Identifying individual,community and country-level determinants of poor knowledge of mother-to-child transmission of HIV among women in sub-Saharan Africa: spatial and multilevel analysis

Abstract

Background

Mother-to-child transmission (MTCT) of HIV accounts for over 90% of new HIV infections among children. In 2018, a report from the World Health Organization showed that about 14.8 million children between the ages of 0 and 14 years were HIV-exposed globally, of which 13.2 million reside in sub-Saharan Africa (SSA). This study aimed to determine the spatial variation and identify factors of poor Knowledge of MTCT of HIV among women in SSA.

Methods

A cross-sectional study was conducted using secondary data of 318,354 women collected during a Demographic Health Survey (DHS) in SSA countries from the 2006-2024. Spatial cluster detection was conducted using SaTScan version 10.1, spatial mapping was performed in ArcGIS version 10.7, and statistical analyses were carried out using Stata version 17. Multilevel logistic regression was used to identify the associations of health and demographic variables with poor knowledge of MTCT.

Results

The spatial analysis revealed that poor knowledge of the MTCT of HIV among women of reproductive age significantly varied across countries. The spatial window analysis identified the primary clusters in Madagascar, the Congo Democratic Republic and parts of Angola. Overall, 46% of women had poor knowledge of MTCT of HIV. In multivariable analysis, age of 15 to 24 years (AOR: 1.11, 95% CI 1.04,1.18), no media exposure (AOR: 1.13, 95% CI 1.08, 1.19), being poorest (AOR: 1.17, 95% CI 1.06, 1.28), not working (AOR: 1.07, 95% CI 1.02, 1.12), never tested for HIV (AOR: 1.38, 95% CI 1.21, 1.57), lack of comprehensive knowledge (AOR: 1.16, 95% CI 1.10, 1.21), low community media exposure (AOR: 1.11, 95% CI 1.03, 1.19), Western Africa region (AOR: 1.27, 95% CI 1.20, 1.40), low income (AOR: 1.97, 95% CI 1.51, 2.56), and lower middle income (AOR: 1.53 95% CI 1.19, 1.95) were associated with poor knowledge of MTCT.

Conclusion

Spatial analysis demonstrated that poor knowledge of MTCT of HIV among women was unevenly distributed, with significant regional variation across the SSA countries. This study found that individual, community and country-level variables were significantly associated with poor knowledge of MTCT of HIV. The finding highlights the need for targeted, multilevel HIV education and testing programs that prioritize hotspot areas and address disparities in knowledge of MTCT of HIV across SSA.

Keywords

poor knowledge MTCT reproductive women sub-Saharan Africa

Introduction

Human immunodeficiency virus (HIV) is an infection that attacks the body’s immune system, specifically the white blood cells called CD4 cells and reduces a person’s immunity against opportunistic infections, such as tuberculosis, fungal and severe bacterial infections, and some cancers.¹ According to the United Nations Acquired Immune Deficiency Syndrome (UNAIDS) Global Fact Sheet 2022; 38.4 million people were living with HIV in 2021. Of these, 36.7 million were adults (15 years or older), and 54% of all people living with HIV were women and girls.² In the same year, approximately 650,000 deaths worldwide were attributed to illnesses related to HIV, which shows a 30.8% decrease compared to 2017.^2,3 It was estimated that around 20.2 million people were living with HIV worldwide in 2021, of which women and girls accounted for 63% of all new HIV infections in sub-Saharan Africa (SSA). This report revealed that a total of 425,100 AIDS-related deaths occurred in Africa, and 98.8% of these were accounted in SSA.²

The transmission of HIV from HIV-positive mothers to their infants is called mother-to-child transmission (MTCT).⁴ It can occur during pregnancy, childbirth or breastfeeding and is the most significant source of HIV infection in children and infants.⁵ The transmission risk ranges from 15%-45% in the absence of intervention,⁶ with some variations ranging from 5% -10% during pregnancy, 10%-15% during labor/delivery, and 5%-20% through breastfeeding. However, the risk of HIV transmission can be reduced to 2% or less if a woman receives antiretroviral drugs during pregnancy and childbirth, and the baby receives antiretroviral prophylaxis for 4–6 weeks after delivery.^7,8 In the absence of treatment, half of the infected children died before 2 years of age.⁹ In developing countries, particularly in eastern and southern Africa, there is a high burden of HIV,¹⁰ and with approximately 25.6 million HIV-positive individuals in this region in 2015, 59% of whom were women of reproductive age.^7,11 In addition, there are nearly 1.2 million infants exposed to HIV, according to a report by the United Nations International Children’s Emergency Fund (UNICEF).¹²

According to a 2020 UNICEF report, approximately 310 children died each day from AIDS-related causes in 2019, largely due to inadequate access to HIV prevention, care and treatment services.² Additionally, the 2017 WHO report estimated that 62000 children aged 0–14 years were living with HIV in Ethiopia in 2016, representing 8.7% of the country’s total HIV infections.¹³

Although HIV causes many deaths globally, the magnitude of poor knowledge of MTCT is high in different African countries.^14,15 A systematic review of 33 published articles reported a pooled prevalence of 7.68% for MTCT of HIV in East Africa.¹⁶ Evidence from various studies conducted across Ethiopia indicates that 34.5% to 81% of women of reproductive-age (15 to 49 years) lack knowledge about MTCT of HIV.^14,17,18 These studies further identified several factors associated with women’s knowledge of MTCT, including place of residence, attainment of higher education, antenatal care utilization, receiving information from health care providers, wealth status, exposure to mass media, partners’ level of education and occupation.^18,19 Preventing HIV transmission from infected women to their children and families remains a critical entry point for comprehensive HIV care for women, their partners and infants.^20,21 Consequently, most SSA countries launched option B+ in 2013, which involves lifelong antiretroviral treatment.²² Even though many countries are implementing the above-mentioned interventions, the lack of knowledge on MTCT makes the interventions inefficient, and thus MTCT remains a major public health issue. Moreover, to the best of our knowledge, no study has shown the spatial distribution of poor knowledge of MTCT among women of reproductive age in SSA.

Therefore, identifying poor knowledge of MTCT of HIV and its determinants among women is crucial to tackling the problem. Additionally, this study answers the spatial variation of poor knowledge of MTCT of HIV among women of reproductive age, providing preliminary evidence to inform policymakers and program planners in designing targeted interventions to improve knowledge of MTCT of HIV, including prioritizing hotspot areas for resource allocation, integrating MTCT education with in maternal health services, expanding HIV testing and media outreach and addressing gender-related barriers to improve MTCT knowledge.

Methods

Study design, period and setting

A community-based cross-sectional study was conducted in SSA countries. Sub-Saharan Africa is a region of Africa that lies south of the Sahara Desert with four sub-regional states (East Africa, West Africa, Central Africa and Southern Africa). Therefore, this study was conducted based on recent demographic and health survey reports in each sub-Saharan African country from 2006 to 2024 survey years. It has a population of nearly 1.2 billion, and 42 % of the population is under 15 years of age.

Data source

This study utilized secondary data obtained from the recent Demographic Health Survey (DHS), accessed through the DHS program database. The DHS surveys were implemented by the Central Statistical Agency (CSA) of each sub-Saharan African country, with technical and logistical support from International Classification of Functioning, Disability and Health (ICF) under the global DHS program,^23,24 which is funded by the United States Agency for International Development (USAID). The DHS employs standardized data collection procedures and three validated instruments: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These tools were further adapted and contextualized by national governmental and non-governmental stakeholders while maintaining methodological consistency and validity. For this study, variables anticipated to be associated with women’s poor knowledge about MTCT of HIV were extracted from the women’s dataset, informed by a review of relevant literature and then prepared for further analyses.

