Factors influencing HIV Testing among young women aged 15-24 in the 2015-2016 Malawi Demographic and Health Survey

doi:10.21203/rs.3.rs-2610791/v1

Download PDF

Research Article

Factors influencing HIV Testing among young women aged 15-24 in the 2015-2016 Malawi Demographic and Health Survey

https://doi.org/10.21203/rs.3.rs-2610791/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Purpose

Adolescent girls and young women exhibit two of three global new HIV infections every day, yet they perceived little risk of acquiring it, thus neglecting their status. HIV Testing is fundamental to its prevention and achieving the UNAIDS three 90s.

Methods

We obtained data from the 2015–2016 Malawi Demographic and Health Survey on women to establish the HIV testing factors. We employed SPSS bivariate and multivariate logistic regression to determine HIV testing factors among young women aged 15–24.

Results

Of all 10422 young women, 69.5% have done the test, and most were rural dwellers. Ages 20–24 were more than 2.6 times (AOR = 2.69; 95% CI = 2.11–3.43) more likely to do HIV testing than those aged 15–19. Reading newspapers or magazines less than once a week and listening to the radio at least once a week increased HIV testing by 1.9 and 1.5 times, respectively. A visit to a health facility in the past 12 months influenced HIV testing by 3 (AOR = 3.06; 95% CI = 2.41–3.89) times. Contraceptives and condoms used influenced HIV testing 4.4 (AOR = 4.40; 95% CI = 3.24–5.96) and 2.1 (AOR = 2.17; 95% CI = 1.09–4.30) times. Employed and wealthy young women were 1.6 times more likely to be tested. Husbands’ education positively influenced HIV testing while living in rural areas negatively influenced it.

Conclusions

Our findings highlight potential factors in improving HIV testing and indicate that robust strategies need to be implemented to increase HIV testing among young women in Malawi. Future research should focus on motivating factors that will enhance HIV testing uptake in Malawi young women.

HIV testing

Malawi

Women

Malawi Demographic and Health Survey

HIV/AIDS remains a significant public health burden in sub-Saharan Africa (SSA). The prevalence of HIV in Malawi, an SSA country, is among the highest globally, accounting for 9.2% of adults (aged 15–49) population [1]. HIV disproportionately affects women in Malawi, with a prevalence of 10.8% in women and 7.0% in men living with HIV between the ages of 15–49 years in 2018 [1]. This disparity of HIV prevalence significantly affects young women aged 15–25 years, with 9,900 of the 38,000 new infections in 2018, compared to 4200 young men in the same age group [1, 2]. Despite declined HIV prevalence from 11.5% in 1996 to 4.2% in 2019 among young women in Malawi [3], 70% of the new infection among youth occurred in females. [4]. Furthermore, reports showed a considerable reduction in HIV/AIDS-related deaths, yet young women continue to bear the highest burden of the epidemic [5]. While they contribute to a third of new HIV infections, half of the young people living with HIV appear to neglect their HIV status [6].

Malawi has implemented several interventions to combat the HIV/AIDS pandemic; including the United Nations Program on HIV/AIDS (UNAIDS) 90-90-90 treatment targets by 2020. The UNAIDS 90-90-90 targets focus on eradicating HIV by the year 2030 through ensuring 90% of all people living with HIV know their status, 90% of all those diagnosed with HIV commence and maintain sustain Antiretroviral Therapy (ART), and 90% of people on Highly Active Antiretroviral Therapy (HAART) have suppressed viral load [7]. Additionally, Malawi has also implemented the Test and Treat Management of HIV irrespective of the World Health Organization’s (WHO) HIV staging of Viral Load and CD4 count [8]. Since early 2000, rapid HIV testing services have been provided on a free-of-charge basis at all public health facilities and some private facilities [9].

HIV testing is a significant gateway in achieving the UNAIDS target of 90-90-90 by 2020. While the current adult population is on track to meeting HIV testing targets, the young people still lag, with only half of young women knowing their HIV status [4]. In Malawi, HIV testing made notable progress among the adult population, with 90% of the adult population aware of their HIV status; the young aged 15 to 24 was estimated at 53% HIV Testing [4, 10]. Despite HIV testing rates being higher for females of this age group than males [10], it remains of paramount importance for young women aged 15–24 to know their HIV status before conception since most young women experience their sexual debut within this age bracket. Young women who are aware of their HIV status may prevent risky sexual behavior for fear of acquiring the virus or adopt better behaviors to prevent secondary HIV infection and transmission of the virus to others or their unborn child. The acute phase of HIV is the most infectious without ARVs since it can rapidly replicate in the earliest stage when the body is naïve [11]. However, HIV Testing can enhance the timely commencement of ARV’s to reduce this rapid replication [7, 12]. Moreover, HIV testing has been shown to strategically contribute to reducing HIV related morbidity, mortality and improving patient outcome [13].

