Prenatal care coverage and correlates of HIV testing in sub-Saharan Africa: Insight from demographic and health surveys of 16 countries.

BACKGROUND: Prenatal screening of pregnant women for HIV is central to eliminating mother-to-child-transmission (MTCT) of HIV. While some countries in sub-Saharan Africa (SSA) have scaled up their prevention of MTCT programmes, ensuring a near-universal prenatal care HIV testing, and recording a significant reduction in new infection among children, several others have poor outcomes due to inadequate testing. We conducted a multi-country analysis of demographic and health surveys (DHS) to assess the coverage of HIV testing during pregnancy and also examine the factors associated with uptake.
METHODS: We analysed data of 64,933 women from 16 SSA countries with recent DHS datasets (2015-2018) using Stata version 16. Adjusted and unadjusted logistic regression models were used to examine correlates of prenatal care uptake of HIV testing. Statistical significance was set at p<0.05.
RESULTS: Progress in scaling up of prenatal care HIV testing was uneven across SSA, with only 6.1% of pregnant women tested in Chad compared to 98.1% in Rwanda. While inequality in access to HIV testing among pregnant women is pervasive in most SSA countries and particularly in West and Central Africa sub-regions, a few countries, including Rwanda, South Africa, Zimbabwe, Malawi and Zambia have managed to eliminate wealth and rural-urban inequalities in access to prenatal care HIV testing.
CONCLUSION: Our findings highlight the between countries and sub-regional disparities in prenatal care uptake of HIV testing in SSA. Even though no country has universal coverage of prenatal care HIV testing, East and Southern African regions have made remarkable progress towards ensuring no pregnant woman is left untested. However, the West and Central Africa regions had low coverage of prenatal care testing, with the rich and well educated having better access to testing, while the poor rarely tested. Addressing the inequitable access and coverage of HIV testing among pregnant women is vital in these sub-regions.

Introduction

With the introduction of antiretroviral treatments (ART), there has been a 40% decline in new HIV infections from 2.9 million in 1997 to 1.7 million in 2019 [1]. Also, new infections have declined by 52% among children from 310 000 in 2010 to 150 000 in 2019 [1]. However, with over 150,000 new infections among children in 2018, mother to child transmission of HIV (MTCT) remains a major public health concern [1]. What is more, 61% of global new infections among children in 2019 occurred among children in sub-Saharan Africa (SSA), with western and central African countries having the highest record of children infected [1]. Success in eliminating MTCT is vastly uneven in SSA. While Southern and Eastern African countries such as Comoros, Malawi, Rwanda, South Africa, Burundi, and Uganda have seen a remarkable decline in MTCT transmission of HIV, many countries in West and Central Africa like Angola, Equatorial Guinea, the Gambia, Mali, and Niger have experienced an increase in transmission rate [2]. The intensification of the existing PMTCT program and scaling up of a nationwide program to accelerate MTCT elimination in Malawi, Zimbabwe, and South Africa have resulted in MTCT at 6-12weeks postpartum rate comparable to the ‘global north’ [3-7].

HIV testing before and during pregnancy is critical to initiating ART and eliminating MTCT of HIV [8, 9]. The risk of MTCT of HIV ranges from 15–45%, without ART but could be reduced to < 5% with prenatal care testing and initiation of ART [10]. Global north countries with a universal prenatal care HIV screening among pregnant women have nearly eliminated MTCT of HIV [11-14]. However, SSA countries, particularly West and Central African countries, still have a low prevalence of prenatal care HIV testing among pregnant women. The high rate of MTCT of HIV in SSA reflects gaps in the PMTCT programmes in SSA, particularly in screening and diagnoses of pregnant women. It is, however, worth noting that the health system's response and dynamics of PMTCT implementation in SSA are markedly different from country to country. For instance, in Rwanda and Uganda, it is mandatory for every pregnant mother who attends antenatal care (ANC) to test for HIV, rather than a voluntary choice of the mother [15, 16]. In South Africa and Botswana and like most SSA countries, the opt-out policy was implemented and pregnant women are educated about HIV. However, despite the acceptability and implementation of the opt out strategy [17], there is evidence a large proportion of women who received ANC are not tested for HIV in some SSA countries [2, 18].

Attendance of ANC is generally high in East and Southern African countries relative to West and Central Africa countries, thus explaining the contrasting successes recorded in PMTCT across SSA [19]. Even though HIV testing is generally available in all West African countries, the challenge remains to ensure women access prenatal care and also ensure all women who do are tested as part of prenatal care. In Nigeria, for example, about 30% of women who received prenatal care were not tested for HIV [2]. As such, significant gaps remain in terms of universal testing of women who present for antenatal care. Nevertheless, the larger percentage of unreached women remains those who never received prenatal care.

Some of the reasons why pregnant women do not test for HIV in SSA include inaccessibility of healthcare facility [20], perceived lack of confidentiality, stigma, and discrimination [21, 22], cost, illiteracy, and inability to secure husband’s permission, attitude, and skills of health workers and inadequate resources [23, 24]. Health behavioural theories such as the Health belief model (HBM) [25] and Capability, Opportunity, and Motivation Model of Behaviour (COM-B) model [26] have also highlighted barriers and enablers of uptake of health behaviours and behavioural change among individuals, including deciding to test for HIV during pregnancy. Most of these theories and models explain or predict how internal and external factors influence an individual’s health behaviour. The Health Belief Model (HBM) could provide an understanding of the relationship between individual factors such as knowledge of MTCT and prenatal care uptake of HIV testing. Based on the proposition of HBM, mothers who have good knowledge of MTCT can assess their susceptibility correctly, understand the severity of MTCT of HIV, and the benefit of testing [27-29]. Lack of knowledge of MTCT could constitute a significant barrier to the uptake of HIV testing, as mothers may not see the need for testing or inaccurately assess their risk. According to the COM-B model, behaviour is a product of three necessary conditions; capability, opportunity, and motivation. Capability can be psychological (knowledge) or physical (skills), opportunity can be social (societal influences) or physical (environmental resources) while motivation can be automatic (emotion) or reflective (beliefs, intentions). COM-B model provides a comprehensive analysis of all factors influencing prenatal HIV testing [26].

Given the lack of recent data on prenatal testing in SSA and drawing from the COM-B model and HBM theoretical propositions, we examined factors associated with uptake of HIV testing during pregnancy in 16 sub-Saharan African countries. These countries are representative of all the sub-regions of SSA. Our study aligns with the global effort to achieve the UNAIDS first 95% by 2030. The findings of the study will be useful for scaling up HIV testing in SSA. It will facilitate cross-country comparison and could be used to support advocacy. In the present study, we first estimated the prevalence of HIV testing during pregnancy in SSA and the 16 countries included in our analysis. We then assessed the effects of knowledge of MTCT, relevant individual and community level factors on HIV testing in these countries.

Methods

This cross-sectional study used recent Demographic and Health Surveys (DHS) data (2015–2018) to examine MTCT knowledge and uptake of HIV testing during pregnancy in SSA. DHS is a nationally representative survey collected every five years across low- and middle-income countries. We focused on 16 countries with recent DHS to assess the landscape of HIV testing during pregnancy in SSA. Countries were included in the study if they had complete information on HIV testing during pregnancy, MTCT knowledge, and have recent DHS (2015–2018) (see Fig 1). The countries included were Benin, Guinea, Mali and Senegal from West Africa, Angola, Cameroon and Chad from Central Africa, Burundi, Ethiopia, Rwanda and Uganda from East Africa and Malawi, Mozambique, South Africa, Zambia and Zimbabwe from Southern Africa.

Fig 1

Overview of country selection.

The study population was women aged 15 to 49 who gave birth within two years preceding the surveys. Weightings were applied to obtain unbiased estimates, according to the DHS guidelines. Not using weights can bias results toward the oversampled sub-populations. The study sample was, thus, a weighted sample of 65,107 women (Table 1). Details on the sampling methodology and data collection used by the DHS are published elsewhere [30].