Study population and sampling procedure

The DHS follows a multistage sampling design using a two-stage stratified random sampling technique. In the first stage, clusters (or census enumeration areas) were selected from each geographic stratum using a probability proportional to size, based on the required number of households. In the second stage, households were selected within the chosen clusters. Data were collected using standard questionnaires employed at both the household and individual levels. Enumeration areas were selected with a probability proportional to size with independent selection in each sampling stratum. In the next stage, 200-300 households per enumeration area were selected randomly. All women of reproductive age (15-49 years) residing in the selected enumeration areas of SSA countries with complete records of the standard DHS were included, while women with incomplete observations in the DHS dataset were excluded from this study. By using this procedure, 463,138 eligible women (15–49 years) were identified and interviewed in the parent study and were included as the source population for the current study (Table 1).

Table 1.

Study participants interviewed by country and their respective year of survey.

Country	Year of survey	Frequency	Percent (100%)
Angola	2023/24	14,717	3.12
Benin	2017/18	15,928	3.38
Burundi	2016/17	17,269	3.66
Congo Democratic Republic	2023/24	27,583	5.85
Cameroon	2018	13,527	3.11
Ethiopia	2016	15,683	3.33
Gabon	2020/21	9,507	2.34
Ghana	2022	15,014	3.19
Gambia	2019/20	11,797	2.52
Guinea	2018	10,771	2.31
Comoros	2012	4,583	1.12
Liberia	2019/20	8,065	1.71
Lesotho	2023/24	6,413	1.36
Madagascar	2021	18,596	4
Mali	2023/24	20,354	4.32
Mauritania	2019/21	15,714	3.33
Malawi	2015/16	24,562	5.21
Mozambique	2023	13,183	2.8
Nigeria	2024	39,050	8.29
Niger	2012	10,842	2.36
Namibia	2013	9,176	2.13
Rwanda	2019/20	14,634	3.11
Sierra Leone	2019	15,574	3.31
Senegal	2011/12	15,688	3.33
Eswatini	2006/07	4,987	1.06
Chad	2014/15	17,264	3.76
Togo	2013/14	9,369	2.01
Uganda	2016	18,506	3.93
South Africa	2016	8,514	1.81
Zambia	2024	13,951	2.96
Zimbabwe	2015	9,955	2.11
Total		463,138	100

Study variables

Outcome variable

For our analysis, four questions (Q1. Can HIV be transmitted to the baby during pregnancy?, Q2. Can HIV be transmitted to the baby during delivery?, Q3. Can HIV be transmitted to the baby during breastfeeding?, and Q4. Any drug to avoid HIV transmission to the baby during pregnancy?); were used to identify the level of respondents’ poor knowledge on MTCT of HIV, with category 1 for ‘yes’ and 0 for ‘no’. Women who responded “no” and “don’t know” to the survey questions were classified as having no knowledge in this study, in accordance with the DHS guidelines. The outcome of interest for this analysis is “women’s poor knowledge of MTCT of HIV”, and is defined as “yes” if the women answered at least one question incorrectly, and “no” if the respondent answered all four questions correctly.^25,26

Independent variable

In this study, factors at the individual-, community- and country-level were included as independent variables. The individual-level factors were women’s current age, marital status, level of education, working status, partner’s educational status, partner’s working status, comprehensive knowledge about HIV, HIV test exposure, exposure to media and wealth index. The place of residency, distance from health institutions, community media exposure, community poverty, community women’s education and community comprehensive knowledge about HIV were the community-level variables. Country income status and sub-regions were the country-level variables considered in this study.

Operational definition

Poor knowledge of MTCT: Respondents were considered to have poor knowledge of mother-to-child transmission of HIV if they were missing one or more of the four key questions (HIV transmission during pregnancy, during delivery, during breastfeeding and/or drugs to prevent transmission).

Comprehensive knowledge about HIV: is when the respondents fulfill all the following criteria (know using a condom and having one uninfected partner as primary prevention methods, knowing that a healthy-looking person can have HIV, rejecting the two common misconceptions that HIV can be transmitted by supernatural means and transmitted by mosquito bite.

Media exposure: media exposure was assessed using four indicators: watching television, listening to the radio, reading newspapers, and internet use (all coded Yes/No). We summed these to generate, whether women have media exposure if they used at least one of these sources and unexposed if they used none.²⁷

Country-level wealth status: Countries were classified as low-income, lower-middle-income, and upper-middle-income categories based on the 2022 World Bank List of Economies.²⁸

Data quality assurance

Data quality assurance for secondary DHS data is ensured through standardized questionnaires across all nations. Data extraction and handling of missing values were performed according to the precise definition of the DHS guideline. The DHS program employs validated data entry systems with consistency checks, conducts regular data cleaning, and implements procedures to minimize missing and inconsistent responses. Moreover, sampling weights and survey design variables are provided to enhance the accuracy, reliability and representativeness of the data used for the analysis.

Data management and analysis

Data extraction, cleaning, coding, and analysis were performed using Stata version 17. Sampling Weights were applied to ensure representativeness and appropriate statistical estimates.

Spatial analysis

Spatial analyses were conducted using ArcGIS version 10.7 and SaTScan version 10.1.

Spatial autocorrelation

The Global Moran’s I was used to assess whether poor knowledge of MTCT was dispersed, clustered or randomly distributed. Moran’s I value ranges from -1 to +1; values near to +1 indicate clustering, near −1 indicate dispersion, and near 0 indicates random distribution of poor knowledge of MTCT of HIV. A significant Moran’s I (P<0.05) indicated spatial autocorrelation.²⁹

Hot spot analysis

Getis-OrdGi statistics were used to identify hotspots (areas with high prevalence of poor knowledge), and cold spots (areas with low prevalence of poor knowledge). Significant Z scores indicate areas of clustering.³⁰

Spatial interpolation

Ordinary Kriging interpolation technique was used to predict poor knowledge of MTCT in unsampled areas of the country based on observed measurements. Even though there are different deterministic and geostatistical methods, the ordinary Kriging interpolation incorporates spatial autocorrelation and optimizes the weights statistically.

Spatial scan analysis

A Bernoulli-based model in SaTScan was used to identify statistically significant clusters of women with poor knowledge of MTCT. Women with poor knowledge of MTCT were taken as cases, and those with good knowledge were considered as controls. Clusters were evaluated using Likelihood Ratio tests, p-value, and relative risk, with the cluster of maximum likelihood identified as the primary cluster. For these significant clusters, the log-likelihood ratio (LLR), relative risk (RR), population, and number of cases were reported.

Multilevel analysis

Given the hierarchical structure of DHS data, intra-class correlation coefficients (ICC) were calculated to assess clustering. A significant ICC revealed the use of multilevel mixed-effect logistic regression. Bivariable analysis was performed to select variables for multivariable analysis. Six models were fitted (Null model, Model-I (individual variables only), Model-II (community-level variables only), Model-III (country-level variables only), Model-IV (both individual and community-level variables), and Model-V (finally all individual, community and country-level variables)), and model fitness was checked using information criteria (DIC). The model with the lowest DIC was selected as the best-fit. Variables with p<0.05 in the final model were considered significant predictors of poor knowledge of women about MTCT of HIV.

