i
The Determinant for the Uptake of HIV testing among
women aged 15-49 years in Liberia. A cross-sectional
study based on the 2013 Demographic and Health
Survey of Liberia
Name: Courage Sundberg Word count:12204
Master’s degree in Global Health, 30 credits, Spring 2020 Department of Women’s and Children’s Health (IMCH), International Maternal and Child Health
1 ABSTRACT
Background: Human immunodeficiency virus (HIV) remains a global challenge with an estimate of 37.9 million confirm cases. Testing remains a critical indicator of HIV diagnosis and treatment, as it is the primary step towards taking accountability for the protection of oneself and the protection of others.
Aim: This secondary analysis of Liberia Demographic and Health Survey data from 2013 aimed to statistically describe the association between Knowledge, Attitude, and Practices (KAP), demographics factors such as age, education, marital status, wealth, religion, place of residence, and testing uptake.
Methods: A cross-sectional study of Liberian women (n=7353, aged 15 - 49 ) was
performed. Statistical analysis of chi-square test and logistics regression was employed to identify the association between independent variables (KAP), selected demographics factors and the dependent variable (HIV testing).
Results: The key findings of the study are that there is a statistically significant relationship between comprehensive knowledge and testing for HIV. The attitude in terms of stigmatizing behavior is also significantly associated with testing uptake. Demographic factors age,
education, being ever married, being a Muslim, belonging to the middle class and living in the rural area are positive determinants of HIV testing in Liberia. The study confirmed that there is no statistically significant association between high-risky behavior and testing uptake among women aged 15-49 in Liberia.
Conclusion: Knowledge, stigmatizing attitude and some demographic factors are determinants of testing uptake in Liberia.
Keywords: Knowledge, stigma, risky sexual practices, age, PLWH
ABSTRACT wordcount:240
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TABLE OF CONTENT
ABSTRACT ...1
TABLE OF CONTENT ...2
LIST OF ABBREVIATIONS AND ACRONYMS ...4
INTRODUCTION ...5
Global burden of HIV ... 5
Transmission Path and testing for HIV ... 5
Global cohesion and commitment in terms of HIV... 6
Positive and negative predictors of HIV testing uptake... 7
Liberia settings ... 9
Health system of Liberia ... 9
Liberia Health Policies ... 10
THEORETICAL FRAMEWORK ... 10
Theory of Planned Behavior (TPB) ... 10
The Importance of KAP ... 11
Definition of knowledge ... 12
Definition of attitude ... 12
Definition of practice ... 12
Justification/ Rationale ... 13
Overall Aim ... 14
Specific aims ... 14
Hypothesis ... 14
METHODOLOGY ... 15
Study design ... 15
Study population ... 17
Sample size ... 17
Data collection ... 18
Validity ... 19
Reliability ... 19
Measure ... 19
Demographic factors ... 20
Dependent variable ... 22
Statistical analyses ... 22
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Ethical Approval ... 23
RESULTS ... 23
Descriptive statistics... 23
Inferential statistics ... Fel! Bokmärket är inte definierat. DISCUSSION ... 29
Introduction ... 29
Findings on the demographic data ... 30
Knowledge, demographic characters, and testing... 33
Stigma ... 34
Practices in terms of risky sexual behavior, demographic characters and testing uptake ... 35
The implication of not applying weighted data ... 37
Limitation and Strength ... 38
Study findings in terms of the Theory of Planned behavior ... 39
CONCLUSION ... 40
ACKNOWLEDGMENT ... 41
References ... 43
ANNEX ... 53
4 LIST OF ABBREVIATIONS AND ACRONYMS AIDS Acquired Immune Deficiency Syndrome ART Antiretroviral Therapy
ARV Antiretroviral medicine
CDC Center for Disease and Control EVD Ebola Virus Disease
HDI Human Development Index HIV Human Immunodeficiency Virus ICF International Coach Federation
LDHS Liberia Demographic and Health survey
LISGIS Liberia Institute of Statistics and Geo-Information Services LMIS Liberia Malaria Indicator Survey
MOHSW Ministry of Health and Social Welfare NACP National AIDS Control Program PLWH People Living With HIV
SDG Sustainable Development Goals
SPSS Statistical Package for the Social Sciences TPB Theory of Planned Behavior
UNAIDS United Nations Aids Program
UNCRC United Nations Convention on the Rights of the Child VCT Voluntary Counselling and Testing
WHO World Health Organization
5 INTRODUCTION
Background
Global burden of HIV
Human immunodeficiency virus HIV remains a global challenge with estimates from the World Health Organization( WHO) epidemiological data confirming that 37.9 million people living with the disease in 2018(1). In 2011, 69% of all people living with HIV were from sub- Saharan Africa. One in 20 adults had HIV, making this region the most affected, with women accounting for about 58% of those infected(2). Approximately 1.7 million people died of HIV in 2011, and about over a million died yearly from HIV in Africa (3).
It was estimated that nearly 35.3 million people were living with the disease in 2012 (4), and about 75 million people have been affected from the inception of this disease up to present, with approximately 32 million lives lost globally to this disease(5, 6). About 71% of all HIV related deaths are said to be people living in Africa (7). In west Africa, the prevalence of HIV is about 2%, except for the Ivory Coast and Nigeria, where adult prevalence estimated at 3.4 and 3.5%, respectively(8). In Liberia, it is estimated that about 55 000 people are living with HIV, 3000 of which became newly infected in 2016 with a higher prevalence being among individuals aged 15-24(9). According to the 2007 Liberia Demographic and Health Survey (LDHS)(10), the prevalence of HIV is 1.5 times higher among women compared to men in Liberia.
Transmission Path and testing for HIV
HIV is most commonly transmitted through penetration of body fluids from one individual to another, by factors such as risky sexual behavior in the form of unprotected oral, vaginal, and anal intercourse (11), sharing of injected drugs needles, and transmission during pregnancy, childbirth, and during breastfeeding if there is no safeguard (12). In Liberia, the most common transmission path of HIV is through heterosexual contact and perinatal transmission(13).
According to WHO (14), the transmission of mother to child scales between 15% to 45% in the nonexistence of intervention and can be decreased to below 5% due to effective
intervention during pregnancy, labor, delivery, and breastfeeding(14). However, testing is the primary step to know the mother’s status to help facilitate intervention. Achieving below 5%
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of mother to child transmission requires knowledge. The lack of comprehensive knowledge regardless of collective awareness has impeded testing uptake(15). Knowledge is an essential aspect of HIV as it helps an individual make a vital decision that could save theirs and the lives of others around them(16). HIV is an incurable disease; nevertheless, it is no longer a deadly disease but rather a chronic disease due to the accessibility of antiretroviral drugs (ARVs) (17). With the availability of over 30 different drugs, people living with
HIV(PLWH) can live a symptom-free life(18).
