Measuring socioeconomic inequalities in prenatal HIV test service uptake for prevention of mother to child transmission of HIV in East Africa: A decomposition analysis.

BACKGROUND: Despite efforts made towards the elimination of mother-to-child HIV transmission, socioeconomic inequality in prenatal HIV test uptake in East Africa is not well understood. Therefore, this study aimed at measuring socioeconomic inequalities in prenatal HIV test uptake and explaining its main determinants in East Africa.
METHOD: We analysed a total weighted sample of 45,476 women aged 15-49 years who birthed in the two years preceding the survey. The study used the most recent DHS data from ten East African countries (Burundi, Comoros, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Uganda, Zambia, and Zimbabwe). The socioeconomic inequality in prenatal HIV test uptake was measured by the concentration index and illustrated by the concentration curve. Then, regression based Erreygers decomposition method was applied to quantify the contribution of socioeconomic factors to inequalities of prenatal HIV test uptake in East Africa.
RESULTS: The concentration index for prenatal HIV test uptake indicates that utilization of this service was concentrated in higher socio-economic groups with it being 15.94% higher among these groups in entire East Africa (p <0.001), 40.33% higher in Ethiopia (p <0.001) which was the highest and only 1.87% higher in Rwanda (p <0.01) which was the lowest. The decomposition analysis revealed that household wealth index (38.99%) followed by maternal education (13.69%), place of residence (11.78%), partner education (8.24%), watching television (7.32%), listening to the radio (7.11%) and reading newsletters (2.90%) made the largest contribution to socioeconomic inequality in prenatal HIV test in East Africa.
CONCLUSION: In this study, pro-rich inequality in the utilization of prenatal HIV tests was evident. The decomposition analysis findings suggest that policymakers should focus on improving household wealth, educational attainment, and awareness of mother-to-child transmission of HIV (MTCT) through various media outlets targeting disadvantaged sub-groups.

Introduction

HIV remains to be a major global public health problem [1]. Across the world, approximately 37.7 million people were living with HIV of these 1.7 million were children aged 0–14 years at the end of 2020 [2, 3]. The burden of the epidemic continues to vary considerably between countries and regions. Sub-Saharan Africa remains most severely affected region accounting for more than two-thirds of the people living with HIV followed by Asia and the Pacific worldwide [3, 4].

The spread of HIV/AIDS disproportionately affected women in sub-Saharan Africa (SSA) [5] due to poverty, political inequalities, socially constructed gender inequalities and inadequate coverage of health care services [5, 6]. Moreover, women are at a higher risk of acquiring HIV during pregnant or breastfeeding period [7-9] and those women who acquire HIV during pregnancy or the postpartum period are more likely to pass the infection on to their offspring [10, 11]. As a result, vertical transmission of HIV remains a public health challenge in the region. Evidence showed that 90% of children age less than 15 years who lived with HIV/AIDS found in SSA [12]. Furthermore, in 2018, 61% of new HIV infections [13] and 73% of the deaths occurred in SSA alone [14]. Specifically, 67% of children living with HIV are found in Eastern and Southern Africa [4]. Hence, preventing mother-to-child-transmission of HIV (PMTCT) is an essential HIV/AIDS infection control strategy amongst children in the region.

Early identification of HIV-positive pregnant women during pregnancy, birth, or the postpartum period is vital to prevent the risk of MTCT of HIV as it’s a gateway to prevention of HIV transmission, treatment, care and support services [15]. Provider-initiated HIV testing and counselling (PITC) (sometimes referred to as opt-out HIV testing) is recommended during antenatal care (with repeat testing at birth for those who initially test negative) to detect HIV-positive mothers [16]. Testing remains voluntary and the woman is given the option to ‘opt-out’ of testing [17]. Those women who test positive for HIV are supposed to be immediately provided with Anti-Retroviral Therapy (ART) both to decrease the risk of mother-to-child HIV transmission (MTCT) and to improve maternal health [17]. Evidence has shown that the overall prenatal HIV test service uptake was 80.8% in World Health Organization regions of East African countries (Burundi, Comoros, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Uganda, Zambia, and Zimbabwe). The lowest prenatal HIV testing was measured in Comoros (17%) and highest was in Rwanda (97.9%) [18], and most of these countries were below the minimum 95% required to enable 95% of HIV‐positive pregnant women to have access to ART [19-21].

Various studies have examined the relationship between HIV testing and socioeconomic factors and have indicated that the utilization of HIV testing is highly associated with socioeconomic status (SES) such as differences in age, marital status, educational status, and place of residence [22-30]. These differences associated with socioeconomic and demographic factors are an indication of the presence of socioeconomic inequalities in prenatal HIV test service uptake [31, 32]. However, the level of socioeconomic inequalities and the contribution of each socio-economic and demographic factor on prenatal HIV tests for prevention of mother-to-child transmission of HIV (PMTCT) in East Africa has not been empirically investigated.

Measuring socioeconomic inequalities in health service use can guide the focus of policymakers and assist stakeholders to prioritize specific health care needs [33]. Improving socioeconomic inequalities in healthcare utilization has become one of the most important priorities or a shared goal amongst many policymakers, researchers, and public health practitioners who strive to improve health systems [34]. For instance, the principle of universal health coverage (UHC) states that individuals with equal needs should utilize equal healthcare services without any financial hardship [34]. The United Nation sustainable development goals (SDGs), specifically SDG 3, calls for ensuring healthy lives and promoting well-being for all, whilst SDG 10 calls for reducing inequality within and between countries to promote the inclusion and empowerment of all [35]. Under these broad goals, UNAIDS has initiated a new approach “End Inequalities, to End AIDS Epidemic” [36]. It is a global AIDS strategy to close the gaps that are preventing progress towards ending HIV/AIDS by reducing inequalities in access to treatment and care. Hence, UNAIDS recommend that every country to sets out evidence-based priority actions and targets to reduce inequalities and end AIDS as a public health threat by 2030 [14, 36, 37].

Therefore, the purpose of this study was to estimate the level of socioeconomic inequality in the uptake of prenatal HIV test services for PMTCT in East African countries using concentration index decomposition approach. Firstly, with concentration index (CI) we measure the extent/magnitude of socioeconomic inequalities in prenatal HIV test service utilization [38, 39]. Secondly, with the regression-based decomposition approach, we quantified the contributions of the socio-economic factors for the observed inequalities in the uptake of prenatal HIV test services. Hence, quantifying prenatal HIV test service uptake inequalities that exist, identification and quantification of covariates that explain the inequalities will assist policy maker to prioritize and develop tailored policies, strategies, and actions aimed at reducing the existing inequalities between the poor and rich. It can be also helpful to assess/evaluate the extent to which PMTCT services are better targeted toward underprivileged in Eastern Africa countries.