Parameter estimation

Fixed effects: used to estimate the association between explanatory variables and poor knowledge of MTCT. The strength of associations has been reported using the Adjusted Odds Ratio (AOR) with 95% confidence intervals.

Log (\frac{π ij}{1 - π ij}) = β o + β 1 xij + β 2 xij + \dots uj + eij

Where,

πij: the probability of poor knowledge of MTCT,

1 − πij: the probability of good knowledge of MTCT,

β1xij are explanatory variables for the i^th woman in the j^th cluster, ß’s are fixed coefficients. While ß₀ is the intercept noted that the effect of poor knowledge of MTCT. The uj indicates the random effect of the community, and eij is the residual error.^31,32

Random effects: is employed to estimate the average distribution of effects and for comparison of two clusters selected randomly. This cluster-level variation was assessed using the median odds ratio (MOR), ICC, and proportional change in variance (PCV).

The MOR is the odds ratio between the areas of the highest risk and the lowest risk of two clusters randomly selected.

MOR = exp. [√(2 × VA) × 0.6745], or MOR=e^0.95√VA, where; VA is the area level variance.^33,34

The PCV shows variation in the prevalence of women’s MTCT knowledge explained by variables. PCV= $\frac{V_{null} {- V}_{A}}{V n u l l}$ ∗100, and

The ICC measures the variation of women’s MTCT knowledge between clusters, is calculated as: ICC= $\frac{VA}{VA + 3.29}$ ∗100%, where VA = cluster level variance, V_null = variance of the null model.^34,35

Ethical consideration

This study was a secondary analysis of publicly available DHS data from sub-Saharan African countries. Ethical approval was not required. Data access was requested and granted via the DHS program office (https://www.dhsprogram.com). All survey data were anonymized by the DHS during the collection.

Result

Socio-demographic characteristics of the study population

A total weighted 318,354 reproductive women were considered in this study. Two-thirds (71%) of the women were between the age of 15 to 34 years. Of the total weighted women, 57.52% were living in rural areas. About 196,387 (62%) were married, and 93,327 (29.32%) of them were single. One-third (32.20%) of the women had primary education. Around 122,737 (72.26%) of the reproductive women had media exposure (Table 2).

Table 2.

Sociodemographic characteristics of women in the reproductive age; DHS of sub-Saharan Africa, 2023.

Variables	Categories	Frequency		Weighted percentage (%)
Variables	Categories	Unweighted	Weighted	Weighted percentage (%)
Age	15-24 years	122,812	123,626	38.83
	25-34 years	100,167	102,911	32.32
	35-49 years	91, 567	91,817	28.85
Sex of household	Male	219,814	223,618	70.24
Sex of household	Female	94,702	94,735	29.76
Marital status	Single	91,852	93,327	29.32
	Married	194,018	196,387	61.69
	Widowed	8,146	8,026	2.52
	Divorced	20,500	20,614	6.48
Women Educational attainment	No education	60,567	60,457	22.38
	Primary education	87,535	87,002	32.20
	Secondary & above	118,531	122,737	45.43
Media exposure	Yes	169,387	173,007	72.26
Media exposure	No	66,944	66,406	27.74
Wealth index	Poorest	55,199	49,170	15.45
	Poor	55,959	55,333	17.38
	Middle	60,928	60,761	19.09
	Rich	65,467	70,771	22.23
	Richest	76,963	82,321	25.86
Women working status	Working	179,707	183,159	57.56
Women working status	Not working	134,648	135,023.	42.44
Partner working status	Working	183,535	186,071	93.83
Partner working status	Not working	12,228	12,243	6.17
Ever been tested for HIV	No	112,433	114,573	39.75
Ever been tested for HIV	Yes	172,892	173,661	60.25
Comprehensive knowledge of HIV	Not knowledgeable	168,398	169,255	58.84
Comprehensive knowledge of HIV	Knowledgeable	116,434	118,374	41.16
Tested for HIV as part of antenatal visit	No	28,914	29,031	28.66
Tested for HIV as part of antenatal visit	Yes	72,111	72,274	71.34
visited by fieldworker	No	244,689	248,640	85.03
visited by fieldworker	Yes	44,428	43,783	14.97
Visited health facility	No	137,303	139,637	45.03
Visited health facility	Yes	168,601	170,428	54.97
Community level factors
Perception of distance from health center	Big problem	107,344	106,622	34.45
Perception of distance from health center	Not big problem	198,129	202,913	65.55
Community media exposure	Low	117,521	116,922	48.83
Community media exposure	High	118,816	122,500	51.16
Community poverty	Low	166,817	177,121	55.64
Community poverty	High	147,699	141,233	44.36
Community education status	Low	116,612	115,951	42.91
Community education status	High	150,021	154,245	57.09
Community comprehensive knowledge	Low	121,795	120,980	42.00
Community comprehensive knowledge	High	163,412	167,045	58.00
Residence	Urban	129,302	135,234	42.48
Residence	Rural	185,214	183,120	57.52
Country level factors
Sub-Regions	Western	118,088	119,673	37.59
	Central	43,435	45,356	14.25
	Eastern	126,153	126,328	39.68
	Southern	26,840	26,997	8.45
Country income status	Low income	185,068	186,368	58.54
	Lower middle income	107,740	109,935	34.53
	Upper middle income	21,708	22,052	6.93
Survey Year	After SDG (2016-2024)	332,304	333,771	80.26
Survey Year	Before SDG (2006-2015)	81,864	82,100	19.74

Overall prevalence of poor knowledge on MTCT of HIV

The pooled prevalence of poor knowledge of MTCT of HIV among women in SSA was 46% (95%CI: 40-51%). It covers a range of 26% in South Africa to 85% in Comoros. As indicated from the forest plot, the high I² value shows that the observed variation across the 31 sub-Saharan African countries was due to heterogeneity other than chance (Figure 1).

Figure 1.

Forest plot showing pooled prevalence of poor knowledge of MTCT of HIV among women of reproductive age, DHS of sub-Saharan Africa.

Most of the respondents (88.93%) and (87.50%) knew MTCT of HIV could occur through breastfeeding, and during delivery, respectively (Table 3).

Table 3.

Poor Knowledge of MTCT of HIV among women (15–49 years) in sub-Saharan Africa.

Variable	Category	Frequency		Weighted Percentage (%)
Variable	Category	Unweighted	Weighted	Weighted Percentage (%)
HIV transmitted during pregnancy	Yes	260,105	263,549	82.78
HIV transmitted during pregnancy	No	54,411	54,804	17.22
HIV transmitted during delivery	Yes	275,151	278,558	87.50
HIV transmitted during delivery	No	39,365	39,796	12.50
HIV transmitted during breastfeeding	Yes	280,912	283,117	88.93
HIV transmitted during breastfeeding	No	33,604	35,237	11.07
Drugs to avoid HIV transmission to baby during pregnancy	Yes	243,312	246,229	77.34
Drugs to avoid HIV transmission to baby during pregnancy	No	71,204	72,125	22.66

Spatial analysis of poor knowledge of MTCT of HIV

Spatial distribution of poor knowledge on MTCT

The spatial analysis revealed that a high proportion of poor knowledge on MTCT among women in SSA was spatially clustered in Madagascar, Uganda, Chad, Democratic Republic of Congo, Senegal, Mauritania, Angola and parts of Ethiopia. In contrast, South Africa, Zimbabwe, Mozambique, Cameroon, Ghana and Malawi exhibited the lowest proportion of poor knowledge on MTCT (Figure 2).

