Snapshot of civil registration and vital statistics systems of Kenya

Library has French versionThe Ministry of Interior and Coordination of National Government is responsible for civil registration in Kenya. A large backlog, along with delayed registrations of births and deaths is attributed to the lack of demand for services and the lack of easy access to registration centres, especially in rural areas. The Kenya National Bureau of Statistics (KNBS) has the legal mandate for the collection, compilation, analysis, publication and dissemination of “vital occurrences and morbidity” and the co-ordination of the national statistical system. Coordination and collaboration among key stakeholders are a necessary condition for the improvement of CRVS systems.Global Affairs Canad

The informal sector and universal health coverage: crucial considerations

http://www.bu.edu/pardee/publications-library/issues-in-brief/issues-in-brief-no-37-june-2019/Published versio

Trends in childhood mortality in Kenya: the urban advantage has seemingly been wiped out

Background: we describe trends in childhood mortality in Kenya, paying attention to the urban–rural and intra-urban differentials.Methods: we use data from the Kenya Demographic and Health Surveys (KDHS) collected between 1993 and 2008 and the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) collected in two Nairobi slums between 2003 and 2010, to estimate infant mortality rate (IMR), child mortality rate (CMR) and under-five mortality rate (U5MR).Results: between 1993 and 2008, there was a downward trend in IMR, CMR and U5MR in both rural and urban areas. The decline was more rapid and statistically significant in rural areas but not in urban areas, hence the gap in urban–rural differentials narrowed over time. There was also a downward trend in childhood mortality in the slums between 2003 and 2010 from 83 to 57 for IMR, 33 to 24 for CMR, and 113 to 79 for U5MR, although the rates remained higher compared to those for rural and non-slum urban areas in Kenya.Conclusions: the narrowing gap between urban and rural areas may be attributed to the deplorable living conditions in urban slums. To reduce childhood mortality, extra emphasis is needed on the urban slums

Detectable HIV Viral Load in Kenya: Data from a Population-Based Survey.

IntroductionAt the individual level, there is clear evidence that Human Immunodeficiency Virus (HIV) transmission can be substantially reduced by lowering viral load. However there are few data describing population-level HIV viremia especially in high-burden settings with substantial under-diagnosis of HIV infection. The 2nd Kenya AIDS Indicator Survey (KAIS 2012) provided a unique opportunity to evaluate the impact of antiretroviral therapy (ART) coverage on viremia and to examine the risks for failure to suppress viral replication. We report population-level HIV viral load suppression using data from KAIS 2012.MethodsBetween October 2012 to February 2013, KAIS 2012 surveyed household members, administered questionnaires and drew serum samples to test for HIV and, for those found to be infected with HIV, plasma viral load (PVL) was measured. Our principal outcome was unsuppressed HIV viremia, defined as a PVL ≥ 550 copies/mL. The exposure variables included current treatment with ART, prior history of an HIV diagnosis, and engagement in HIV care. All point estimates were adjusted to account for the KAIS 2012 cluster sampling design and survey non-response.ResultsOverall, 61·2% (95% CI: 56·4-66·1) of HIV-infected Kenyans aged 15-64 years had not achieved virological suppression. The base10 median (interquartile range [IQR]) and mean (95% CI) VL was 4,633 copies/mL (0-51,596) and 81,750 copies/mL (59,366-104,134), respectively. Among 266 persons taking ART, 26.1% (95% CI: 20.0-32.1) had detectable viremia. Non-ART use, younger age, and lack of awareness of HIV status were independently associated with significantly higher odds of detectable viral load. In multivariate analysis for the sub-sample of patients on ART, detectable viremia was independently associated with younger age and sub-optimal adherence to ART.DiscussionThis report adds to the limited data of nationally-representative surveys to report population- level virological suppression. We established heterogeneity across the ten administrative and HIV programmatic regions on levels of detectable viral load. Timely initiation of ART and retention in care are crucial for the elimination of transmission of HIV through sex, needle and syringe use or from mother to child. Further refinement of geospatial mapping of populations with highest risk of transmission is necessary