Testing remains a critical indicator for HIV diagnosis and antiretroviral therapy(ART), and it is the primary step towards taking responsibility for the protection of oneself and the
protection of others(19). Irrespective of the above mentioned, in 2018, about 8.1million people living with HIV did not know that they had HIV due to not testing(4,16).
Global cohesion and commitment in terms of HIV
In 2016, Heads of States and other governmental representatives at the United Nations reaffirmed their commitment to reducing HIV and end the Acquired Immunodeficiency Syndrome (AIDS) epidemic by 2030 (21). This strong global cohesion and commitment led to a mandate to Fast-Track the AIDS response by 2020 and 2030 through the framework of the 90 90 90 and the 95 95 95 targets respectively(22).The 90 90 90 targets are to ensure that by 2020, 90% of PLWH should know their status, 90% of people that know their status should be on treatment, and 90% of people on treatment should have an unnoticeable viral load (23).
In alliance with the 90 targets, the 95 targets are to ensure that by 2030, 95% of people living with HIV should have diagnosed, 95% of people that have diagnosed so be on treatment and 95% of people on treatment should have suppressed viral loads. This target also aims to annually reduce the total number of new HIV infection cases among adults from 500 000 to 200 000 and to achieve zero discrimination for those infected with HIV (24). Through achieving these targets, PLWH will have an extended lifespan. The first step to achieving these targets is again by knowing one status, which is done through testing, which is the gateway to the prevention, treatment, and cares for PLWH(25). Though we have grasped into 2020, the 90 90 90 targets are yet to be achieved in some countries due to shortcoming in testing uptake, service availability, and lack of knowledge and awareness(26).
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Positive and negative predictors of HIV testing uptake
In consultation with literature, it was established that testing uptake has been influenced by many different factors which include age (27, 28) , education (29), marital status(30), socio- economic position (31), fear (32), knowledge (33), stigma (34), and risky behavior (35).
Age
In a study conducted to determine the acceptability of intrapartum HIV testing at two referral hospitals in Cameroon, it was established that women age 20- 29 and women aged ≥30 were more likely to refuse HIV testing compared to younger women aged <20 (27). In a study conducted in Burkina Faso to assess determinant of HIV testing uptake at individual and community level, a less likelihood of testing was revealed among participant aged >35 compare to those age 15-24 years, whereas participant age 25-34 were 1.2times more likely to get tested for HIV compare to those aged 15-24(28).
Education
Findings from a study conducted to “identify the effect of educational attainment on HIV testing among African-Americans” where level of education was stratified, it was observed that the higher level of education an African American attained, the higher likelihood was reported for HIV testing uptake. In this study, compare with all other strata, participant who had attained a masters or professional degree was found to have the highest likelihood of testing for HIV(29). Higher level of education was equally found to be positively associated to testing uptake when participant with a tertiary education in the study of Chimoyi et al. (30) showed 1.6 times more likelihood to get tested compare to those with a primary school education(30).
Marital status
In a venue-based intercept survey conducted in Johannesburg a positive association was established between being married and testing uptake. Women who were married or cohabiting were 1.5 times more likely to get tested compare to those that were not married(30).
Socio-economic position
A study conducted in Ethiopia to determine factors affecting HIV testing and counseling among women aged 15-49, found a positive associated between socio-economic position and testing uptake. Women with high socio-economic status were 8.2 times more likely to get
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tested for HIV compare to those with low economic status, similarly those with middle economic status had a 3.3 times more likelihood to get tested compare to those with low social economic status (31).
Fear
Other studies have even found fear to be a negative predictor of testing uptake. Fear of being known as an HIV carrier, gender norms, fear of losing community respect, and fear of incompetency to sustain oneself were all factors of that led individuals to the unwillingness of HIV testing uptake(32). According to Maeri et al. (32), fear of being abandoned by one’s partner, and enduring violence from a partner are some of the factors that leads to not discussing testing uptake. Fear related to the possibility of becoming HIV positive has also been identified as predictor of not testing for HIV (33).
Knowledge
Knowledge has also been identified to have an influence to testing uptake. A study conducted in Ethiopia Gambella Region identified a 2.3 times likelihood of refusing HIV testing among pregnant women who had no knowledge that breastfeeding was a pathway for mother to child transmission(33).
Stigma
Stigma has also been identified as a factor influencing testing uptake. In a study conducted in Zimbabwe to investigate HIV stigma as an obstacle to testing uptake, 77% of women
revealed that due to stigma and social rejection, they would not want to disclose if a family member was HIV positive (34). These above mentioned shows the level of barrier that stigma profligates on testing uptake.
Risky sexual behavior
Risky sexual behavior has been recognized as a factor associated to testing uptake. In a study carried on in Ghana to determine risky sexual behavior and testing uptake among youth, it was evident that risky sexual behavior in term of irregular sex partner was positively associated to testing uptake (35). This study in Ghana also established that participant who used condom irregularly during sexual intercourse where 4 times more likely to get tested for HIV compared to those who used condom regularly. Similarly, participant with multiple sex partner were 1.9 times more likely to get tested compared to those that had one partner(35).
In Liberia however, the young people do not understand the risk of HIV and they continues
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engaged into factors such as multiple partnerships, transactional sex, alcohol and drugs usages, and low levels of condoms used(36). Regardless of these behaviors, they still do not seek testing, counseling, nor treatment due to other social factors such as a high level of unemployment, poverty, and fragmented educational and healthcare system (36).
Liberia settings
Liberia is located on the west coast of Africa, north of Guinea, south of the Atlantic ocean, on the east by Ivory Coast, on the northwest by the Republic of Sierra Leone. It has a population of approximately 5 million people and has a square kilometer of about 96,320 000(37). About 1.1 million of its population lives in Monrovia, the capital. Liberia has five geographic five regions, which include 15 counties and 90 districts. Liberia has a hot and humid tropical climate, with a geographical feature of swamp and rain forest(38).The primary export source of earnings is through its natural resources of gold, iron ore, and rubber(39). However, the country is presently at an economic halt with increased inflation from about 26% in 2018 to 31.3%in 2019. It also experienced a decrease in GDP from about 23.4 % in
2018 to 21.1% in 2019 (40). Liberia life expectancy is 63.7 years, the mean year of schooling is 4.7years (41), and about 55.4% of the population age 15-24 and 32.22% of the population age 65 and above are literate(42). Being a low-income country, about over 60% of its population lives on less than $1.25 a day. Liberia is even listed 181 out of 189 countries by the Human Development Index (HDI) (43).