Figure 2.

Spatial distribution of poor knowledge on MTCT of HIV among women of reproductive age in sub-Saharan Africa, 2023. Source: United Nations Geoscheme for Africa, 2013.

Spatial autocorrelation analysis of poor knowledge on MTCT

Global Spatial autocorrelation demonstrated significant clustering of poor knowledge about MTCT was found to be spatially clustered in sub-Saharan Africa, with a Global Moran’s I of 0.906812 (p < 0.001). Given the z-score of 14.2800 indicates that there is less than a 1% probability that this clustered pattern occurred by chance, confirming strong spatial dependence (Figure 3).

Figure 3.

Spatial autocorrelation of poor knowledge of MTCT of HIV among women of reproductive age in sub-Saharan Africa, 2023. Source: United Nations Geoscheme for Africa, 2013.

Spatial hot spot (Getis-ord gistatistic) analysis of poor knowledge on MTCT

Hot spot analysis identified statistically significant clusters of poor knowledge of MTCT. High risk (hot spot) areas observed in Madagascar, Uganda, Democratic Republic of Congo, Chad, Senegal, Mauritania and Angola, whereas low risk (cold spot) areas were identified in South Africa, Zimbabwe, Mozambique, Cameroon, Ghana and Malawi (Figure 4).

Figure 4.

Hot-spot Analysis of poor knowledge of MTCT of HIV among women of reproductive age in sub-Saharan Africa, 2023. Source: United Nations Geoscheme for Africa, 2013.

Interpolation of poor knowledge on MTCT

Spatial interpolation using the Kriging method was employed to predict the likelihood of poor knowledge of MTCT of HIV in unsampled areas. Regions shaded in green denoted a higher predicted likelihood of poor knowledge of MTCT of HIV among women of reproductive (Figure 5).

Figure 5.

Kriging Interpolation of poor knowledge of MTCT of HIV among women of reproductive age in sub-Saharan Africa, 2023. Source: United Nations Geoscheme for Africa, 2013.

Spatial SaTScan analysis

Spatial scan statistics identified a total of 6,369 significant clusters, including 893 primary (most likely) clusters, and 361 secondary clusters. The primary clusters were located in Madagascar, the Congo Democratic Republic and parts of Angola which was centered at 17.373672 S, 49.402946 E) and 1009.61 km radius, a Log-Likelihood Ratio (LRR) of 3143.69, and a Relative Risk (RR) of 1.21 at p-value< 0.001, indicating that women within these clusters were at higher risk of poor knowledge compared to those outside the window. Secondary clusters were identified in Mauritania, Senegal, Guinea and some parts of Mali, centered at 2.981212 S, 22.052814 E and 761.82 km radius, LRR of 2551.46, RR of 1.88 (p< 0.001), suggesting significantly lower knowledge among women within these spatial windows (Figure 6).

Figure 6.

SaTScan Analysis of poor knowledge of MTCT of HIV among women in sub-Saharan Africa, 2023. Source: United Nations Geoscheme for Africa, 2013.

Factors associated with poor knowledge of reproductive women about MTCT

Bivariable mixed-effect logistic regression identified several individual-, community- and country-level variables associated with poor knowledge of MTCT at p<0.20. In multilevel mixed-effect logistic regression model, statistically significant factors (p≤0.05) included individual level variables (age, marital status, sex of household head, educational status, women working status, partner’s working status, wealth index, media exposure, ever been tested for HIV, tested for HIV as part of antenatal visit, visited by fieldworker, visited health facility, comprehensive knowledge about HIV), community level variables (residency, community poverty, community comprehensive knowledge, community media exposure, distance from health facility and community women education), and from country level variables (sub-regions, survey year and country income status).

Women aged 15-24 years had 1.11 times higher odds of poor knowledge of MTCT of HIV (AOR= 1.11, 95% CI 1.04, 1.18) compared to those aged 35-49 years. Women with no media exposure were more likely to have poor knowledge (AOR = 1.13, 95% CI 1.08, 1.19). Compared with richest women, those in the poorest, poor, middle and rich wealth quantile had significantly higher odds of poor knowledge, (AOR 1.17, with 95% CI 1.06, 1.28), (AOR 1.16, with 95% CI 1.06, 1.27), (AOR 1.14, with 95% CI 1.05, 1.25); and (AOR 1.14 with a 95% CI 1.05, 1.23), respectively. Non-working women were also more likely to have poor knowledge of MTCT (AOR=1.07, 95% CI 1.02, 1.12). Furthermore, women who had never been tested for HIV had higher odds of poor knowledge of MTCT (AOR = 1.38, 95% CI 1.21, 1.57), as did women without comprehensive knowledge of HIV (AOR = 1.16 with 95% CI 1.10, 1.21). Women who are not tested for HIV as part of antenatal care were more likely to have poor knowledge (AOR =1.44, 95% CI 1.28, 1.63). At the community-level variables, women residing in communities with low media exposure (AOR=1.11 with 95% CI 1.03, 1.19), and Low comprehensive HIV knowledge (AOR=1.63 with 95% CI 1.52, 1.76) had higher odds of poor knowledge. Perceiving distance to a health facility as a major problem was associated with lower odds of poor knowledge (AOR=0.92 with 95% CI 0.88, 0.96). At country level, women living in western Africa had higher odds of poor knowledge on MTCT (AOR= 1.27 with 95%CI of 1.20, 1.40) in comparison with Eastern Africa. The survey year of being before sustainable development goal endorsement (SDG) was significantly associated with women’s poor knowledge of MTCT (AOR= 1.47 with 95%CI of 1.22, 1.77) than survey year after SDG endorsement. Moreover, women residing in low-income (AOR=1.97 with 95% CI 1.51, 2.56) and lower-middle-income countries (AOR=1.53 with 95% CI 1.19, 1.95), were more likely to have poor knowledge of MTCT of HIV compared to those in upper-middle-income countries (Table 4).

Table 4.

Multilevel multivariable logistic regression of associated factors with poor knowledge MTCT of HIV among reproductive women, sub-Saharan Africa, 2023.