Table 1

Sampling distribution and countries.

Countries and sub-region	Data Collection Year	Unweighted observations remaining	Weighted observations remaining
Central Africa
Angola	2016	5,631	5,220
Cameroon	2018	3,575	3,692
Chad	2015	6,283	6,439
West Africa
Benin	2018	5,242	5,258
Guinea	2018	3,131	3,090
Mali	2018	3,875	4,090
Senegal	2017	4,251	3,906
East Africa
Burundi	2017	5,006	5,157
Ethiopia	2016	3,810	3,993
Rwanda	2016	3,103	3,167
Uganda	2016	5,645	5,535
Southern Africa
Malawi	2016	6,159	6,148
Mozambique	2015	2,044	2,223
South Africa	2016	1,301	1,308
Zambia	2018	3,699	3,623
Zimbabwe	2015	2,178	2,258
Total		64,933	65,107

Variables and measurement

Outcome of interest

The outcome variable of this study was HIV testing during pregnancy. Participants were asked if they tested for HIV as part of antenatal care and responses were dichotomous (“Yes” or “No”).

Explanatory variables

Using the COM-B Model and HBM, we included both internal and external factors in our analysis. The COM-B model and HBM propose that knowledge and capability are important prerequisites for HIV testing. Capability can be psychological (knowledge) or physical (skills). Knowledge of the benefit of testing and the severity of not testing could influence women to test for HIV or not. As such, we included knowledge of MTCT in our analysis. Three main questions on the transmission of HIV from mother to child during pregnancy, delivery and breastfeeding were used to assess MTCT knowledge. Each respondent was given a score of 1 for each correct answer. The available scores ranged from 0–3. We categorised a score of “0” as low, a score of “1–2” as moderate, and a score of “3” as high. Also, according to COM-B model, opportunity and motivation are critical in factors that influence the uptake of HIV testing. Education, marital status and wealth status would not only influence an individual’s capability to navigate and overcome the barriers to testing for HIV during pregnancy but also provide opportunity to access antenatal care and get tested. Age was categorised as “15–19 years”, “20–24 years”, ‘‘25–34 years” and “35–49 years”. Marital status was classified as “Never married”, “Currently married”, “Previously married” and “Cohabiting”. Education was measured by asking participants to report their highest level of education. Their responses were classified as “No formal education”, “Primary education” and “Secondary and Higher education”. Wealth status was assessed as an index that combines household assets and utilities. The index was then categorised as “Poor”, “Middle” and “Rich”. Also, we included health insurance coverage, which is a binary choice variable of Yes or No. All countries in the study sample had responses except for Senegal and Rwanda. The COM-B model emphasises the role of environmental factors in access and uptake of HIV testing. Where women live is an important factor that could determine if HIV testing is available and accessible. Health resources are unevenly distributed, and women in rural areas are disadvantaged in terms of access to health facilities that provide quality health care services. As such, we included residential area, which is divided into “Rural” and “Urban”. Also, we included geopolitical zones, classified based on each country’s setting.

According to the COM-B model, motivation is an important predictor of uptake of HIV testing. Even though the DHS did not directly measure women’s motivation, other factors could influence one’s motivation. We believe exposure to media programme on the importance of HIV testing could not only educate women on the need to test but only motivate them to test. Media exposure was constructed from three variables on the frequency of exposure to three different media outlets, which are print media, radio, and television using principal component analysis. The respondents were assigned 0 for “not at all”, 1 for “less than once a week” and 2 for “at least once a week”. These were added up across all respondents with overall scores of 0 to 6. We classified a score of “0”as "Low media exposure",”1 to 3” as "Moderate media exposure", and 4 or greater as “High media exposure". This classification was applicable to all countries except Zambia. For Zambia, there was an additional category–“almost every day”–which we coded as 3. The available score for Zambia is 0 to 9. We classified “0” as "Low media exposure", “1 to 3” as "Moderate media exposure", and 4 or greater as “High media exposure".

We included countries as a variable to account for variation in the implementation of the “Opt-out” policy and the use of antenatal care services across the region. The “Opt-out” policy and rate of antenatal service utilisation are important factors influencing uptake of HIV testing during pregnancy. However, all women that did not receive antenatal care services also did not test for HIV. As such, we are unable to include antenatal care attendance in our regression model. However, countries become an important proxy to account for the role of the disparate rate of antenatal care attendance across SSA counties. Also, the extent of implementation of the “Opt-out” policy, which is widely accepted and is implemented in SSA, varies. Therefore, countries become an important variable to account for these variations.

Statistical analyses

The analyses were carried out using STATA Version 16.0. The analysis began by computing descriptive statistics, such as frequencies and percentages for the main explanatory variable and the outcome variable. We also performed Pearson’s chi-square test analysis to examine the relationship between knowledge of MTCT and uptake of testing. We pooled the dataset of all countries to create a single dataset, yielding a total weighted sample of 65,106. Multivariable logistic regression models were used to examine factors associated with uptake of HIV testing during pregnancy in SSA. We stratified the regression analysis by countries to show the factors associated with prenatal testing in each country included. The results of the regression analyses were presented as odds ratios (OR), with their corresponding 95% confidence intervals (CI) signifying significance and precision of the reported OR.

Ethical considerations

The DHS surveys are conducted after approval of ethical review bodies and authorisation by the country of the study. De-identified datasets are freely available on the DHS website (https://dhsprogram.com/data/available-datasets.cfm). Since this is a secondary analysis, we do not need to obtain a separate ethical approval other than those obtained when the primary data was collected.

Results

Descriptive results

We presented the prevalence of uptake of HIV testing during pregnancy in each of the 16 SSA countries included in the study in Fig 2. We found that the uptake of HIV testing ranges from about 6.1% in Chad to about 98.1% in Rwanda. We observed that the uptake of HIV testing was highest among women in Southern and Eastern African countries, whereas the uptake of HIV testing was lowest among women in Western and Central Africa countries (Fig 2).

Fig 2

Uptake of HIV testing during pregnancy across SSA countries.

Table 2A and 2B report the proportion of women who had HIV testing during pregnancy by background characteristics across the 16 SSA countries. Prevalence of prenatal HIV testing was higher among women older than 19 compared to women aged 19 or below in all countries except in Mozambique, South Africa and Zimbabwe. The proportion of never-married women who tested for HIV during pregnancy were higher compared to married women in Angola, Cameroon, Chad, Benin, Guinea, Mali, Ethiopia, Mozambique, Zambia and Zimbabwe. Also, the prevalence of HIV testing was highest among women who owned health insurance, resided in urban areas, had a high media exposure, belonged to rich wealth quintile and had secondary or higher level of education in all SSA countries studies. In all countries, uptake of HIV testing during pregnancy was significantly higher among women with moderate to high knowledge of MTCT than among those with low knowledge of MTCT. Even countries where uptake of HIV testing during pregnancy was high, women who had a high knowledge of MTCT were more likely to test for HIV compared to women who had low knowledge of MTCT (Rwanda 98.1% vs 87.1%, Malawi 91.7% vs 63.5%, South Africa 92.5% vs 60.9%, Uganda 92.4% vs 72.1%, Zambia 96.9% vs 54.9%, Zimbabwe 92.7% vs 46.6%).

Table 2

Relationship between background factors and prenatal care uptake of HIV testing stratified by country.

