Trends and correlates of HIV testing amongst women: Lessons learnt from Kenya

African Journal of Primary Health Care and Family Medicine, Volume 5, No. 1, Year 2013

Interpretation

Background: A majority of women in Kenya do not know their HIV status and are therefore unable to take preventive measures or medication in order to prolong their lives. Objectives: This study investigates the key determinants of HIV testing in Kenya and documents how these changed over the 1998-2008 period. Method: This study uses data from the 1998, 2003 and 2008 Kenya Demographic and Health surveys. Principal components analysis was used to compute indices of HIV knowledge, HIV-related stigma, media exposure and decision making. Survey logistic regression analysis was used to determine factors that had a statistically-significant association with ever having been tested for HIV. Results: Testing was significantly higher in 2008 compared with the previous surveys. In 1998, 14.7% of the women had tested for HIV. The rate increased to 15.0% in 2003 and then to 59.2% in 2008. In the 1998 and 2003 Kenya Demographic and Health surveys, respondents’ age, region of residence, education, knowledge of someone who had died from HIV-related illness and media exposure were the main determinants of testing. In the 2008 study, HIV-related stigma, occupation and the partner’s level of education were found to be associated with HIV testing. Conclusion: Despite efforts to scale up voluntary counselling and testing in Kenya over the 1998-2008 period, HIV testing amongst women is still quite low. Prevention and control programmes in Kenya need to focus on reducing HIV-related stigma, increasing access to testing in rural areas and increasing access amongst women with little or no education. Data on HIV testing and other relevant covariates were extracted from the 1998, 2003 and 2008 Kenya Demographic and Health Surveys (KDHSs). These surveys were designed to provide data that can be used by various stakeholders in order to monitor the population and health situation in Kenya. Data were collected on fertility, family planning and maternal and child health. Other data collected also included HIV prevalence, domestic violence and malaria statistics. The 1998 KDHS was the third national demographic and health survey conducted in the country. Based on a multistage cluster-sampling approach, a nationally-representative sample of 7881 women aged 15–49 and 3407 men aged 15–54 were interviewed. The 2003 KDHS was implemented using a similar sampling methodology to interview 8195 women aged 15–49 and 3578 men aged 15–54. Finally, the 2008 KDHS was the fifth national demographic and health survey conducted in the country. A total of 9057 households were selected using a multistage cluster-sampling process whereby 8444 women aged 15–49 and 3465 men aged 15–54 were interviewed. The samples in each case provide estimates for Kenya as a whole, for urban and rural areas in each of the eight provinces. This study was based on secondary data with all participant identifiers removed. Survey procedures and instruments were approved by the Scientific and Ethical Review Committee of the Kenya Medical Research Institute (KEMRI) and by the Ethics Committee of the Opinion Research Corporation Macro International Incorporated (ORC Macro Inc.), Calverton, USA. Ethical permission for use of the data in the present study was obtained from ORC Macro Inc. Details concerning the data-collection protocols are available on the Measures Demographic and Health Surveys (DHS) website (http://www.measuredhs.com/). In this study, we restricted our analysis to women aged 15–49 years and considered the response to the question, ‘Have you ever been tested for HIV?’ to be our primary response variable. Several other variables identified from the literature were cross-classified with this variable. A Pearson’s chi-square test was then used to detect any association between the response variable and the categorical variables identified. Survey logistic-regression analysis, with stepwise elimination, was carried out using STATA 11.0 under the svy command and statistically-significant covariates were identified. All p-values less than 0.05 were considered to be significant. Separate statistical analysis was carried out for each of the 1998, 2003 and 2008 data sets. The independent variables entertained included: Principal component analysis20 was used to generate indices regarding HIV knowledge, stigma, media exposure and decision making. The HIV knowledge-perception index was created based on responses to the following questions: ‘Can a person reduce risk of getting AIDS (Acquired immune deficiency syndrome) by not having sex at all?’, ‘Can a person reduce the chances of AIDS by always using condoms during sex?’, ‘Can a person reduce the chance of AIDS by having one sex partner with no other partner?’, ‘Can a person get AIDS from mosquito bites?’, ‘Can a person get AIDS by sharing food with person who has AIDS?’ and ‘Can a healthy person have AIDS?’. On the basis of their factor scores, respondents were classified as having low-, moderate- or high knowledge with regard to the causes of HIV and the basic issues surrounding the disease. Stigma has been defined in the literature as an attribute or label that sets a person apart from others and links the labelled person to undesirable characteristics.21 Specifically, stigma related to AIDS has been defined as ‘the prejudice, discounting, discrediting, and discrimination that are directed at people perceived to have AIDS’.22 A stigma index was created based on responses to the questions: ‘Willing to care for relative with AIDS’; ‘Person with AIDS allowed to continue teaching’; and ‘Would buy vegetables from vendor with AIDS’. Based on factor scores, respondents were classified as having low-, medium- or high HIV-related stigma. A media-exposure index was also computed using principal component analysis based on responses to questions posed on the frequency of watching television, listening to radio and reading newspapers. The respondents were then classified as having low-, medium- or high media exposure. The decision-making index was computed based on the respondents’ answers to the questions: ‘Final say on own health care’; ‘Final say on making large household purchases’; ‘Final say on making household purchases for daily needs’; ‘Final say on visits to family or relatives’; ‘Final say on food to be cooked each day’; and ‘Final say on deciding what to do with money husband earns’. The decision-making index was a trichotomous variable with levels ‘independent’, ‘consults’ and ‘subservient’. For the 1998 KDHS, the decision-making index was based only on the response to the question, ‘Who decides how to spend money?’, as the other proxy questions were not included in the survey questionnaire.

