STRENGTHS AND LIMITATIONS OF THIS STUDY

Nationally representative data were used for each sub-Saharan African country, as was a large sample size.

The pooled prevalence of cervical screening uptake was employed.

Erreygers concentration index, curve and Wagstaff decomposition analyses are appropriate statistical models for demonstrating the extent and direction of socioeconomic inequality in cervical cancer screening.

A causal relationship cannot be established between cervical cancer screening and predictors because of the use of cross-sectional data.

Unable to investigate all variables because the nature of the data was secondary.

Introduction

Cancer has become the leading cause of death in both developing and developed countries.1 2 Due to the increasing prevalence of risk factors, the burden of cancer has increased over time.3 There has been a recent surge of non-communicable diseases, including cancer, in many low- and middle-income countries, which has led to a considerable economic burden.4

Cervical cancer is the fourth most common cancer in women, and globally, approximately 7.5% of cancer-related deaths among women are attributed to it.5–7 Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide. HPV has more than 130 low-risk and high-risk serotypes: high-risk serotypes (HPV 16, 18) can cause cancers such as cervical cancer, although low risk causes benign warts.8 In women, HPV infection may become chronic, and precancerous lesions may progress to invasive cervical cancer.6

Cervical cancer is a prevalent public health concern and is among the leading causes of death among women globally.9 In 2022, an estimated 661 044 women were diagnosed with cervical cancer, and approximately 350 000 deaths from the disease occurred worldwide; approximately 94% of women who died from cervical cancer were from low- and middle-income countries.6 10 In sub-Saharan Africa (SSA), cervical cancer is the second leading cause of cancer in women, accounting for 22.5% of all cancer cases. Moreover, approximately 117 316 women are diagnosed annually with the disease.11 Additionally, cervical cancer is the most common cause of death among women in sub-Saharan African countries.12 The disease burden in SSA is high because of a lack of accessible screening and treatment options, health system barriers, low levels of awareness and challenges with health-seeking behaviours, all of which increase the burden of cervical cancer high in sub-Saharan African countries.13–15

HPV vaccination and regular screening are recommended to reduce the burden of cervical cancer in low- and middle-income countries.16 Cervical cancer screening aims to identify precancerous cervical lesions in healthy women before they progress to cancer,17 18 thereby establishing it as one of the most effective strategies for cancer control. This screening method plays a crucial role in the early detection of cervical cancer, expands treatment options for affected women, and increases cancer survival rates.19 20

The WHO recommends cervical cancer screening every 5–10 years. Moreover, it encourages a minimum of two lifetime screens with a high-performance HPV test by the age of 35 and again by the age of 45.6 In line with these recommendations, the Sustainable Development Goals aim to address universal health coverage, ensuring healthy lives and promoting well-being for all people.21 Currently, the prevention, control and treatment of cervical cancer have become a worldwide public health priority.22 Early detection of cancers leads to better prognoses and less costly treatment,23 24 and cervical cancer screening, including the Papanicolaou (Pap) smear, has significantly reduced associated morbidity and mortality globally.25

Cervical cancer disproportionately affects poor, socially marginalised women around the world, which may be attributed to low cervical cancer screening uptake among poor populations. Previous evidence also indicated that cervical cancer screening was concentrated among wealthier women.26 There are several sociodemographic characteristics that are linked to the uptake of cervical cancer screening, including age, marital status, place of residence and education level.27–29

Assuring equitable access to cervical cancer screening services is thus critical to decreasing the burden of cervical cancer in low- and middle-income countries. Therefore, this study aimed to assess the presence of socioeconomic inequality in the uptake of cervical cancer screening services among women and their contributors in SSA. We estimate the contribution of each of the determinants to socioeconomic inequality in the uptake of cancer screening services using the decomposition analysis proposed by Erreygers.30 The results of this study will provide useful information for policymakers to address socioeconomic disparities in the uptake of cervical cancer screening services.