Methods and materials

Study design and data sources

Data were extracted from the most recent Demographic and Health Surveys (DHS), consisting ten East Africa countries with complete data for the period from 2011 to 2018 [40, 41]. Countries were Burundi (DHS,2016–17), Comoros (DHS,2012), Ethiopia (DHS,2016), Kenya (DHS,2014), Malawi (DHS,2015–16), Mozambique (DHS,2011), Rwanda (DHS,2014–15), Uganda (DHS,2016), Zambia (DHS,2018), and Zimbabwe (DHS,2015).

The DHS are based on nationally representative samples that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition [42] and therefore, its an important source of data on health of families in developing countries.

The DHS employed a cross-sectional study design with a stratified two-stage sampling strategy. First, each country was divided into enumeration areas (clusters) based on the census frames in the country, and then, households were randomly selected within each cluster. Furthermore, since the DHS surveys were intended to address household-based health issues, strata for urban and rural households were used for the selection of respondents. The DHS follows a standard procedure of data collection and presentation (similar questionnaires) and uses the same definition of terms in each country. The DHS data were collected by the country-specific department of health and population, in collaboration with Inner City Fund (ICF) International using standardized household questionnaires. The detailed methodology of the survey design, sample selection, survey tools, and data collection are described elsewhere [43, 44].

Pooled DHS datasets from ten East African countries were structured by creating a country-specific cluster and country-specific strata. Hence, in this study, we used a total weighted sample of 45,476 women aged 15–49 years who birthed in the two years preceding the survey. The information on the HIV testing during antenatal care and birth in the two years preceding the survey for each woman in the sample was found in the women’s individual records of DHS to measure socioeconomic inequalities in prenatal HIV test service uptake for PMTCT

This study used data from the Measure DHS/ ICF International which is secondary in nature. The data were anonymous and are available to apply for online. Therefore, Approval was received from DHS/ ICF International to use.

Variables

Outcome variable

The outcome variable in this study included socioeconomic inequality in prenatal HIV test service uptake. Prenatal HIV testing was defined as the proportion of women who tested for HIV and received the test result during antenatal care or before birth [45]. The variable was coded as “1” if a woman was tested for HIV and also received the HIV test result otherwise coded as “0” if the woman did not test for HIV or tested but did not receive the test result during antenatal care or before birth.

Socioeconomic and demographic factors

Based on previously published literature from low and middle-income countries [18, 19, 24], the following socioeconomic and demographic factors were selected for this study and examined: country of participants, place of residence (urban and rural), maternal age (categorized as 15–24 years, 25–34 years, and 35–49 years), and maternal and partner educational level (categorized as no education, primary education, secondary and higher education). The sex of the household head was coded as ‘male’ if the participants lived in the male-dominated household, or ‘female’ if otherwise. The household size was classified as 1–3, 4–6, or ≥7 members and employment status were categorized as not working, formal employment, and non-formal employment. Women’s access to mass media was categorized as yes or no in relation to access to the radio, watching television, and reading magazines/newspapers.

Socioeconomic status measure

The calculation of concentration indices (CI) requires a single indicator to capture respondents’ SES. Hence, the DHS household wealth index was used as a measure of the SES of mothers in East Africa [46]. The household wealth index stands out to be the most appropriate measure of SES for national surveys in comparison with direct measures of living standards such as income, consumption, or expenditure [47]. The wealth index was constructed via principal components analysis (PCA) [48] using data such as household assets (e.g. radio, televisions, refrigerators, farmland, farm animals) [48, 49], housing characteristics (e.g. type of water access, type of flooring), and access to basic services (e.g. electricity supply, source of drinking water and sanitation facilities) [46]. PCA is a multivariate statistical method that is widely used as a data reduction technique [48]. The detailed methodology of the wealth index construction is described elsewhere [46, 49]. In this study, wealth index score was divided into quintiles, each category comprising 20% of the population. The lowest 20% quintile was assigned to the poorest households, the next 20% quintile to generally poor households, followed by another 20% quintile for the middle-class households, and finally the top 40% quintile for the wealthier and wealthiest households.

Analytical strategy

The analytical approach for this study was undertaken in three stages. Firstly, sample characteristics and prevalence of prenatal HIV test service uptake were described using frequencies and percentages. Secondly, concentration indexes (CIs) were determined to examine the extent of socioeconomic inequalities in prenatal HIV test service uptake in East Africa. Thirdly, we decomposed the concentration indices to understand the contribution of various factors to inequality. The methods are discussed in detail as follows.

Concentration index and curve

Concentration index (CI) has become a popular tool to measure health and health care inequality in the field of health policy and health economics research [50]. It assumes values between -1 and +1 [38]. When the concentration index is positive, it reflects higher uptake of HIV test services tend to the rich. If it is negative, it reflects higher uptake of HIV test services tend to the poor. In the absence of any inequalities, the value of the concentration index is zero [38]. It can be estimated as:

Where CI is the concentration index for health service use (prenatal HIV test service uptake in this case), yi is health service use for individual i, μ is the mean of health service use, and R is the fractional rank or asset score of the i individual in the living standards distribution/socio-economic rank with i  =  1 for the poorest and i  =  n for the wealthiest. The concentration index depends only on the relationship between the health variable (yi) and the rank of the living standard variable (Ri) and not on the variation of the living standard variable itself.

When the health variable of interest is dichotomous, the concentration index is not bounded within the range of (–1,1) [51]. The lower bound is then equal to , the upper bound is equal to [38]. For large samples, the terms vanish, and the maximum and the minimum values tend to μ−1 and 1−μ respectively [51]. To consider the bounded nature of prenatal HIV test service uptake, Erreygers (2009) proposed a modified version of the concentration index called Erreygers normalized concentration index (ECI) [52]. This is defined as: where ECI is Erreygers concentration index, CI is the generalized concentration index and μ is the mean of prenatal HIV test service uptake. The Erreygers normalized concentration index (ECI) method was also applied to decompose prenatal HIV test service utilization inequality in this study [39, 52]. Furthermore, with concentration curve, the cumulative proportion of women ranked by SES (on the x-axis) against the cumulative proportion of prenatal HIV test service uptake (on the y-axis) were plotted. If the concentration curve lays below the 45-degree line, it suggests prenatal HIV test service use is concentrated more amongst women from rich households and vice versa.

Decomposition of the concentration index

Decomposition of the healthcare inequality relies on the assumption that healthcare is a linear function of the outcome variables [38]. However, since the outcome variable of this study is binary, an appropriate statistical technique for non-linear settings is needed. According to O’Donnell, et al. [38], a linear approximation when dealing with a discrete change from 0 to 1 is to use marginal or partial effects (dh/dx) estimates as follows:

Where βkm is the marginal effects (dy/dx) of each xk; e indicates the error term generated by the linear approximation. In this study, we performed the decomposition analysis using a generalized linear model (GLM) with a binomial distribution and a logit link function as a linear approximation to capture the partial effects of socioeconomic factors on prenatal HIV test service uptake [53]. GLM is a suitable regression to provide consistent results for the decomposition of binary outcomes regardless of the choice of the reference category [53]. Therefore, given the relationship between y and xk in Eq [3], we decomposed the concentration index of prenatal HIV test service uptake y(CI) into its contributory factors as follows

In this expression, is the means of explanatory variables, βkm is the patrial effect on explanatory variable xk (dy/dxk), CIk is the concentration index for determinant xk, and GCIe is the generalized concentration index for the error term.