(A)
	SSA	Central Africa			West Africa
Variables		Angola	Cameroon	Chad	Benin	Guinea	Mali	Senegal
Age group in years
15–19	56.2	48.4	72.5	5.4	16.3	23.6	16.6	50.1
20–24	62.7	53.5	78.0	5.7	19.8	24.1	20.4	62.9
25–34	59.9	54.0	75.8	6.4	21.1	23.2	21.0	66.9
35–49	58.3	47.0	71.6	6.5	23.2	18.8	19.7	68.3
Marital Status
Never Married	75.3	54.8	87.6	11.4	30.5	34.0	23.7	62.7
Currently Married	54.9	42.7	70.5	5.7	19.2	21.6	20.0	64.8
Previously	70.4	52.1	73.3	8.3	18.2	39.3	19.8	64.5
Cohabiting	69.2	53.0	83.5	7.7	25.0	25.7	16.4	92.5
Education level
None	34.2	22.8	47.7	3.7	16.2	16.9	13.2	58.7
Primary	72.1	46.7	74.6	8.1	25.0	28.1	23.4	71.3
Sec. & Higher	81	82.7	93.2	14.1	31.7	48.1	46.5	77.4
Wealth Status
Poor	52.4	21.6	59.5	4.4	13.9	11.7	8.1	51.7
Middle	59.5	62.3	81.6	4.2	19.9	17.6	11.1	68.7
Richest	69.9	87.3	93.5	9.1	28.6	40.4	39.0	82.4
Residential Areas
Rural	56	21.4	65.2	4.3	17.2	14.0	13.0	56.9
Urban	70.2	72.1	88.7	13.7	26.4	44.2	46.8	79.9
Media Exposure
Low	48	23.0	58.5	4.1	14.4	12.7	8.5	41.3
Moderate	65.2	58.6	87.0	9.5	22.1	23.5	16.9	61.4
High	75.7	84.6	95.5	17.6	34.7	44.8	35.3	75.7
Health Insurance Cover
No	59	51.3	74.9	5.9	20.5	22.1	18.1
Yes	85.7	66.7	96.6	90.6	50.3	71.4	63.1
Know MTCT
Low	15.1	12.6	41.5	0.8	3.0	8.8	10.7	56.8
Moderate	74.5	61.3	74.8	20.4	52.7	27.9	31.2	66.7
High	76	74.8	81.2	26.3	49.3	32.1	23.1	68.5

Multivariable findings

We fitted two models to examine the factors associated with uptake of HIV testing during pregnancy. Model 1 was an unadjusted model with no covariates. Women aged 20–49 had a higher likelihood of prenatal uptake of HIV testing than women aged 15–19 years. Married women had lower odds of prenatal HIV testing relative to never-married women. Secondary education or higher, high wealth status, and high media exposure were associated with a higher likelihood of prenatal HIV testing. Ownership of health insurance was associated with higher odds of prenatal testing. In all countries, women who had moderate to high knowledge of MTCT had higher odds of HIV testing uptake during pregnancy compared with women who had low MTCT knowledge (Table 3).

Table 3

Multivariable models showing factors associated with uptake of HIV testing in SSA.

Variables	Uptake of HIV testing during pregnancy
	UOR [95% CI]	AOR [95% CI]
Age group in years
15–19	Ref	Ref
20–24	1.31 [1.24,1.38]***	1.21 [1.11,1.31]***
25–34	1.15 [1.09,1.21]***	1.30 [1.20,1.41]***
35–49	1.07 [1.01,1.13]*	1.33 [1.21,1.46]***
Marital Status
Never Married	Ref	Ref
Currently married	0.42 [0.39,0.44]***	0.98 [0.88,1.09]
Previously married	0.86 [0.78,0.94]**	0.94 [0.82,1.07]
Cohabiting	0.79 [0.73,0.85]***	1.08 [0.97,1.21]
Education level
None	Ref	Ref
Primary	5.67 [5.45,5.89]***	1.60 [1.51,1.70]***
Secondary & Higher	8.84 [8.43,9.28]***	2.57 [2.38,2.77]***
Wealth Status
Poor	Ref	Ref
Middle	1.37 [1.31,1.43]***	1.37 [1.29,1.46]***
Rich	2.04 [1.97,2.11]***	1.83 [1.70,1.97]***
Residence
Rural	Ref	Ref
Urban	1.69 [1.63,1.75]***	1.68 [1.58,1.80]***
Media Exposure
Low	Ref	Ref
Moderate	2.12 [2.05,2.19]***	1.43 [1.35,1.51]***
High	3.30 [3.14,3.48]***	1.77 [1.62,1.94]***
Health Insurance Cover
No	Ref	Ref
Yes	3.96 [3.54,4.43]***	1.41 [1.21,1.64]***
Knowledge of MTCT
Low	Ref	Ref
Moderate	19.46 [18.33,20.65]***	5.92 [5.49,6.38]***
High	20.29 [19.32,21.32]***	7.08 [6.66,7.53]***
Countries
Angola	Ref	Ref
Cameroon	3.47 [3.17,3.81]***	2.84 [2.53,3.19]***
Chad	0.06 [0.06,0.07]***	0.14 [0.12,0.17]***
Benin	0.29 [0.26,0.31]***	0.40 [0.36,0.45]***
Guinea	0.30 [0.27,0.33]***	0.32 [0.28,0.37]***
Mali	0.26 [0.24,0.28]***	0.22 [0.20,0.25]***
Senegal	1.63 [1.50,1.77]***	2.13 [1.90,2.39]***
Burundi	10.71 [9.60,11.94]***	10.16 [8.92,11.58]***
Ethiopia	0.73 [0.67,0.80]***	0.91 [0.81,1.03]
Rwanda	56.17 [43.18,73.05]***	36.38 [27.74,47.71]***
Uganda	13.58 [12.13,15.19]***	10.14 [8.92,11.54]***
Malawi	9.73 [8.82,10.73]***	9.38 [8.28,10.62]***
Mozambique	3.44 [3.07,3.86]***	4.15 [3.61,4.77]***
South Africa	10.81 [8.89,13.15]***	5.58 [4.47,6.97]***
Zambia	15.48 [13.46,17.81]***	15.72 [13.36,18.48]***
Zimbabwe	11.07 [9.47,12.94]***	5.88 [4.92,7.02]***

AOR is the adjusted odds ratio, UOR is the unadjusted odds ratio, ref is the reference; Exponentiated coefficients; 95% confidence intervals in brackets.

* p < 0.05

** p < 0.01

*** p < 0.001.

Model 2 is the adjusted model where we included sociodemographic factors, media exposure, and health insurance as covariates. After controlling for countries in the adjusted model, women were more likely to test for HIV as part of antenatal care if they had a high knowledge of MTCT (AOR: 7.08; 95% CI: 6.66, 7.53), aged 35–49 (AOR: 1.33; 95% CI: 1.21, 1.46), had a secondary or higher level of education (AOR:2.57; 95% CI: 2.38, 2.77), belong to rich wealth quintile(AOR: 1.83; 95% CI: 1.70, 1.97), resided in the urban area (AOR: 1.68; 95% CI: 1.58, 1.80), exposed to the media (AOR: 1.77; 95% CI: 1.62, 1.94), and owned a health insurance cover (AOR: 1.41; 95% CI: 1.21, 1.64). We did not include ANC in the model, given that the prevalence of HIV testing uptake was 0% among women who did not receive ANC. However, we presented the result of testing prevalence and ANC attendance for all countries included, and the results show that testing the proportion of women who tested during pregnancy was higher in countries with HIV coverage of ANC than in those with low coverage.

We stratified the results by country to report on the homogeneity and heterogeneity of the results. The findings are shown in S1–S6 Tables. In both the adjusted and unadjusted models, the odds of uptake of HIV testing as part of antenatal care was higher among women with high MTCT knowledge in all countries studied.

Older age (aged 20 to 49) was significantly associated with a higher likelihood of uptake of HIV testing during pregnancy only in Angola, Benin, Mali, Senegal, and Burundi. There was heterogeneity in the result of the association between marital status and uptake of HIV testing. In most countries, marital status was not significantly related to prenatal uptake of testing. Married women had higher odds of uptake of HIV testing in Burundi compared to never-married women. However, the contrast was found in Zimbabwe, where married women were 63% less likely to test during antenatal care. In all countries studied, except Malawi and South Africa, women who had a secondary or higher level of education were more likely to test for HIV as part of ANC compared to women who had no formal education. Rich wealth status was significantly associated with higher odds of uptake of HIV testing in all countries, except Malawi, Zimbabwe, Zambia, South Africa, Uganda, Rwanda, and Chad.