Health system of Liberia
Liberia is a country that has experienced over 14-year of civil unrest, which has left the country with a deteriorated health system and enormous scarcity of health workforce(44).
The health service delivery of Liberia is strongly dependent on donors(45), which includes non-governmental organizations (NGOs), including faith-based organizations, and fewer private health centers(45). Donors funds to the health sector of Liberia decreased by 15% in 2008, which inflicted a challenge to the government in financing its health sector (46). In 2013, only 11% of the government budget was allocated to the health system(47). Liberia health sector faces a severe constraint in the form of low budget, human resources in terms of doctors, specialists, pharmacists, and laboratory technicians, including diagnostic equipment (48). Still recuperating from its 14 years of civil war and trying to rebuild the health system, the country was hit by the 2014 Ebola virus disease (EVD), which further deteriorated the
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fragile health system (45), by claiming the lives of many health workers, which led to a 50%
reduction in the HIV access services and reduced implementation of testing(49). Conversely, the number of functional health facilities increased by 27% between 2010 and 2016, with one in five people living with HIV (21%) having access to ARV therapy in 2015 (50). According to WHO “ it is evident that for all health interventions there are still large proportions of the population who are not reached”(50).
Liberia Health Policies
To improve resilient and quality health services, the Ministry of Health (MOH), in
collaboration with other international partners, created a 6-year investment health plan(51).
In 2014, Liberia developed a National Strategic plan 2015-2020 to halt new HIV infections, promote health and well-being, and increase the life span for PLWH. In July of 2017, Liberia was a part of the endorsement of a regional catch-up plan that seeks to promptly accelerate access to HIV treatment for the west and central African countries. Liberia also created a 2019–2020 national Fast-Track plan to identify barriers and help adopt essential programs and policies that will increase the country's "test and treat figures" that will warrant the best service delivery for HIV positive people (52). Irrespective of all the above mention policies and goals of a holistic health plan to substantially recuperate the health and social welfare of the population on an equitable basis, HIV remains on the rise in Liberia(53).The inadequate capacity of the health system in terms of poor infrastructure, unavailability of equipment, and incompetent staff continuously furthermore serve as an impediment to HIV services in Liberia(13).
THEORETICAL FRAMEWORK
Theory of Planned Behavior (TPB)
Icek Ajzen (54), developed the theory of Reasoned Action in 1975 with its aim of explaining how attitude influences the behaviors of people towards factors or issues in society. The theory was instrumental in predicting people's intention of taking action. However, limited by norms that drove people to take action such as availability of funds, knowledge, time, and expectation form other people, resulted in an improvement of the theory by coming up with the Theory of Planned Behavior (TPB). The theory assumes that individuals would not intend
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to behave in a certain way if it is physically impossible for them to behave in that specific manner to unpredicted obstacles.
According to Ajzen, three determinant explains behavior; One's Attitude, Subjective Norms (expectation and another opinion), and perceived behavior control, which predicts the intention, which in turn determines the behavior. From the TBP theory, knowledge, attitude, and practices (KAP) is an essential factor that influences and controls the sequence of practice in response to a specific health intervention. Knowledge informs attitude and practices and leads to an individual intention. In the case of this study, the intention is to determine how the three variables influence the individual decision of HIV testing uptake.
Theory of planned behavior (TPB)
Fig 1- A photo showing the TPB(54)
The Importance of KAP
Surveys covering Knowledge, Attitudes and Perception (KAP) are significant in gathering information surrounding public health initiatives. KAP surveys helps one identify knowledge about a particular topic or disease, the approach toward the said topic, and people’s behavior toward the topic, and are a type of study that serves as an educational establishment to
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explore a disease or topic(55). KAPs is known as a fundamental foundation in the fight against HIV. In a KAP study to evaluate peoples of Iran knowledge, attitudes and practices toward HIV, only half of the respondents (51.5%), knew that breast milk for an example was a transmission path, and about (52.9%) did not know that HIV cannot transmit through cough, saliva, or sneezing (56). This shows the essentiality of KAP in the fight against HIV.
Even with adequate knowledge about HIV, there is still a risk for factors such as stigma and risky practiced behavior which could have a negative influence on testing uptake(57).
Definition of knowledge
Knowledge
Knowledge is the beginning of ideas, achievement, imagination, perception, judgment, generalization, and reasoning(58). Knowledge empowers an individual to achieve the ability to differentiate between right and wrong(59). There are many misconceptions about people's knowledge of HIV(60), with adequate knowledge regarding HIV, there is a enhance
possibility to promote positive attitudes and safer practices(61). The knowledge determined to be identified in this study is participant knowledge and comprehension of HIV causes transmission path, and prevention measures. For example, knowing if one can reduce risk of getting HIV by always use condoms during sex or knowing whether HIV is transmitted from mother to child during pregnancy, delivery or breastfeeding.
Definition of attitude
Attitude is the propensity of reacting to a situation in a certain way. Attitude is defined as observed behavior that helps a person's reasoning and intellectual capabilities concerning the handling of information (59). Usually, the term attitude is a fixed assertiveness that has been built up through knowledge and experiences and becomes a kind of template used to express
"being for" or "against" something(63). The attitude determines to be identified in this study's is the scandalous orientation involving perceived stigmatizing behavior toward someone with HIV; for example, not wanting an HIV positive person that is not sick to teach one's kid or not wanting to buy vegetables from an symptom free HIV-positive vendor.
Definition of practice
The word practice is the application of one's knowledge that leads to one's action. Practices are an assimilated concept of an individual or group adaptation to their environment through
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their action, traditions, behaviors, or belief (59). Practices in this study refer to risky behavior such as engaging in unprotected sex, having multiple sex partners, and the practice of early (62).
Justification/ Rationale
According to the United Nations aids programs (20), about 1.7 million people became newly infected with HIV in 2018, 64% of whom are from western and central Africa. Four in five new infections are said to be among adolescent girls living in Sub-Saharan Africa, and 6000 young women between the ages of 15–24 years get infected weekly (20). According to the Liberia Institute of statistics and geo-information services (LISGIS) (63), 1.9% of Liberians aged 15-49 are HIV-positive with a higher prevalence among women than among men (13).
The 2016 Liberia HIV and AIDS Response Progress Report pointed out a high illiteracy rate, traditional norms, poverty, sexual exploitation, community unwillingness, and stigma toward PLWH as some of the main challenges in addressing HIV in Liberia(13), this report,
however, did not mention the frequency of testing up of the population. It is, however,
essential to emphasize that there have been no empirical studies regarding HIV testing uptake in Liberia, as per the researcher's understanding. The 2016 HIV and AIDS response report of Liberia is based solely on the findings of the 2007/2013 LDHS, whose aim was to provide information on socio-demographic characteristics, HIV prevalence, and other health indicators among the general population.
