Household Food Security Status and Child Health Outcomes in Kenya

Interminable access to sufficient, nutritious, and safely prepared food is a human right. Attributed to insufficient food and nutrient intake, malnutrition is a major health burden in developing economies that has maimed socioeconomic development. In children, undernourishment impairs the functioning of the immune system, increases susceptibility to diseases, and undermines physical and cognitive development. In Kenya, there exists a paucity of empirical corroboration of the effect of household food security status (HFSS) on child health outcomes. Using data drawn from the 2014 Kenya Demographic and Health Survey, this paper focuses on analyzing the causal link between HFSS and child health outcomes and to provide evidencebased policy recommendations to promote child health outcomes. We employed three measures of HFSS: households that lacked food/enough money to purchase food, the Reduced Coping Strategy Index (CSI), and the Food Consumption Score (FCS). The child health production function was estimated using the two-stage residual inclusion (2SRI) technique to control for potential endogeneity. The results indicate that households that lacked food/enough money to purchase food were significantly associated with stunted, wasted, and underweight growth in children. Similarly, the Reduced CSI was a significant determinant of stunted and underweight growth in children. However, the effect was insignificant relative to wasted growth. The findings also indicate that FCS contributes significantly to improvements in child health outcomes. Our evidence has the potential to inform policies on the promotion of child health outcomes. We recommend implementation of programs such as social assistance, integration of nutrition and WASH, and capacity-building to promote women’s knowledge of health, nutrition, and better child-care practices

Health Expenditures and Health Outcomes in Kenya

Health inputs are critical in attaining a healthy nation and improving health outcomes. Kenya, like other developing countries, grapples with limited health expenditures and poor population health indicators. Specifically, Kenya is yet to achieve the allocation of least 15% of the government’s annual budget to improve the health sector as enshrined in the Abuja Declaration. Though there is an improvement with regards to infant mortality rate decreasing from 96.6 per 1, 000 live birth in 1970 to 30.6 per 1, 000 live birth in 2018. This indicator of population health outcome is currently far below the Sustainable Development Goals (SDGs) target of reducing the under five mortality rate to as low as 12 deaths per 1,000 live births by 2030. The literature suggests that increase in government’s budgetary allocation to the health sector can improve country’s health outcomes. Evidence on the impact of health expenditures on health outcomes is mixed and limited in developing countries. This study aims to analyze the impact of public health expenditures on health outcomes, among other control variables in Kenya. The study uses time series data from 1970 to 2018. The variables are found to be integrated of different orders suggesting the choice of Autoregressive Distributed Lag (ARDL) model. ARDL provides a useful link between long run equilibrium relationships and short run disequilibrium dynamics is estimated. The ARDL bounds test suggests presence of cointegration thus leading to the estimation of Error Correction Model (ECM). The findings suggest that improvements in public health expenditures enhance health outcomes in Kenya. The control variablesthat are found to be important determinants of infant mortality rate in Kenya include the national income and number of hospital beds per 100, 000. The study recommends that Kenyan government should increase annual budgetary allocation to health sector. Such increase is likely to lead to investments in physical and human capital in the health sector thus translating to improved health outcomes in Kenya

Catastrophic Health Expenditures And Impoverishment In Kenya

Background: Out-of-pocket health expenditures leave households exposed to the risk of financial catastrophe and poverty whenever they entail significant dissaving or the sale of key household assets. Even relatively small expenditures on health can be financially disastrous for poor households and similarly, large health care expenditures can lead to financial catastrophe and bankruptcy for rich households. Objective: There is increasing evidence that out-of-pocket expenditures act as a financial barrier to accessing health care, and are a source of catastrophic expenditures and impoverishment. This paper estimates the burden of out-of-pocket payments in Kenya; the incidence and intensity of catastrophic health care expenditure and impoverishment in Kenya. Methods: Using Kenya Household Health Expenditures and Utilization Survey data of 2007, the study uses both descriptive and econometric analysis to investigate the incidence and intensity of catastrophic health expenditures and impoverishment as well as the determinants of catastrophic health expenditures. To estimate the incidence and intensity of catastrophic expenditures and impoverishment, the study used both Wagstaff and van Doorslaer, (2002) and Xu et al. (2005) and applied various thresholds to demonstrate the sensitivity of catastrophic measures. For determinants of catastrophic health expenditures, a logit model was employed. Findings: Among those who utilized health care, 11.7 percent experienced catastrophic expenditures and 4 percent were impoverished by health care payments. In addition, approximately 2.5 million individuals were pushed into poverty as a result of paying for health care. The poor experienced the highest incidence of catastrophic expenditures. Conclusion: The paper recommends that the government should establish avenues for reducing the burden of out-of-pocket expenditures borne by households. This could be through a legal requirement for everyone to belong to a health insurance and targeting the poor, the elderly and chronically ill through the devolved system of the government and devolved funds