Despite the free HIV testing and treatment in Malawi and its numerous advantages, young women aged 15–24 failed to meet the UNAIDS HIV Testing target in 2020. Here, we aimed to explore the factors influencing HIV testing among young women aged 15–24 using data from Malawi Demographic and Health Survey (MDHS) for better intervention

Data Source

This study analyzed secondary data from the 2015-16 MDHS; a national representative cross-sectional survey conducted every five years to provide current estimates of essential demographic and health indicators. The survey uses four questionnaires (Household, Women's, Men's and the Biomarker questionnaire) to collect data from women and men aged 15–49 and 15–54 years old, respectively. The women’s questionnaire data were analyzed to determine the factors of HIV testing among young women in Malawi. The MDHS is an implemented program by the National Statistical Office (NSO) in collaboration with the Malawi Ministry of Health, International Classification of Functioning, Disability and Health (ICF), United States Agency for International Development (USAID), the United Nations Children's Fund (UNICEF), and other financing organizations. The DHS conducts cross-sectional, national representative surveys in over 90 countries.

Study Setting, Sampling And Study Design

The study was conducted in Malawi with three regions and 28 districts. The MDHS used a two-stage probability sampling method to produce a nationally representative sample. Firstly, the 28 districts were stratified into urban and rural settings, resulting in 56 sampling strata. The strata were further segmented into 850 Enumeration Areas (EAs) or clusters using proportional probability. The 850 EAs had 173 urban and 677 rural clusters derived from the latest Malawi population and Sampling Housing Census frame. Systematic random sampling was done on 30 households per urban cluster and 33 per rural cluster. All women within the selected households were eligible to participate. A total of 26,564 occupied households were enumerated at the time of data collection, but 26,361 households were interviewed, accounting for 99% coverage. Of the 24,562 women interviewed (98% response rate), we extracted 10,422 young women aged 15–24 years for our analysis. Detailed methodology and standard MDHS questionnaires used for interview have been described elsewhere [14]

Study Variables And Measurements

Dependent variable

The outcome variable was "Ever been tested for HIV" with a binary response “Tested” and “Never tested.”

Independent Variables

The independent or predictor variables were: age in 5 years, region, residence, frequency of reading newspaper or magazine, frequency of listening to a radio, owns a mobile, visited health facility last 12 months, employment, condom use during last sex with a most recent partner, currently pregnant, husband/partner's education level, wealth index (poor, middle and rich) [15] and account in a bank or financial institution.

Ethical Consideration

Data use permission was obtained from the MEASURE DHS, the demographic health survey's global monitoring and evaluation body (DHS). The MDHS obtained ethical clearance from Malawi's National Health Sciences Research Committee (NHSRC), and all participants provided informed consent.

Data Analysis

We used IBM SPSS version 24 and selected variables based on previous literature. Data were cleaned, recoded and weighed to accounts for the national representation of study subjects. Descriptive statistics described variable characteristics of young women aged 15–24 and Chi-square test to evaluate the association (P < 0.001) between the dependent variable and independent variables. Predictor variables associated with HIV testing at p ≤ 0.25 on binary logistic regression were included in the final model at a 95% confidence interval to identify potential factors [16]. The crude (unadjusted) odds ratio and adjusted odds ratios and their 95% confidence were estimated and reported.

Characteristics of young women aged 15–24 years in Malawi with regards to ever been tested for HIV

Table 1. shows the weighted socio-demographic characteristics of young women who responded to questions relating to ever being tested for HIV in the 2015/2016 Malawi DHS. Of the 10422 young women that responded, 69.5% have done HIV tests with a Mean ± SD of 19.38 ± 2.84 years. The population of young women was almost equally distributed between the two age group categories. The central and the southern regions accounted for nearly 90% of the young women population, with more than 80% residing in rural areas. The majority of young women had low media exposure. Half of the young women (50.6%) are employed, but only 5.2% had an account in a bank or financial institution. The wealth index was almost equally distributed between the poor (40.3%) and the rich (41.3%). Almost 70% used contraceptives, but few (16.8%) used a condom during their last sex with their partners. About half of the young women had educated husbands with primary school level (53.4%) and secondary level 35.2%. More than half of the young women have visited a health facility in the last 12 months, and 10.2% of the young women were pregnant.