Similarly, urban-dwelling was associated with a higher likelihood of HIV testing uptake in all countries, except in Zambia, Zimbabwe, South Africa, Uganda and Rwanda. Surprisingly, women were significantly less likely to test for HIV during ANC if they reside in urban areas in South Africa. High media exposure was associated with a higher odd of prenatal care uptake of HIV testing in all countries studied, except in Rwanda, Uganda, and South Africa. Lastly, ownership of health insurance was significantly associated with uptake of HIV testing as part of ANC in all countries, except in Benin, Burundi, Uganda, South Africa, Zambia, Zimbabwe, Malawi, and Mozambique.

Discussion

We examined the coverage and factors associated with HIV testing among pregnant women in SSA. Our study is timely, considering the global need for evidence to support the achievement of the UNAIDS 95-95-95 targets [31]. Understanding the gaps in coverage of HIV testing during pregnancy is critical for designing programmes to address the gaps and ensure even progress in SSA [2, 32]. Our analysis showed uneven progress in expanding access to HIV testing during pregnancy in SSA. East and Southern African countries like Rwanda, Malawi, Zimbabwe and South Africa have expanded access to prenatal HIV testing for most women, irrespective of their education level, wealth status and place of residence. In contrast, prenatal care HIV testing coverage was low in all West, and Central African countries studied. We also found a higher uptake of HIV testing among women in Southern and Eastern African countries, compared to Western and Central African countries.

There are several pathways to understand these findings. First, the Southern and Eastern African regions are most affected by HIV in the world (S7 Table) and are home to the largest number of people living with HIV (20.6 million) [1]. In line with this, several countries in the region such as Botswana, Kenya, Uganda, Malawi, and Rwanda have not only implemented national campaigns to encourage uptake of HIV testing and counselling (HTC), they have also implemented effective and efficient PMTCT, ensuring that most pregnant women are tested for HIV and those diagnosed are placed on treatment [33-35]. They have deployed community-based testing, which supports provider-initiated testing. Also, workplace and door-to-door testing and self-testing, using rapid diagnostic tests, are being implemented in these sub-regions [9].

Due to the heavy burden of HIV in the Eastern and Southern Africa regions, attention and efforts of global developmental partners are concentrated on the region. Also, the governments of countries in these regions, particularly South Africa, have invested heavily towards reducing new HIV transmission and eliminating AIDS. The partnership of the local and global effort in ending AIDS and preventing HIV transmission have no doubt bear tremendous results in East and Southern Africa, including expanded access to HIV testing for pregnant women and reduced MTCT of HIV. It is therefore imperative to focus more attention on West and Central African countries to replicate the results recorded in East and Southern African in their region.

Apart from these explanations, the high knowledge of MTCT and high HIV testing among women in the Southern and Eastern African regions could also be linked to the high uptake of antenatal care services (see S7 Table). Women in Central and West Africa were, on average, less likely to receive antenatal care compared to women in Southern and Eastern Africa (see S7 Table). Nevertheless, despite the remarkable progress, particularly in South Africa, Rwanda, and Uganda and Zimbabwe, no country has universal testing of pregnant women. As such, programme implementers must make an effort to reach the missing pregnant women in the HIV care cascade.

Also consistent with previous research [2, 34, 36] women who had secondary or higher education, who owned health insurance and who resided in the wealthiest households were more likely to be tested for HIV during pregnancy compared to those who did not have formal education, did not have health insurance and lived in the poorest households. This finding was significant in most countries studied, indicating socioeconomic gaps in ANC coverage of HIV testing. Only a few countries in SSA (Rwanda, South Africa, Zimbabwe, Malawi and Zambia) have managed to eliminate wealth and education inequality in access to HIV testing for pregnant women. These countries have demonstrated that eliminating inequalities in access to prenatal HIV testing is possible with the implementation of equitable policies. As such, other countries in the region could draw lessons from these countries to provide universal access to care and ensure that no one is left behind.

In most SSA countries studied, we observed rural-urban disparity in coverage of HIV testing during pregnancy, highlighting the need to scale up prenatal HIV coverage in rural areas. Rwanda, Zambia, Zimbabwe, Malawi, and Mozambique are the few countries where the rural and urban disparity in coverage of prenatal HIV testing has been eliminated. Equitable access to prenatal testing is vital for the health of women and babies in SSA. West and Central African countries must address the rural and urban disparity in coverage of prenatal HIV testing to save the lives of mothers and babies. Scaling up access to HIV testing in rural areas alone will remarkably increase uptake in the West and Central Africa. Our findings on the association between marital status and prenatal care uptake of HIV testing suggest no significant association in 14 of the 16 countries studied. Married women were more likely to test for HIV in Burundi but less likely to test in Zimbabwe. More studies are needed to understand this result.

The findings from this study have implications for policy and practice regarding the prevention of MTCT in SSA. As shown in S7 Table, new HIV infections are high in Central and West African countries, and these countries also have lower uptake of antenatal care, limiting the level of prenatal HIV screening. Efforts to reduce or eliminate MTCT should focus on West and Central Africa. Such efforts must target women who do not seek antenatal care services. Our findings also underscore the need for PMTCT programmes to pay particular attention to knowledge on MTCT and socioeconomic inequality. Community-based interventions delivered through women and church leaders could help improve women’s MTCT knowledge in underserved settings, resulting in increased knowledge and uptake of HIV testing.

Strength and limitations

The strength of the study lies in the use of nationally representative survey data and the large sample size. Notwithstanding, the study is without limitations. First, the use of the cross-sectional study design, as employed in the DHS, limits the capacity of the authors to attribute causality to the findings. Again, the study adopted self-reporting of past events (prenatal care uptake of HIV testing), which is subjected to social desirability and recall bias. The DHS, however, limits responses to this question to two years to reduce the impact of recall bias. Lastly, there are several other unmeasured confounders such as couple testing, availability of testing services and how testing is done, which could potentially influence the uptake of HIV testing during ANC.

Conclusion

The findings of the present study highlight the between countries and sub-regional disparities in prenatal care uptake of HIV testing in SSA countries. Even though no country has a universal coverage of HIV testing of pregnant women, East and Southern African countries have made remarkable progress towards ensuring no pregnant woman is not tested. However, the coverage of testing is incredibly low in West and Central Africa, with the rich and well educated having better access to testing, while the poor are rarely tested. Addressing the inequitable access and coverage of HIV testing among pregnant women is vital in these sub-regions. In all countries studied, knowledge of MTCT was linked with a higher likelihood of testing, which calls for appropriate interventions to increase awareness of MTCT. Such interventions could be delivered through community leaders and mass media. In doing this, priority should also be given to pregnant women in underserved settings, especially in West and Central Africa.

Adjusted and unadjusted logistic regression models showing factors associated with prenatal uptake of HIV testing in Angola, Cameroun and Chad.

(DOCX)

Click here for additional data file.

Adjusted and unadjusted logistic regression models showing factors associated with prenatal uptake of HIV testing in Benin, Guinea and Mali.

(DOCX)

Click here for additional data file.

Adjusted and unadjusted logistic regression models showing factors associated with prenatal uptake of HIV testing in Senegal, Burundi and Ethiopia.

(DOCX)

Click here for additional data file.

Adjusted and unadjusted logistic regression models showing factors associated with prenatal uptake of HIV testing in Rwanda, Uganda and South Africa.

(DOCX)

Click here for additional data file.

Adjusted and unadjusted logistic regression models showing factors associated with prenatal uptake of HIV testing in Zambia, Zimbabwe and Malawi.

(DOCX)

Click here for additional data file.