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

The state of emergency care in the Republic of Kenya

Approximately 580,000 km2 in size, the Republic of Kenya is as big as Botswana but only half the size of countries like South Africa, Mali, and Angola. Kenya is comprised of eight provinces: Central, Coast, Eastern, Nairobi, North Eastern, Nyanza, Rift Valley, and Western. The 2009 census revealed a population of over 38 million people, with a population density of approximately 66 persons per square kilometre. Majority of the population (68%) lives in rural areas, as compared with the sub-Saharan African average of approximately 62%. With a gross domestic product (GDP) per capita of US $1,600 in 2010, Kenya is considered a low-income country–– with 50% of the population living below the poverty line. HIV/AIDS disproportionately affects the country’s mortality and morbidity. Although its prevalence is higher than the regional average at 6.3% for people ages 15–49 years, it is much lower than many other sub-Saharan African countries. In addition to HIV/AIDS, tuberculosis, malaria, and diarrheal diseases are major killers. The burden of communicable diseases is high, with malaria as the leading cause of morbidity (30%), followed by respiratory diseases (24.5%). Malaria prevalence is 14%, and HIV prevalence is 7.4%, with a higher rate in women (8.5%) compared to men (5.6%). The non-communicable disease burden is also on the rise with diabetes prevalence at 3.3%, a threefold increase over the last 10 years. Mental health issues and road traffic injuries are also on the rise. Thirteen percent of school-age children aged 13–15 years are active cigarette smokers. These put Kenya in the company of other low-income countries predicted by the World Health Organization (WHO) to face the ‘‘double hump’’ burden of communicable and chronic disease over the next several decades

Qualitative-Quantitative Integrated Analysis for Kenya: Well-being in the Context of Food Price Volatility

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Effect of A Community Health Worker Led Intervention on Skilled Birth Care in Rural Mwingi West Sub-County, Kenya. A Quasi Experiment

Community Health Workers (CHWs) are key to improving utilization of Skilled Birth Care (SBC) in Africa. Evidence from a quasi-experiment conducted in Kenya indicates that the Community Health Strategy, which is a Community Health Worker led intervention increased utilization of Skilled Birth Care by 1.6 times

Spatial and temporal distribution of notified tuberculosis cases in Nairobi County, Kenya, between 2012 and 2016

Background: Tuberculosis (TB) is an infectious disease of major public health concern globally. The disease has showed space‐time variations across settings. Spatial temporal assessment can be used to understand the distribution and variations of TB disease.Objective: To determine the spatial and temporal distribution of notified TB cases in Nairobi County, Kenya, between 2012 and 2016Design: A cross sectional studySetting: Nairobi County, KenyaSubjects: Tuberculosis cases notified in Tuberculosis Information for Basic Units from 2012 to 2016Results: A total of 70,505 cases of TB were notified in Nairobi County between 2012 and 2016, with male to female ratio of 3:2 and HIV coinfection rate of 38%.The temporal analysis showed a declining trend of the notified cases. The spatial clusters showed stability in most areas while others varied annually during the study period. The space‐time analysis also detected the four most likely clusters or hotspots. Cluster 1 which covered the informal settlements of Kibera, Kawangware and Kangemi with 4,011observed cases against 2,977expected notified TB cases(relative risk (RR) 1.37, p<0.001). Further, Cluster 2 covered Starehe and parts of Kamukunji, Mathare, Makadara, Kibra and Dagoretti North Constituencies (RR 1.93, p<0.001; observed and expected cases were 4,206 and 2,242, respectively.Conclusion: This study identified high TB case notifications, declining temporal trends and clustering of TB cases in Nairobi. Evidence of clustering of TB cases indicates the need for focused interventions in the hotspot areas. Strategies should be devised for continuous TB surveillance and evidence based decision making