In summary, the contribution of the determinants is calculated in four steps. Firstly, the regression model of the health outcome variable is performed for all xk to obtain the marginal effects of determinants (βkm), which demonstrate the associations between the determinants and prenatal HIV test service uptake (positive signs indicate positive associations whilst negative signs indicate negative associations). Secondly, the elasticity of the health variables was calculated for each x (xk), which is the sensitivity of prenatal HIV test service uptake to changes in the determinants . It denotes the change in the dependent variable (socioeconomic inequality in prenatal HIV test service uptake in this case) associated with a one-unit change in the explanatory variables. Thirdly, the CIs are calculated for the prenatal HIV test uptake and each explanatory variable (CIk). Fourthly, the contribution of each explanatory variable to the overall CI is calculated by multiplying the elasticity of each determinant by its concentration index .

In this study, we accounted for the multistage survey design during descriptive, regression, and decomposition analyses by creating country-specific clustering, country-specific strata, and population-level weight. All statistical analyses were conducted using STATA version 14.2 (Stata Corp, College Station, TX, USA).

Results

Characteristics of the study participants

In this study, a weighted number of 45,476 women were included in the analysis from ten East African countries (Burundi, Comoros, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Uganda, Zambia, and Zimbabwe) between the ages of 15–49 years who birthed two years before the survey. Most study participants were from rural areas (n = 35365, 77.8%), aged between 25–34 years (n = 20365, 44.8%), and had educational qualifications of at least primary education (n = 23739, 52.2%). Three quarters of the respondents (n = 34746, 76.4%) were from a male-headed household, (n = 7727, 17.0%) read newsletters, (n = 26282, 57.8%) listened to the radio and (n = 12510, 27.5%) watched television [

The overall weighted prevalence of prenatal HIV test uptake was 80.8% (95% CI: 79.7–81.7%) with the highest prevalence in Rwanda (97.9%, 95% CI: 97.2–98.3%) and lowest in Comoros (17.0%, 95% CI: 13.9–20.7%) [Tables Furthermore, the distribution of prenatal HIV test service uptake across countries and study factors is shown in Tables 1 and 2. For instance, women who resided in rural areas of Burundi (n = 4384, 81.0%), Malawi (n = 5131, 76.7%), Rwanda (n = 2612, 80.7%), and Uganda (n = 4185, 70.9%) had the highest proportion of prenatal HIV test services use. Amongst women who attained secondary and higher education, those who resided in Zambia (n = 1517, 38.9%) and Zimbabwe (n = 1534, 62.5%) reported the highest level of usage of HIV test services during pregnancy, whilst lower levels of usage was reported in Burundi (n = 603, 11.2%), Comoros (n = 111, 8.6%) and Ethiopia (n = 296, 6.8%)

Table 1

Study participant characteristics and percentage of prenatal HIV test service users for PMTCT by study factors in East African countries (N = 45,476; Demographic and Health Survey, 2011–2018).

	Total sample (N = 45476)		Burundi (N = 5412)		Comoros (N = 1298)		Ethiopia (N = 4308)		Kenya (N = 7357)
Characteristics	N (%)	Yes# n (%)	N (%)	Yes# n (%)	N (%)	Yes#n (%)	N (%)	Yes# n (%)	N (%)	Yes# n (%)
Residency
Urban	10111(22.2)	9133(20.1)	489(9)	471(8.7)	368(28.4)	96 (7.4)	520(12.1)	410(9.5)	2618(35.6)	2530 (34.4)
Rural	35365(77.8)	27588(60.7)	4923(91)	4384(81.0)	929(71.6)	125(9.6)	3788(87.9)	1069(24.8)	4739(64.4)	4306 (58.5)
Maternal age
15–24	17049(37.5)	13973(30.8)	1407(26)	1255(23.2)	404(31.2)	68 (5.3)	1260(29.3)	453 (10.5)	2864(38.9)	2688 (36.5)
25–34	20365(44.8)	16536(36.4)	2742(50.7)	2481(45.9)	622(47.9)	111 (8.5)	2189(50.9)	766 (17.8)	3432(46.7)	3186 (43.3)
35–49	8024(17.7)	6194(13.6)	1259(23.3)	1117(20.6)	271(20.9)	42 (3.2)	854(19.9)	260 (6.0)	1061(14.4)	963 (13.1)
Maternal education
No education	10312(22.7)	6116(13.5)	2365(43.7)	2096(38.7)	562(43.3)	64 (4.9)	2606(60.5)	612 (14.2)	834(11.3)	615 (8.4)
Primary	23739(52.2)	20035(44.1)	2398(45.3)	2155(39.8)	323(24.9)	46 (3.5)	1319(30.6)	571 (13.3)	4023(54.7)	3775 (51.3)
Secondary and higher	11420(25.1)	10570(23.2)	650(12)	603(11.2)	413(31.8)	111 (8.6)	383(8.9)	296 (6.8)	2499(34)	2445(33.2)
Maternal occupation
Not working	15331(37.0)	11127(26.8)	388(7.2)	360 (6.7)	816(65.1)	139 (11.1)	2510(58.8)	776 (18.2)	1885(53.4)	1730 (49.0)
Professional work	6219(15.0)	5345(12.9)	489(9.1)	453 (8.4)	203(16.2)	43 (3.4)	703(16.5)	331 (7.8)	577(16.3)	559 (15.8)
Nonprofessional work	19922(48.0)	16570(39.9)	4502(83.7)	4009(74.5)	234(18.7)	21 (1.7)	1054(24.7)	349 (8.2)	1068(30.3)	988 (28.0)
Partner education
No education	7051(19.6)	4187(11.6)	1775(36.2)	1589(32.3)	459(36.9)	39 (3.1)	1838(45.1)	461 (11.3)	304(9.5)	220 (6.3)
Primary	17778(49.3)	14214(39.5)	2532(51.5)	2270(46.2)	322(25.9)	43 (3.5)	1651(40.5)	545 (13.4)	1609(50.6)	1500 (47.1)
Secondary and Higher	11191(31.1)	9988(27.7)	605(12.3)	559 (11.4)	462(37.1)	117(9.4)	588(14.4)	393 (9.6)	1270(39.9)	1237 (38.9)
Household head
Male	34746(76.4)	27919(61.4)	4436(82.0)	3973(73.4)	886(68.2)	159(12.2)	3725(86.5)	1275(29.6)	5435(73.9)	5079(69.0)
Female	10730(23.6)	8802(19.4)	976(18.0)	882(16.3)	412(31.8)	62(4.8)	583(13.5)	204(4.7)	1922(26.1)	1757(23.9)
Household size
1–3	7588(16.7)	6293(13.8)	809(14.9)	724(13.4)	165(12.7)	28(2.2)	537(12.4)	241(5.6)	1356(18.4)	1282(17.4)
4–6	22908(50.4)	18706(41.1)	2783(51.4)	2507(46.3)	581(44.7)	85(6.6)	2197(51.0)	834(19.4)	3757(51.0)	3531(48.0)
> = 7	14980(32.9)	11722(25.8)	1820(33.6)	1624(30.0)	552(42.5)	107(8.3)	1574(36.5)	404(9.4)	2244(30.5)	2022(27.5)
Household wealth index
Poorest	8148(17.9)	6006(13.2)	1161(21.5)	1005(18.6)	226(17.4)	11(0.9)	355(8.2)	43(1.0)	812(11.0)	605(8.2)
Poorer	9159(20.1)	6965(15.3)	1157(21.4)	1025(18.9)	262(20.2)	32(2.5)	887(20.6)	171(3.9)	1349(18.3)	1234(16.8)
Middle	9611(21.1)	7496(16.5)	1149(21.2)	1038(19.2)	263(20.2)	42(3.3)	1225(28.4)	325(7.5)	1480(20.1)	1385(18.8)
Wealthier	9525(21.0)	7876(17.3)	1106(20.4)	999(18.4)	269(20.7)	59(4.6)	1281(29.7)	504(11.7)	1614(21.9)	1550(21.1)
Wealthiest	9032(19.9)	8378(18.4)	840(15.5)	788(14.6)	279(21.5)	76(5.8)	559(13.0)	436(10.1)	2102(28.6)	2062(28.0)
Reads newspaper
No	37733(83.0)	29553(65.0)	5181(95.7)	4643(85.8)	1105(85.1)	154 (11.9)	4004(93)	1266(29.4)	5214(70.9)	4753 (64.6)
Yes	7727(17.0)	7159(15.8)	231(4.3)	212 (3.9)	193(14.9)	67 (5.2)	304(7)	213 (4.9)	2141(29.1)	2081 (28.3)
Listens to the radio
No	19179(42.2)	14101(31.0)	3045(56.3)	2664(49.2)	649(50)	92 (7.1)	3115(72)	888 (20.6)	1564(21.3)	1317(17.9)
Yes	26282(57.8)	22610(49.7)	2367(43.7)	2191(40.5)	649(50)	129 (9.9)	1193(28)	591 (13.7)	5790(78.7)	5516 (75.0)
Watches television
No	32950(72.5)	25862(56.9)	5022(92.8)	4488(82.3)	444(34.2)	51 (3.9)	3494(81.1)	986 (22.9)	4243(57.7)	3813 (51.9)
Yes	12510(27.5)	10848(23.8)	390(7.2)	366 (6.8)	854(65.8)	170 (13.1)	814(18.9)	493 (11.4)	3108(43.3)	3018 (41.1)
HIV test uptake (%), 95% CI	80.8% [CI: 79.7–81.7%]		89.7% [CI: 88.4–90.8]		17.0% [CI: 13.9–20.7%]		34.3% [CI: 30.3–38.6]		92.9% [CI: 92.1–93.7]

