Effect of Cash Transfers on Household Consumption amongst the Vulnerable Population in Awendo Sub-County

Despite the national cash transfer programme, poverty rate among the Kenyan population is still high at a prevalence rate of 48.9% in 2016 up from 45% in 2008. In Kenya, poverty prevalence is disproportionately spread across the 47 counties and sub-counties. In Awendo sub-county, poverty prevalence rate is 49.8 % in 2018 which is higher than the national figure prompting this empirical study. The purpose of this study was to analyze the effects of cash transfer on consumption among vulnerable households in Awendo Sub-County. The study was anchored on the Life Cycle hypothesis of consumption and savings, the study used a correlational design to aid the determination of relationship and association between cash transfers and status of household consumption. Using stratified sampling method, a total of 390 respondents were selected. However, the response rate was 98.7 %. Cronbach’s alpha coefficient was estimated to test for reliability and the value was 0.782 which was greater than the threshold of 0.7. The study used multiple linear regression models which indicated that, there is a positive significant effect of cash transfer on consumption (α1=0.060;p=0.046). In conclusion; cash transfer is an important factor determining levels of consumption of the vulnerable population in Awendo Sub-County. The study recommends for policy to enhance allocations among the vulnerable population in Awendo and Kenya in general. The study provides empirical evidence on the current body of knowledge for researchers and policy makers

County Poverty, Urban–Rural Classification, and the Causes of Term Infant Death

Objective Higher mortality among full-term infants (term infant deaths) contributes to disparities in infant mortality between the United States and other developed countries. We examined differences in the causes of term infant deaths across county poverty levels and urban–rural classification to understand underlying mechanisms through which these factors may act. Methods We linked period birth/infant death files for 2012-2015 with US Census poverty estimates and county urban–rural classifications. We grouped the causes of term infant deaths as sudden unexpected death in infancy (SUDI), congenital malformations, perinatal conditions, and all other causes. We computed the distribution and relative risk of overall and cause-specific term infant mortality rates (term IMRs) per 1000 live births and 95% CIs for county-level factors. Results The increase in term IMR across county poverty and urban–rural classification was mostly driven by an increase in the rate of SUDI. The relative risk of term infant deaths as a result of SUDI was 1.6 (95% CI, 1.5-1.8) times higher in medium-poverty counties and 2.3 (95% CI, 1.2-2.5) times higher in high-poverty counties than in low-poverty counties. Cause-specific IMRs of congenital malformations, perinatal conditions, and death from other causes did not differ by county poverty level. We found similar trends across county urban–rural classification. Sudden infant death syndrome was the main cause of SUDI across both county poverty levels and urban–rural classifications, followed by unknown causes and accidental suffocation and strangulation in bed. Conclusions Interventions aimed at reducing SUDI, particularly in high-poverty and rural areas, could have a major effect on reducing term IMR disparities between the United States and other developed countries.

Choice between home and health institutional births in the era of Kenya’s maternity subsidy

Background Adverse maternal and neonatal outcomes disproportionately afflict low and middle-income countries, which experience high-unmet need for safe and skilled attendance at birth. This study sought to investigate how choices for place of birth were made in Kenya during the era of a national maternal subsidy. Methods The study employed data from the Kenya Demographic Health Survey 2014 and involved data from women who experienced a birth around the time when the maternity subsidy was in place. After testing for multi-collinearity of variables and addressing endogenous endogeneity using two-stage residual inclusion, probit regression models were estimated. The choice for place of birth was employed as a binary outcome variable. Results Overall, data from 4,772 women were included in the analysis. The women’s mean age was 27.7 years and majority (83.8%) were married or staying with their sexual partners. Among these women, 2748 (57.5%) had elected institutional births. Regression analysis illustrated that woman’s age, the woman and partner’s education, economic empowerment, low parity, low county poverty headcount rate and access to medical insurance. Further, access to complementary reproductive services such as antenatal care and family planning and the existence of the maternity subsidy were associated with increased likelihood to choose deliveries in health facilities. Conclusions The existence of the maternal subsidy confers women increased potential to elect health institutions as a preferred place for birth, although this was influenced by other factors. These findings imply that investments, which prevent teen pregnancies, reduce domestic and national poverty, increase education attainment, expand autonomy of women in decision making and promote access to the continuum of reproductive health services can optimize choice making during the existence of the maternity subsidy favoring access to safe institutional births.

Abstract 315: Community Characteristics Associated With Out of Hospital Cardiac Arrest Mortality: The Intersection of Urbanicity, Poverty, and Race

Introduction: Variation in survival for out-of-hospital cardiac arrest (OHCA) has been described, but the intersection of urbanicity, race, and poverty and the impact on OHCA outcomes remains unclear. We sought to test whether rurality was associated with increased in-hospital mortality compared to urban and suburban communities when accounting for differences in poverty and race. Methods: We performed a retrospective analysis using 2013-2014 Medicare claims for inpatient stays originating in the emergency department. OHCA Patients (≥65 years) were identified by ICD-9-CM diagnosis code. Urbanicity was assigned based on county of residence using Rural-Urban Continuum Codes. Census data were used for county poverty and racial composition measures. Multivariate logistic regression was used to estimate the association of in-hospital mortality with urbanicity, percent of resident population in poverty, and percent black residency. Also included were individual, hospital, and community characteristics. Results: A total of 246,736 OHCA cases were identified of which 53% were male, 23% non-white, and 36% >75 years. Survival to discharge was 22%. Over 95% of OHCA patients resided in urban (85%) or suburban (11%) areas. Predicted probabilities of death (Figure) were lowest in suburban communities with moderate poverty and small black populations (0.76, CI 0.75-0.76) and highest in urban areas with moderate poverty and larger black populations (0.80, CI 0.80-0.81). All areas with high poverty and larger black populations had similar predicted probabilities (0.77-0.78), regardless of urbanicity. Conclusions: Suburban residence was associated with lower odds of mortality, even in communities with high levels of poverty. Communities with moderate poverty showed the greatest spread of outcomes in all 3 urbanicity categories. Further work should explore access to care, social determinants of health, and hospital factors that lead to the observed disparities.

Poverty, a risk factor overlooked: a cross-sectional cohort study comparing poverty rate and cardiovascular disease outcomes in the state of Florida

The purpose of this study is to examine the relationship between poverty rate and heart disease in our state. A cross-sectional data analysis was performed using figures provided by the Center for Disease Control’s Interactive Atlas of Heart Disease and Stroke Tables. Spearman’s correlations and simple regressions were used to determine if there was a relationship between poverty and cardiovascular hospitalization rate and cardiovascular death rate. There was a positive monotonic correlation between poverty rate and cardiovascular hospitalization rate (Rho=0.384, P=0.001). There was a positive monotonic correlation between poverty rate and cardiovascular death rate (Rho=0.646, P<0.0001). County poverty rate had a statistically significant positive relationship with cardiovascular hospitalization and cardiovascular mortality in the state of Florida.