Table 1

Characteristics background of young women aged 15–24 years in the 2015–2016 Malawi Demographic and Health Survey
			NEVER TESTED		TESTED
Characteristics	N	%	N	%	N	%	P-Value
Age in years							< 0.001*
15–19	5263	50.5	2690	84.8	2572	35.5
20–24	5159	49.5	483	15.2	4676	64.5
Mean ± SD of age (years)	19.38 ± 2.842
Region							< 0.001*
Northern	1159	11.1	278	8.8	881	12.2
Central	4536	43.5	1553	48.9	2983	41.2
Southern	4727	45.4	1342	42.3	3384	46.7
Residence
Urban	1892	18.2	492	15.5	1400	19.3
Rural	8529	81.8	2681		5848
Reading News p or M							0.017*
Not at all	8085	77.6	2516	79.3	5569	76.8
< than a week	1364	13.1	375	27.5	989	72.5
At least once a week	973	9.3	283	8.9	690	9.5
Listening Radio							< 0.001*
Not at all	5551	53.3	1820	57.3	3731	51.5
> once a week	1873	18.0	569	17.9	1304	18.0
At least once a week	2998	28.8	785	24.7	2213	30.5
Own Mobile Phone							< 0.001*
No phone	7813	75.0	2734	86.1	5079	70.1
phone	2609	25.0	440	13.9	2169	29.9
Visited HF Last 12 Months							< 0.001*
No	4772	45.8	2249	70.9	2523	34.8
Yes	5650	54.2	925	29.1	4725	65.2
Employment							< 0.001*
not employed	5277	50.6	1974	62.2	3302	45.6
employed	5145	49.4	1199	37.8	3946	54.4
Contraceptive use							< 0.001*
Not using	7203	69.1	2966	93.4	4237	58.5
Using	3219	30.9	208	6.6	3011	41.5
Condom used during last sex							< 0.001*
No	5428	83.2	621	67.9	4807	85.7
Yes	1098	16.8	294	32.1	803	14.3
Currently Pregnant							< 0.001*
No or Unsure	9361	89.8	3044	95.9	6317	87.2
Yes	1061	10.2	130	4.1	931	12.8
Husband Education							< 0.001*
No education	320	6.5	82	21.8	238	5.3
Primary	2608	53.4	207	55.1	2401	53.2
Secondary	1722	35.2	79	21.0	1644	36.4
Higher	176	3.6	1	0.3	175	3.9
Don’t know	62	1.3	7	1.9	55	1.2
Wealth Index Combined							< 0.001*
Poorest	4201	40.3	1169	36.8	3032	41.8
Middle	1945	18.7	666	21.0	1279	17.6
Rich	4276	41.0	1338	42.2	2938	40.5
Account in Bank or Financial Institution							< 0.001*
No	9882	94.8	3117	98.2	6765	93.3
Yes	540	5.2	57	1.8	483	6.7
*Significant (P < 0.05)

The Chi-square test showed the socio-demographic characteristics of young women were significantly associated with HIV testing at P < 0.001.

Determinants Of Hiv Testing Among Young Women Aged 15–24 In Malawi

Table 2 shows the associates of HIV testing among young women in Malawi. The determinants are based on logistic regression's crude and adjusted odds ratio.