N = Total weighted number of eligible women; Yes#n = Weighted number of women who tested for HIV; % = weighted proportions of women who were tested for HIV by study factors in each East African country.

Table 2

Study participants characteristics and percentage of prenatal HIV test service users for PMTCT by study factors in East African countries (N = 45,476; Demographic and Health Survey, 2011–2018).

	Malawi (N = 6693)		Mozambique (N = 4913)		Rwanda (N = 3236)		Uganda (N = 5901)		Zambia (N = 3905)		Zimbabwe (N = 2454)
Characteristics	N (%)	Yes# n (%)	N (%)	Yes# n (%)	N (%)	Yes# n (%)	N (%)	Yes# n (%)	N (%)	Yes# n (%)	N (%)	Yes# n (%)
Residency
Urban	911(13.6)	848 (12.6)	1356(27.6)	1050 (21.4)	561(17.3)	554 (17.1)	1258(21.3)	1214 (20.6)	1340(34.3)	1307(33.5)	689(28.1)	653 (26.6)
Rural	5781(86.4)	5131 (76.7)	3557(72.4)	1884 (38.3)	2675(82.7)	2612(80.7)	4643(78.7)	4185 (70.9)	2564(65.7)	2328(59.6)	1765(71.9)	1563 (63.7)
Maternal age
15–24	3083(46.1)	2717 (40.6)	2015(41.2)	1223 (24.9)	871(26.9)	852(26.3)	2511(42.6)	2290 (38.8)	1666(42.7)	1544(39.6)	966(39.4)	882 (36.0)
25–34	2584(38.7)	2361 (35.3)	2000(40.8)	1250 (25.5)	1666(51.5)	1634(50.5)	2473(41.9)	2306 (39.1)	1514(38.8)	1413(36.2)	1144(46.6)	1029 (41.9)
35–49	1018(15.2)	897 (13.4)	882(18)	455 (9.3)	699(21.6)	680(21.0)	912(15.5)	799 (13.6)	724(18.5)	678(17.4)	343(14)	305 (12.4)
Maternal education
No education	794(11.9)	673 (10.0)	1747(35.6)	817 (16.6)	439(13.6)	417 (12.9)	566(9.6)	499 (8.5)	371(9.5)	296(7.6)	32(1.3)	28 (1.1)
Primary	4480(66.9)	3983 (59.5)	2546(51.8)	1552 (31.6)	2316(71.6)	2276(70.3)	3577(60.6)	3200 (54.2)	1970(50.5)	1822(46.7)	787(32.1)	654 (26.7)
Secondary and higher	1419(21.2)	1323(19.8)	620(12.6)	565(11.5)	481(14.8)	474(14.6)	1757(29.8)	1699(28.8)	1564(40.0)	1517(38.9)	1635(66.6)	1534(62.5)
Maternal occupation
Not working	2213(33.1)	1910 (28.5)	2699(55.2)	1765 (36.1)	294(9.1)	289 (8.9)	1193(20.2)	1082 (18.3)	1953(50.0)	1813(46.4)	1381(56.8)	1264(52.1)
Professional work	616(9.2)	572 (8.6)	518(10.6)	415 (8.5)	434(13.4)	426 (13.2)	1235(21.0)	1188 (20.1)	730(18.7)	710(18.2)	713(29.4)	649 (26.7)
Nonprofessional work	3863(57.7)	3498 (52.3)	1670(34.2)	738 (15.1)	2507(77.5)	2450(75.7)	3469(58.8)	3126 (53.0)	1220(31.3)	1113(28.5)	335(13.8)	279 (11.5)
Partner education
No education	637(11.4)	522 (9.3)	1376(29.6)	662 (14.3)	462(16)	449 (15.5)	458(9.2)	399 (8.0)	179(6.2)	149(5.2)	27(1.3)	20 (1.0)
Primary	3018(54)	2735 (48.9)	2412(51.9)	1359 (29.2)	2051(71)	2007(69.5)	2618(52.7)	2342 (47.1)	1082(37.5)	994(34.5)	483(23.1)	418 (20.0)
Secondary and Higher	1935(34.6)	1803(32.2)	856(18.5)	697 (15.0)	374(13)	372 (12.9)	1896(38.1)	1804(36.3)	1622(56.3)	1555(53.9)	1582(75.6)	1449(69.3)
Sex of the head of household
Male	5093(76.1)	4565(68.2)	3474(70.7)	1993(40.6)	2565(79.3)	2516(77.8)	4470(75.7)	4057(68.7)	3111(79.7)	2900(74.3)	1552(63.3)	1403(57.2)
Female	1600(23.9)	1414(21.1)	1439(29.3)	941(19.2)	671(20.7)	650(20.1)	1431(24.3)	1343(22.8)	793(20.3)	735(18.8)	902(36.7)	813(33.1)
Household size
1–3	1329(19.8)	1144(17.1)	757(15.4)	422 (8.6)	663(20.5)	644(19.9)	986(16.7)	901(15.3)	497(12.7)	458(11.7)	490(20.0)	449(18.3)
4–6	3534(52.8)	3209(47.9)	2515(51.2)	1448(29.4)	1842(56.9)	1809(55.9)	2727(46.2)	2536(43.0)	1729(44.3)	1616(41.4)	1243(50.7)	1130(46.0)
> = 7	1829(27.3)	1626(24.3)	1641(33.4)	1064(21.7)	731(22.6)	713(22.0)	2188(37.1)	1962(33.2)	1679(43.0)	1562(40.0)	720(29.3)	636(25.9)
Household wealth index
Poorest	1452(21.7)	1278(19.1)	1268(25.8)	509(10.4)	645(19.9)	621(19.2)	966(16.4)	852(14.4)	701(18.0)	601(15.4)	561(22.8)	480(19.6)
Poorer	1374(20.6)	1214(18.1)	1110(22.6)	550(11.2)	668(20.6)	652(20.1)	1097(18.6)	965(16.3)	711(18.2)	648(16.6)	544(22.2)	475(19.3)
Middle	1375(20.5)	1209(18.1)	992(20.2)	599(12.2)	682(21.1)	670(20.7)	1189(20.1)	1073(18.2)	732(18.7)	679(17.4)	525(21.4)	476(19.4)