This study will help generate empirical evidence and help the general public and
policymakers acquire empirical results about determinant of testing uptake among women aged 15-49 within Liberia. This study will furthermore facilitate as a foundation in
responding to stigma and discrimination, which is known to be one of the most impediments to HIV testing uptake, as pointed out in the Liberia 2016 report.
As the first study on HIV testing uptake, this study will contribute to identifying the age group most vulnerable to contracting HIV, which in itself will help in innovative
implementation. Irrespective of the reduction of HIV-related death globally, there remains a significant need to increase HIV testing uptake to help promote earlier diagnosis of HIV infection(64). Executing this study is essential for several reasons, which include beneficial
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for equitable treatment assets and identification of risk populations to obtain routinely help for prevention, treatment, and care services(65).
This study will furthermore contribute to the debate on the role of HIV testing and treatment services and gives health care providers the need to examine the implications of health- seeking behavior and behavior change among the Liberia population. The researcher extrapolates that this study will fill the gap in serving as the first sustainable empirical evidence to help facilitate other HIV studies in Liberia and help policymakers prioritize where the resources should be allocated.
Overall Aim
The overall aim is to determine associations between KAP, demographics factors, and HIV testing uptake among women aged 15-49 in Liberia. The study aim will be established by analyzing 2013 data from Liberia Demography Health Survey(LDHS).
Specific aims
1. To determine the association of demographic factors such as age, education, marital status, wealth, religion, and place of residence, and HIV testing uptake.
2. To determine knowledge association to HIV testing uptake
3. To determine the association between stigmatizing attitudes and HIV testing uptake 4. To determine the association between risky sexual behavior in terms of condoms use, the number of sex partners, age at first sex, and participant HIV testing uptake.
Hypothesis
RQ: What are the associations of knowledge, stigma, and risky behavior relative to HIV testing uptake among women aged15-49 in Liberia?
Hypothesis 1: There is an association between knowledge , stigma, Risky sexual behaviors and HIV testing uptake
Null hypothesis: There is no association between
knowledge , stigma, Risky sexual behaviors and HIV testing uptake P and HIV testing uptake
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Hypothesis 2: There is an association between demographics factors and HIV testing uptake
Null Hypothesis: There is no association between demographics factors e and HIV testing uptake
METHODOLOGY Study design
A study design is a general strategy used by the researcher to integrate different study components into a coherent and reasonable manner. This study adopted a population-based cross-sectional study design to describe the determinant of HIV testing uptake in Liberia.
According to Kothari(66), a cross-sectional research design is an effective and inexpensive way of conducting research based on readily available data, which can be sourced by either primary or secondary methods of data collection(66).
Kothari furthers indicates that Cross-sectional research design enables researchers to measure both prevalence and outcomes of certain factors in a study group. In this case, this study intended to determine the KAP association to the uptake of HIV testing among women aged between 15-49 years old in Liberia. A cross-sectional design will be viable for this study since it allows the researcher only to work with respondents who fit the selection criteria(66).
The 2013 DHS study design was developed by the Liberia Institute of Statistics and Geo- Information Services (LISGIS) and was similar to that of the 2009 and 2011 Liberia Malaria Indicator Surveys (LMIS), except that the classification of localities as urban or rural was updated through the application of standardized definitions which differs markedly from that used for the 2007 LDHS, which was based on the 1984 NPHC(63).
Study setting
The LDHS was conducted in Liberia between 2012-2013, and a two-stage sampling design was adopted. Stratification made included the entire country, rural and urban areas separately, greater Monrovia and other urban areas separately, and for the15 counties of Liberia. These 15 counties were divided into five regions to allow a precise estimate of geographical differentials for specific demographic indicators. These five regions are,
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North Western: Bomi, Grand Cape Mount, and Gbarpolu South Central: Montserrado, Margibi, and Grand Bassa South Eastern A: Rivercess, Sinoe, and GrandGedeh South Eastern B: River Gee, Grand Kru, and Maryland North Central: Bong, Nimba, and Lofa
The first stage adopted a cluster of enumeration areas (EAs) delineated for the 2008 National Population and Housing Census( NPHC), which included 322 sample points, 119 in urban areas, and 203 in rural areas (63). Forty-four sampling points were designated to Montserrado and greater Monrovia, whereas 16-24 sample points selected in each of the other 14 counties.
Stage two of the selection employed a systemic sampling of households (63). A listing of the household carried out in all the selected EAs from mid-September to mid-October 2012.
A total of 9,677 households was achieved, from which approximately 30 households selected from each sample point. According to the 2013 LDHS (67), weighting factors were not self- weighing at the national level but were added to the data file so that the results will be proportional at the national level, due to approximately equal sample sizes in each region
The map of Liberia
Fig 2- A photo of Liberia Map(68)
17 Study population
Those eligible to participate in the study were all women age 15-49 who permanently resided in the selected households or visitors who had stayed at the house the night before the survey.
All women who consented to interview were asked questions about the various topics, including Knowledge, stigma, and practices regarding HIV and other sexually transmitted diseases. According to the LDHS 2013(67), testing for HIV was implemented through the taking of finger-prick blood from adults age 15-49. The procedure for HIV testing was approved by the Liberia Institute for Biomedical Research, the Institutional Review Board of ICF International, and the U.S. Centers for Disease Control and Prevention in Atlanta, Georgia (67).
Sample size
The original sample size of the LDHS included 9,462 identified women, of which 9,239 were interviewed, and 4,318 identified men, of which 4,118 men were interviewed, giving a total of 13357 interviewed respondents. The exclusion criteria of the LDHS sampling included nomadic and institutional populations, such as residents of hotels, barracks, and prisons. Due to the aim of this thesis study, all men were excluded from this study. Participants in this study include nationally representative samples of 9,239 women ages 15-49. This study excluded four hundred twenty-six of the 9239 women after they responded that they had never heard of HIV. The decision to exclude these participants was made because all DHS questions relating to HIV knowledge and stigma was not applicable to them since they had never heard of HIV. The participants with over 50% missing response rate was also excluded from this study (69). The total participant of this thesis study is N=7353. Please see the flow chart
Fig 3- flow chart of how sample size was arrived at
18 Data collection
The data used in this thesis is Secondary data from the 2013 Liberia Demographic Health Survey (LDHS) which happens to be the most recent National Survey on HIV testing in the country. Data collection was performed through the authorization of the Ministry of Health and Social Welfare (MOHSW) and implemented by the Institute of Statistics and Geo- Information Services (LISGIS) (67). Other collaborators of this survey were the National AIDS Control Program of Liberia and the International Classification of Functioning,
Disability, and Health (ICF International), who rendered technical assistance. Data collection took place from March 10 to July 19. Data collection was per the DHS standard protocol, and data collection was executed by a field team that consisted of a supervisor, a field editor, and four interviewers. A biomarker collection in terms of blood samples was collection for HIV testing from eligible respondents(67). The survey questionnaires used in this thesis is the Woman’s Questionnaire. The questions in the questionnaires were broken down into Liberian pidgin English which is nationally spoken in Liberia as Liberia has about 16 languages that are not taught in schools.