MethodsData source and sampling procedure

The data were collected through the Demographic and Health Survey (DHS) programme; more information about the surveys can be found in each country’s DHS reports. The study participants were chosen using a multistage stratified sampling method. The first stage involved the random selection of Enumeration Areas, followed by the selection of households. Women in the chosen household were asked about their cervical cancer screening to determine their cervical screening uptake status. DHS contains a number of datasets, one of which we used in this study: the Individual Record (IR) file. Using the literature, we extracted data from the IR dataset and appended it with the STATA command ‘append’. Data analysis commonly involves the appending of separate datasets. This results in a unified dataset that can be managed and analysed more efficiently within STATA. The study’s final sample size was 139 801 women aged 15–64 years (online supplemental table 1).

Study variables and measurementsDependent variable

The dependent variable was socioeconomic-related inequality in cervical cancer screening uptake among women in SSA. Cervical cancer screening uptake was classified as ‘yes/no’. The respondents who had been examined or tested for cervical cancer were categorised, and those who had not were categorised as ‘no’. Socioeconomic inequality in cervical cancer screening was demonstrated by using the covariance between cervical cancer screening uptake and the wealth index. Then, it was categorised as either pro-poor, pro-rich, or no inequality.

Independent variables

For the independent variables, women’s age, religion, educational level, employment status, wealth index, media exposure, health insurance coverage, residence, and income level of the country were considered.

Socioeconomic status

The wealth index from the DHS dataset was used to measure socioeconomic status. The index used principal component analysis and was classified as poorest (wealth quintile 1), poorer (wealth quintile 2), middle (wealth quintile 3), richer (wealth quintile 4) or richest (wealth quintile 5).31 Media exposure was created by aggregating the following three variables: listening to the radio, watching television and reading a newspaper are classified as ‘yes’ if a woman has had exposure to any of the three media sources and ‘no’ if she has not. Country income status was classified as low- or lower-middle-income on the basis of the list of economies classified by the World Bank in 2019.32

Data management and analysis

This study used DHS data from the official DHS measure website. DHS data in STATA format were cleaned, transformed and appended to generate favourable variables for the final analysis. STATA V.17 software was used to compute descriptive and analytical statistics for the data from 11 countries. The analyses used sampling weights to account for unequal sample selection probabilities and potential response rate differences. Pearson’s χ2 test was used to determine p values on the basis of the distribution of respondents’ background characteristics, expressed as frequencies with percentages. The ‘metan prop’ STATA command was used to compute a pooled estimate of cervical cancer screening uptake among women in SSA. It was estimated using the proportion of cervical cancer screenings in each SSA, as well as the SE calculated from the proportion and sample size of each country.

The concentration curve33 was used to visualise the presence of socioeconomic inequality; for some variables, it was prominent at some points compared with others, and the concentration index (CI) was used to measure and compare socioeconomic inequality in health variables.34 35

The CI ranges from −1 to +1 and is equal to two times the area between the concentration curve and the line of equity. The sign of CI indicates the direction of the relationship between cervical cancer screening uptake and income distribution (wealth status). A CI of 0 indicates that the distribution is proportional; a CI of 1 indicates that the richest person has all the health variables and a CI of −1 indicates that the poorest person has all the health variables.36 37 However, in the present study, the outcome variable was binary (yes or no), and the bounds of C depended on the mean (µ) of the outcome variable and were not between 1 and −1. Thus, the bounds of C ranged from µ−1 (lower bound) to 1−µ (upper bound). To account for this, the current study employed the Erreygers normalised concentration index (ECI), which is a modified version of the CI.30

The ECI can be mathematically defined as follows:

CI(y) is the generalised CI and μ is the mean of cervical cancer screening uptake. In this study, the ECI was reported along with the SE. To graphically depict socioeconomic inequality in cervical cancer screening uptake, concentration curves plot the cumulative percentage of cervical cancer screening uptake (Y-axis) against the cumulative share of the population ranked by the wealth index, beginning with the poorest and ending with the richest (X-axis).30

The ECI would be a 45°-line run from the bottom left corner to the top right corner, indicating that there is no inequality (ECI=0). Furthermore, the concentration curve above and below the equality line (45°) indicated that the health variable is disproportionately concentrated between poor (pro-poor or ECI <0) and rich (pro-rich or ECI >0) individuals.30 38 Visual inspection of a concentration curve can reveal whether it lies above or below the line of equality. To determine the statistical significance of the difference between the concentration curve and the line of perfect equality (45° or diagonal line), the ECI and p value were computed.