Wealthier	1259(18.8)	1140(17.0)	863(17.6)	662(13.5)	662(20.5)	652(20.2)	1237(21.0)	1139(19.3)	792(20.3)	757(19.4)	441(18.0)	413(16.8)
Wealthiest	1232(18.4)	1138(17.0)	679(13.8)	615(12.5)	579(17.9)	571(17.6)	1412(23.9)	1370(23.2)	968(24.8)	950(24.3)	382(15.6)	372(15.2)
Reads newspaper
No	5601(83.7)	4970(74.2)	4412(89.8)	2503 (50.9)	2543(78.7)	2479(76.7)	4778(81)	4321 (73.2)	3298(84.5)	3055(78.2)	1606(65.5)	1413 (57.6)
Yes	1091(16.3)	1010(15.1)	501(10.2)	431 (8.8)	689(21.3)	682 (21.1)	1123(19)	1079 (18.3)	607(15.5)	581(14.9)	848(34.5)	803 (32.7)
Listens to the radio
No	3569(53.3)	3127(46.7)	1705(34.7)	977 (19.9)	600(18.5)	576 (17.8)	1623(27.5)	1429 (24.2)	2226(57)	2058(52.7)	1094(44.6)	980(39.9)
Yes	3124(46.7)	2852(42.6)	3208(65.3)	1957 (39.8)	2636(81.5)	2590(80.0)	4278(72.5)	3971 (67.3)	1679(43)	1578(40.4)	1359(55.4)	1235(50.4)
Watches television
No	5656(84.5)	5028(75.1)	3590(73.1)	1883 (38.3)	2023(62.6)	1977(61.2)	4288(72.7)	3866 (65.5)	2646(67.8)	2419(61.9)	1549(63.1)	1352 (55.1)
Yes	1037(15.5)	951 (14.2)	1323(26.9)	1051 (21.4)	1207(37.4)	1184(36.7)	1614(27.3)	1533 (26.0)	1258(32.2)	1217(31.2)	905(36.9)	864 (35.2)
HIV test uptake [95% CI]	89.3% [CI: 88.2–90.4]		59.7% [CI: 56.2–63.1]		97.9% [CI: 97.2–98.3%]		91.5% [CI: 90.2–92.7]		93.1% [CI: 91.9–94.2]		90.3% [CI: 87.8–92.4]

N = Total weighted number of eligible women; Yes#n = Weighted number of women who tested for HIV; % = weighted proportions of women who were tested for HIV by study factors in each East African country

Socioeconomic inequality in prenatal HIV test service uptake in East Africa

Fig 1 illustrates the concentration curve of prenatal HIV test service uptake in East Africa. The concentration curves lay below the 45° line (the line of equality), indicating that the prenatal HIV test uptake was more concentrated amongst the rich or higher socioeconomic groups. The overall concentration index for prenatal HIV test service uptake in East Africa was 0.1594 (SE = 0.0042, P<0.001). The positive sign of concentration indices for prenatal HIV test use reveals the existence of pro-rich inequalities. This means women in higher SES utilized more HIV testing services than their counterparts or wealthy women had a higher rate of prenatal HIV testing than those with lower economic status. The magnitude 0.1594 indicates that prenatal HIV test service uptake is 15.94% higher among the rich. Across East African countries, the larger concentration index was measured in Ethiopia with a CI of 0.4033 (SE = 0.0159, P <0.001), while the smallest concentration index was in Rwanda with a CI of 0.0187 (SE = 0.0059, P <0.01). This means that prenatal HIV test service uptakes are 40.33% and 1.87% higher among the rich in Ethiopia and Rwanda respectively. The concentration indices and concentration curves across East Africa countries are presented as Supplementary file [

Fig 1

The concentration curve for prenatal HIV test service uptake for PMTCT in East Africa.

Decomposing socioeconomic inequality in prenatal HIV test service uptake in East Africa

Table 3 and Fig 2 presents the results of the decomposition analysis and the contribution of each explanatory variable to the socioeconomic inequality in prenatal HIV test service utilization. Based on the marginal effect, except for the sex of the household head, all determinants had a significant effect on the likelihood of prenatal HIV testing. For example, women from urban areas had a 4.4% higher chance of being HIV tested compared to those from rural residents. Likewise, more educated women had an increased probability of being HIV tested by 3.6% and 8.3% for primary and secondary/higher level of education respectively as compared with non-educated.

Table 3

A decomposition analysis of socioeconomic inequalities in prenatal HIV testing for PMTCT in East Africa.