AI Digest

Study Justification:

The study investigates the key determinants of HIV testing in Kenya and documents how these factors changed over the 1998-2008 period. The justification for this study is that a majority of women in Kenya do not know their HIV status, which prevents them from taking preventive measures or medication to prolong their lives.

Highlights:

– HIV testing significantly increased from 1998 to 2008, but overall testing rates among women in Kenya are still low.
– In 1998 and 2003, factors such as age, region of residence, education, knowledge of someone who had died from HIV-related illness, and media exposure were the main determinants of testing.
– In 2008, HIV-related stigma, occupation, and the partner’s level of education were found to be associated with HIV testing.

Recommendations:

– Prevention and control programs in Kenya should focus on reducing HIV-related stigma.
– Access to testing in rural areas needs to be increased.
– Access to testing should be improved for women with little or no education.

Key Role Players:

– Government health departments
– Non-governmental organizations (NGOs) working in HIV prevention and control
– Healthcare providers and clinics
– Community leaders and influencers
– Educators and schools

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers
– Development and dissemination of educational materials on HIV testing and prevention
– Outreach programs and community awareness campaigns
– Infrastructure and equipment for testing facilities
– Transportation and logistics for reaching rural areas
– Monitoring and evaluation of program effectiveness
Please note that the cost items provided are general categories and not actual cost estimates.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study uses data from multiple surveys over a 10-year period, which provides a good basis for analysis. The use of statistical analysis techniques, such as principal component analysis and logistic regression, adds rigor to the study. However, the abstract does not provide specific details about the sample size or representativeness of the surveys, which could affect the generalizability of the findings. Additionally, the abstract does not mention any limitations or potential biases in the study design or data collection. To improve the strength of the evidence, the authors could provide more information about the sample size and representativeness of the surveys, as well as acknowledge any limitations or potential biases in the study design.