Table 2

Univariate and multivariate logistic regression analyses of independent factors associated with HIV testing among young women aged 15–24 years in Malawi
Characteristics	COR	95% C I		P-value	AOR	95% CI		P-value
Age in 5 years
15–19	Ref.				Ref.
20–24	10.13	9.09-	11.282	< 0.001*	2.69	2.11-	3.43	< 0.001*
Region
Northern	Ref.				Ref.
Central	0.61	0.52-	0.70	< 0.001*	0.82	0.55-	1.23	0.330**
Southern	0.795	0.69-	0.92	0.003*	1.04	0.70-	1.55	0.839**
Residence
Urban	Ref.				Ref.
Rural	0.77	0.69-	0.86	< 0.001*	0.35	0.17-	0.71	0.004*
Frequency of reading newspaper or magazine
Not at all	Ref.				Ref.
< once a week	1.19	1.05-	1.35	0.007*	1.94	1.09-	3.45	0.024*
At least once a week	1.10	0.95-	1.28	0.189**	1.43	0.77-	2.64	0.258**
Frequency of listening to radio
Not at all	Ref.				Ref.
> once a week	1.12	1.00-	1.25	0.054**	1.36	0.95-	1.95	0.091**
At least once a week	1.375	1.25-	1.52	< 0.001*	1.50	1.09-	2.06	0.013*
Owns a mobile
No	Ref.				Ref.
Yes	2.66	2.37-	2.97	< 0.001*	0.73	0.52-	1.01	0.056**
Visited health facility last 12 months
No	Ref.				Ref.
Yes	4.56	4.16-	4.99	< 0.001*	3.06	2.41-	3.89	< 0.001*
Employment
not employed	Ref.				Ref.
employed	1.97	1.81-	2.14	< 0.001*	1.60	1.25-	2.05	< 0.001*
Contraceptive use
Not using	Ref.				Ref.
Using	10.15	8.75-	11.77	< 0.001*	4.40	3.24-	5.96	< 0.001*
Condom use during last sex with most recent partner
No	Ref.				Ref.
Yes	0.35	0.30-	0.41	< 0.001*	2.17	1.09-	4.30	0.027*
Currently Pregnant
No or Unsure	Ref.				Ref.
Yes	3.45	2.86-	4.17	< 0.001*	1.30	0.98-	1.73	0.071**
Husband/partner's education level
No education	Ref.				Ref.
Primary	4.00	3.00-	5.34	< 0.001*	3.24	2.30-	4.56	< 0.001*
Secondary	7.19	5.13-	10.08	< 0.001*	5.11	3.41-	7.67	< 0.001*
Higher	97.42	7.94-	1195.72	< 0.001*	35.53	2.76-	457.67	0.006*
Don’t Know	2.64	1.17-	5.99	0.020*	2.21	0.85-	5.77	0.104**
Wealth Index
Poor	Ref.				Ref.
Middle	0.74	0.66-	0.83	< 0.001*	0.97	0.71-	1.33	0.852**
rich	0.85	0.77-	0.93	< 0.001*	1.65	1.13-	2.41	0.009*
Account in Bank or Financial Institution
No	Ref.				Ref.
Yes	3.93	2.97-	5.19	< 0.001*	0.63	0.30-	1.34	0.228**
AOR adjusted odds ratio, COR crude odds ratio, Ref reference category
Significant (P < 0.05); *Non-significant (P > 0.05)

In particular, young women aged 20–24 were 2.6 times (AOR = 2.69; 95% CI = 2.11–3.43) more likely to do HIV tests than those aged 15–19 years. The frequency of reading newspapers or magazines less than once a week influenced HIV testing by 1.9 (AOR = 1.94; 95% CI = 1.09–3.45) times compared to those who did not. Similarly, listening to a radio at least once a week influenced HIV testing by 1.5 (AOR = 1.50; 95% CI = 1.09–2.06) times than those who did not. Young women who had visited a health facility for the past 12 months were 3 (AOR = 3.06; 95% CI = 2.41–3.89) times more likely to do HIV tests than those who did not. Contraceptives and condoms use influenced HIV testing by 4.4 (AOR = 4.40; 95% CI = 3.24–5.96) and 2.1 (AOR = 2.17; 95% CI = 1.09–4.30) times respectively compared to those who did not. Young women married to educated husbands/partners were 35.5 (AOR = 35.53; 95% CI = 2.76-457.67), 5.1 (AOR = 5.11; 95% CI = 3.41–7.67) and 3.2 (AOR = 3.24; 95% CI = 2.30–4.56) times more likely to do HIV test for higher, secondary and primary schools respectively as compared to those not married/married to uneducated husbands. Employed (AOR = 1.60; 95% CI = 1.25–2.05) and rich (AOR = 1.65; 95% CI = 1.13–2.41) young women were 1.6 times more likely to do HIV tests than unemployed and poor, respectively. Conversely, young women in rural residences were 0.3 times (AOR = 0.35; 95% CI = 0.17–0.71) less likely to do HIV testing than urban residents. Other variables such as region, owning a phone, current pregnancy, and bank/financial accounts were only significant at the univariate but not at the multivariate level

HIV testing is crucial in eradicating the AIDS epidemic by 2030 through identifying influential associated factors. These influencing factors vary in a population of different age groups and geographical regions. Here, we document the socio-demographic factors associated with HIV testing among young women aged 15–24 in Malawi using DHS data. Nearly seventy per cent (69.5%) of young women know their status, similar to men [17]. However, almost three-quarters of people living with HIV in SSA are young women aged 15–24 years who acquired it a decade ago [18].