Adjusted and unadjusted logistic regression models showing factors associated with prenatal uptake of HIV testing in Mozambique.

(DOCX)

Click here for additional data file.

Antenatal care, HIV prevalence and new HIV infections.

(DOCX)

Click here for additional data file.

28 Aug 2020

PONE-D-20-21800

Knowledge of mother-to-child transmission of HIV and uptake of HIV testing during pregnancy: findings from 14 sub-Saharan African countries

PLOS ONE

Dear Dr. Ajayi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Many thanks for your submission which highlights important discrepancies in and consequences of vertical transmission of HIV policies across the region. Both reviewers have highlighted concerns regarding the presentation of the methodology and the conclusions drawn. The premise that there is a straight-forward causal relationship between knowledge and testing needs to be examined in more detail to account for study design, confounding and colinearity. Here regional PMTCT policy and practice are important: education and counselling as part of PMTCT, opt-out testing versus opt-in etc. Similarly,as noted in the discussion, ANC attendance is vital.

Please check the formatting of the tables including denominators (e.g. Table 2), units (e.g. Table 3) and general formatting (e.g. Table 5) where appropriate.

Please note, one set of comments is included as an attachment.

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Reviewer #1: Knowledge of mother-to-child transmission of HIV and uptake of HIV testing during

pregnancy: findings from 14 sub-Saharan African countries

Awopegba OE, et al

Comments

Abstract

Line 49-51:‘The odds of prenatal care HIV testing was significantly higher [21.21, CI=20.03,22.46] in countries where most women knew of MTCT compared to countries where most women did not know of MTCT routes of transmission.’

Comment

While from the these results are true based on the data used, but there some confounding factors, for example in Uganda it is kind of a must for every pregnant mother who attends ANC to test for HIV, rather than a voluntary choice of the mother. So these health systems differences across the countries have to be taken into account, before we conclude that the testing rates of HIV during prenatal are due to the level of knowledge. Like I have mentioned in these countries like Rwanda and Uganda, HIV testing is ‘mandatory’ during ANC, and the mothers are also given health education. One would love to know the health systems dynamics for PMTCT in West Africa, where testing and knowledge are low, so as to put these findings into context.

Line 53-56: The conclusion of ‘educating women of the risk of MTCT could be an important strategy for increasing HIV testing uptake, especially in West and Central Africa, where the rate of testing during pregnancy remains low, and the rate of MTCT remain high, is not the only solution, when other factors are not addressed, because knowledge alone without addressing health systems barriers is not enough.

Line 77-78.’ Global north countries with a universal prenatal care HIV screening among pregnant women have nearly eliminated MTCT of HIV.’ Need to provide the source of this information. But also this statement underscores the importance of having the prenatal care HIV screening among pregnant women. It is not clear if these services are available in West Africa, where MTCT HIV rates are high and testing is low.

Line 89-91: The authors emphasize the importance of knowledge in facilitation HIV testing among prenatal mothers, but literature has shown that knowledge alone is not enough.

‘Knowledge of MTCT is considered essential to facilitate HIV testing since women with knowledge of HIV transmission have a better understanding and appreciation of the need for HIV testing and the perceived benefit of testing not only for them but also for their infant 17’.

Line 92-107: The authors are using The Health Belief Model (HBM) with the premise that knowledge is enough for mothers to test. I feel they should have used more comprehensive models of behaviour change e.g. the approach the Capability, Opportunity and Motivation Model of Behaviour (COM-B) model (Michie S 2011), see figure below. According to the model, behaviour is a product of three necessary conditions; capability, opportunity, and motivation. Capability can be psychological (knowledge) or physical (skills), opportunity can be social (societal influences) or physical (environmental resources) while motivation can be automatic (emotion) or reflective (beliefs, intentions). Such model like COM-B gives a comprehensive analysis of the issues that affect prenatal HIV testing. Therefore, the current analysis gives a narrow picture.

Figure 1. COM-B Model Michie S 2011.

Therefore, the authors need to recognize the limitations of their theoretical approach, is not comprehensive enough in exploring the factors that affect prenatal HIV testing. But also the secondary data limitations, not able to have collected other data such as health systems factors e.g availability of testing services, how testing is done, etc.

Line 210-2013: Table 3. The authors don’t give the reference point and also should provide the confidence intervals.

Line 2017-2025: In all countries, women who had moderate to high knowledge of MTCT had higher odds of HIV testing uptake during pregnancy compared with women who had low MTCT 219 knowledge. In Model 2, we added sociodemographic factors, media exposure, and health insurance as covariates. After adjusting for covariates, the magnitude and direction of effect persisted, indicating a strong and robust effect of MTCT knowledge on uptake of HIV testing during pregnancy. The odds of prenatal HIV testing uptake was higher among women with high MTCT 223 knowledge, especially in Chad (AOR: 38.30; 95% CI: 26.23, 55.93), Benin (AOR: 40.65; 95% CI: 224 31.95, 51.72), Angola (AOR:8.14; 95% CI:6.77, 9.78), Burundi (AOR: 53.37; 95% CI: 36.00, 225 79.11), and Zimbabwe (AOR: 19.1; 95% CI: 10.4, 35.0).

Comment: Though this data analysis is correct, it has limitation of health systems issues on the ground.

Table 4; Also does not provide reference points/measurements.

Comment: It would also be important for this study to give us a clue on the prevalence of couple testing, given that the pregnant mother testing alone without the partner leaves a gap in terms of achieving the PMTCT targets.

The authors should acknowledge the limitations of this secondary data analysis.

Reviewer #2: Review attached separately .

**********

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Submitted filename: PlosOne paper review_DHS_PMTCT_Aug2020.docx

Click here for additional data file.

8 Oct 2020

PlosOne paper review: PONE-D-20-21800 –

Title of paper: Knowledge of mother-to-child transmission of HIV and uptake of HIV testing during pregnancy: findings from 14 sub-Saharan African countries

Dear Editor:

I am pleased to re-submit the revised version of our manuscript. We appreciate the time taken to critically review this paper and the constructive comments we received from you and the anonymous reviewers. We believe that addressing all of your concerns as well as theirs, has contributed significantly to our paper. We have considered all the comments and have addressed each of the concerns as outlined below.

On behalf of all authors

Editors comments

Many thanks for your submission which highlights important discrepancies in and consequences of vertical transmission of HIV policies across the region. Both reviewers have highlighted concerns regarding the presentation of the methodology and the conclusions drawn. The premise that there is a straight-forward causal relationship between knowledge and testing needs to be examined in more detail to account for study design, confounding and collinearity. Here regional PMTCT policy and practice are important: education and counselling as part of PMTCT, opt-out testing versus opt-in etc. Similarly, as noted in the discussion, ANC attendance is vital.

Response: We thank the editor and the reviewers for all these insightful comments. We have addressed these comments, making changes to the methods, results, and addressing the issues of collinearity and regional PMTCT policy and practice. We have specifically emphasized why ANC attendance is critical to the uptake of HIV testing during pregnancy. Also, our country stratified analysis suggests that knowledge of MTCT is important for HIV testing uptake given this variable was significant for all countries included in the analysis. We have now focused on all factors associated with uptake of HIV testing during pregnancy.

Comment

Please check the formatting of the tables, including denominators (e.g. Table 2), units (e.g. Table 3) and general formatting (e.g. Table 5) where appropriate.

Response: We have made these corrections to the tables. Many thanks for suggesting these corrections.

Reviewer 1

Comments

Reviewer #1: Knowledge of mother-to-child transmission of HIV and uptake of HIV testing during pregnancy: findings from 14 sub-Saharan African countries

Awopegba OE, et al

Comments

Abstract

Line 49-51:‘The odds of prenatal care HIV testing was significantly higher [21.21, CI=20.03,22.46] in countries where most women knew of MTCT compared to countries where most women did not know of MTCT routes of transmission.’

