Gendered Foundations of Partner Violence and HIV

LORI HEISE, PHDDirector, Gender Violence and Health CentreLondon School of Hygiene & Tropical Medicine

What do we know about gender-based violence?

IPV is the most common form of violence in women’s lives, (even in areas of conflict)

Health effects of violence are long term and cumulative Types so violence (physical, sexual, emotional) frequently overlap “Life Burden” of violence

Violence is preventable

Sasa!, Uganda. Community mobilization programme based on promoting shared power between women and men reduced IPV by 52% over 3 yrs.

Stepping stones, South Africa: Community reflection groups reduced physical violence reported by men by 38% at 2 years post-intervention.

Give Direcly, Kenya: Transfers lead to a 30-50% reduction in reports of physical IPV and a 50-60% reduction in forced sex within marriage.

Ujaama, Kenya: Girls who participated in a self-defense and empowerment training had 62% lower rate of rape than non-participants, 10.5 months post intervention.

Levels of partner violence vary greatly between settings

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Percent of women physically or sexually abused by a partner in the past 12 months

WHO Multi-country Study on Domestic Violence & Women’s Health

3.7%53.7%

Even within regions and neighborhoods, variation is profound

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Percent of women in cluster reporting partner violence

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Percent of clusters/neighborhoods reporting different levels of IPV

What accounts for the geographic differences in levels of partner violence?

The origins of violence are multi-causal

Infectious disease Heart Disease

The origins of violence are multi-causal

Ecological Model

• Genetic endowment• Developmental

history• Beliefs, behaviours

Macrosocial

Community

WomanRelationship

Conflict Arena

The Man

IPV The Man

Economic structures

Religious ideologies

Consumerism

Gender regimes

Market ideology

Honor cultures

poor communication

marital conflict

Life history

Gender socialization

Genetic endowment

TWO LEVELS OF ANALYSIS

What accounts for risk of IPV to INDIVIDUAL women?

What drives levels of IPV at the POPULATION LEVEL?

Predictors of individual risk of female victimization

Increase risk Childhood exposure to violence

Child sexual abuse Witnessing violence as a child Other forms of childhood trauma

Attitudes accepting wife beating Young age

Protective Completing secondary school Social support

Impact varies by context Female employment Participation in credit schemes Owning land or other assets

Partner-related factors of female victimization/perpetration

Violence in Childhood Harsh physical punishment Witnessing parental violence Experience of abuse

Psychological Dysfunction Adult attachment issues Anti-social behavior

Delinquent peers Violence against other

men

Problematic alcohol use Attitudes & Beliefs

Acceptability of wife beating Male authority in the family

Socio-demographic factors Low education Young age

MASCULINITIES

Predictors at a community/neighbourhood level

Norms and beliefs appear key in low and middle income countries Acceptability of wife beating and norms related to male authority & control

over female behaviour Norms of family privacy and male honour linked to female purity

Bulk of reduction in recent SASA! impact evaluation was mediated through change in norms on acceptability of wife beating

Percent of women interviewed who believe that a man is justified in beating his wife if…

Wife disobeys

Wife refuses sex

Bangladesh province 38.7 23.3Brazil province 10.9 4.7Ethiopia province 77.7 45.6Namibia capital 12.5 3.5Peru province 46.2 25.8Samoa 19.6 7.4Thailand province 25.3 7.3Tanzania province 49.7 41.7

Source: WHO Multi-Country Study on Domestic Violence and Women’s Health

Cultural beliefs perpetuate abuse

Predicting geographic distribution of IPVNEIGHBORHOOD/COMMUNITY FACTORS

MACRO-LEVEL FACTORS

Ecological analysis

Asks: “Why does this population have this particular level of partner violence?” as opposed to asking, “Why did this particular woman get beaten?