Moreover, adolescents and young women perceived themselves at a lower risk of acquiring HIV, including those at the highest risk. Therefore, it is significant to study this age group since their trend prevalence of HIV better reflects a country’s overall trend of HIV incidence and risk behavior. Here we employed multivariate regression analysis to evaluate the influencers of HIV testing. Our study suggests that age influences HIV testing among young women in Malawi.

Specifically, young women aged 20–24 were more likely to be tested for HIV than those aged 15–19 years. This finding confirms several studies demonstrating that the young age population (15–19 years) have lower odds of HIV testing irrespective of gender [17, 19, 20]. This discrepancy could be attributed to the several barriers, including physical and legal limitations adolescents face in accessing HIV testing in most countries.[21]. Furthermore, Malawi HIV testing guidelines limit young people below 18, requiring a guardian's permission or important reason/or order to test people below 18 years [8]. Additionally, adolescents engaging in premarital sex may be shy to access HIV testing services. However, adolescents and young women must be significantly considered in HIV testing programs as a study showed age 15 years to be the defining threshold for early sexual debut [22]. In a recent study, the age was even lower with first sexual activity for boys at age 11.5 years and girls at age 13 [23]. Adolescents are faced with a lot of peer pressure and are easily influenced by their opposite counterparts. Hence, targeting the young aged and leveraging the HIV testing guidelines in Malawi will significantly harness UNAIDS 90-90-90 targets to end the Aids pandemic by 2030.

In agreement with other studies, our findings exhibit rural-urban discrepancy in terms of HIV testing. The result indicates that young women in rural residents were less likely to do HIV testing than urban residents. Similar studies have also found lower odds of HIV testing among rural dwellers compared to urban residents regardless of gender [17, 24, 25]. The lower odds of HIV testing among rural residents could be attributed to lesser and distant health facilities. Generally, there are fewer health facilities in rural areas than in urban areas, explaining this finding. Additionally, stigmatization [26] might decrease HIV testing among rural residents since healthcare workers usually know community members or clients. Another reason could be the higher exposure of urban residents to various media [19] in which they are easily informed.

A visit to a health facility was a significant influencer of HIV testing in our model. Young women who had visited a health facility in the past 12 months had higher odds of HIV testing than those who did not. A similar study in Ethiopia also finds that young women who visited health care facilities had higher odds of being tested for HIV [24]. Furthermore, antenatal care services provided in health facilities, including free HIV testing, could account for the high proportion of HIV testing among young women visiting these health facilities. For example, a study in Mozambique revealed that 75.4% of pregnant women received HIV tests during ANC visits [27]. Young women should, therefore, be encouraged to visit health facilities to improve HIV testing service uptake.

Media exposure such as the frequency of reading newspapers or magazines and listening to radio significantly influenced HIV testing among young women. The frequency of reading a newspaper or magazine for at least less than a week was more likely to influence HIV testing. This conforms to other studies that showed increased HIV test uptake with the frequency of reading newspapers or magazines [28] or being able to read compared to those that cannot [29]. The influence of reading on HIV testing can be explained in terms of educational attainment as the educated can better comprehend health and other information with reasonable deduction. Our study also showed the frequency of listening to a radio at least once a week is positively associated with increased HIV testing in concordance with other studies [28]. This positive association could be due to the dissemination of health knowledge through media like radio and television. A study in Uganda evidenced a significant association between mass media exposure with HIV related knowledge [30]. Although there was a significant association between the frequency of listening to a radio and HIV testing, other media such as owning a phone was only marginally significant at the multivariable level.

Young women in the rich wealth index were more likely to do an HIV test, agreeing with earlier studies [25, 31]. Several studies have also confirmed that rich people [32] are more likely to accept HIV VCT than the poor. These differences could be explained in terms of the greater exposure of the rich to social amenities such as diverse media and better health facilities.

Husband educational level was significantly associated with HIV testing among young women. Husband's educational level influenced young women HIV testing from primary, secondary and higher academic levels. Many studies have found educational status to influence HIV testing [33]; however, we found an indirect association through husbands/partners. Studies have shown that men were more knowledgeable in HIV and influenced HIV testing decisions. Data showed that husbands are primary confidants for HIV testing among married women [34, 35]. Our study did not directly associate HIV testing and educational level among young women, which might be due to fear of stigma and other unknown factors.