Comment

While from the these results are true based on the data used, but there some confounding factors, for example in Uganda it is kind of a must for every pregnant mother who attends ANC to test for HIV, rather than a voluntary choice of the mother. So these health systems differences across the countries have to be taken into account, before we conclude that the testing rates of HIV during prenatal are due to the level of knowledge. Like I have mentioned in these countries like Rwanda and Uganda, HIV testing is ‘mandatory’ during ANC, and the mothers are also given health education. One would love to know the health systems dynamics for PMTCT in West Africa, where testing and knowledge are low, so as to put these findings into context.

Line 53-56: The conclusion of ‘educating women of the risk of MTCT could be an important strategy for increasing HIV testing uptake, especially in West and Central Africa, where the rate of testing during pregnancy remains low, and the rate of MTCT remain high, is not the only solution, when other factors are not addressed, because knowledge alone without addressing health systems barriers is not enough.

Response: We thank the reviewer for these comments. We have used this insight to improve our introduction and discussion. Also, we have revised the conclusion to highlight the role of other factors in our findings.

Comment

Line 77-78.’ Global north countries with a universal prenatal care HIV screening among pregnant women have nearly eliminated MTCT of HIV.’ Need to provide the source of this information. But also this statement underscores the importance of having prenatal care HIV screening among pregnant women. It is not clear if these services are available in West Africa, where MTCT HIV rates are high and testing is low.

Response: We have provided a reference to this sentence. HIV testing is generally available in all West African countries; however, the challenge remains to ensure women access prenatal care and also ensure all women who do are tested as part of prenatal care. In Nigeria, for example, not all women who received prenatal care are tested. As such, significant gaps remain in terms of universal testing of women who present for antenatal care. Nevertheless, the larger percentage of unreached women remains those who never received prenatal care.

Comment

Line 89-91: The authors emphasize the importance of knowledge in facilitation HIV testing among prenatal mothers, but literature has shown that knowledge alone is not enough.

‘Knowledge of MTCT is considered essential to facilitate HIV testing since women with knowledge of HIV transmission have a better understanding and appreciation of the need for HIV testing and the perceived benefit of testing not only for them but also for their infant 17’.

Response: We agree with this comment and have revised the manuscript accordingly. We have highlighted the role of PMTCT policies, health system strengthening, and addressing of demand and supply factors in improving coverage of prenatal care testing.

Comment

Line 92-107: The authors are using The Health Belief Model (HBM) with the premise that knowledge is enough for mothers to test. I feel they should have used more comprehensive models of behaviour change e.g. the approach the Capability, Opportunity and Motivation Model of Behaviour (COM-B) model (Michie S 2011), see figure below. According to the model, behaviour is a product of three necessary conditions; capability, opportunity, and motivation. Capability can be psychological (knowledge) or physical (skills), opportunity can be social (societal influences) or physical (environmental resources) while motivation can be automatic (emotion) or reflective (beliefs, intentions). Such model like COM-B gives a comprehensive analysis of the issues that affect prenatal HIV testing. Therefore, the current analysis gives a narrow picture.

Figure 1. COM-B Model Michie S 2011.

Therefore, the authors need to recognize the limitations of their theoretical approach, is not comprehensive enough in exploring the factors that affect prenatal HIV testing. But also the secondary data limitations, not able to have collected other data such as health systems factors e.g availability of testing services, how testing is done, etc.Response: We appreciate the reviewer for not only providing comments but also suggesting a theory. We have read this theory and have applied it in this study. The theory provides a comprehensive analysis of barriers and enablers of HIV testing and we have related the constructs of the theory to our study.

Line 210-2013: Table 3. The authors don’t give the reference point and also should provide the confidence intervals.

Response: Table 3 is shows the cross-tabulated results of knowledge and prenatal care testing from a chi-square test of independence. Hence, it does not require confidence intervals intervals nor reference point as required in a binary logistic regression table.

Line 2017-2025: In all countries, women who had moderate to high knowledge of MTCT had higher odds of HIV testing uptake during pregnancy compared with women who had low MTCT 219 knowledge. In Model 2, we added sociodemographic factors, media exposure, and health insurance as covariates. After adjusting for covariates, the magnitude and direction of effect persisted, indicating a strong and robust effect of MTCT knowledge on uptake of HIV testing during pregnancy. The odds of prenatal HIV testing uptake was higher among women with high MTCT 223 knowledge, especially in Chad (AOR: 38.30; 95% CI: 26.23, 55.93), Benin (AOR: 40.65; 95% CI: 224 31.95, 51.72), Angola (AOR:8.14; 95% CI:6.77, 9.78), Burundi (AOR: 53.37; 95% CI: 36.00, 225 79.11), and Zimbabwe (AOR: 19.1; 95% CI: 10.4, 35.0).

Comment: Though this data analysis is correct, it has a limitation of health systems issues on the ground.

Response: We have highlighted this limitation under the strengths and limitations of our study.

Table 4; Also does not provide reference points/measurements.

Comment: It would also be important for this study to give us a clue on the prevalence of couple testing, given that the pregnant mother testing alone without the partner leaves a gap in terms of achieving the PMTCT targets.

The authors should acknowledge the limitations of this secondary data analysis.

Response: The reference point in Table 4 is denoted by “Ref”. In relation to couple testing, we have acknowledged this as a limitation since the variable was not available in the datasets.

Review 2

Comments:

- The DHS is a cross-sectional study, which means you can’t draw directional associations, or cause-and-effect. Therefore, you cannot say that education caused testing (direction of effect), because it’s just as plausible that testing access resulted in greater knowledge of MTCT, especially since education is usually part of the pre-test process in ANC. To be able to draw the conclusion you’ve drawn, you’d have to have some indication that women had been educated on MTCT prior to their test, something hard to do in a cross-sectional study if there wasn’t a question specifically targeting when the person gained knowledge about PMTCT

Response: We did indicate this under our study limitation.

- There’s no reflection in your methods, results or discussion about the possibility of collinearity and confounding variables (ie. media exposure and knowledge) – this is a gap that needs to be addressed

Response: We have now addressed this.

- Your main independent variable and your dependent variable are almost certainly highly correlated. I’d suggest that this is because education on PMTCT happens in ANC as part of the pre-test counseling process, so knowledge=testing. This is really important and isn’t addressed anywhere in your paper or accounted for in your analysis plan.

Response: The correlation coefficient was approximately 0.5. We agree that this is high and have discussed it.

- I think your discussion needs to dig a little deeper into what your results are telling you:

o It’s clear that media exposure is associated with testing, which implies that the knowledge influencing behavior (testing) comes from somewhere other than the ANC setting. But the analysis doesn’t account for this, and there’s no mention of this in the discussion

- Response: We have included this in our discussion.

-

o Looking at marital status and testing – the relationship is the inverse of what you’d want to see; marriage/cohabitating is associated with not testing – other national PMTCT evaluations from SSA show similar findings and it’s worth discussing this in your paper because it appears to hold true across multiple countries in your analysis

- Response: We have discussed the effect of marital status. However, we found a significant association between marital status and tesing only in 2 of the 16 countries.

- In the intro section more work needs to be done to highlight the major variation in PMTCT success within Africa. Several countries have demonstrated successful MTCT at the levels seen in developed nations (see Zim, SA and Malawi publications), but this doesn’t come out clearly when all grouped together as ‘PMTCT programmes in SSA’.

Response: We have specifically referenced these countries in the introduction. We have further highlighted the remarkable progress made particularly by southern African countries, including Zim, SA and Malawi.

- Media exposure classification for Zambia needs to be revised – this appears to be inconsistent with classification for all other countries – if ‘almost every day’, then this should be grouped with the high media exposure, not moderate media exposure

Response: We have grouped ‘almost every day’as high media exposure.

- It’s unclear from the description in the methods whether weighting was done correctly during analysis. Weight variables are usually provided with DHS datasets and the use of these in your analysis should be described in your methods, and your results presented as ‘weighted’

Response: Women’s sample weights were appropriately applied to obtain unbiased estimates according to the DHS guidelines. We have made this clear in the methods.