An Econometric Analysis Of Health Care Utilization In Kenya

Background: Increasing access to health care has been a policy concern for many governments, Kenya included. The Kenyan government introduced and implemented a number of initiatives in a bid to address the healthcare utilization challenge. These initiatives include 10/20 policy, exemptions for user fees for some specific health services (treatment of children less than five years, maternity services in dispensaries and health centers, Tuberculosis treatment in public health facilities), and increase in the number of health facilities and health workforce. These initiatives notwithstanding, healthcare utilization in Kenya remains a challenge. The Kenya Household Health Expenditure and Utilization Survey of 2007 found that 17 percent of those who needed health care services could not access the services from both government and private health facilities largely due to financial constraints. This paper employed econometric analysis to examine what could be constraining health care utilization in Kenya despite all the efforts employed. Methods: Using the 2007 Kenya Household Health Expenditures and Utilization Survey (KHHEUS) data (n = 8414), this paper investigates the factors that affect health care utilization in Kenya by estimating a count data negative binomial model. The model was also applied to public and private health facilities to better understand the specificities of poverty in these two facility types. Common estimation problems of endogeneity, heterogeneity, multicollinearity and heteroskedasticity are addressed. Findings: The econometric analysis reveals that out-of-pocket expenditures, waiting time, distance, household size, income, chronic illness area of residence and working status of the household head are significant factors affecting health care utilization in Kenya. While income and distance are significant factors affecting public health care utilization they are not significant in explaining healthcare utilization in private facilities. In addition, working status of the household head, insurance cover and education are significant in explaining private and not public health care utilization. A striking finding is the positive relationship between distance and health care utilization implying that people will travel long distances to obtain treatment. This is perhaps associated with expectations of higher quality of care at far away higher level facilities, especially in rural areas. Conclusion: The paper confirms the existing evidence of the negative effects of Out-of-Pocket (OOP) expenditures and other determinants of health care utilization. With a better understanding of why people use or do not use health services, health care organizations can seek to improve the quality of human life. The bypassing of health facilities for higher level far away facilities implies that it is not so much about availing health facilities, but the quality of the services offered in those facilities. The government should therefore assure quality to increase utilization of the lower level facilities, especially in the rural areas

Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Kenyan blood donors.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Africa is poorly described. The first case of SARS-CoV-2 in Kenya was reported on 12 March 2020, and an overwhelming number of cases and deaths were expected, but by 31 July 2020, there were only 20,636 cases and 341 deaths. However, the extent of SARS-CoV-2 exposure in the community remains unknown. We determined the prevalence of anti-SARS-CoV-2 immunoglobulin G among blood donors in Kenya in April-June 2020. Crude seroprevalence was 5.6% (174 of 3098). Population-weighted, test-performance-adjusted national seroprevalence was 4.3% (95% confidence interval, 2.9 to 5.8%) and was highest in urban counties Mombasa (8.0%), Nairobi (7.3%), and Kisumu (5.5%). SARS-CoV-2 exposure is more extensive than indicated by case-based surveillance, and these results will help guide the pandemic response in Kenya and across Africa

COVID-19 transmission dynamics underlying epidemic waves in Kenya

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of SARS-CoV-2 transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or when infection spreads to susceptible sub-populations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socio-economic and urban/rural population structure are critical determinants of viral transmission in Kenya

Temporal trends of SARS-CoV-2 seroprevalence during the first wave of the COVID-19 epidemic in Kenya.

Observed SARS-CoV-2 infections and deaths are low in tropical Africa raising questions about the extent of transmission. We measured SARS-CoV-2 IgG by ELISA in 9,922 blood donors across Kenya and adjusted for sampling bias and test performance. By 1st September 2020, 577 COVID-19 deaths were observed nationwide and seroprevalence was 9.1% (95%CI 7.6-10.8%). Seroprevalence in Nairobi was 22.7% (18.0-27.7%). Although most people remained susceptible, SARS-CoV-2 had spread widely in Kenya with apparently low associated mortality

SARS-CoV-2 seroprevalence and implications for population immunity: Evidence from two Health and Demographic Surveillance System sites in Kenya, February-December 2022.