19 Validity
Creswell et al. (70), emphasized the importance of validity in every study. Validation is vital as it creates credibility, accuracy, and dependability of a study. To validate weather the variables selected from the DHS questionnaire were relevant for this study aims and
objectives, the researcher repeatedly read and assessed the suitability of the selected variables by critical self-justification and reflection over the essentiality of the question to be removed or retained. The selected variables from the DHS questionnaire were shared with the research supervisor for consultation. Comments were given by supervisor on researcher decision of which variable to be retained and removed, and adjustments were then made as per
supervisor recommendations. These validation was made in consultation with” Guidelines for developing, translating, and validating a questionnaire in perioperative and pain medicine”(71).
Reliability
It is indicated that reliability is a measure of the degree to which a research instrument yields reliable results or data after repeated trials. Reliability in research is influenced by the random error of which if it is high, then reliability is low(72). There was no mention made of a
reliability test in the DHS report. However, data processing consisted of editing, coding of open-ended questions, and editing computer-identified errors with the assistance of data entry clerks, data editors, data entry supervisor, and administrators of questionnaires(67). Checked recheck was performed to identify that the clusters were completed per the sample selection and that all members of the household eligible for individual interview were identified(67).
Measure
Independent Variable
The study's independent variables are knowledge, attitude in terms of stigmatizing behavior, and practices in terms of risky sexual behavior (KAP), was determined as an independent variable through the consultation of previous studies. Nine-9 points HIV related knowledge scored based on three key spheres was adopted:
1. Four questions on the mode of transmission
2. Three questions about knowledge of mother to child transmission 3. Two questions based on a misconception of HIV transmission.
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These questions were assessed via yes and no options. Questions with accurate responses obtained 1 point respectively, and questions with incorrect responses obtain 0 points. The cut of the score was as follows; score >7 was termed as comprehensive knowledge and score 0-6 was termed as low knowledge. Comprehensive knowledge in this work is having an in-depth understanding rather than a general understanding of the studied phenomenon.
Stigmatizing attributes were measured based on four questions. Each respond that shows no stigma obtaining zero point and responses that portrays stigma obtaining 1 point. 0 to 1 point was classified as no stigma, 2 to four points responses were classified as stigma.
Risky Sexual behavior was investigated through three questions, which include 1. The number of sex partners
2. Condom used with the last partner for the last 12 months 3. Age at first intercourse.
All three questions were computed with a 0-3 score. Zero scores were classified as no risk and obtaining a score of 3 was classified as high risk.
Age at first intercourse was coded as legal age at first intercourse and illegal age at first intercourse. Legal ages at first intercourse were characterized by first intercourse at first union and first intercourse at age 18 upward. Intercourse at illegal age was characterized by intercourse age 9-17 years. The decision to categorized illegal age as those under 18 was made per the United Nations Convention on the Rights of the Child, (UNCRC) that everyone under 18 years of age is a child (73). (Table 1 and 2).
Risky Sexual behavior was investigated through three questions, which include 1. Condom used with the last partner for the last 12 months
2. Number of sex partners, excluding spouse, in last 12 months 3. Age at first intercourse.
Condom used was coded as (0= yes) respondent had used condom with last partner the last 12 month and (1 = no), respondent had not used condom with last partner the last 12 month.
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Number of sex partners excluding spouse, in last 12 months was code as (0= no ), meaning that respondent had no other sex partner excluding spouse in the last 12 months , and (1=
yes), meaning, respondent had other sex partner excluding spouse in the last 12 months.
Age at first intercourse was coded as legal age at first intercourse and illegal age at first intercourse. Legal ages at first intercourse were characterized by first intercourse at first union or first intercourse at age18 upward. Intercourse at illegal age was characterized by intercourse aged 9-17. The decision to categorized illegal age as those under 18 was made per the United Nations Convention on the Rights of the Child, (UNCRC), that everyone under 18 years of age is a child (73).
Age at first intercourse was then coded as (1= yes) respondent age at first intercourse was at illegal age, and (0= no), respondent age at first intercourse was not at illegal age . All three questions ( condom used, Age at first intercourse, and the number of sex partner except spouse), were computed with the score of 0-3; with 0-1 being no risk and 2-3 being high risky behavior.
Demographic factors
The independent variables in terms of the participant's demographic information age, education, wealth index, place of residence in terms of rural and urban areas, religion and marital status were considered significant for this study as these factors postulates a detailed description of the nature and characteristics of the study participants. The respondents’ ages were categorized into three age groups to identify the frequency of testing among different age ranges; classifications made included age 15-24 as a youth, 25-34 as young adults, and >35 as adults. The categorization of age was made per the United Nations definition of youth for statistical purposes "youth' as those between the ages of 15 and 24 years" (74).
Marital status was terms as being ever married and never married. Being ever married was characterized by participant that were either married, living with a partner, divorced or widow. Being never married was categorized by those that has never been married and those that have never lived together with a partner.
Education was categorized into higher education, secondary education, primary education, and no education. Wealth Index primarily coded by DHS as (poorest, poorer, middle, richer,
22
and richest) was re-coded by this researcher as the following: Poorest and poorer coded as poor, middle as middle class, richer and richest coded as wealthy. All religion practiced in Liberia were included in this study, and they are as followed, Christian, Muslim, traditional, and no religion.
Dependent variable
The dependent variable or outcome of the study is “ever tested for HIV”. Participants were asked if they had ever tested for HIV in their lifetime, binary variable (yes = 1, no =0).
Statistical analyses
The Data analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 24. The result of this study is presented as both descriptive and inferential statistics to give a clearer understanding of the participant’s characteristics. A descriptive analysis was performed to describe the frequency distribution of the participant’s demographic factors.
Descriptive of demographics factors such as age, marital status, education, region, religion, wealth index, and independent variable KAP is expressed in frequency (percentages).