Variable	Category	Null model	Model II	Model III	Model IV	Model V	Model VI
Variable	Category	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)
Age	15-24 years	-	1.13 (1.06, 1.20)***	-	-	1.12 (1.06, 1.19)***	1.11 (1.04, 1.18)**
	25-34 years	-	1.04 (0.98, 1.10)	-	-	1.03 (0.98, 1.09)	1.03 (0.97, 1.08)
	35-49 years	-	1	-	-	1	1
Sex of household head	Male	-			-	1	1
	Female	-	1.02 (0.96, 1.08)		-	1.01 (0.95, 1.07)	1.02 (0.96, 1.08)
Marital status	Married	-	1	-	-	1	1
	Single	-	0.88 (0.54, 1.42)	-	-	0.75 (0.47, 1.21)	0.79 (0.49, 1.27)
	Widowed	-	1.32 (.48, 3.64)	-	-	1.17 (0.43, 3.21)	1.22 (0.45, 3.35)
	Divorced	-	1.08 (0.55 2.13)	-	-	0.93 (0.47,1.82)	0.97 (0.49, 1.90)
Educational attainment	No education	-	1.05 (0.98, 1.12)	-	-	0.98 (0.92, 1.05)	0.95 (0.89, 1.02)
	Primary education	-	0.97 (0.92, 1.03)	-	-	0.99 (0.93, 1.04)	0.97 (0.92, 1.03)
	Secondary & above	-	1	-	-	1	1
Media exposure	Yes	-	1	-	-	1	1
	No	-	1.09 (1.04, 1.15)***	-	-	1.13 (1.07, 1.19)***	1.13 (1.08, 1.19)***
Wealth index	Poorest	-	1.12 (1.03, 1.21)***		-	1.14 (1.03, 1.25)**	1.17 (1.06, 1.28)**
	Poor	-	1.12 (1.037, 1.21)***	-	-	1.14 (1.04, 1.24)**	1.16 (1.06, 1.27)**
	Middle	-	1.10 (1.02, 1.19)***	-	-	1.12 (1.03, 1.21)*	1.14 (1.05, 1.25)**
	Rich	-	1.11 (1.03, 1.20)***	-	-	1.12 (1.04, 1.20)**	1.14 (1.05, 1.23)**
	Richest	-	1		-	1	1
Women working	Working	-	1	-	-	1	1
	Not working	-	1.07 (1.02, 1.12)***	-	-	1.05 (1.00, 1.10)*	1.07 (1.02, 1.12)**
Partners working status	Working	-	1	-	-	1	1
	Not working	-	0.91 (0.83, 0.99)**	-	-	0.92 (0.85, 1.01)	0.92 (0.85, 1.01)
Ever been tested for HIV	Yes	-	1	-	-	1	1
	No	-	1.57 (1.38, 1.78)***	-	-	1.41 (1.24,1.60)***	1.38 (1.21, 1.57)***
Comprehensive knowledge of HIV	Knowledgeable	-	1	-	-	1	1
	Not Knowledgeable	-	1.27 (1.21, 1.32)***	-	-	1.15 (1.10, 1.21)***	1.16 (1.10, 1.21)***
Tested for HIV as part of antenatal visit	Yes		1	-	-	1	1
	No		1.54 (1.37, 1.73)***	-	-	1.45 (1.28, 1.63)***	1.44 (1.28, 1.63)***
Visited by fieldworker	Yes		1	-	-	1	1
	No		0.99 (0.94, 1.04)	-	-	1.0 (0.95, 1.05)	1.01 (.96, 1.06)
Visited health facility	Yes		1	-	-	1	1
	No		1.03 (0.98, 1.08)	-	-	1.01 (0.96, 1.07)	1.02 (0.97, 1.07)
Perception of distance from health center	Big problem	-	-	0.96 (0.94, 0.98)***	-	0.91 (0.87, 0.96)***	0.92 (0.88, 0.96)***
	Not big problem	-	-	1	-	1	1
Community media exposure	High	-	-	1	-	1	1
	Low	-	-	0.92 (.88, 0.97)***	-	1.13 (1.04, 1.22)**	1.11 (1.03, 1.19)***
Community poverty	High	-	-	1.02 (0.97, 1.07)	-	1.01 (0.93, 1.10)	1.04 (0.96, 1.13)
	Low	-	-	1	-	1	1
Community education status	High	-	-	1	-	1	1
	Low	-	-	1.30 (1.24, 1.36)***	-	1.18 (1.10, 1.27)***	1.03 (0.95, 1.12)
Community comprehensive knowledge	High	-	-	1	-	1	1
	Low	-	-	1.81 (1.74, 1.89)***	-	1.65 (1.54, 1.77)***	1.63 (1.52, 1.76)***
Residence	Urban	-	-	1	-	1	1
	Rural	-	-	0.95 (0.90, 1.01)	-	0.97 (0.89, 1.06)	0.94 (0.86, 1.02)
Sub-Regions	Eastern	-	-	-	1	-	1
	Central	-	-	-	1.96 (1.84, 2.08)***	-	1.08 (0.92, 1.62)
	Western	-	-	-	1.50 (1.44, 1.57)***	-	1.27 (1.20, 1.40)***
	Southern	-	-	-	1.13 (1.03, 1.23)***	-	1.03 (0.63, 1.12)
Country income status	Low income	-	-		2.44 (2.22, 2.68)***	-	1.97 (1.51, 2.56)***
	Lower middle income	-	-	-	1.81 (1.66, 1.98)***	-	1.53 (1.19, 1.95)***
	Upper middle income	-	-	-	1	-	1
Survey year	After SDG (2016-2024)	-	-	-	1	-	1
	Before SDG (2006-2015)	-	-	-	0.71 (0.67, 0.75)**	-	1.47 (1.22, 1.77)***

AOR Adjusted Odds Ratio, CI Confidence IntervalValues expressed in Table 4

^aP-value < 0.05^**P value < 0.01^***P value < 0.001.

Random effect result

In the null model, the ICC was 0.230, indicating that 23% of the total variation in poor knowledge on MTCT was attributable to differences between clusters, while 77% was due to individual-level factors.

The MOR value of 2.56 demonstrated substantial between-cluster heterogeneity.

In the final model, the PCV was 24.9, indicating that one-quarter of the observed variation in poor knowledge of MTCT of HIV was explained by the included covariates in the last model. Model fitness was assessed using deviance statistics, and the model with the lowest deviance was selected as the best-fitting (Model-VI= 65,144) (Table 5).

Table 5.

Random effect results.

Parameters	Null model	Model II	Model III	Model IV	Model V	Model VI
Cluster level variance	0.982	0.787	0.629	0.891	0.751	0.737
ICC	0.230*	0.193*	0.161*	0.213*	0.186*	0.183*
MOR	2.564	2.323	2.283	2.124	2.278	2.260
PCV	Reference	0.198	0.359	0.093	0.235	0.249
Model fitness
Deviance	399,496.54	65,546	263,618.52	398,258	65,230.878	65,144
Mean VIF	-	1.84	1.42	3.69	1.91	2.71

ICC: Inter cluster correlation coefficient, MOR: Median odds ratio, PCV: Proportional Change in variance, VIF: Variance Inflation Factors.^*P-value < 0.05.

Discussion

This study assessed the spatial distribution and predictors of poor knowledge of MTCT of HIV among women of reproductive age in SSA. The result of this study revealed that the pooled prevalence of poor knowledge of MTCT of HIV among women is 46% with 95% CI (40, 51), which is comparable to a study conducted in Tanzania.³⁶ However, this prevalence was lower than reports in Ethiopia (65.1%)³⁷ and in Uganda 50%,³⁸ while it was higher than findings in Zimbabwe (29.5%)³ and 22% in Nigeria.³ The difference might be due to an increased health care services, HIV related interventions and dissemination of health-related information from time to time within/across the country.

Significant spatial variation was observed in the distribution of poor knowledge of MTCT of HIV across SSA countries among reproductive age women. Hotspot areas with high burden of poor knowledge of MTCT of HIV were identified in Uganda, Congo Democratic Republic, Chad, Senegal, Madagascar, Mauritania and Angola, whereas cold spot areas were observed in Mozambique, Cameroon, Ghana, South Africa, Zimbabwe and Malawi. These spatial disparities may be attributed to unequal distribution of maternal health services, health infrastructure and access to HIV-related education.³⁹ Furthermore, women living in hotspot regions may have limited exposure to health education delivered through health facilities and mass media, contributing to lower awareness of MTCT of HIV.