Characteristics	Marginal effect	Elasticity	Concentration index (ECI)	Contribution
				Value	Percentage	Summed %
Countries
Burundi	Base	Base	Base	Base	Base	0.95
Comoros	-0.5056***	-0.0577	0.0018	-0.0001	-0.06
Ethiopia	-0.3245***	-0.1229	0.0208	-0.0025	-1.60
Kenya	-0.0260**	-0.0168	0.0895	-0.0015	-0.94
Malawi	-0.0251***	-0.0147	-0.0308	0.0004	0.28
Mozambique	-0.2219***	-0.0959	-0.0594	0.0056	3.57
Rwanda	0.1421***	0.0404	-0.0102	-0.0004	-0.25
Uganda	-0.0311***	-0.0161	0.0270	-0.0004	-0.27
Zambia	-0.0043	-0.0014	0.0159	-0.00002	-0.01
Zimbabwe	-0.0846***	-0.0182	-0.0207	0.0003	0.23
Residence
Urban	0.0436***	0.0388	0.4838	0.0187	11.78	11.78
Rural	Base	Base	Base	Base	Base
Maternal age
15–19 years	Base	Base	Base	Base	Base	0.98
20–34 years	0.0149 ***	0.0267	0.0597	0.0015	1.00
35–49 years	0.0021	0.0015	-0.0254	-0.00003	-0.02
Maternal education
No education	Base	Base	Base	Base	Base	13.69
Primary	0.0359***	0.0751	-0.1523	-0.0114	-7.17
Secondary and higher	0.0833***	0.0837	0.3975	0.0332	20.86
Maternal occupation
Not working	Base	Base	Base	Base	Base	4.25
Professional work	0.0068	0.0041	0.2251	0.0009	0.58
Nonprofessional work	-0.0101*	-0.0195	-0.3000	0.0058	3.67
Partner education
No education	Base	Base	Base	Base	Base	8.24
Primary	0.0173***	0.0343	-0.1841	-0.0063	-3.96
Secondary and Higher	0.0400***	0.0497	0.3911	0.0194	12.20
Sex of household head
Male	Base	Base	Base	Base	Base	-0.16
Female	0.0070	0.0066	-0.0395	-0.0002	-0.16
Household size
1–3	Base	Base	Base	Base	Base	-0.36
4–6	0.0153**	0.0310	-0.0217	-0.0006	-0.42
> = 7	0.0032	0.0042	0.0242	0.0001	0.06
Household wealth index
Poorest	Base	Base	Base	Base	Base	38.99
Poorer	0.0203***	0.0164	-0.3548	-0.0058	-3.65
Middle	0.0395***	0.0334	-0.0230	-0.0007	-0.48
Wealthier	0.0674***	0.0565	0.3296	0.0186	11.69
Wealthiest	0.0991***	0.0787	0.6365	0.0501	31.43
Read newspaper
No	Base	Base	Base	Base	Base	2.90
Yes	0.0286***	0.0194	0.2378	0.0046	2.90
Listened to the radio
No	Base	Base	Base	Base	Base	7.11
Yes	0.0162***	0.0375	0.3020	0.0113	7.11
Watched television
No	Base	Base	Base	Base	Base	7.32
Yes	0.0224***	0.0247	0.4725	0.0116	7.32
Explained						95.69
Residual						4.31

Note: Significance level

***p < 0.001

**p < 0.01

*p < 0.05

Fig 2

Contribution of various factors to prenatal HIV test service uptake for PMTCT in East Africa.

The third column of Table 3 shows the elasticity of prenatal HIV test service uptake for each explanatory variable. The value of elasticity for urban resident women was 0.0388, indicating that a change in women’s place of residence from a rural area to an urban area would result in a 3.8% increment in pro-rich socioeconomic inequality of prenatal HIV test service uptake. Likewise, while a change in the country of residence from Burundi to Rwanda (4.0%) increased the pro-rich socioeconomic inequality of prenatal HIV test uptake by 4%, the change in countries of residence from Burundi to Comoros (-5.7%), Ethiopia (-12.2%), Kenya (-1.6%), Malawi (-1.4%), Mozambique (-9.5%), Uganda (-1.6%) and Zimbabwe (-1.8%) reduced the pro-rich socioeconomic inequality in prenatal HIV testing by the percent stated.

The Erreygers concentration index was estimated for each factor related to prenatal HIV test uptake in the fourth column of Table 3. It indicates how the specific variable is distributed across SES. Accordingly, factors such as urban resident (ECI = 0.4838), women aged 20–34 years (ECI = 0.0597), secondary and higher educational level (ECI = 0.3975 for maternal and ECI = 0.3911 for paternal education), higher household wealth index (ECI = 0.3296 for wealthier and ECI = 0.6365 for wealthiest), being exposed to media (ECI = 0.2378 for reading a newsletter, ECI = 0.3020 for listening to the radio, and ECI = 0.4725 for watching TV), had positive Erreygers concentration index, implying that these factors were concentrated amongst women with higher SES or played major contributory roles to inequalities favouring the rich in the use of prenatal HIV test service. The policy implications of these results are that socioeconomic inequality in prenatal HIV testing could be improved by reducing disparity in the distribution of these factors across the economic spectrum, for instance by creating education and media access opportunities to economically disadvantaged groups.

Finally, the fifth and sixth column of Table 3 presents the absolute and percentage contribution of each contributory factor to overall inequality for Prenatal HIV test service uptake. Accordingly, the findings revealed that the major contributors to prenatal HIV test service uptake inequalities in East Africa were household wealth status, maternal education level, and residence. Women from wealthier and wealthiest households explained 43.12% of inequality, followed by women with secondary and higher-level education (20.86%) and urban dwellers (11.78%). Other important contributors to socioeconomic inequality of prenatal HIV test service uptake were partner education (8.24%), listening to the radio (7.11%), and watching television (7.32%). The result furthermore indicates that country of residence, age of woman, and household size played a less important role in determining the prenatal HIV test uptake inequality in East Africa. Overall, included variables in our model explained 95.69% variability of the estimated socioeconomic inequality in prenatal HIV test uptake in East Africa. Unexplained variability or variability explained by factors not included in the model was 4.31% [

The contribution of specific factors varied across countries in East Africa. For instance, in Burundi, listening to the radio (68.2%) and maternal occupation (32.9%) contributed most to inequalities in utilization of prenatal HIV test service, whereas in Comoros the greatest contributors were wealth index (32.5%) and partner’s education (29.8%). In Ethiopia, wealth index (38.8%) and maternal education (8.3%) were found to be the largest contributors to inequalities in prenatal HIV test service uptake while the same in Mozambique were wealth index (33%) and mother’s occupation (14.2%) [

Discussion

Reducing socioeconomic inequality in the utilization of PMTCT services is a major health policy challenge in East African countries. This study showed the existence of socioeconomic inequality in prenatal HIV test service utilization in East Africa with this being 15.94% higher among the rich than the poor (p<0.001). This result is consistent with most studies in developing countries, in which women with higher SES had a high probability of using a range of maternal health services including HIV test services during pregnancy [31, 54–56]. However, the finding contradicts a study in South Africa [39] that found pro-poor inequality of HIV testing during pregnancy among women attending public health facilities. The contradictory finding might be attributable to the fact that underprivileged women are more likely to visit public health facilities than those who are not underprivileged and hence there is a selection bias towards them. Furthermore, the magnitude of pro-rich inequalities in prenatal HIV tests varied across East African countries. The inter-country difference might be the fact that countries involved in this study are East African countries, where political context, economic development, health, and the social policies underway are not the same. It could also be related to variations in the study periods, in countries with the earliest surveys there would be different interventions that have been implemented to improve HIV testing.