This study showed that preventive pregnancy measures significantly influenced HIV testing among young women. Sexually active young women using contraceptives and condoms were more likely to do HIV tests than those not using those preventive methods. Our finding was consistent with other studies that associated condoms usage to increase HIV testing in partners [36]. In another study investigating the relationship between contraceptives and HIV uptake in four SSA countries among women, Mozambique women using modern forms of contraception were more likely to do HIV tests, while Congo, Nigeria and Uganda women did not demonstrate such positive association [37]. Another study has suggested that consistent condom use reduces the likelihood of getting an HIV test [20]. However, that study did not provide much information to the existing literature.

Our study showed that young women's employment was positively associated with HIV testing. This is consistent with a review that showed employment to be positively associated with HIV testing, management and medication adherence in Western Europe, the United States, and Canada [38]. Similar studies conducted in Africa also showed that employment positively contributes to HIV testing. However, some studies conducted in Africa and Asia showed that employed women had a lower chance of being tested for HIV/AIDS than unemployed women [39]. Differences in results may be due to the type of occupation.

Strengths And Limitations

Like all other survey data, it is imperative to acknowledge a set of study limitations. Firstly, our study variables were limited to those collected by MDHS. Therefore, cultural and traditional beliefs that would better inform the differences in HIV testing and other social factors were not captured. However, the variables collected by MDHS are very significant in determining HIV testing factors. Secondly, since data was obtained from the participants by self-reporting, it is substantive to be report bias. However, our results tallied with several cross-sectional data of such nature, making our estimates unlikely to be subjected to bias error. Thirdly, the factors influencing HIV testing cannot draw a causal inference since MDHS data are cross-sectional. Finally, not all households and young women were interviewed. However, the MDHS sample design is representative of the national population of Malawi.

Additionally, there was a more than 90% response rate of women, including young women. Further studies are needed to establish cultural, traditional beliefs and other associated factors. Despite the limitation, our findings of HIV testing among young women aged 15–24 years can be generalized to the entire country

Available evidence showed that adolescent girls and women exhibit a lopsided HIV burden representing two of three new infections occurring every day globally [40]. There is a substantial need to strengthen HIV testing uptake among this population to achieve the three UNAIDS 90s in eradicating the AIDS epidemic by 2030. Our findings highlight possible targets for Malawi policymakers. Adolescents (15–19) living in the rural, husbands’ educational level and media exposure are fundamental in HIV testing uptake among Malawi's young women. Therefore, targeting young women (15–19) living in rural areas, increasing access to media will promote HIV uptake among young women and other groups. Our study indicates that additional robust strategies need to be implemented to increase HIV testing among young women in Malawi. Future studies should focus on specific reasons for low HIV testing uptake among young women in rural Malawi and the motivating factors to the HIV test.

ANC: Antenatal Care

AOR: Adjusted Odd Ratio

ART: Antiretroviral Therapy

CI: Confidence Interval

COR: Crude Odd Ratio

DHS: Demographic and Health Survey

HAART: Highly Active Antiretroviral Therapy

HIV VCT: HIV Voluntary Counseling and Testing

HIV/AIDS: Human Immunodeficiency Virus / Acquired Immune Deficiency Syndrome

ICF: International Classification of Functioning Disability and Health

MDHS: Malawi Demographic and Health Survey

NHSRC: National Health Sciences Research Committee

NSO: National Statistical Office

SD: Standard Deviation

SSA: Sub-Saharan Africa

UNAIDS: Joint United Nations Program on HIV/AIDS

UNICEF: United Nations Children's Fund

USAID: United States Agency for International Development

WHO: World Health Organization

Ethical approval and consent to participate

We sought approval from the DHS in order to access the data. The original study was approved by the Malawi Ethics and Scientific Review Committee, and the survey protocol was evaluated and approved by the ICF Institutional Review Board. Moreover, before taking part in the survey, all individuals verbally consented to participate in the study.

Consent for publication

Not applicable

Availability of data and materials

The datasets analyzed during the current study are available in the Demographic Health Survey website, https://dhsprogram.com/

Competing interests

The authors declare that they have no competing interests.

Funding

This study did not receive any funding as at the point of submission.