A crosstabulation using a Chi-square test was executed to determine the association between the independent variables (KAP), demographic factors, and dependent variable. After that, a univariate logistic regression was conducted with each variable against the outcome to find patterns and associations between independent variables and dependent variable testing.
Background factors such as age, marital status, education, religion, and wealth index were tested as covariates against the dependent variable testing. The multivariate analysis was built to identify associated between the demographic factor’s independent variables, and the dependent variable.
The multivariate analysis was run in three models. Model 1 shows the analysis of KAP with ever been tested for HIV. Model 2 shows the logistics regression analysis of demographic factors such as age, marital status, education wealth index, area of residence, and religion concerning ever being tested for HIV among Women in Liberia, 2013. All variables of KAP and demographic factors/ covariates were included in model three to capture potential individualized predictors and associations of testing. The strength of the associations was presented as odds ratios (OR) and 95% confidence intervals CI, and likelihood of testing,
23
(Table 7,8 and 9 respectively). A Pearson correlation matrix was also adopted to see the influence of KAP towards one another.
Ethical Approval
Ethical approval was obtained from the University of Liberia ethical board. Testing for HIV was approved by the Liberia Institute for Biomedical Research, the Institutional Review Board of ICF International, and the U.S. Centers for Disease Control (CDC) and Prevention in Atlanta, Georgia. Verbal consent for HIV testing was obtained from each respondent following completion of the individual interview, and all participants, whether consented or not, were given an informational brochure on HIV and a list of nearby sites providing voluntary counselling and testing (VCT) services (67). Participants were informed about the testing procedure and the confidentiality of the data. Participants were also informed that the test results would not be made available to them. Finally, approval was obtained from DHS to use the LDHS data for this study.
RESULTS
Descriptive statistics
The descriptive presentation contains frequency distribution of demographic data and KAP, which are presented in frequency tables 1.
Demographic characteristics of the respondent
Data presented in table 1 shows evidence that a large proportion (34.4%) of the respondents were youth and were in the age range of 15-24 years. The data also shows that the ages of respondents were evenly distributed. The result shows that (78.5%) of the respondents had been married, (39.5%) had no education, and only 2.4% had a higher level of education. The result further indicates that (51.1)% of the respondent were poor, with (59.2%) being from rural areas. Furthermore, most (85%) of the respondents were Christian, and (59.2%) were from the rural area.
Bivariate analysis
Findings from the crosstabulation using the chi-square test shows a statistically significant association (P<0.001) between demographic factors age, marital status, education, wealth index, place of residence, religion, knowledge, stigma, and testing uptake. There was
24
however no statically significant association found between risky behavior and testing uptake (P=0.089) (Table 1).
Table1 illustrates the Bivariate analysis of the association between sociodemographic factors and knowledge, stigma and risky sexual behaviors
Variables Total sample
N=7,353
N= 3441 (45.8%) Tested for HIV Yes
N= (54.2%) Tested for HIV No
P-Value
Socio-demographic factors and Bivariate analysis Age
15-24 Youth 25-34 Young adults 35-49 Adults
2527 (34.4) 2425 (33) 2401 (32.6)
1287 (50.9) 1520 (62.7) 1105 (46)
1240 (49.1) 905 (37.3) 1296 (54)
P<0.01*
Place of residence Urban
Rural
3000 (40.8) 4353 (59.2)
1740 (58) 2172 (49.9)
1260 (42) 2181 (50.1)
P<0.01*
Education level No education Primary Secondary Higher
2904 (39.5) 2396 (32.6) 1874 (25.5) 179 (2.4)
1343 (46.2) 1258 (52.5) 1168 (62.3) 143 (79.9)
1561 (53.8) 1138 (47.5) 706 (37.7) 36 (20.1)
P<0.01*
Religion Christian Muslim
Traditional belief No religion
6305 (85.7) 844 (11.5) 27 (0.4) 177 (2.4)
3344 (53) 483 (57.2) 11 (40.7) 74 (41.8)
2961 (47) 361 (42.8) 16 (59.3) 103 (58.2)
P<0.01*
Wealth index Poor
Middle Wealthy
3758 (51.1) 1661 (22.6) 1934 (26.3)
1804 (48) 945 (56.9) 1163(60.1)
1954 (52) 716 (43.1) 771 (39.9)
P<0.01*
Marital status Never married Ever married
1578 (21.5) 5775 (78.5)
693 (43.9) 3219 (55.7)
885 (61.1) 2556 (44.3)
P<0.01*
Knowledge, stigma and risky sexual behaviour score Knowledge Scores
Low knowledge Comprehensive
3067 (41.7) 4286 (58.3)
1306 (42.6) 2606 (60.8)
1761 (57.4) 1680 (39.2)
P<0.01*
Stigma Score Low Stigma High Stigma
2716 (36,9) 4637(63.1)
1233(45.4) 2679(57.8)
1483(54.6) 1958(42.2)
P<0.01*
Risky sexual Behavior Low risk
High risk
1683 (22.9) 5670 (77.1)
926(55.0) 2986(52.7)
757(45.0) 2684(47.3)
P<0.01*
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Results from table 2 confirms that the majority (80%) of the respondent knew that having a single faithful partner could reduce HIV transmission. The result shows that (75.9%) of respondents knew that proper condom used could reduce HIV transmission, and the majority (65%) of respondents knew that HIV is not transmitted by sharing food. However, (34.2%) responded that HIV was transmitted by mosquito bite, and (33.1%) of respondents did not know about mother to child transmitted during delivery.
Stigmatizing attitude
The distribution demonstrated in table 2 shows that stigma related attribute was high among the participants. Approximately (55.8%) of the participants would rather keep HIV infection of a family member as secret, (58%) would not buy vegetables from an HIV positive vendor, and (53.8%) would not want an HIV positive female teacher that is not sick to teach their child. Nevertheless, the majority (68%) of the respondents were willing to take care of an HIV positive relative.
Risky sexual behavior
As shown in table 2, there were attributes of risky sexual behavior found among participants.
The majority (74.1%) of the respondents age of first sex was between age 9-17 years. The absence of condom use was one of the most common risky sexual behavior observed among respondents, as (92.5%) of the respondents had not used a condom with a partner the last 12months, and (33%) of the respondent had other sex partners except their spouses.
However, (53.2%) of all respondents had HIV tested during their lifetime.