Given free-of-charge maternal health services including PMTCT services have been implemented in most East African countries [57-59], the pro-rich inequality of prenatal HIV test service uptake amongst women is a great concern and has policy implications. From this evidence, we can understand that minimizing financial barriers to improve access amongst different socio-economic groups does not automatically result in equitable utilization of the services. Even if the services are provided free of charge or do not involve patient fees, unavailability of services in some areas, and inaccessibility to these services due to costs for transportation [30] could remain a challenge in the region for the poor. Furthermore, studies showed that acceptability and utilization of HIV test services by pregnant women can be negatively influenced by lack of awareness about MTCT of HIV [18, 60], HIV related stigma and social exclusion [29, 61].

The decomposition analysis of this study identified different significant contributors to socioeconomic inequality in prenatal HIV test services uptake. Place of residence, maternal education, household wealth index, and exposure to media were the variables with the highest contribution to the overall inequality in prenatal HIV test uptake. Women living in urban area were disproportionately rich (positive concentration indices) and they contributed 11.78% to the total observed inequality in prenatal HIV testing in East Africa. Urban women do not face the same barriers to physical access that rural women do; poor roads and greater difficulty of access to health services, low quality and weak integration of PMTCT services with maternal and child health services in rural women, even though services are free [62]. Rural areas are characterized by higher unemployment, lower levels of education, as well as increased poverty. In contrast, in an urban setting, there could be higher employment, education access and successful implementation of the PMTCT programs in terms of services quality, access, and integration [63]. Therefore, ensuring that health facilities are provided quality services and extending the home or community-based HIV testing and counselling programme in rural regions could help to improve access and consequently reduce inequality in HIV testing.

Consistent with other studies [31], a higher education level for both women and their partners contributed 13.69% and 8.24% to this service utilization inequality respectively. Possible explanations of this finding could be that in developing countries, educated partners have higher chance of employment relative to their counterparts, so that employment translates to a better economic situation and can act as an enabling factor for the utilization of healthcare services. Education is also strongly associated with health behaviours and increases individual preventive health care services uptake through improved knowledge, attitudes, and practice. An educated person is more likely to know the risks/consequences of not being tested for HIV during pregnancy [64]. Furthermore, the contribution of secondary and higher education in pro-rich utilization might be explained by people who are more educated being more likely to identify and access affordable healthcare services [18] and make informed decisions about their health [64]. Hence, bridging inequalities in maternal and partner educational levels is an important responsibility to reduce inequalities in the use of prenatal HIV test services.

Many studies have proved that household wealth index is associated with HIV testing [18, 19, 56]. Likewise, in this study, as compared with the poorest groups, the likelihood of HIV testing was 6.7% for women from poorer groups and 9.9% for women from the wealthiest groups. The possible justification may be that pregnant women from the wealthiest households are more likely to receive WHO-recommended antenatal care visits (ANC) (at least four ANC visits) and this would, in turn, increase the chance of receiving HIV testing for PMTCT [65, 66]. Furthermore, previous research has found that there is high self-initiated HIV testing amongst wealthy individuals in countries such as Kenya, Malawi, and Uganda [67]. Based on the decomposition analysis of this study, if the wealth index were equally distributed across women, the socioeconomic-related inequality in prenatal HIV test uptake would have been decreased by 39% or if wealth did not affect the uptake of HIV testing, then uptake of HIV testing amongst the poor would have been increased by 61%.

In this study, women’s access to media also significantly contributed to the pro-rich inequality of prenatal HIV test uptake (7.32% for watching television, 7.11% for listening to the radio, and 2.90% for reading newspapers/magazines). This is because the consumption of media is higher amongst wealthier, urban residents and educated women as linked with media infrastructure, electricity access and ownership of private television or radio. Therefore, expanding media coverage or access to radio, television, and newspapers programs amongst women in the lower socioeconomic groups could be an important measure to address the observed inequality of prenatal HIV test uptake associated with it in East Africa. In this regard, establishing radio stations, distributing radio for mothers in need and broadcasting essential public health information in their local language especially in rural area is recommended. Media can positively influence underprivileged people’s health-seeking behaviour by informing, motivating, and reminding them to access healthcare services [68] and therefore, can be used as a strategy to reduce prenatal HIV test service uptake inequality by disseminating well-defined behaviourally focused PMTCT related messages to large audiences, repeatedly and over time [68]. Furthermore, media can be used to improve the general socio-economic and political landscape of any society in the long term.

Our study had several strengths and limitations that need to be acknowledged. To the best of our knowledge, this is the first article that has investigated inequalities in prenatal HIV test uptake from a population-based household survey in East Africa. Evidence provided by this study is important to facilitate the formulation of appropriate strategies to reduce inequalities in prenatal HIV test services uptake in the PMTCT program. The results are comparable across East African countries as all the variables used were similarly described across countries. As a limitation, since this was a cross-sectional study, we were unable to establish any causal relationship between prenatal HIV test service uptake and its determinants. Finally, there is a possibility of recall bias as there is a gap between the time of services use and the timing of data collection.

Conclusions

We found a pro-rich distribution of prenatal HIV test services utilization in East Africa. However, the performance of the Burundi, Malawi, Rwanda, Uganda, Zambia, and Zimbabwe health systems is promising in narrowing socioeconomic inequalities in prenatal HIV tests for PMTCT. The highest contributors to the inequality of prenatal HIV test service uptake in East Africa included household wealth status, woman’s education, access to media and residence. We believe that inequalities observed in this study attributed by these factors are unjust or unfair and are therefore avoidable. The concentration of prenatal HIV test services amongst the high SES groups in East Africa have valuable health policy implications for the elimination of mother-to-child HIV transmission. Implementation of governmental policies and programmes to reduce the gap between the rich and poor (e.g. poverty reduction), higher educational attainment and HIV testing targeting socio-economically disadvantaged women (e.g. Home/community-based HIV testing and counselling) are highly recommended.

Erreygers’ normalized concentration indexes and curves for prenatal HIV test uptake across East African countries.

(DOCX)

Click here for additional data file.

A decomposition analysis of socioeconomic inequalities in prenatal HIV testing for PMTCT among mother aged 15–49 years in Burundi and Comoros.

(DOCX)

Click here for additional data file.

11 Apr 2022

Measuring socioeconomic inequalities of prenatal HIV test uptake for prevention of mother to child transmission of HIV in East Africa: A decomposition analysis

PLOS ONE

Dear Dr. Astawesegn,

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.

I would like to sincerely apologise for the delay you have incurred with your submission. It has been exceptionally difficult to secure reviewers to evaluate your study. We have now received two completed reviews; the comments are available below. The reviewers have raised significant scientific concerns about the study that need to be addressed in a revision.