Authors’ contributions

All authors contributed to the conceptualization of this study. MMK acquired the data, conducted the preliminary analysis, and drafted the first version of the manuscript. KC, JAK, and JRY contributed to the final analysis and drafting of the result section. DJX, JW, and YCD contributed to the literature search and manuscript preparation. PKS re-wrote the manuscript's final version which was verified by all the authors. All the authors reviewed the manuscript.

Acknowledgments

Not applicable

UNAIDS. Country factsheet Malawi 2019. 2019 [cited 2020 October 11]; Available from: https://www.unaids.org/en/regionscountries/countries/malawi.
Averts. HIV AND AIDS IN MALAWI. 2018 25 August 2020 [cited 2020 October 12]; Available from: https://www.avert.org/professionals/hiv-around-world/sub-saharan-africa/malawi.
BANK, T.W. Prevalence of HIV, female (% ages 15-24) - Malawi. 2020 [cited 2020 October 14]; Available from: https://data.worldbank.org/indicator/SH.HIV.1524.FE.ZS?locations=MW.
Avert. HIV and AIDS in Malawi, 2017. 9 October 2018 [cited 2018 7 October]; Available from: https://www.avert.org/professionals/hiv-around-world/sub-saharan-africa/malawi.
UNAIDS. Malawi. 2016 [cited 2018 9 October]; Available from: http://www.unaids.org/en/regionscountries/countries/malawi.
UNAIDS, Global HIV and AIDS statistics—2018 fact sheet, in Joint United Nations Programme on HIV/AIDS,. 2018: Geneva
UNAIDS. 90 90 90: An ambitious treatment target to help end AIDS Epidemic. 2014 [cited 2018 11 October]; Available from: www.unaids.org/sites/default/files/media_asset/90-90-90_en.pdf.
Ministry of Health, Malawi HIV Testing guidelines, Ministry of Health, Editor. 2016, Ministry of Health,: Lilongwe, Malawi. . p. 62.
Malawi, M.o.H. Malawi HIV Testing Services Guidelines. 2016.
National Statistics Office, Malawi Demographic and Health Survey. 2017.
CDC. HIV Basics. 2019 April 24, 2019 [cited 2019 30th April]; Available from: https://www.cdc.gov/hiv/basics/whatishiv.html.
Arthur, G., et al., Behaviour change in clients of health centre-based voluntary HIV counselling and testing services in Kenya. Sexually transmitted infections, 2007. 83(7): p. 541-546.
Li, J., et al., The epidemiological impact and cost-effectiveness of HIV testing, antiretroviral treatment and harm reduction programs. AIDS, 2012. 26(16): p. 2069-78.
Office, S., ICF International (Firm, & Measure Dhs (Program. (2017). Malawi demographic and health survey, 2015-16. Zomba, Malawi: National Statistics Office; Rockville, Maryland, USA.
Rutstein, S.O. and K. Johnson, The DHS wealth index, in DHS Comparative Reports No. 6. 2004, ORC Macro: Calverton, Maryland, USA.
Sun, G.-W., T.L. Shook, and G.L. Kay, Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. Journal of Clinical Epidemiology, 1996. 49(8): p. 907-916.
Mandiwa, C. and B. Namondwe, Uptake and correlates of HIV testing among men in Malawi: evidence from a national population-based household survey. BMC health services research, 2019. 19(1): p. 203-203.
UNAIDS, THE GAP REPORT [Internet]. [cited 2022 09 January]; Available from: https://unaids-test.unaids.org/sites/default/files/unaids/contentassets/documents/unaidspublication/2014/UNAIDS_Gap_report_en.pdf
Ajayi, A.I., et al., Low coverage of HIV testing among adolescents and young adults in Nigeria: Implication for achieving the UNAIDS first 95. PLoS One, 2020. 15(5): p. e0233368.
Takarinda, K.C., et al., Factors Associated with Ever Being HIV-Tested in Zimbabwe: An Extended Analysis of the Zimbabwe Demographic and Health Survey (2010–2011). PLOS ONE, 2016. 11(1): p. e0147828.
Staveteig, S., et al., Reaching the ‘first 90’: Gaps in coverage of HIV testing among people living with HIV in 16 African countries. PLOS ONE, 2017. 12(10): p. e0186316.
Pettifor, A., et al., Early Age of First Sex: a Risk Factor for HIV Infection among Women in Zimbabwe. AIDS (London, England), 2004. 18: p. 1435-42.
