95% UI, 4.1–6.7) in 2019, whereas moderate anaemia prevalence increased from 16.8% (95% UI, 14.7–19.1) in 2016 to 30.1% (95% UI, 27.5–32.8) in 2019 and severe anaemia prevalence increased from 7.0% (95% UI, 5.6–8.6) in 2016 to 16.6% (95% UI, 14.5–18.9) in 2019. Overall, 45.1% (95% UI: 45.0–45.2) of the estimated cases were in malaria-endemic sub-counties, with the coastal endemic zone having the highest proportion 72.8% (95% UI: 68.3–77.4) of sub-counties with severe prevalence. Conclusion As the number of women of reproductive age continues to grow in Kenya, the use of routinely collected data for accurate mapping of poor maternal outcomes remains an integral component of a functional maternal health strategy. By unmasking the sub-county disparities often concealed by national and county estimates, our study findings reiterate the importance of maternal anaemia prevalence as a metric for estimating malaria burden and offers compelling policy implications for achieving national nutritional targets.

Joint spatio-temporal modelling of adverse pregnancy outcomes sharing common risk factors at sub-county level in Kenya, 2016–2019

“…In low-resource sub-counties in northern Kenya, macronutrient/maternal undernutrition - caused by inadequate intake of essential minerals and vitamins; contributes to anaemia in pregnancy, which might also precipitate APOs [ 66 ]. Additionally, infectious diseases such as malaria and human immunodeficiency virus (HIV) have previously been associated with an increased risk of pre-term deliveries, stillbirths, and neonatal deaths.…”

Prevalence and risk factors of malaria and anaemia and the impact of preventive methods among pregnant women: A case study at the Akatsi South District in Ghana

“…The prevalence of AiP recorded in this study represents a severe public health problem [ 7 , 9 , 35 ]. This prevalence is higher than the crude prevalence of 26.2% reported in Kenya [ 36 ], the 19.2% in Rwanda [ 37 ], 18.0% in Northern Tanzania [ 9 ] and the 34.85% in Eastern Africa [ 38 ]. The prevalence as recorded in this study is also higher than the national prevalence of 42.4% documented in 2014 among women aged 15–49 years [ 39 ].…”

Reporting of rapid diagnostic testing in Kenya's health system, 2018-2020: triangulation of routine and survey data

“…Figure 6 Discussion Routine health data is essential for quantifying health care utilisation, estimating the reach of interventions in the community and monitoring progress toward national and global targets such as SDGs [28]. However, data quality concerns, primarily due to the non-reporting of health facilities, have continued to persist, impacting the accurate assessment of the performance of a country's health system [15,[19][20][21]23]. These concerns become even more signi cant when attempting to estimate the current supply and demand for point-of-care testing to guarantee an adequate diagnostics supply.…”

“…Since 2011, the platform has provided routine data on tests done at a facility level every month in Kenya. Despite the availability of routine data across the country for an entire year, data quality has been a concern, primarily due to nonreporting and incomplete entries across health facilities [13,15,[19][20][21][22][23]. Dealing with these challenges requires an understanding of the causes driving non-reporting.…”