Table 2 illustration of respondent’s response toward mother to child transmission, stigmatizing attitude, and respondent's Risky sexual behavior
Variables N=yes Yes( %) N= No No (%)
HIV related Knowledge
Reduce chances by one uninfected sex partner
5881 (80) 1472 (20)
Reduce chances of AIDS by always using condom
5579 (75.9) 1774 (24.1)
Can get HIV through mosquito bite
2512 (34.2) 4841 (65.8)
Can get AIDS through sharing food
1861 (25.3) 5492 (74.7)
Can get HIV through witchcraft or superpower
1387 (18.9) 5966 (81.1)
Can a healthy-looking person have HIV
5184 (70.5) 2169 (29.5)
26 Inferential Statistics
Binary logistics regression was carried out to test the association between, demographic factors, variables such as knowledge, stigma, risky sexual behaviors, and testing uptake. A logistic regression was performed to illustrate and explicate the relationship between the above-mentioned variables, demographic factors and dependable variable HIV testing.
A correlation matrix was also performed to comprehend knowledge, stigma and risky sexual behaviors influence on each other toward testing uptake as shown in table 1,3 and 4
respectively
The findings presented in Table 3 show the logistics regression analysis adjusted for sociodemographic factors , knowledge, stigma, risky sexual behaviors, and dependent
variable HIV testing. The choice of these demographic factors was made in consultation with other studies that identified these selected factors to have a significant association with testing uptake. There is a statistically significant relationship between age and testing for HIV. Compare to the reference group aged15-24 , being age 25-34 was positively and significantly associated to testing uptake (OR. 1.28, CI:1.120-1.468), whereas being age 35
Can HIV transmit during pregnancy
5317) (72.3 2036 (27.7)
Can HIV transmit during delivery 4922) (66.9 2431 (33.1)
Can HIV transmit during breastfeeding
5324 (72.4) 2029 (27.6)
HIV related stigma
Would want HIV infection in family to remain secret
4106 (55.8) 3247 (44.2)
Willing to care for sick HIV relative
4998 (68) 2355 (32)
Would buy vegetables from vendor with HIV vendor
3070 (41.8) 4283 (58.2)
A female teacher infected with HIV, but is not sick, should be allowed to continue teaching
3394 (46.2) 3394 (53.8)
Risky sexual behavior
Other sex partner except spouse 2424 (33) 4929 (67)
Age at first intercourse
First intercourse at legal age 1908 (25.9) 5445 (74.1)
Condom used with recent partner the last 12 months
555 (7.5) 6798 (92.5)
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and above was found to have a negative association to testing as this age range shows a 33%
less likely odds to get tested (OR: 66, CI: .576-.767).
There was furthermore a positive and statistically significant association found between HIV testing uptake and demographic factors such as being ever married (OR, 2.43, CI, 2.099- 2.819), being a Muslim (OR 1.30, CI:1.115-1.516), being wealthy(OR: 1.19,CI: 1.020-1.385) and belonging to a middle class (OR.1.26, CI: 1.111-1.441). This study correspondingly confirmed to a statistically significant positive association between level of education and testing uptake; as seen in both the unadjusted and adjusted, compare to no education, the higher the level of education the higher the odds of testing (OR 3.51, CI: 2.365-5.225).
A positive association was also established between comprehensive knowledge and testing for HIV; this was evident in both the unadjusted (OR 2.09, CI: 1.903-2.298) and the adjusted models (OR 1.74 CI: 1.576-1923), P=.000. There was nevertheless no significant association between risky behavior and HIV testing uptake, as seen in both unadjusted (OR.909, CI:.815- .1.014), P>0.05, and adjusted (OR 998,CI: 886-1.124), P-value = .976. There was no
statistically significant association established between living in urban areas and testing uptake as seen in adjusted (OR 1.06; CI, 937-1.202)P-value= .347. However, a statistically significant association was established between living in urban area compare to rural area in the unadjusted. Findings from this study reveal that belonging to different religions in Liberia has different associations to testing. Compared to the reference group (Christian), being a Muslim gives 1.3 times more likelihood to get tested (OR.1.30, CI, 1.115-1.516), whereas belonging to traditional religion shows a 23% less likelihood to get tested (OR .77, 95% CI .344-1.726) P-value .526. Belonging to no religion gives a statistically significant negative association to testing when unadjusted (OR.64, CI, .470 -.861), although no significant association was perceived in the adjusted (OR: .82, 95% CI, 596-1.125) P-value.218.
Table 3: Logistics regression analysis of independent variable of demographics factors, knowledge , stigma , risky behavior, and dependent variable ever tested for HIV among Women in Liberia, 2013
Variable
Unadjusted
OR (95%CI) P-value
Adjusted
OR (95%CI) P-value
Sociodemographic factors Age
15-24 Youth Ref Ref
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25-34 Young adults 35-49 Adults
1.62(1.445-1.812) 0.82 (0.735-.919)
P<0.01*
P<0.01*
1.28(1.120-1.468) 0.66(0.576-.767)
P<0.01*
P<0.01*
Place of residence Rural
Urban
Ref
1.39(1.263-1.523) P<0.01*
Ref
1.06(.937-1.202)
P=0.347 Education level
No education Primary Secondary Higher
Ref
1.28 (1.153 -1.432) 1,92 (1.708 -2.165) 4.61 (3.181 – 6.702)
P<0.01*
P<0.01*
Ref
1.35(1.198-1.525) 1.77(1.527-2.043) 3.51(2.365-5.225)
P<0.01*
P<0.01*
P<0.01*
Religion Christian Muslim
Traditional belief No religion Wealth index Poor
Middle Wealthy Marital status Never married Ever married
Ref
1.18 (1.025 -1.370) 0.61 (0.282 -1.314) 0.64 (0.470 -.861) Ref
1.43(1.273-1.606) 1.63(1.462-1.826) Ref
1.61 (1.438-1.799)
P=0.22 P=0.206 P<0.01*
P<0.01*
P<0.01*
P<0.01*
Ref
1.30(1.115-1.516) 0.77(0.344-1.726) 0.82(0.596-1.125)
1.26(1.111-1.441) 1.19(1.020-1.385)
2.43(2.099-2.819)
P<0.01*
P=0.526 P=0.218
P<0.01*
P<0.027
P<0.01*
Knowledge, stigma and risky sexual behaviour Knowledge
No knowledge
Comprehensive knowledge Stigma
Ref
2.09(1.903-2.298) P<0.01*
Ref
1.74(1.576-1.923) P<0.01*
No stigma High stigma
Ref
1.65(1.496-1.810) P<0.01*
Ref
1.36(1.225-1.504) P<0.01*
Risky sexual behaviour Low risk
High risk
Ref
0.909(.815-.1.014) P=0.089
Ref
0.998(.886-1.124) P=0.976 OR = Odds ratio, CI = Confidence interval, Ref = the reference group , *Significant results at a level <.05.