Please revise the manuscript to address all the reviewer's comments in a point-by-point response in order to ensure it is meeting the journal's publication criteria. Please note that the revised manuscript will need to undergo further review, we thus cannot at this point anticipate the outcome of the evaluation process.

Please submit your revised manuscript by May 26 2022 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'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

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: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Miquel Vall-llosera Camps

Senior Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf.

2. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please delete it from any other section.

3. We noticed you have some minor occurrence of overlapping text with the following previous publication, which needs to be addressed:

- https://pubmed.ncbi.nlm.nih.gov/34065689/

The text that needs to be addressed involves the Materials and Methods section.

In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Summary:

The authors investigated wealth-related socio-economic inequality in prenatal HIV testing, in 10 East African countries using DHS data. They report a pro-rich inequality index indicating better prenatal HIV testing amongst pregnant women of higher socioeconomic status. They also found that place of residence, maternal education, household wealth index, and exposure to media contributed significantly to the overall inequality of prenatal HIV test uptake. Urban dwellers and higher educated women for example had better uptake of HIV testing. The gap between the rich and poor with respect to access and utilization of antenatal healthcare services needs to be dealt with urgently in this region.

The study is very well written and methodologically sound. I have essential but not major revision recommendations outlined below.

Minor Essential Revisions:

OVERALL:

- Please explain abbreviations at the first time you use them. E.g. MTCT in the abstract, PMTCT & SES in the introduction. are not explained the first time they are mentioned. Terms explained already in the introduction should then use abbreviations thereafter – I realize you still used “prevention of mother-to-child transmission of HIV” under Materials & methods.

- The phrase “PMTCT of HIV” can be avoided if you define ‘PMTCT’ to include HIV, but you have not defined PMTCT.

- Run the manuscript through a plagiarism score check

ABSTRACT: Useful to indicate the period of data collection in years.

INTRODUCTION: I would re-order the paragraphs on the Introduction:

- move paragraphs 3,4,5 to be the first, second & third paragraphs, respectively as these nicely introduce the main health issue and then zoom into the specific topic of the paper. Here I recommend listing the East African countries which were included in the 81% estimate (I recommend this because some countries like Mozambique, Zambia & Zimbabwe are sometimes grouped under Southern Africa in global statistics reports).

- The fourth paragraph would start with the sentence: “Measuring socioeconomic inequalities in health service use can guide the focus of researchers and assist policymakers to prioritize specific health care needs (38)” . This way you can reduce current paragraphs 1 & 2 into a single paragraph (new paragraph 4) to be less broad but focus on defining socioeconomic inequalities and how they are measured. This paragraph would touch on one or two examples of methods for measuring these inequalities – thus an opportunity to introduce important terms such as “decomposition analysis’ which you mention in your objectives paragraph, and ‘concentration indices’ which you mention in the methods, but both not explained there. Some sentences can be easily moved from the Methods section, e.g. “CI is a popular tool to measure health and health care inequality in the field of health policy and health economics research (56).” AND “The concentration index (CI) was used to measure the extent/magnitude of socioeconomic inequalities in prenatal HIV test service utilization (54, 55).” The current first two paragraphs have some useful content but some of it is not necessary and makes the intro wander away slightly.

- You may end with your objectives paragraph.

RESULTS: It might be useful to the reader in understanding why some percentages do not add up to 100%, if you indicate with a character like * in Table 1 those variable/country cells where there was missing information. E.g., Burundi ‘Urban = 8.7% and rural = 81.0%.

DISCUSSION: Could some of the inter-country differences somehow influence the differences in results between countries? I would imagine the surveys conducted in 2011-2012 (Comoros & Mozambique) would be not comparable to those conducted in 2016 and beyond given the change in PMTCT guidelines over time. Authors may review changes in PMTCT guidelines around prenatal HIV testing across the survey years and discuss whether this could have impacted on the survey data. The authors may decide whether this is a simple discussion point or a limitation.

Discretionary Revisions:

At the author’s choice – would using South-East and East Africa be more accurate given the geographical location of Mozambique, Zambia and Zimbabwe is accurately South-East Africa and the latter two are commonly grouped under Southern Africa as well.

Reviewer #2: 1. I would suggest focusing the intro on the topic and not on explaining what are inequalities and inequities. That could be a paragraph with references.

2. The intro could explain why the topic is relevant and we already know about it. As the authors stated, there is already evidence of differences by socioeconomic status in HIV testing, so what is the value-added of characterizing those using the CI? How this metric could contribute to informing better policies? That is, for example, knowing that income level is a relevant contributor to testing inequalities could modify policies?

3. In terms of the data, how using a period of 7 years of surveys could affect the analysis? That is, during this period (2011-2018), there were interventions to improve testing in those countries, in particular in the countries with the earliest surveys?

4. How comparable is the wealth index across countries? That is, the metric is relative to households in the country, not between countries. Also, my understanding is that the WI uses a set of variables that could differ county by country as some assets are less or more relevant depending on the context.

5. The WI was used as a continuous measure or the quintiles?

6. There is a ? mark on formula 3 and the text that follows

7. It seems to me that the authors interpretation of the CI is not adequate. The CI is a measure of the degree of inequality, that is, how concentrated is the health indicator among individuals with higher (lower) socioeconomic level. The value of the CI is not a percentage difference between two categories (rich/poor) as it is estimated on the complete spectrum of SE level.

8. The interpretation on the decomposition also seems not adequate. The coefficients are the relation between the CI for testing and the CI for the explanatory variable, so indicates how the inequality on the explanatory variable is related to inequality in testing.

9. Interpretations as a change of residence to decrease inequalities seem not really informative. It would be important to discuss this from social determinants of health perspective. Suggesting that distributing radios —for example— could improve equality on testing may be missing the structural factors that may be related to listening to the radio in a particular context.

10. The discussion could be improved by discussing the findings using the framework of the social determinants of health, rather than trying to discern how listening to the radio or watching tv are related to testing.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Juan Pablo Gutierrez

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

21 Jun 2022

I have attached one by one response to the editor and reviewers as a separate file.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

10 Aug 2022

Measuring socioeconomic inequalities in prenatal HIV test service uptake for prevention of mother to child transmission of HIV in East Africa: A decomposition analysis

PONE-D-21-35264R1

Dear Dr. Astawesegn,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Joseph Donlan

Staff Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed all my comments.

One minor error needs to be noted.

ABSTRACT: The ‘MTCT’ abbreviation can be introduced in the first sentence of the ABSTRACT where authors mention ‘mother-to-child transmission of HIV’, and then simply used in the ‘Conclusion’

The authors may consider indicating in the ABSTRACT the years for which the reported data apply.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

**********

12 Aug 2022

PONE-D-21-35264R1

Measuring socioeconomic inequalities in prenatal HIV test Service uptake for prevention of mother to child transmission of HIV in East Africa: A decomposition analysis

Dear Dr. Astawesegn:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr Joseph Donlan

Staff Editor

PLOS ONE