- Table content and formatting: The table selection, content and formatting needs to be worked on. Suggest reviewing other PMTCT papers with regression analyses and identifying the ‘typical’ flow of tables included in the results sections.

Response: We have revised the tables and formatted them appropriately.

Specific comments:

Abstract

Line 34 – ‘inefficient’ isn’t a good word here. Please identify a better descriptor: ie. ‘Incomplete’? ‘Inadequate coverage of’? ‘inadequate uptake’?

Response: We thank the reviewer for this correction. We have effected the change.

Line 38 – what software was used?

Response: We used Stata version 16 and have indicated this at the statistical analyses section.

Introduction

Line 64-66 – sentences 1 and 2 are repetitive. Combine into a single sentence.

Response: Done

Line 77 – update your literature review to include National PMTCT evaluations which have been published since 2015 from Malawi, Zimbabwe and South Africa – all have demonstrated in nationally representative studies that MTCT at 6-12wks postpartum is comparable to ‘global north’. Suggest you focus the intro section on the wide variation of PMTCT success within Africa for greater impact to the reader.

Response: We thank the reviewer for the positive feedback. We have focused on introduction on the wide variation in PMTCT success within Africa

Line 80-82 – revise sentence based on guidance provided in previous comment

Response: We thank the reviewer for important suggestions. We have revised the sentence.

Line 86 – not clear what ‘operate through knowledge’ means – revise sentence

Response: deleted

Line 94 – please provide reference(s) for first sentence in the paragraph

Response: Done

Line 102 – remove ‘in the main’. Suggest starting sentence with “we theorized that”

Response: We have deleted the sentence

Lines 108-118 – move to methods section, and remove the sentences about how the findings can be used (those below in your conclusions section if still relevant after doing the analysis)

Response: done

Methods

Line 120 – ‘uses’ should be ‘used’ – make sure you use past tense consistently throughout the paper (ie see lines 124, 125 and 126 which also contain present tenses)

Response: We have changed to past tense.

Line 124 – Some of the countries excluded from the study would have met the criteria as described (ie. Malawi). Please be more specific if there were questions/data specifically needed for the analysis that excluded some countries.

Response: We have added two more countries(Malawi and Mozambique).

Line 130 -the sentence on weighted sounds like a conclusion from the previous sentence. Weighting needs to be described briefly as it’s own step in the analysis, not as part of the study population

Response: We described weighting under data analysis. Here we only indicated the weighted sample included in the study as shown in Table 1.

Line 159 – remove ‘also’.

Response: Done

Line 167 – why would ‘almost every day’ be moderate and not high exposure, when the other questions of ‘at least once a week’ count as high for each type of media? If the ‘3’ refers to the individual question (ie. 3 on a scale of 0-2), then that should be made clear, and then the high media category would become >4 rather than 4-6.

Response: We have revised this accordingly.

Line 172 – sample weights should be described as ‘women’s sample weights’, but just as ‘Weighting’

Response: We have revised accordingly

Line 180 – ‘pooled the data to create a single dataset’ is more accurate

Response: We have revised the sentence accordingly.

Results

Line 189 – use present tense ‘present’, not ‘presented’ (sorry, a bit confusing but now that you are presenting your results in the paper, you use the present tense)

Response: Corrected

Lines 215-222 – The description of your analysis belongs in your methods section. The results section should only include results.

Response: We have deleted the sentences belonging to the methods.

Line 216-217 – is Model 1 not just your unadjusted analysis presented above in Table 3?

Response: Table 3 is a descriptive table. The p-values are from Pearson chi-square.

Where is the univariable analysis that shows the association between each of the covariates and the outcome? They should be included in your Table 3, with additional columns – it’ll likely take up an entire page in landscape orientation but that’s fine.

Response: We have revised table 3. It now shows association between the covariates and the outcomes.

For model 2, did you identify which covariates were significantly associated with transmission in before fitting them in the model? Or did you just include everything in the model? Need to explain how multivariates were selected

Response: We included all these variables based on previous studies indicating they were significantly associated with testing.

Line 220 – “Direction of effect” cannot be established from a cross-sectional study design. You cannot claim cause and effect. You can only demonstrate an association between variables.

Response: We have revised accordingly.

Submitted filename: _Reponse to Reviewers PONE-D-20-21800.docx

Click here for additional data file.

14 Oct 2020

PONE-D-20-21800R1

Prenatal care coverage and correlates of HIV testing in sub-Saharan Africa: Insight from demographic and health surveys of 16 countries

PLOS ONE

Dear Dr. Ajayi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Thank you submitting your revised manuscript. The manuscript has benefitted from the change in focus and the additional analyses included as Supplementary Data. However,  some important issues raised by the initial reviewers have only been addressed in part. I have summarized the outstanding issues below.

Please submit your revised manuscript by Nov 28 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Emma K. Kalk

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

PONE-D-20-21800R1

Prenatal care coverage and correlates of HIV testing in sub-Saharan Africa: Insight from demographic and health surveys of 16 countries

The authors have submitted a revised manuscript the focus of which has been broadened to include multiple factors associated with antenatal HIV testing uptake in sub-Saharan Africa. This widening of scope addresses some of the main concerns with the initial report in that 1) the study design (cross-sectional surveys) did not support a causal relationship between HIV-knowledge and test uptake; 2) confounding and collinearity, particularly related to country-specific PMTCT policies which may include mandatory/opt-out ANC education and testing, and country-specific uptake of ANC services in general. However, some of the issues raised have not been completely addressed.

General

The Title, Abstract and Discussion have been revised to reflect the change in focus i.e. “coverage of HIV testing during pregnancy and also examine the factors associated with uptake.” The Results section remains focused on Knowledge. As noted in previous reviewer comments: attendance of ANC=pretest counselling=knowledge. Attendance of ANC=test. Do you have any data on ANC uptake?

Please apply consistency with respect to numbers i.e. numerals or words.

Specific

Line 84-84. ANC testing is standard of care in Botswana and South Africa (every 3 months in the latter) with an opt-out policy. HIV education is part of the testing process.

line 108. Add: “According to the COM-B model….” You have described the summary of the COM-B model suggested by the reviewer. Please could you apply this to the analysis.

Line 134. You appropriately use the weighted datasets from the DHS surveys. As noted before, please provide a brief description of what this means in the text of Methods.

Line 143. Why did you select “knowledge” as the main explanatory variable of interest? As noted, this is likely colinear with local PMTCT policy and ANC attendance itself, both of which may be more relevant. Perhaps “knowledge” could be included as one of several variables. You should also note that COUNTRY was a variable included in your models.

Line 158. The Media Exposure classification is still unclear. You present 2 classifications: low = 0; moderate =1-3 or 1-4; high=4-6 or 5-9. Do the latter apply to Zambia only? If it isn’t possible to apply a single classification, please be explicit as to which system is applied to which country.

Line 169. Detail on weightings as noted above. You only need provide this once.

FIGURES: Has Figure 1 been deleted? There is no longer a legend. Please relabel all the Figures starting at 1 in the text and Figure legends.

Table 2. What is the Total column? It looks like the total number of women included i.e. your denominator? The cause of confusion is use of the comma which you haven’t used in the frequency columns. Please be consistent with the numbers.

Line 220. Typo – “never” is floating.. “never-married”? Proportion is singular so “proportion …. was higher…”

Line 224. Sentence incomplete.

Line 236. “To examine the factors associated with uptake of HIV testing during pregnancy, we fitted two models and presented the results in (Table 4).”

Line 245 – 246. Please could you include COUNTRY as a variable in both models in Table 4. ANC uptake and PMTCT policy, which are captured in the COUNTRY variable, are key factors in ANC testing uptake. I note stratified results are presented in Supplementary tables.