A correlation matrix was performed to see the influence of KAP towards one another.
Results as presented in the correlation matrix table below confirm to a statistically
significant correlations between Knowledge and stigma p<0.05 = .003. Whereas on the other hand no statistical correlation between knowledge and risky behaviors Pvalue.866= -. 002 However there is a statistically significant correlation between Stigma and risky behaviors as correlation is .042**,p-value=.000. This indicates that one-unit increase in knowledge about HIV/AIDS has a significant implication on the attitudes towards Testing for HIV/AIDS.
However, an increase in Knowledge does not influence participants with risky behaviors HIV/AIDS testing uptake. Also, a unit increase in attitude(stigma) has significant effect on participants with risky behaviors uptake of HIV/AIDS testing.
Table 4: Associations of knowledge with attitudes and practices on each other
Knowledge Stigma Risky behavior
Knowledge
Pearson Correlation 1 .173** -.002
Sig.(2-tailed) .000 .866
29 DISCUSSION
Introduction
Findings from this study show that comprehensive knowledge is strongly and positively associated with HIV testing uptake. Having a high stigmatizing behavior was found to have a significant influence on testing uptake as well. On the contrary, risky sexual behavior was found to have no statistically significant association with testing uptake. Moreover,
demographic factors such as age, education, being ever married, being wealthy, belonging to the middle class, and being a Muslim were established as positive determinants of testing uptake among women age 15-49 in Liberia.
HIV testing uptake is the gateway for both prevention and care for individuals who are at risk of conducting the ailment, specifically in Sub-Saharan Africa, where the prevalence is higher than in other parts of the world. Being a global health issue, it affects the social and economic structures of many developing nations(75). The flexibility of HIV adapting to its host
immune system has made it challenging to find an appropriate cure for the disease (76).
However, tremendous efforts have been made to boost the immunity of PLWH by providing them with Antiretroviral (ARVs) drugs that enable them to reduce the harsh side effects of the disease and live healthy lives(77). These are accomplished only by knowing the status of the individual, which is done by testing.
Despite the efforts made globally, the spread of HIV has not been adequately combated; - hence behavioral change approaches are dimmed to play a significant role in reducing the
N 7353 7353 7353
Stigma
Pearson Correlation .173** 1 .042**
Sig.(2-tailed) .000 .000
N 7353 7353 7353
Risky Behavior Pearson Correlation -.002 .042** 1
Sig.(2-tailed) .866 .000
N 7353 7353 7353
** correlation is significant at the 0.01 level (2tailed)
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effects of this global health issue(78).These efforts have primed the establishments of DHS and other surveys in Sub-Saharan Africa countries whose population is at high risk of contracting HIV. Since then, HIV testing Uptake has increased in most of these countries with countries such as Nigeria, Mozambique, Ghana, and Cong recording double increase in the number of people testing for HIV (79). However, testing uptake in Liberia has not been to the scale of other Sub-Saharan countries. Hence this study was carried out to establish how factors such as knowledge, attitude, and practices determine HIV testing uptake in the country concerning women in the aged15-49.
Findings on the demographic data
This study found demographic factors such as age, higher level of education, being ever married, belonging to the middle class, being wealthy, and being a Muslim to have significant associations to testing uptake. The findings on level of education in this study evident that compare to no education attained, the higher level of education an individual has, the higher the odds of the individual getting tested. Primary education (OR.1.35, CI:1.198-1.525), secondary education (OR.1.77, CI: 1.527-2.043) and higher education (OR.3.51, CI: 2.365- 5.225). The findings on education in this study corresponded with Tenkorang et al. (81)who in a study to determine correlation of HIV testing among women in Ghana, found an
statistically significant increase likelihood of having been tested with level of education and for both primary (AOR = 1.15) and secondary (AOR = 1.36) compared to no education.
A study conducted by Staveteig et al. (79) on demographic patterns of HIV testing uptake in Sub-Saharan Africa established that factors such as age, having higher education, and being married, to a great extent determine uptake of HIV testing. This finding was in line with the results of this study that showed the majority of the participants who confirmed to testing being within the age bracket of 24-34 years old, with secondary and tertiary levels of education. However, the finding of this study differs from Staveteig et al. (79), where they find that living in urban area was significantly associated with testing uptake. Also in line with the findings of this study was Muyunda et al. (81) who in their study establish HIV testing uptake to be high among women (25–34 years) compared to the younger women (15–
24 years).
The low frequency of testing among age 15-24 is worrisome as this age group is known for early sex debut (82), and are less likely to use condoms(83). It is essential to highlight that
31
over half of all new HIV infected globally falls between ages 15-24 (84).This low frequency of testing calls for an awareness to increase testing uptake among this age group, and to fight against negative factors associated with testing uptake globally. There is a need for behavior factors re-modeling, to help increase testing uptake among the population with different community norms indiscriminatingly so that their influence on factors such as early marriage, societal norms, and inequality are favorable and can contribute to reducing these young women vulnerability to HIV(85). It is estimated that about 250 million young girls are married before the age of 15, with child marriage predominant in South Asia and Sub-
Saharan Africa (86). Being forced into marriage at the age of 15 or before 18 leaves one with many vulnerabilities, one of which is gender inequality. According to UNAIDS (87),
statistics from a population-based survey data from 28 countries between 2011-2016
demonstrated that 52% of adolescent girls and young women aged 15-24 are unable to make decisions about their health (87),this report shows how vulnerable these young women are to contracting HIV. A young girl that is married before the age of 15 could endure inequality of decision making, where they are not in the position of negotiating condom use or even make a decision to get tested for HIIV. This overemphasizes the significance of the SDG 5 (88), which talks about the achievement of gender equality and the empowerment of all girls for an equitable society and opportunities (89).
The empowerment of all women and girls, can be done by accentuating the SDG 4 (90), which talks about ensuring inclusive, impartial quality education and promotion of learning opportunities for all (90). Efforts of transformation that give women and girls equal rights and enhance their self-sustainability and self-efficacy could lead to self-decision taking , which could also lead to an increment of testing uptake and reduction of HIV among girls and women.
Being ever married was found to have a positive association with testing uptake. This gives rise to the question of why is ever married positively associated with HIV testing uptake among women? In a qualitative study carried out in Uganda by Larsson et al. (91) to identify reasons why men refuse couple HIV testing during antenatal visits with their wives, It was identified that the cultural and traditional norms of this setting allowed men to have sexual relationships outside of the marriage. With this establishment, one may assume that ever- married women seem to test themselves due to fear of their partner's risky sexual behaviors.
The societal norms of gender inequality in terms of the acceptability of extramarital affairs