Bakeera-Kitaka, S., et al., Factors Influencing the Risk of Becoming Sexually Active Among HIV Infected Adolescents in Kampala and Kisumu, East Africa. AIDS and Behavior, 2019. 23(6): p. 1375-1386.
Bekele, Y.A. and G.A. Fekadu, Factors associated with HIV testing among young females; further analysis of the 2016 Ethiopian demographic and health survey data. PloS one, 2020. 15(2): p. e0228783-e0228783.
Lakhe, N.A., et al., HIV screening in men and women in Senegal: coverage and associated factors; analysis of the 2017 demographic and health survey. BMC infectious diseases, 2019. 20(1): p. 1-1.
Corno, L. and D. de Walque, Socioeconomic determinants of stigmatization and HIV testing in Lesotho. AIDS Care, 2013. 25 Suppl 1: p. S108-13.
Yaya, S., et al., Determinants of prenatal care use and HIV testing during pregnancy: a population-based, cross-sectional study of 7080 women of reproductive age in Mozambique. BMC Pregnancy and Childbirth, 2019. 19(1): p. 354.
Agegnehu, C.D. and G.A. Tesema, Effect of mass media on comprehensive knowledge of HIV/AIDS and its spatial distribution among reproductive-age women in Ethiopia: a spatial and multilevel analysis. BMC Public Health, 2020. 20(1): p. 1420.
Kim, S.W., et al., A method for measuring spatial effects on socioeconomic inequalities using the concentration index. Int J Equity Health, 2020. 19(1): p. 9.
Lompo, M.L. and J.-L. Bago, How Does Exposure to Mass Media affect HIV Testing and HIV-Related Knowledge Among Adolescents? Evidence From Uganda. Global Journal of Health Science, 2018. 10: p. 1.
Diress, G., et al., Barriers and Facilitators for HIV Testing Practice Among Ethiopian Women Aged 15-24 years: Analysis of the 2016 Ethiopian Demographic and Health Survey. HIV AIDS (Auckl), 2020. 12: p. 963-970.
Ha, J.H., et al., Gendered relationship between HIV stigma and HIV testing among men and women in Mozambique: a cross-sectional study to inform a stigma reduction and male-targeted HIV testing intervention. BMJ Open, 2019. 9(10): p. e029748.
Kirakoya-Samadoulougou, F., K. Jean, and M. Maheu-Giroux, Uptake of HIV testing in Burkina Faso: an assessment of individual and community-level determinants. BMC Public Health, 2017. 17(1): p. 486.
Karamagi, C.A., et al., Antenatal HIV testing in rural eastern Uganda in 2003: incomplete rollout of the prevention of mother-to-child transmission of HIV programme? BMC Int Health Hum Rights, 2006. 6: p. 6.
Bajunirwe, F. and M. Muzoora, Barriers to the implementation of programs for the prevention of mother-to-child transmission of HIV: a cross-sectional survey in rural and urban Uganda. AIDS Res Ther, 2005. 2: p. 10.
Hearst, N., et al., Demographic and health surveys indicate limited impact of condoms and HIV testing in four African countries. Afr J AIDS Res, 2013. 12(1): p. 9-15.
Center, K.E., et al., Contraceptive Use and Uptake of HIV-Testing among Sub-Saharan African Women. PLoS One, 2016. 11(4): p. e0154213.
Maulsby, C.H., et al., A scoping review of employment and HIV. AIDS and Behavior, 2020. 24(10): p. 2942-2955.
Worku, M.G., A.B. Teshale, and G.A. Tesema, Prevalence and Associated Factors of HIV Testing Among Pregnant Women: A Multilevel Analysis Using the Recent Demographic and Health Survey Data from 11 East African Countries. HIV AIDS (Auckl), 2021. 13: p. 181-189.
L'Engle, K.L., et al., Evaluating feasibility, reach and potential impact of a text message family planning information service in Tanzania. Contraception, 2013. 87(2): p. 251-6.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Factors influencing HIV Testing among young women aged 15-24 in the 2015-2016 Malawi Demographic and Health Survey

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusions

Introduction

Methods And Materials

Data Source

Study Setting, Sampling And Study Design

Study Variables And Measurements

Dependent variable

Independent Variables

Ethical Consideration

Data Analysis

Results

Determinants Of Hiv Testing Among Young Women Aged 15–24 In Malawi

Discussion

Strengths And Limitations

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1