Abstract

Data on HIV testing and other relevant covariates were extracted from the 1998, 2003 and 2008 Kenya Demographic and Health Surveys (KDHSs). These surveys were designed to provide data that can be used by various stakeholders in order to monitor the population and health situation in Kenya. Data were collected on fertility, family planning and maternal and child health. Other data collected also included HIV prevalence, domestic violence and malaria statistics. The 1998 KDHS was the third national demographic and health survey conducted in the country. Based on a multistage cluster-sampling approach, a nationally-representative sample of 7881 women aged 15–49 and 3407 men aged 15–54 were interviewed. The 2003 KDHS was implemented using a similar sampling methodology to interview 8195 women aged 15–49 and 3578 men aged 15–54. Finally, the 2008 KDHS was the fifth national demographic and health survey conducted in the country. A total of 9057 households were selected using a multistage cluster-sampling process whereby 8444 women aged 15–49 and 3465 men aged 15–54 were interviewed. The samples in each case provide estimates for Kenya as a whole, for urban and rural areas in each of the eight provinces. This study was based on secondary data with all participant identifiers removed. Survey procedures and instruments were approved by the Scientific and Ethical Review Committee of the Kenya Medical Research Institute (KEMRI) and by the Ethics Committee of the Opinion Research Corporation Macro International Incorporated (ORC Macro Inc.), Calverton, USA. Ethical permission for use of the data in the present study was obtained from ORC Macro Inc. Details concerning the data-collection protocols are available on the Measures Demographic and Health Surveys (DHS) website (http://www.measuredhs.com/). In this study, we restricted our analysis to women aged 15–49 years and considered the response to the question, ‘Have you ever been tested for HIV?’ to be our primary response variable. Several other variables identified from the literature were cross-classified with this variable. A Pearson’s chi-square test was then used to detect any association between the response variable and the categorical variables identified. Survey logistic-regression analysis, with stepwise elimination, was carried out using STATA 11.0 under the svy command and statistically-significant covariates were identified. All p-values less than 0.05 were considered to be significant. Separate statistical analysis was carried out for each of the 1998, 2003 and 2008 data sets. The independent variables entertained included: Principal component analysis20 was used to generate indices regarding HIV knowledge, stigma, media exposure and decision making. The HIV knowledge-perception index was created based on responses to the following questions: ‘Can a person reduce risk of getting AIDS (Acquired immune deficiency syndrome) by not having sex at all?’, ‘Can a person reduce the chances of AIDS by always using condoms during sex?’, ‘Can a person reduce the chance of AIDS by having one sex partner with no other partner?’, ‘Can a person get AIDS from mosquito bites?’, ‘Can a person get AIDS by sharing food with person who has AIDS?’ and ‘Can a healthy person have AIDS?’. On the basis of their factor scores, respondents were classified as having low-, moderate- or high knowledge with regard to the causes of HIV and the basic issues surrounding the disease. Stigma has been defined in the literature as an attribute or label that sets a person apart from others and links the labelled person to undesirable characteristics.21 Specifically, stigma related to AIDS has been defined as ‘the prejudice, discounting, discrediting, and discrimination that are directed at people perceived to have AIDS’.22 A stigma index was created based on responses to the questions: ‘Willing to care for relative with AIDS’; ‘Person with AIDS allowed to continue teaching’; and ‘Would buy vegetables from vendor with AIDS’. Based on factor scores, respondents were classified as having low-, medium- or high HIV-related stigma. A media-exposure index was also computed using principal component analysis based on responses to questions posed on the frequency of watching television, listening to radio and reading newspapers. The respondents were then classified as having low-, medium- or high media exposure. The decision-making index was computed based on the respondents’ answers to the questions: ‘Final say on own health care’; ‘Final say on making large household purchases’; ‘Final say on making household purchases for daily needs’; ‘Final say on visits to family or relatives’; ‘Final say on food to be cooked each day’; and ‘Final say on deciding what to do with money husband earns’. The decision-making index was a trichotomous variable with levels ‘independent’, ‘consults’ and ‘subservient’. For the 1998 KDHS, the decision-making index was based only on the response to the question, ‘Who decides how to spend money?’, as the other proxy questions were not included in the survey questionnaire.

Materials

N/A

Innovations

Based on the information provided, here are some potential innovations that could improve access to maternal health:

1. Mobile Clinics: Implementing mobile clinics that travel to rural areas and provide maternal health services, including HIV testing, to women who may not have easy access to healthcare facilities.

2. Community Health Workers: Training and deploying community health workers who can educate women about the importance of HIV testing and provide testing services in their communities.

3. Telemedicine: Using telemedicine technology to connect women in remote areas with healthcare professionals who can provide counseling, guidance, and HIV testing remotely.

4. Awareness Campaigns: Conducting targeted awareness campaigns to reduce HIV-related stigma and increase knowledge about the benefits of HIV testing among women.