A decomposition of the ECI was performed to determine the relative contributions of various factors to socioeconomic disparities in cervical cancer screening uptake.30 38 39 A linear additive regression model of health outcomes (y) was used.30

The concentration index for y, CI, is given as:

where ‘y’ is the health outcome variable (in this study, socioeconomic-related inequality of cervical cancer screening uptake), is a set of socioeconomic determinants of cervical cancer screening uptake, α is the intercept, is the coefficient of , µ is the mean of y, is the mean of , is the CI for , is the generalised CI for the error term ( ∈ ), and is the elasticity of y with respect to .39 40

Patient and public involvement

The public/patient was not involved in the design, conduct, reporting or dissemination plans of this review.

ResultsSociodemographic characteristics of women in SSA

A total of 139 801 women were included in this study. More than one-third (38.13%) of the women were aged 15–24 years, with a median age of 28 years (IQR: 16), and more than half of the women (52.26%) were rural residents. Approximately 40.17% had completed secondary school, and 24.49% came from the richest households. The majority (69.40%) of the women were married, and 58% of the sub-Saharan African countries included in the study had a lower income (tables 1 and 2).

Table 1

Socioeconomic characteristics of women in a study of socioeconomic inequality in sub-Saharan Africa, 2024

Table 2

Bivariable analysis of cervical cancer screening among women in SSA 2024

Pooled prevalence of cervical cancer screening uptake among women in SSA

The pooled prevalence of cervical cancer screening among women in sub-Saharan African countries was 10.51% (95% CI: 7.54% to 13.48%) (figure 1). We conduct subgroup analysis by year and country. The results of the subgroup analysis by country revealed that cervical screening was high in Namibia (39%), followed by Kenya and Gabon (16.81% and 16.41%, respectively); Mauritania had the lowest percentage (0.46%) (figure 2). Moreover, the subgroup analysis by year revealed that screening for cervical cancer was high before 2020: 14.77% (95% CI: 6.89% to 22.64%) (figure 3).

Figure 1

Forest plot depicting the pooled prevalence of cervical cancer screening uptake among women in SSA, 2024. DHS, Demographic and Health Survey; ES, Effective Size; SSA, sub-Saharan Africa.

Figure 2

Subgroup analysis of cervical cancer screening among women in SSA by country. DHS, Demographic and Health Survey; ES, Effective Size; SSA, sub-Saharan Africa.

Figure 3

Subgroup analysis of cervical cancer screening among women in SSA by year. DHS, Demographic and Health Survey; ES, Effective Size; SSA, sub-Saharan Africa.

Socioeconomic inequality of cervical cancer screening

The wealth-related inequality of cervical cancer screening using the weighted ECI was 0.084, with an SE of 0.0037 (p value <0.0001). This finding shows that cervical cancer screening is disproportionately concentrated among the rich (pro-rich). Correspondingly, the concentration curve revealed that the concentration of cervical cancer screening was above the line of equality, which indicated that the distribution of cervical cancer screening was concentrated in rich households (pro-rich distribution) (figures 4 and 5). The analysis of educational status-related inequality in cervical cancer screening uptake using the weighted ECI yielded a result of 0.080, with an SE of 0.0035 (p value <0.0001). This finding indicates that cervical cancer screening uptake was disproportionately higher among women with higher education. Additionally, the concentration curve revealed that the graph of cervical cancer screening uptake was above the line of equality, suggesting that the uptake was concentrated among women with higher education (figure 6).