BACKGROUND: We sought to estimate SARS-CoV-2 antibody seroprevalence within representative samples of the Kenyan population during the third year of the COVID-19 pandemic and the second year of COVID-19 vaccine use. METHODS: We conducted cross-sectional serosurveys among randomly selected, age-stratified samples of Health and Demographic Surveillance System (HDSS) residents in Kilifi and Nairobi. Anti-spike (anti-S) immunoglobulin G (IgG) serostatus was measured using a validated in-house ELISA and antibody concentrations estimated with reference to the WHO International Standard for anti-SARS-CoV-2 immunoglobulin. RESULTS: HDSS residents were sampled in February-June 2022 (Kilifi HDSS N = 852; Nairobi Urban HDSS N = 851) and in August-December 2022 (N = 850 for both sites). Population-weighted coverage for ≥1 doses of COVID-19 vaccine were 11.1% (9.1-13.2%) among Kilifi HDSS residents by November 2022 and 34.2% (30.7-37.6%) among Nairobi Urban HDSS residents by December 2022. Population-weighted anti-S IgG seroprevalence among Kilifi HDSS residents increased from 69.1% (65.8-72.3%) by May 2022 to 77.4% (74.4-80.2%) by November 2022. Within the Nairobi Urban HDSS, seroprevalence by June 2022 was 88.5% (86.1-90.6%), comparable with seroprevalence by December 2022 (92.2%; 90.2-93.9%). For both surveys, seroprevalence was significantly lower among Kilifi HDSS residents than among Nairobi Urban HDSS residents, as were antibody concentrations (p < 0.001). CONCLUSION: More than 70% of Kilifi residents and 90% of Nairobi residents were seropositive for anti-S IgG by the end of 2022. There is a potential immunity gap in rural Kenya; implementation of interventions to improve COVID-19 vaccine uptake among sub-groups at increased risk of severe COVID-19 in rural settings is recommended

SARS-CoV-2 seroprevalence in three Kenyan health and demographic surveillance sites, December 2020-May 2021

Background Most of the studies that have informed the public health response to the COVID-19 pandemic in Kenya have relied on samples that are not representative of the general population. We conducted population-based serosurveys at three Health and Demographic Surveillance Systems (HDSSs) to determine the cumulative incidence of infection with SARS-CoV-2. Methods We selected random age-stratified population-based samples at HDSSs in Kisumu, Nairobi and Kilifi, in Kenya. Blood samples were collected from participants between 01 Dec 2020 and 27 May 2021. No participant had received a COVID-19 vaccine. We tested for IgG antibodies to SARS-CoV-2 spike protein using ELISA. Locally-validated assay sensitivity and specificity were 93% (95% CI 88–96%) and 99% (95% CI 98–99.5%), respectively. We adjusted prevalence estimates using classical methods and Bayesian modelling to account for the sampling scheme and assay performance. Results We recruited 2,559 individuals from the three HDSS sites, median age (IQR) 27 (10–78) years and 52% were female. Seroprevalence at all three sites rose steadily during the study period. In Kisumu, Nairobi and Kilifi, seroprevalences (95% CI) at the beginning of the study were 36.0% (28.2–44.4%), 32.4% (23.1–42.4%), and 14.5% (9.1–21%), and respectively; at the end they were 42.0% (34.7–50.0%), 50.2% (39.7–61.1%), and 24.7% (17.5–32.6%), respectively. Seroprevalence was substantially lower among children (&lt;16 years) than among adults at all three sites (p≤0.001). Conclusion By May 2021 in three broadly representative populations of unvaccinated individuals in Kenya, seroprevalence of anti-SARS-CoV-2 IgG was 25–50%. There was wide variation in cumulative incidence by location and age. </jats:sec

Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics in three Kenyan referral hospitals: Repeated cross-sectional surveys 2020-21.

INTRODUCTION: The high proportion of SARS-CoV-2 infections that have remained undetected presents a challenge to tracking the progress of the pandemic and estimating the extent of population immunity. METHODS: We used residual blood samples from women attending antenatal care services at three hospitals in Kenya between August 2020 and October 2021and a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. We fitted a two-component mixture model as an alternative to the threshold analysis to estimate of the proportion of individuals with past SARS-CoV-2 infection. RESULTS: We estimated seroprevalence in 2,981 women; 706 in Nairobi, 567 in Busia and 1,708 in Kilifi. By October 2021, 13% of participants were vaccinated (at least one dose) in Nairobi, 2% in Busia. Adjusted seroprevalence rose in all sites; from 50% (95%CI 42-58) in August 2020, to 85% (95%CI 78-92) in October 2021 in Nairobi; from 31% (95%CI 25-37) in May 2021 to 71% (95%CI 64-77) in October 2021 in Busia; and from 1% (95% CI 0-3) in September 2020 to 63% (95% CI 56-69) in October 2021 in Kilifi. Mixture modelling, suggests adjusted cross-sectional prevalence estimates are underestimates; seroprevalence in October 2021 could be 74% in Busia and 72% in Kilifi. CONCLUSIONS: There has been substantial, unobserved transmission of SARS-CoV-2 in Nairobi, Busia and Kilifi Counties. Due to the length of time since the beginning of the pandemic, repeated cross-sectional surveys are now difficult to interpret without the use of models to account for antibody waning