Perhaps mention that you have looked at ANC uptake (Supplementary 7). Is there a reason you couldn’t use this as a variable in the models?

Line 301. Delete “also”

Lines 320-324. The focus of the manuscript has broadened. I feel that emphasis on Knowledge limits the discussion. As noted, Knowledge is colinear with many other variables which may be more important. The study proposition is no longer defining a causal relationship between knowledge and testing (this is not possible with the study design). Lines 330-332 are more important.

Line 332. The COM-B theoretical model (mentioned in the Introduction) could be very useful here as it would address some of the issues with confounding. It would be useful to discuss it’s application to your analysis here, as you do with the HBM model.

Line 335. Does media exposure differ by urban-rural area of SES status?

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23 Oct 2020

PONE-D-20-21800R1

Prenatal care coverage and correlates of HIV testing in sub-Saharan Africa: Insight from demographic and health surveys of 16 countries

Dear Editor,

Many thanks for giving us another opportunity to revise our manuscript. We believe your constructive comments have further helped us improve our paper. Please see below our response to all comments you have raised.

Best Regards

Anthony

The authors have submitted a revised manuscript the focus of which has been broadened to include multiple factors associated with antenatal HIV testing uptake in sub-Saharan Africa. This widening of scope addresses some of the main concerns with the initial report in that 1) the study design (cross-sectional surveys) did not support a causal relationship between HIV-knowledge and test uptake; 2) confounding and collinearity, particularly related to country-specific PMTCT policies which may include mandatory/opt-out ANC education and testing, and country-specific uptake of ANC services in general. However, some of the issues raised have not been completely addressed.

Response: We thank the editor for the comment. We have broadened our focus, and have now emphasised other factors and particularly the role of antenatal care attendance. The opt-out strategy is widely implemented in SSA, including in west African countries; however, other challenges exist which we have highlighted in the manuscript. We did not indicate a causal relationship between knowledge of MTCT and testing in the manuscript. Also, we did not include antenatal care attendance in the Model, given that the rate of testing is 0% among women who did not attend antenatal care. However, the country as a variable is a proxy for the rate of antenatal care utilisation rate as well as the difference in the implementtion of the Opt-out strategy. We also presented table S7 to highlight the role of antenatal care attendance further. Countries with a high rate of antenatal care utilisation generally have a high rate of prenatal care testing.

General

The Title, Abstract and Discussion have been revised to reflect the change in focus i.e. “coverage of HIV testing during pregnancy and also examine the factors associated with uptake.”

Response: we thank the editor for the positive feedback

The Results section remains focused on knowledge.

Response: We have further revised the result section by deleting the results presented on knowledge and Table 2 and figure 3.

As noted in previous reviewer comments: attendance of ANC=pretest counselling=knowledge. Attendance of ANC=test. Do you have any data on ANC uptake?

Response: The statement that attendance of ANC=pretest counselling=knowledge. Attendance of ANC=test, even though could be implied, is not completely supported by our analysis. The correlation coefficient of knowledge of MTCT and testing was 0.6. It is not too high to be left out of our Model. It is important to note that so many women are still not tested even though they received antenatal care across SSA and especially in West Africa. In fact, ensuring the all women who receive ANC are tested will increase the prevalence of testing significantly, especially in West Africa.

There is data on ANC; however, the rate of testing among those who did not attend ANC is 0%, not allowing for estimating the odds ratio. However, the point on the role of ANC is well made in the manuscript with the information presented in Table S7. Countries with a high rate of ANC use had a high rate of testing of pregnant women. The country was included in our Model, which we consider to be a proxy for differences in the policy context and ANC use across SSA.

Please apply consistency with respect to numbers i.e. numerals or words.

Response: we have used numeral throughout

Specific

Line 84-84. ANC testing is standard of care in Botswana and South Africa (every 3 months in the latter) with an opt-out policy. HIV education is part of the testing process.

Response: We have added a sentence to reflect this insight. We thank the editor for this comment.

line 108. Add: “According to the COM-B model….” You have described the summary of the COM-B model suggested by the reviewer. Please could you apply this to the analysis.

Response: We have explained how this Model informed our analysis under the variable measure section.

Line 134. You appropriately use the weighted datasets from the DHS surveys. As noted before, please provide a brief description of what this means in the text of Methods.

Response: We thank the editor for the feedback. We have indicated what it means.

Line 143. Why did you select “knowledge” as the main explanatory variable of interest? As noted, this is likely colinear with local PMTCT policy and ANC attendance itself, both of which may be more relevant. Perhaps “knowledge” could be included as one of several variables. You should also note that COUNTRY was a variable included in your models.

Response: we have revised our manuscript such that this could no longer be implied. Also, we have shown the results for country as we included country in our model. Please see Table 3.

Line 158. The Media Exposure classification is still unclear. You present 2 classifications: low = 0; moderate =1-3 or 1-4; high=4-6 or 5-9. Do the latter apply to Zambia only? If it isn’t possible to apply a single classification, please be explicit as to which system is applied to which country.

Line 169.

Response. We have revised the description as indicated.

Detail on weightings as noted above. You only need provide this once.

Response: we have deleted the sentence.

FIGURES: Has Figure 1 been deleted? There is no longer a legend. Please relabel all the Figures starting at 1 in the text and Figure legends.

Response: No, we have added the label

Table 2. What is the Total column? It looks like the total number of women included i.e. your denominator? The cause of confusion is use of the comma which you haven’t used in the frequency columns. Please be consistent with the numbers.

Response: we have deleted table 2 so as not to make the paper focus on knowledge of MTCT.

Line 220. Typo – “never” is floating.. “never-married”? Proportion is singular so “proportion …. was higher…”

Response- we have corrected this. Thank you

Line 224. Sentence incomplete.

Response: we have completed the sentence

Line 236. “To examine the factors associated with uptake of HIV testing during pregnancy, we fitted two models and presented the results in (Table 4).”

Response: sentence has been revised.

Line 245 – 246. Please could you include COUNTRY as a variable in both models in Table 4. ANC uptake and PMTCT policy, which are captured in the COUNTRY variable, are key factors in ANC testing uptake. I note stratified results are presented in Supplementary tables.

Perhaps mention that you have looked at ANC uptake (Supplementary 7). Is there a reason you couldn’t use this as a variable in the models?

Response: we included country in the Model, we have now shown the result in the paper. ANC was not included because the prevalence of testing among women who did not receive ANC was 0%. We could not estimate odds ratio given that there is nothing to reference. We have indicated the result presented in Table S7 in results.

Line 301. Delete “also”

Response: deleted

Lines 320-324. The focus of the manuscript has broadened. I feel that emphasis on knowledge limits the discussion. As noted, knowledge is colinear with many other variables which may be more important. The study proposition is no longer defining a causal relationship between knowledge and testing (this is not possible with the study design).

Response: we agree with this comment and have revised accordingly. Again, we did not infer a causal relationship; rather, we indicated that knowledge is associated with the uptake of testing. We did test for collinearity, as indicated earlier.

Lines 330-332 are more important.

Line 332. The COM-B theoretical Model (mentioned in the Introduction) could be very useful here as it would address some of the issues with confounding. It would be useful to discuss it’s application to your analysis here, as you do with the HBM model.

Response: we have discussed the relevance of COM-B

Line 335. Does media exposure differ by urban-rural area of SES status?

Response: we expected that media exposure would differ by place of residence and socio-economic status, given that access to media is generally more in urban areas.

26 Oct 2020

Prenatal care coverage and correlates of HIV testing in sub-Saharan Africa: Insight from demographic and health surveys of 16 countries

PONE-D-20-21800R2

Dear Dr. Ajayi,

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Academic Editor

PLOS ONE

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29 Oct 2020

PONE-D-20-21800R2

Prenatal care coverage and correlates of HIV testing in sub-Saharan Africa: Insight from demographic and health surveys of 16 countries

Dear Dr. Ajayi:

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