Figure 4

Concentration curve for cervical cancer screening uptake in SSA. SSA, sub-Saharan Africa.

Figure 5

Bar-graph for socioeconomic inequality of cervical cancer screening uptake among women in SSA, 2024. SSA, sub-Saharan Africa.

Figure 6

Concentration curve of educational status inequality of cervical cancer screening uptake in SSA, 2024. SSA, sub-Saharan Africa.

Decomposing socioeconomic-related inequalities in cervical cancer screening

Income-related inequality in cervical cancer screening was shown after assessment using the CI and curve. After the Erreygers normalised the CI, a decomposition analysis was conducted to understand the determinant factors that contribute to socioeconomic inequality, the CI, the coefficient, elasticity and the percentage contribution.

The sensitivity of cervical cancer screening for each factor was elasticity. The direction and degree of socioeconomic disparities in cervical cancer screening with respect to specific explanatory variables are depicted by the CI for each variable. By calculating the absolute contribution, which involves multiplying the elasticity of each factor by its CI, the degree of inequality to which the explanatory variables have contributed can be determined. The percentage contribution signifies the contribution of each variable to the overall CI (table 3).

Table 3

Contributing factors of socioeconomic inequality in cervical cancer screening in sub-Saharan Africa 2024

In this study, educational level was the primary contributing factor to overall socioeconomic inequality in cervical cancer screening, accounting for 33.18% of the variation. This was followed by the residence and wealth indices, which contributed 25.45% and 19.33%, respectively, to the observed inequality. Moreover, 8.19% of pro-rich inequalities in cervical cancer screening among women were explained by media exposure.

Discussion

Cervical cancer incidence and mortality rates are notably high in sub-Saharan African countries, which are attributed to significant disparities caused by inadequate access to cervical screening and treatment services, as well as socioeconomic factors. Recognising and reducing avoidable socioeconomic disparities and other factors that influence cervical cancer screening are important steps toward eliminating cervical cancer in SSA. The purpose of this study was to determine the pooled estimate, socioeconomic disparities in cervical cancer screening, and factors that influence it among women in SSA.

The findings of the current study revealed that the overall pooled prevalence of cervical cancer screening among women in sub-Saharan African countries was 10.51%. This number is lower than that reported in studies conducted in Malaysia41 and China-Canadian.42 The possible reason for the lower uptake of cervical cancer screening in SSA could be due to poor access to screening facilities, sociocultural influences and socioeconomic determinates.43 The findings from the subgroup analysis revealed that cervical screening was high in Namibia (39%) and lowest in Mauritania (0.46%). The differences in cervical cancer screening rates between Namibia and Mauritania can be largely attributed to disparities in healthcare infrastructure, public awareness, government policies, cultural factors and economic conditions. Namibia has established comprehensive screening programmes and public health campaigns that effectively educate women about the importance of cervical cancer prevention, supported by stronger healthcare funding and international partnerships.44 In contrast, Mauritania faces challenges such as limited healthcare access, lower levels of public awareness and cultural stigmas that hinder women’s participation in screening initiatives.45 Moreover, the results of the subgroup analysis according to year revealed that cervical cancer screening is high before 2020 than after 2020. The possible reason could be attributed to the COVID-19 pandemic, which disrupted healthcare services and created a fear of visiting medical facilities.46 Additionally, persistent barriers such as limited access to healthcare, socioeconomic challenges, lack of awareness and cultural stigmas further exacerbate the situation.47