5. Integration of Services: Integrating HIV testing services with existing maternal health programs and services to ensure that women receive comprehensive care during pregnancy and childbirth.

6. Improving Education: Enhancing educational programs to increase knowledge about HIV transmission, prevention, and the importance of testing among women, especially those with little or no education.

7. Strengthening Health Systems: Investing in the improvement of healthcare infrastructure, staffing, and supply chains to ensure that maternal health services, including HIV testing, are readily available and accessible to all women.

These innovations aim to address the barriers identified in the study, such as HIV-related stigma, limited access to testing in rural areas, and low education levels. By implementing these recommendations, it is hoped that access to maternal health, including HIV testing, can be improved in Kenya.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health would be to implement targeted interventions that address the key determinants of HIV testing among women in Kenya. These interventions should focus on reducing HIV-related stigma, increasing access to testing in rural areas, and improving access for women with little or no education.

To reduce HIV-related stigma, awareness campaigns and educational programs can be implemented to promote understanding and acceptance of individuals living with HIV/AIDS. These campaigns should aim to dispel myths and misconceptions surrounding HIV/AIDS and encourage empathy and support for those affected.

To increase access to testing in rural areas, mobile testing units can be deployed to reach remote communities. These units can provide on-site testing and counseling services, making it easier for women in these areas to access HIV testing without having to travel long distances.

To improve access for women with little or no education, community health workers can be trained to provide information and support for HIV testing. These health workers can visit households and engage with women directly, addressing their concerns and providing them with the necessary information to make informed decisions about testing.

Overall, a multi-faceted approach that addresses the social, cultural, and geographical barriers to HIV testing is essential to improve access to maternal health in Kenya. By implementing these recommendations, more women will be able to know their HIV status and take the necessary preventive measures to protect their health and the health of their children.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthen community-based education and awareness programs: Implement targeted education and awareness campaigns to increase knowledge about maternal health, including the importance of prenatal care, safe delivery practices, and postnatal care. These programs can be conducted through community health workers, local clinics, and mobile health units.

2. Improve access to healthcare facilities: Increase the number of healthcare facilities, particularly in rural areas, and ensure they are equipped with necessary resources and skilled healthcare providers. This can be achieved through infrastructure development, recruitment and training of healthcare professionals, and provision of essential medical supplies and equipment.

3. Enhance transportation services: Establish reliable transportation systems, such as ambulances or transportation vouchers, to facilitate access to healthcare facilities for pregnant women, especially in remote areas. This can help overcome geographical barriers and ensure timely access to emergency obstetric care.

4. Strengthen referral systems: Develop and strengthen referral systems between primary healthcare facilities and higher-level facilities, such as hospitals, to ensure seamless and timely transfer of pregnant women requiring specialized care. This can be achieved through improved communication channels, standardized protocols, and training of healthcare providers on referral procedures.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators to measure the impact of the recommendations, such as the number of pregnant women receiving prenatal care, the percentage of deliveries attended by skilled birth attendants, and the maternal mortality rate.

2. Collect baseline data: Gather baseline data on the selected indicators from existing sources, such as national health surveys, health facility records, and population data.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population demographics, healthcare infrastructure, transportation networks, and referral systems.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. Vary the parameters related to the recommendations, such as the coverage of education programs, the number of healthcare facilities, and the availability of transportation services.

5. Analyze results: Analyze the simulation results to determine the projected changes in the selected indicators. Assess the effectiveness of each recommendation individually and in combination, and identify the most impactful interventions.

6. Refine and validate the model: Refine the simulation model based on feedback and validation from experts in the field of maternal health. Incorporate additional data sources and refine the parameters to improve the accuracy of the simulations.

7. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community leaders. Use the results to advocate for the implementation of the recommended interventions and to guide resource allocation and planning efforts.

By following this methodology, policymakers and healthcare professionals can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions to prioritize and implement the most effective strategies.

Author

Dima Health

Statistics:

Citations: 6

Identifiers:

DOI: 10.4102/phcfm.v5i1.547

ISSN: 20712928

Research Areas:

Community Interventions, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Violence and Injury

Study Countries:

Kenya

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female