Presentation on theme: "Abortion Stigma Correlates: Comparing Two Kenyan Counties Erick K. Yegon Peter Mwaniki Elizabeth Echokah Joachim Osur 1 st AMREF HEALTH AFRICA INTERNATIONAL."— Presentation transcript:
Abortion Stigma Correlates: Comparing Two Kenyan Counties Erick K. Yegon Peter Mwaniki Elizabeth Echokah Joachim Osur 1 st AMREF HEALTH AFRICA INTERNATIONAL CONFERENCE NAIROBI NOVEMBER 26, 2014
Background Increase in unsafe abortion incidence rate (from 32 to 48 per 1000 WRA) in the last 10 years in Kenya (APHRC 2013) In 2012, 465,000 women treated for complications from incomplete or unsafe abortions 119,912 women treated for induced abortion complications WRA (in 000’s) Induced Abortion Rate per 1,000 WRA Induced Abortion Ratio per 100 Total96004830 Central & Nairobi21863220 Coast & N.Eastern12985132 Eastern13822013 Nyanza & Western23296339 Rift Valley24046440 Induced Abortion Rates and Ratios
Background Abortion is a very sensitive issue with providers and women treated as outcasts (Kumaret al., 2014) Abortion often viewed as an abnormal event and women who have them are deviant (Kumar et al., 2013) Women feel embarrassment, shame, guilt and fear of disclosure – effectively silencing them from discussing their experience (Cockril et al., 2013) Women experience rejection, exclusion or discrimination as a result of seeking an abortion or when their abortion is voluntarily or involuntarily revealed to others (Shellenberg et al., 2014)
The Social Construct of Stigma LabelStereotype Separate Discriminate
Study Questions What are the levels of abortion stigma at individual and community levels in Machakos and Trans Nzoia counties? Do counties in regions that report higher incidences of unsafe abortion also have higher levels of stigma? What factors are associated with abortion stigma at individual- and community-level in these two counties?
Methodology A cross-sectional survey of general community members in Machakos and Trans Nzoia counties Ethical approval from KEMRI Administrative approval from County Health Directors in the two counties Population All above 18 years old 50% of study population were Men (Married 25%; Unmarried 25%) 50% of study population were women (Married 25%; Unmarried 25%)
Stigmatizing Attitudes, Beliefs and Actions Scale (SABAS) Measures stigma at the individual and community levels 18 items, 3 subscales Negative stereotyping Discrimination and exclusion Potential contagion Scoring Easy summative scoring of Likert scale responses Higher score = more stigmatizing attitudes, beliefs and actions Used sub-scale scores and total score Published in 2014 (Shellenberg et al..)
Data Analysis Data Entry- Epidata Analysis Stata SE ver 12 Regression Analysis Relationship between SABAS scores and age, gender, marital status, educational attainment and religious affiliation
Community Members’ Level of Education, by County (N=718)
Community Members’ Religious Affiliation, by County
Mean Scores for SABAS and its Subscales, by County Trans Nzoia (N=358) Machakos (N=360) p-value Full scale 55.453.10.110 Negative stereotyping 29.728.50.009 Exclusion and discrimination 18.517.60.000 Fear of contagion 7.370.000
Mean Scores for SABAS and its Subscales, by County and Population Density Trans Nzoia (N=358) Machakos (N=360) Urban Semi- urbanRuralUrban Semi- urbanRural p- value Full scale54.957.154.551.352.455.20.004 Negative stereotyping 30.130.428.928.228.528.60.001 Exclusion and discrimination 18.318.918.316.417.119.20.000 Fear of contagion 18.104.22.168.7 7.40.000
Mean Scores for SABAS and Sub scales by County and Level of Education Full scale Negative stereotyping Exclusion and discrimination Fear of contagion EducationTrans N.MachaTrans N.MachaTrans N.MachaTrans N.Macha No educ/ primary 57.057.630.429.519.020.07.68.0 Secondary school 56.252.529.728.419.017.27.57.0 Post secondary 55.448.828.527.316.922.214.171.124 p-value0.000120.00160.00130.00124
Regression Model Independent variables: County/region, age, gender, education, marital status, and religion Dependent variable: SABAS score Significant relationship emerges between SABAS and educational attainment (p-value<0.001) i.e. SABAS scores go down as education level goes up.
Conclusion and Recommendation In this study the County that had higher incidences of unsafe abortions also had higher levels of stigma among general community members. Communities in rural areas were more stigmatizing compared to communities in semi- and urban areas. To reduce unsafe abortions, interventions need to address stigmatizing attitudes in communities, targeting rural communities and less-educated community members.