Post on 13-Apr-2017
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
Banglades
h city
Banglad
esh pro
vince
Brazil c
ity
Brazil p
rovin
ce
Ethiopia
provin
ce
Japan
city
Namibia
city
Peru ci
ty
Peru pro
vince
Samoa
Serbia
city
Thailan
d city
Thailan
d provin
ce
Tanza
nia cit
y
Tanza
nia pro
vince
0
20
40
60
80
100
perc
enta
ge
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
None<2
020
-2930
-3940
-4950
-5960
-69 70+
05
1015
2025
3035
40
6.48
17.15
36.52
11.77
18.09
8.87
1.11
Brazil city Brazil province
Percent of women in cluster reporting partner violence
Perc
ent o
f com
mun
ities
/clu
ster
s
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
TZA2
HTIMDA
GERMAL
AZBUKR
GHA
ZMB
PHLHND
EGY COL
UGA
MWIBGD2 SMA
KHM
IND
RWA
BOL
NGR
ZAR
LIB
PER2DOM
TIM KENZWE
JOR TUR
CMR
020
4060
% w
ith p
ast-y
r phy
sica
l/sex
ual I
PV
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
TZA2
HTIMDA
GERMAL
AZBUKR
GHA
ZMB
PHL
COL
UGA
MWISMA
KHM
IND
RWA
BOL
NGR
LIB
PER2DOM
TIM KENZWE
TUR
CMR
020
4060
% w
ith p
ast-y
r phy
sica
l/sex
ual I
PV
0 10 20 30 40 50prevalence of high male control of female behaviour
National data
Negative association with acceptability of divorce,urban samples
BGD
PER2
IND
MDA BRA
RWAJOR
NZEGER
THA
JPN
EGYPERBGD2
UKR
ZWE
TZA
TUR
ZMB
GHA COL
SRB
DOMAZB
UGA
010
2030
4050
% w
ith p
ast-y
r phy
sica
l/sex
ual I
PV
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