This study revealed that cervical cancer screening uptake in SSA was disproportionately concentrated among wealthy households. According to studies, the uptake of cervical cancer screening is unequal, with poorer women receiving less screening and richer women receiving more screening.48 49 One possible explanation is that the poorest women may struggle financially, which is a hurdle to accessing cervical cancer screening.50 Moreover, this finding can be explained by Michael Grossman’s theories on health capital and healthcare utilisation. Grossman posits that health is a form of capital that individuals invest in, meaning that wealthier individuals typically have more resources for preventive care, such as screenings. Higher socioeconomic status is associated with greater demand for healthcare services because of improved access, health literacy, and the ability to recognise the long-term benefits of preventive measures. As a result, pro-rich individuals are more likely to attend screenings, navigating barriers such as cost and transportation more effectively. This disparity highlights the structural inequities in healthcare access and reinforces the idea that economic factors significantly influence health outcomes.51

In the decomposition analysis, several factors contributed to the rich socioeconomic inequalities in cervical cancer screening. Educational status, residence, wealth index, and media exposure were the major contributors.

Educational status was found to be an important and major contributor to the overall socioeconomic disparities in cervical cancer screening (33.18%). This finding is in line with those of India49 and Iran.52 One possible explanation is that education is associated with better access to and utilisation of healthcare services, such as cervical cancer screening.53 54

Residence was also a significant contributor to socioeconomic inequality in cervical cancer screening (25.45%). This finding is supported by another study conducted in Nepal.55 This could be explained by the fact that the provision of cervical cancer screening services may be hampered in rural areas compared with urban areas due to the lack of screening centres within walking distance, limited transportation options and the cost of transportation.56 57 Furthermore, because information sources are more accessible in urban areas, residents may have more opportunities to access health information, potentially leading to differences in screening utilisation rates between urban and rural populations. Likewise, women from urban areas may have higher educational levels than their counterparts.

This finding revealed that the wealth index was the other contributor to socioeconomic inequality in cervical cancer screening (19.33%). Previous studies in India49 and Iran52 have also highlighted that the wealth index has a positive association with cervical cancer screening uptake. The global distribution of cervical cancer screening and the burden of cervical cancer among economically disadvantaged women remain inexplicable. One possible explanation for this phenomenon could be the financial constraints faced by the poorest women, which, in turn, impedes their access to cervical cancer screening and treatment services.50

Media exposure significantly contributed to socioeconomic inequality in cervical cancer screening (8.19%). This finding is consistent with a study from Nepal.58 The reason could be that exposure to media provides health information, which may enhance women’s understanding of and approach to using healthcare services such as screening.59

Strengths and limitations

The use of weighted nationally representative data for each sub-Saharan African country, as well as a large sample size, is the strength of this study. Furthermore, the ECI, curve and Wagstaff decomposition analyses are appropriate statistical models for demonstrating the extent and direction of socioeconomic inequality in cervical cancer screening uptake across households ranging from poor to wealthy. However, readers should be aware of the study’s limitations, due to the cross-sectional nature of the study; we cannot establish causal or temporal inferences. To evaluate the causal and long-term associations between socioeconomic inequality and cervical cancer screening, future research will need to use longitudinal data.

Conclusion

The cervical cancer screening rate among women in SSA is low, and there are significant wealth-related disparities in screening rates. These disparities are influenced by factors such as educational status, place of residence, wealth index and media exposure. Addressing these inequalities requires focusing on disadvantaged women and their contributing factors.

To increase cervical cancer screening among women in SSA, policymakers and other stakeholders should work with other sectors and prioritise modifiable factors such as improving access to education and household media exposure. For sub-Saharan African countries with lower income levels, a long-term plan is needed to improve individual household economic conditions and the wealth index. Interventions to increase cervical cancer screening uptake should also consider supporting marginalised groups, such as rural residents, to achieve a more equitable approach.

Data availability statement

Data are available in a public, open access repository. The datasets generated and/or analysed during the current study are available at http://www.dhsprogram.com.

Ethics statementsPatient consent for publication

Not applicable.

Ethics approval

The study did not require ethical approval or participant consent because it was a secondary analysis of publicly available survey data from the MEASURE DHS programme. We received permission from http://www.dhsprogram.com to download and use the data for this study. The datasets did not include any person names or addresses.

Acknowledgments

We would like to thank the measure DHS program for providing the datasets.