The study found that those aged of 15 to 24 years had higher odds of poor knowledge of MTCT of HIV. This is similar to the evidence from Rwanda,⁴⁰ and may be explained by young women’s low access to different maternal health services during their pregnancies and limited exposure to HIV-related counseling. It may indicate insufficient targeting of adolescents and young women in current HIV prevention and awareness programs.³

The participants with the poorest, poor, middle and rich wealth were more likely to have poor knowledge about MTCT of HIV. This finding is consistent with studies from Ethiopia,⁴¹ South Africa⁴² and Zimbabwe.³ The possible reason might be those women with better economic status can afford and access various means of Media to get health-related information. Furthermore, women with better economic status can afford and get health care services and information about what they want.^43–45

Having no work was also significantly associated with poor knowledge about MTCT of HIV. This may be due to those women who are working (support workers, porters) have frequent communication with health professionals and seek information about MTCT of HIV. In general, having work creates communication with other individuals and working partners, which helps them to gather and share different ideas and information.⁴⁶

Lack of access to mass media (watching television, listening radio, using internet or reading a newspaper at least once a week), was associated with poor knowledge of MTCT of HIV/AIDS than those with media exposure. This is concordant with DHS-based studies conducted in South Africa and Nigeria. Mass media remains remain powerful tool for disseminating health information to large populations and plays a critical role in improving awareness of HIV transmission and prevention.^47,48

Comprehensive knowledge about HIV was strongly associated with knowledge of MTCT. Women without comprehensive HIV knowledge were more likely to have poor understanding of MTCT of HIV. This result is consistent with several studies.^19,49–51 This highlights the interdependence between general HIV awareness and knowledge of HIV transmission for this positive association is that those women with comprehensive knowledge of HIV may have the chance to know about mother-to-child transmission of HIV and prevention.^52,53

Similarly, women who had never been tested for HIV were more likely to have poor knowledge on MTCT, which is supported by a study conducted in Zimbabwe DHS.³ This association may be attributed to the absence of post-test counseling among women who have never undergone HIV testing, which may reduce their awareness of MTCT.

Low community media exposure was associated with a high likelihood of poor knowledge on MTCT of HIV. Reduced media exposure limits women’s ability to access, share and understand health-related information. In addition, low community-level comprehensive knowledge of HIV was significantly associated with poor knowledge of MTCT of HIV. Women living in communities with higher overall HIV awareness are more likely to be familiar with preventive measures against mother-to-child transmission.

Women residing in western Africa were 1.27 times higher likely to have poor knowledge about MTCT of HIV. The possible reason may be due to inadequate governmental and nongovernmental intervention to reduce vertical transmission of HIV in those countries of the region. Likely, the significant association between survey year and poor knowledge of MTCT suggests the presence of temporal variation across survey periods. The higher odds observed in surveys conducted before sustainable development goals era. This finding reflects improvements over-time in HIV related awareness, maternal health service and health communication following global and national commitments under SDGs.⁵⁴ Since 2015/16 increased investments in HIV prevention programs, expansion of maternal services, and strengthened community-based health education may have contributed to enhanced knowledge among women in comparison with surveys before SDGs.^54,55

Moreover, women who are coming from countries of low and lower middle income status were 1.97 and 1.53 times more likely to have poor knowledge of MTCT of HIV than women from upper middle income countries. This may be because countries with low income levels will have insufficient health intervention programs, and inadequate access to information regarding HIV/AIDS.

Strengths and limitations of the study

One of the key strengths of this study was applying data weights to ensure national and regional representativeness. Therefore, it enhances the generalizability of the findings to all women in sub-Saharan Africa. Additionally, the application of GIS and SaTScan spatial analysis enabled the identification of statistically significant hotspot areas of poor knowledge of MTCT of HIV and providing valuable insights for geographically targeted public health interventions. However, the study has some limitations. First, SaTScan detects only circular clusters and may fail to identify irregularly shaped spatial patterns. This study also does not present spatial distribution of factors associated with poor knowledge of MTCT by country to assess geographic heterogeneity. Moreover, the DHS relies on self-reported data, which may be subject to recall bias and social desirability bias. The cross-sectional nature of the data also limits causal inferences (temporal variations). Lastly, the sample size was based on the existing nationally representative DHS data, not calculated.

Conclusion

This study showed that significant spatial variation in poor knowledge of MTCT of HIV among reproductive women across the regions of SSA. Several countries like Madagascar, Uganda, Congo Democratic Republic, Chad, Senegal, Mauritania and Angola were identified as hotspot areas of poor knowledge of MTCT of HIV/AIDS among women. Individual-, community- and country-level factors such as age, media exposure, wealth status, working status of women, comprehensive knowledge about HIV, ever been tested for HIV, tested for HIV as part of antenatal care, community media exposure, community comprehensive knowledge, sub-regions and country income status were significantly associated with poor knowledge of MTCT of HIV.

Accordingly, it is recommended to give targeted interventions focusing on improving access to maternal health services by increasing contact with health professionals during antenatal care, delivery and postnatal care, expanding HIV related health education, and strengthening media outreach are essential in hotspot areas and low-income countries.

Enhancing comprehensive HIV knowledge with an integrated MTCT education and promoting HIV testing through integrated community and media-based strategies may substantially reduce gaps in awareness of MTCT of HIV.

Healthcare professionals should design and implement interventions that increase comprehensive knowledge of HIV in collaboration with various media platforms and expand their coverage to a large area about HIV testing.

Social protection, women’s economic empowerment and adult education initiatives should be integrated with HIV programs to mitigate the impact of poverty, unemployment on access to health information.

Policy makers and program managers should routinely incorporate spatial analysis into HIV program planning to guide resource allocation, monitor in hotspot areas, and evaluate the effectiveness of targeted interventions overtime.

Supplemental material

Supplemental material - Identifying individual, community and country-level determinants of poor knowledge of mother-to-child transmission of HIV among women in sub-Saharan Africa: spatial and multilevel analysis

Supplemental material for Identifying individual, community and country-level determinants of poor knowledge of mother-to-child transmission of HIV among women in sub-Saharan Africa: spatial and multilevel analysis by Mihret Getnet, Lamrot Yohannes, Nigusu Worku, Adina Yeshambel Belay, Lakew Asmare, Demiss Mulatu Geberu, Asebe Hagos, Melak Jejaw, Misganaw Guadie Tiruneh, Kaleb Assegid Demissie, Yawkal Tsega, Abel Endawkie, Amare Mesfin Workie and Wubshet Debebe Negash in Sage Open Medicine.

Footnotes

Acknowledgements

We would like to acknowledge the Demographic and Health Survey staff and the DHS program for permitting data access.

ORCID iDs

Mihret Getnet

Asebe Hagos

Misganaw Guadie Tiruneh

Abel Endawkie

Ethical considerations

This is a secondary data analysis of the recent DHS of sub-Saharan African countries, so ethical approval is not required. For conducting our study, we registered and requested the dataset from the DHS online archive and received approval to access and download the data files. Permission for data access was obtained from the demographic and health surveys through an online request from .

Consent to participate

According to the DHS report, all participant data were anonymized during the collection of the survey data.

Author Contributions

MG, LY, NW, AYB, LA, HTA and DMG were involved in the foundation of the idea and study design. AH, MJ, KMA, MGT, KAD and YT were responsible for data extraction. AE, AMW, WDN and MG performed the data analysis, drafted and finalized the manuscript. All the authors contributed to the interpretation of the data, reviewing, and approving the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data are available from the corresponding author on reasonable request.*

Supplemental material

Supplemental material for this article is available online.

Appendix

References

Namara-Lugolobi

Namukwaya

Owor

, et al. Twenty years of Prevention of Mother to Child HIV Transmission: research to implementation at a national referral hospital in Uganda. African Health Sciences 2022; 22(2): 22–33. https://doi.org/10.4314/ahs.v22i2.5S

Farhoudi

Ghalekhani

Afsar Kazerooni

, et al. Cascade of care in people living with HIV in Iran in 2019; how far to reach UNAIDS/WHO targets. AIDS care 2022; 34(5): 590–596. https://doi.org/10.1080/09540121.2021.1944603

Masaka

Dikeleko

Moleta

, et al. Determinants of comprehensive knowledge of mother to child transmission (MTCT) of HIV and its prevention among Zimbabwean women: Analysis of 2015 Zimbabwe Demographic and Health Survey. Alexandria Journal of Medicine 2019; 55(1): 68–75. https://doi.org/10.1080/20905068.2019.1667114

Morty

Morris

, World AIDS Day 2021: highlighting the pulmonary complications of HIV/AIDS. In: American Physiological Society; 2021; vol. 321: L1069–L1071. https://doi.org/10.1152/ajplung.00471.2021

Organization WH . Governance for the validation of elimination of mother-to-child transmission of HIV and syphilis: an overview of validation structures and responsibilities at national. regional and global levels. World Health Organization (WHO), 2020.

FmoHo

. National consolidated guidelines for comprehensive HIV prevention, care and treatment. World Health Organization, 2020.

Kebede

Minuye

Bayih

, et al. Knowledge about mother-to-child transmission of HIV (MTCT) and its determinants among reproductive age (15-49 years) women in Ethiopia: multi-regional based study. PAMJ-Clinical Medicine 2021; 7(28): 1-10.

Centers for Disease Control and Prevention . HIV among pregnant women, infants, and children. Department of Health and Human Services (CDC). Atlanta, GA, U.S. 2017. https://www.cdcgov/hiv/group/gender/pregnantwomen/indexhtml2017

Kellerman

Sugandhi

. Pediatric AIDS in the elimination agenda. PLoS medicine 2013; 10(8): e1001503. https://doi.org/10.1371/journal.pmed.1001503

10.

Slogrove

Powis

Johnson

, et al. Estimates of the global population of children who are HIV-exposed and uninfected, 2000–18: a modelling study. The Lancet Global Health 2020; 8(1): e67–e75. https://doi.org/10.1016/S2214-109X(19)30448-6

11.

Stover

Glaubius

Kassanjee

, et al. Updates to the Spectrum/AIM model for the UNAIDS 2020 HIV estimates. Journal of the International AIDS Society 2021; 24: e25778. https://doi.org/10.1002/jia2.25778

12.

Organization WH . Global monitoring framework and strategy for the global plan towards the elimination of new HIV infections among children by 2015 and keeping their mothers alive (EMTCT). World Health organization (WHO), 2012.

13.

Organization WH . Global health sector strategy on HIV 2016-2021. Towards ending AIDS. World Health Organization, 2016.

14.

Gamell

Luwanda

Kalinjuma

, et al. Prevention of mother-to-child transmission of HIV Option B+ cascade in rural Tanzania: The One Stop Clinic model. PloS one 2017; 12(7): e0181096. https://doi.org/10.1371/journal.pone.0181096

15.

Teshale

Tessema

Alem

, et al. Knowledge about mother to child transmission of HIV/AIDS, its prevention and associated factors among reproductive-age women in sub-Saharan Africa: Evidence from 33 countries recent Demographic and Health Surveys. PloS one 2021; 16(6): e0253164. https://doi.org/10.1371/journal.pone.0253164

16.

Belachew

Tewabe

Malede

. Prevalence of vertical HIV infection and its risk factors among HIV exposed infants in East Africa: a systematic review and meta-analysis. Tropical Medicine and Health 2020; 48(1): 1–11. https://doi.org/10.1186/s41182-020-00273-0

17.

Alemu

Habtewold

Alemu

. Mother’s knowledge on prevention of mother-to-child transmission of HIV, Ethiopia: A cross sectional study. PloS one 2018; 13(9): e0203043. https://doi.org/10.1371/journal.pone.0203043

18.

Hailu

Nigussie

Gudeta

, et al. Assessment of knowledge and attitude towards prevention of mother-to child transmission of HIV/AIDS among antenatal care client in Mizan-Aman town public health facilities, Benchi-Maji Zone, South Nation Nationalities and People Region, Southwest Ethiopia, 2017. Clinics Mother Child Health 2018; 15(280): 2. https://doi.org/10.4172/2090-7214.1000280

19.

Birhane

Assefa Tessema

Addis Alene

, et al. Knowledge of pregnant women on mother-to-child transmission of HIV in Meket District, Northeast Ethiopia. Journal of pregnancy 2015; 2015: 960830. https://doi.org/10.1155/2015/960830

20.

Agegnehu

Tesema

. Effect of mass media on comprehensive knowledge of HIV/AIDS and its spatial distribution among reproductive-age women in Ethiopia: a spatial and multilevel analysis. BMC public health 2020; 20(1): 1–12. https://doi.org/10.1186/s12889-020-09536-1

21.

Flynn

Taha

Cababasay

, et al. Prevention of HIV-1 transmission through breastfeeding: efficacy and safety of maternal antiretroviral therapy versus infant nevirapine prophylaxis for duration of breastfeeding in HIV-1-infected women with high CD4 cell count (IMPAACT PROMISE): a randomized, open label, clinical trial. Journal of acquired immune deficiency syndromes 1999) 2018; 77(4): 383–392. https://doi.org/10.1097/QAI.0000000000001612

22.

Tagar

Sundaram

Condliffe

, et al. Multi-country analysis of treatment costs for HIV/AIDS (MATCH): facility-level ART unit cost analysis in Ethiopia, Malawi, Rwanda, South Africa and Zambia. PloS one 2014; 9(11): e108304. https://doi.org/10.1371/journal.pone.0108304

23.

Kheirallah

Alrawashdeh

Alsaleh

, et al. Domestic violence against women in Jordan: analysis of the demographic and health survey dataset 2017-2018. Journal of medicine and life 2023; 16(8): 1264–1273. https://doi.org/10.25122/jml-2023-0111

24.

Mandiwa

Namondwe

. Uptake and correlates of HIV testing among men in Malawi: evidence from a national population–based household survey. BMC health services research 2019; 19(1): 203. https://doi.org/10.1186/s12913-019-4031-3

25.

Croft

Marshall

Allen

. The DHS program-guide to DHS statistics. ICF, 2018.

26.

Eshetu

Kebede

Bogale

, et al. Knowledge of prevention of mother-to-child transmission of HIV among reproductive age women in high HIV/AIDS prevalent countries: a multilevel analysis of recent demographic and health surveys. PLoS One 2023; 18(10): e0292885. https://doi.org/10.1371/journal.pone.0292885

27.

Alem

Yeshaw

Kebede

, et al. Spatial distribution and determinants of early marriage among married women in ethiopia: a spatial and multilevel analysis. BMC Women's Health 2020; 20(1): 1–13. https://doi.org/10.1186/s12905-020-01070-x

28.

. World Bank Country classification by income level. 2022.

29.

Tesema

Teshale

Agegnehu

, et al. Trends and Spatial Variation of Female Genital Mutilation among Reproductive Age Women in Ethiopia based on 2000. 2005, and 2016 Ethiopian Demographic and Health Surveys: Spatial-temporal and Multivariate Decomposition Analysis, Biomed Central. 2019.

30.

Sharma

Acharya

. Spatial variations and determinants of anemia among under-five children in Nepal, DHS (2006–2016). International journal of environmental research and public health 2022; 19(14): 8664. https://doi.org/10.3390/ijerph19148664

31.

Tessema

Teshale

Tesema

, et al. Pooled prevalence and determinants of modern contraceptive utilization in East Africa: A Multi-country Analysis of recent Demographic and Health Surveys. PloS one 2021; 16(3): e0247992. https://doi.org/10.1371/journal.pone.0247992

32.

Belay

Taddese

Gelaye

. Minimum acceptable diet intake and its associated factors among children age at 6–23 months in sub-Saharan Africa: a multilevel analysis of the sub-Saharan Africa demographic and health survey. BMC Public Health 2022; 22(1): 684. https://doi.org/10.1186/s12889-022-12966-8

33.

Merlo

Chaix

Ohlsson

, et al. A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. Journal of Epidemiology & Community Health 2006; 60(4): 290–297. https://doi.org/10.1136/jech.2004.029454

34.

Liyew

Teshale

. Individual and community level factors associated with anemia among lactating mothers in Ethiopia using data from Ethiopian demographic and health survey, 2016; a multilevel analysis. BMC Public Health 2020; 20: 1–11. https://doi.org/10.1186/s12889-020-08934-9

35.

Merlo

Chaix

Yang

, et al. A brief conceptual tutorial of multilevel analysis in social epidemiology: linking the statistical concept of clustering to the idea of contextual phenomenon. Journal of Epidemiology & Community Health 2005; 59(6): 443–449. https://doi.org/10.1136/jech.2004.023473

36.

Wangwe

Nyasinde

Charles

. Counselling at primary health facilities and level of knowledge of antenatal attendees and their attitude on prevention of mother to child transmission of HIV in Dar-es salaam, Tanzania. African health sciences 2013; 13(4): 914–919. https://doi.org/10.4314/ahs.v13i4.8

37.

Luba

Feng

Gebremedhin

, et al. Knowledge about mother–to–child transmission of HIV, its prevention and associated factors among Ethiopian women. Journal of global health 2017; 7(2): 020414. https://doi.org/10.7189/jogh.07.020414

38.

Byamugisha

Tumwine

Ndeezi

, et al. Attitudes to routine HIV counselling and testing, and knowledge about prevention of mother to child transmission of HIV in eastern Uganda: a cross-sectional survey among antenatal attendees. Journal of the International AIDS Society 2010; 13(1): 1–11. https://doi.org/10.1186/1758-2652-13-52

39.

Adugna

. Health instituition and Services in Ethiopia. 2014.

40.

Deynu

Nutor

. Determinants of comprehensive knowledge on mother-to-child transmission of HIV and its prevention among childbearing women in Rwanda: insights from the 2020 Rwandan Demographic and Health Survey. BMC public health 2023; 23(1): 1–14. https://doi.org/10.1186/s12889-022-14925-9

41.

Gebre

Feyasa

Dadi

. Levels of mother-to-child HIV transmission knowledge and associated factors among reproductive-age women in Ethiopia: Analysis of 2016 Ethiopian Demographic and Health Survey Data. PloS one 2021; 16(8): e0256419. https://doi.org/10.1371/journal.pone.0256419

42.

Darteh

EKM

Abraham

Seidu

A-A

, et al. Knowledge and determinants of women’s knowledge on vertical transmission of HIV and AIDS in South Africa. AIDS research and therapy 2021; 18(1): 1–9. https://doi.org/10.1186/s12981-021-00367-7

43.

Ginzberg

. Access to health care for Hispanics. In: Health policy and the Hispanic. edn.: Routledge; 2019: 22–31.

44.

Seidel

Gürbüz

Großkreutz

, et al. The influence of migration on women’s use of different aspects of maternity care in the German health care system: Secondary analysis of a comparative prospective study with the Migrant Friendly Maternity Care Questionnaire (MFMCQ). Birth 2020; 47(1): 39–48. https://doi.org/10.1111/birt.12476

45.

Morón-Duarte

Varela

Bertoldi

, et al. Quality of antenatal care and its sociodemographic determinants: results of the 2015 Pelotas birth cohort, Brazil. BMC Health Services Research 2021; 21(1): 1–14. https://doi.org/10.1186/s12913-021-07053-4

46.

Suandi

Williams

Bhattacharya

. Does involving male partners in antenatal care improve healthcare utilisation? Systematic review and meta-analysis of the published literature from low-and middle-income countries. International health 2020; 12(5): 484–498. https://doi.org/10.1093/inthealth/ihz073

47.

Liyeh

Cherkose

Limenih

, et al. Knowledge of prevention of mother to child transmission of HIV among women of reproductive age group and associated factors at Mecha district, Northwest Ethiopia. BMC Research Notes 2020; 13(1): 1–6. https://doi.org/10.1186/s13104-020-05005-5

48.

Diress

Ahmed

Adane

, et al. Predictors to HIV testing among youth women in Ethiopia: 2016 Ethiopian Demographic and Health Survey. HIV/AIDS-Research and Palliative Care. 2019; 12: 963–970.

49.

Cherkose

Limenih

Yimer

, et al. Knowledge of prevention of mother to child transmission of HIV among women of reproductive age group and associated factors at Mecha district. Northwest Ethiopia. BioMed Central (BMC), 2020.

50.

Malaju

Alene

. Determinant factors of pregnant mothers’ knowledge on mother to child transmission of HIV and its prevention in Gondar town, North West Ethiopia. BMC Pregnancy and Childbirth 2012; 12(1): 1–7. https://doi.org/10.1186/1471-2393-12-73

51.

Abtew

Awoke

Asrat

. Knowledge of pregnant women on mother-to-child transmission of HIV, its prevention, and associated factors in Assosa town, Northwest Ethiopia. HIV/AIDS (Auckland, NZ) 2016; 8: 101–107. https://doi.org/10.2147/HIV.S100301

52.

Liyeh

. Determinant factor of married women’s knowledge on vertical transmission of HIV in Mecha district, Ethiopia; a community-based study. PloS one 2020; 15(12): e0242659. https://doi.org/10.1371/journal.pone.0242659

53.

Yaya

Oladimeji

, et al. Prenatal care and uptake of HIV testing among pregnant women in Gambia: a cross-sectional study. BMC Public Health 2020; 20: 1–10. https://doi.org/10.1186/s12889-020-08618-4

54.

Bekker

L-G

Alleyne

Baral

, et al. Advancing global health and strengthening the HIV response in the era of the Sustainable Development Goals: the International AIDS Society—Lancet Commission. The Lancet 2018; 392(10144): 312–358. https://doi.org/10.1016/S0140-6736(18)31070-5

55.

O’Donovan

Baskin

Stansert Katzen

, et al. Costs and cost-effectiveness of community health worker programs focussed on HIV, TB and malaria infectious diseases in low-and middle-income countries (2015–2024): A scoping literature review. PLOS Global Public Health 2025; 5(5): e0004596. https://doi.org/10.1371/journal.pgph.0004596

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.18 MB

0.00 MB