Builds on three types of data: Variables aggregated upwards from individual respondents National or district level statistics (average level of education) Information from specialized data-bases (e.g. global conflict; laws, etc)

Explanatory domains

Women’s achieved status Secondary & tertiary school completion Rate of child marriage

Level of gender inequality E.g., ratio of male to female completion

of secondary & tertiary school SIGI ownership index Inequality in family law (SIGI) M/F ratio earned income

Norms Acceptability of wife beating Male control of female behavior Acceptability of divorce

Economic participation & rights Women’s economic rights (WECON measure

of CIRI Human Rights Database) Women in formal waged employment Women working for cash

Political participation and rights Women’s political rights Share of women in national parliaments

Log GDP per capita

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5 6 7 8 9 10Level of economic development - national data

National IPV data

Country-level variation in 12 month prevalence of partner violence by GDP per capita

Strong positive association with prevalence of male control of female behavior

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0 10 20 30 40 50prevalence of high male control of female behaviour

National data

Negative association with acceptability of divorce,urban samples

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2 4 6 8Acceptability of Divorce (WVS)- urban data

Urban IPV data

All associations are in the hypothesized direction, except political rightsAlthough some did not achieve statistical significance

For ever log increase in GDP per person, the prevalence of partner violence increases 5.5%

Is per capita GDP likely to be causally related to levels of current IPV?

Statistical modelling suggests NO: Association with GDP disappears when you add norms,

inequality in ownership rights or inequality in family law to the statistical model

Association with norms is maintained, even when controlling for age structure, proportion of women working for cash and in secondary school

This suggests that GDP is serving as a “marker” for a series of social transformations that tend to go in tandem with economic development

Impact of norms and discriminatory asset ownership is strong

Population prevalence of current IPV is 14.6% points higher where 100% of people agree with at least 1 of 6 justifications for abuse, compared to where none do

Discriminatory ownership laws are the strongest predictor (within gender inequality domain) of population levels of partner violence Impact appears largely driven by gender inequality in access to land and

other property in rural areas (rather than unequal access to credit) Multilevel analysis confirms that living in a country that discriminates

against women in access to land and other property is also a strong driver of individual-level risk (0.132, p=.015 and 0.155, p=.003)

Pathways between IPV and HIV

*Strongest data comes from South Africa: Jewkes et al, The Lancet, 2010;Cross sectional data more mixed; methodological limitationsConsistent association found between more severe IPV and HIV risk

5 prospective studies link IPV with Incident HIV or STI

Evidence of Impact: Partner violence

Evidence suggests that multiple pathways (structural, behavioural & biological) behind VAW-HIV link

Exposure to high risk men appears to be a significant path

HIV+ men only

All male partners

Wife beaten

Wife not beaten

Wife beaten

Wife not beaten

In both groups, same likelihood that wife is HIV+ whether beaten or not

12% higher likelihood that wife is HIV+ if beaten

HIV- men only

Stratify

Pooled sample of 9,385 matched couples in which both husband and wife were tested; each country is weighted equally*

It might not all be about GENITAL traumaPhysical abuse, emotional abuse associated with up/down regulation of host genital immunology immune responses

Women who experienced IPV were at increased risk of acquiring HIV with increasingly severe violence associated with increased risk of infection.

Higher rates of depression and lower T-cell function in women who experience chronic abuse.

PTSD associated with dysregulation of cortisol pathways, fight or flight responses.

Potentially important in the maturing genital tract of young women

Immunology of violence

Source: Klot, 2012; Ghosh, 2015;

For more information see:

www.strive.lshtm.ac.ukwww.whatworks@mrc.ac.za

Woman experiencing violence Man using violence

Increased likelihood that woman acquires

HIV

Clustering of HIV risk factors among perpetrators

Binge drinkingConcurrent partnersPurchasing of sex

Potential pathways between intimate partner violence & women’s risk of HIV acquisition

Unprotected sex

Low Adherence

Higher likelihood that partner is infected

Reduced access to information &

prevention

PROXIMATE DETERMINANTS OF HIV

Genital trauma

Man’s childhood exposure to violence

Gender inequality & social norms condoning use of violencePoverty & economic stresses Social constructions

of masculinity & femininity

SHARED STRUCTURAL DRIVERS OF HIV & INTIMATE PARTNER VIOLENCE

Woman’s exposure to child sexual abuse

Inflammation & immune activation

INTIMATE PARTNER VIOLENCE

Psychological distressChronic anxiety

DepressionPTSD

Substance use

Increased sexual riskRe-victimisation

Multiple / concurrent partners, transactional sex,

sex work