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

Factors affecting actualisation of the WHO breastfeeding recommendations in urban poor settings in Kenya

Poor breastfeeding practices are widely documented in Kenya, where only a third of children are exclusively breastfed for 6 months and only 2% in urban poor settings. This study aimed to better understand the factors that contribute to poor breastfeeding practices in two urban slums in Nairobi, Kenya. In-depth interviews (IDIs), focus group discussions (FGDs) and key informant interviews (KIIs) were conducted with women of childbearing age, community health workers, village elders and community leaders and other knowledgeable people in the community. A total of 19 IDIs, 10 FGDs and 11 KIIs were conducted, and were recorded and transcribed verbatim. Data were coded in NVIVO and analysed thematically. We found that there was general awareness regarding optimal breastfeeding practices, but the knowledge was not translated into practice, leading to suboptimal breastfeeding practices. A number of social and structural barriers to optimal breastfeeding were identified: (1) poverty, livelihood and living arrangements; (2) early and single motherhood; (3) poor social and professional support; (4) poor knowledge, myths and misconceptions; (5) HIV; and (6) unintended pregnancies. The most salient of the factors emerged as livelihoods, whereby women have to resume work shortly after delivery and work for long hours, leaving them unable to breastfeed optimally. Women in urban poor settings face an extremely complex situation with regard to breastfeeding due to multiple challenges and risk behaviours often dictated to them by their circumstances. Macro-level policies and interventions that consider the ecological setting are needed

Communities and employers show a high level of preparedness in supporting working mothers to combine breastfeeding with work in rural Kenya

Community Readiness Model (CRM) through pragmatic mixed methods design, combining quantitative CRM survey with qualitative data, was used to assess the level of preparedness and readiness among local leaders, employers and community members in supporting working mothers to combine breastfeeding with work. The study was conducted in one of the tea state farms in Kericho County of Kenya. A total of 17 purposively selected men (fathers), lactating mothers, peer educators, health professionals (doctors, nurses and nutritionists), tea plantation managers and grandmothers were interviewed. The CRM that has six different dimensions was applied to determine the stage of readiness to support working mothers to combine breastfeeding with work. Community Readiness Score (CRS) was calculated descriptively as mean ± standard deviation (SD). Thematic analysis using NVIVO software was used to analyse qualitative data. We found that the mean (±SD) CRS was 7.3 (1.9), which corresponded to the third highest level of the nine stages or the ‘stabilization’ stage of community readiness. Dimensionally, the mean CRS was the highest (8.3 ± 1.9) for leadership followed by community efforts (7.5 ± 2.1), whereas the lowest CRS was observed for knowledge of efforts (6.6 ± 2.3) and availability of resources (6.6 ± 1.9). In conclusion, high level of readiness to support working women to combine work with breastfeeding with suboptimal knowledge of efforts and availability of resources was observed in the area. Future interventions should focus on enabling the community to feel more comfortable and creating detailed and refined knowledge on combining breastfeeding with work

How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses

COVID-19 research has relied heavily on convenience-based samples, which—though often necessary—are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. This registered study analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected online in Canada within the International COVID-19 Awareness and Responses Evaluation Study (www.icarestudy.com). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impacted sampling discrepancies on these outcomes. Significant discrepancies emerged between samples on 73% of outcomes. Participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Our results suggest that online convenience samples may display more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended