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  • Published: 02 December 2022

Barriers to uptake of cervical cancer screening services in low-and-middle-income countries: a systematic review

  • Z. Petersen 1 ,
  • A. Jaca 2 ,
  • T. G. Ginindza 3 , 4 ,
  • G. Maseko 1 ,
  • S. Takatshana 1 ,
  • P. Ndlovu 1 ,
  • N. Zondi 1 ,
  • N. Zungu 1 , 3 ,
  • C. Varghese 5 ,
  • G. Hunting 5 ,
  • G. Parham 5 ,
  • P. Simelela 5 &
  • S. Moyo 1 , 6  

BMC Women's Health volume  22 , Article number:  486 ( 2022 ) Cite this article

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Low-and-middle-income countries (LMICs) bear a disproportionate burden of cervical cancer mortality. We aimed to identify what is currently known about barriers to cervical cancer screening among women in LMICs and propose remedial actions.

This was a systematic review using Medical Subject Headings (MeSH) terms in Google Scholar, PubMed, Scopus, and Web of Science databases. We also contacted medical associations and universities for grey literature and checked reference lists of eligible articles for relevant literature published in English between 2010 and 2020. We summarized the findings using a descriptive narrative based on themes identified as levels of the social ecological model.

We included studies conducted in LMICs published in English between 2010 and 2020.

Participants

We included studies that reported on barriers to cervical cancer screening among women 15 years and older, eligible for cervical cancer screening.

Seventy-nine articles met the inclusion criteria. We identified individual, cultural/traditional and religious, societal, health system, and structural barriers to screening. Lack of knowledge and awareness of cervical cancer in general and of screening were the most frequent individual level barriers. Cultural/traditional and religious barriers included prohibition of screening and unsupportive partners and families, while social barriers were largely driven by community misconceptions. Health system barriers included policy and programmatic factors, and structural barriers were related to geography, education and cost. Underlying reasons for these barriers included limited information about cervical cancer and screening as a preventive strategy, poorly resourced health systems that lacked policies or implemented them poorly, generalised limited access to health services, and gender norms that deprioritize the health needs of women.

A wide range of barriers to screening were identified across most LMICs. Urgent implementation of clear policies supported by health system capacity for implementation, community wide advocacy and information dissemination, strengthening of policies that support women’s health and gender equality, and targeted further research are needed to effectively address the inequitable burden of cervical cancer in LMICs.

Peer Review reports

Key messages

What is already known: Low-and-middle-income countries (LMICs) bear a disproportionate burden of cervical cancer mortality and there is limited knowledge on barriers to cervical cancer screening uptake across LMICS.

Findings: Women in LMICs face individual level, cultural/traditional and religious, societal, health system, and structural barriers to cervical cancer screening. The underlying reasons for these barriers include limited information about cervical cancer and screening as a preventive strategy, poorly resourced health systems without screening policies, poorly implemented policies, generalised limited access to health services, and gender norms that deprioritize the health needs of women.

What the findings imply: There is a need for education, information dissemination, and advocacy to dispel myths about cervical cancer, and implementation of clear cervical cancer policies and guidelines with prerequisite structures and resources across diverse health settings. Policies that support sexual and reproductive health and the rights of women should be strengthened and expanded and account for inequities in access for diverse groups of women. Education and awareness initiatives should be driven by local and community contexts, and engage community members and multiple stakeholders, including traditional and religious figures. In addition, the introduction and roll out of more modern screening approaches in LMICs should be prioritized to ensure more women are reached.

Introduction

Cervical cancer, although preventable and curable, is the fourth most common cancer among women globally [ 1 ]. The burden is greatest in low-and-middle-income countries (LMICs) with age-standardized incidence rates varying from 75/100000 women in highest-risk countries to less than 10/100000 women in lowest risk countries [ 1 ]. In 2018, approximately 90% of deaths occurred in LMICs [ 2 ]. The remarkable geographic contrasts in cervical cancer incidence and mortality reflect differences in social and structural contexts associated with cervical cancer, and inequities in access to information about cervical cancer, prevention, screening, and effective cancer treatment facilities and thus indicate areas with the greatest need for interventions [ 3 ]. Consequently, the World Health Organization’s (WHO) global strategy to accelerate the elimination of cervical cancer proposes a vision of a world where cervical cancer is eliminated as a public health problem by employing measures that are sensitive to women’s needs, their social circumstances, and the personal, cultural, social, structural and economic barriers hindering their access to health services [ 2 ].

With almost all cervical cancer cases (99%) linked to human papillomaviruses infection (HPV), HPV vaccination is a key primary preventive strategy, with secondary prevention – screening - remaining a key component of the cervical cancer elimination toolkit, especially where there is low HPV vaccination availability, access, and uptake [ 3 , 4 ]. Screening coverage of eligible women in most LMICs is on average 19%, compared to 63% in high income countries, and thus it is important to review identified barriers to screening uptake to address the burden in LMICs [ 4 ].

We conducted a systematic review on barriers to uptake of cervical cancer screening services (including poor provision of services) in LMICs. The objectives of the review were to i) document and investigate the underlying reasons for poor uptake of cervical cancer screening services in LMICs, ii) identify research gaps, and iii) provide evidence for decision-making and policy interventions for improved programmes and actions to support the elimination of cervical cancer in LMICs. We used Brofenbrenner’s social ecological model [ 5 , 6 ] to understand the dynamic interrelations among personal and environmental factors. First introduced in the 1970s as a conceptual model, the social ecological model was formalized as a theory in the 1980s and underwent revisions by Bronfenbrenner until his death in 2005. In his initial theory, Bronfenbrenner proposed that to understand human development, the entire ecological system in which growth occurs needs to be considered. In subsequent revisions, the model examines how human beings develop according to their environment, which includes society and the context which impacts behavior and development.

The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and included LMICs, as defined by the World Bank based on per capita gross national income in 2020 [ 7 ]. The research question was framed using the broad population, concept and context (PCC) framework recommended by the Joanna Briggs Institute for Scoping Reviews [ 8 ] and was defined as: “What are the barriers to the uptake of cervical cancer screening services in LMICs?”. The population was women (15 years and older) eligible for cervical cancer screening. Studies that examined HPV vaccination and included girls younger than 15 years old together with older girls and women were also included.

Search strategy

Two authors (AJ and ZP) developed the search strategy. A comprehensive literature search was conducted in February 2021 in Scopus, Web of Science and Pubmed. No language or date restrictions were applied in the initial search. A search in Google Scholar using the keywords ‘cervical cancer screening’ and ‘barriers to cervical cancer screening’ was also conducted, aimed at finding studies that may not have been included in the findings from the major databases that were searched. We also searched the websites of the WHO, the International Agency for Research on Cancer (IARC), and the reference lists of all included studies for additional relevant articles. The search was initiated with keywords and refined by adapting search terms from relevant literature to include a variation of the terminology used in different countries. The detailed search strategy for the three databases is shown in Table  1 .

Studies addressing barriers to and uptake of cervical cancer screening in LMICs and published in English over 10 years (1 January 2010 to December 2020) were eligible for inclusion. Project and academic reports including Master’s and Doctoral theses were also eligible while editorials, commentaries, and abstracts where we could not access full-text articles were ineligible. Working in pairs, the authors independently screened the titles and abstracts of the search output and retrieved the full texts of those considered eligible. The authors then independently assessed the full texts for inclusion and resolved disagreements through discussion and consensus.

Data extraction

A standardized data extraction tool was used. Information was extracted on the country of study, aim/s, design, population, sample size, participant ages, screening type, documented barriers, reported findings, and recommendations. Discrepancies were resolved through discussion and consensus. Two authors assessed the quality of the studies included using the Critical Appraisal Skill Program(CASP) tool [ 8 ]. See Appendix 1 , Quality Assessment of studies.

Search Results

The literature search yielded a total of 2148 articles: 385 from PubMed, 1280 from Scopus, and 461 from Web of Science, 18 from Google and Google scholar. After removing 20 duplicates, we screened titles for eligibility and 1882 irrelevant articles were excluded (Fig.  1 ). Full texts of the 246 remaining articles were assessed for eligibility, and 92 met the inclusion criteria. Thirteen review articles were excluded, leaving 79 articles based on individual studies.

figure 1

Search strategy flow diagram

Characteristics of included studies

The included studies were undertaken in 28 LMICs; with 61% undertaken in Africa, 21% in Asia, 5% in North America, 9% in South America, 1% in Oceania and 3% in Europe. The characteristics of the included studies are shown in Table  2 . Of the included individual studies, 45 (57%) were quantitative, 27 (34%) qualitative and 4 (5%) used a combination of qualitative and quantitative methods. Four studies were based on secondary data analysis [ 9 , 10 , 11 , 12 ]. The quantitative studies were largely cross-sectional surveys, while the qualitative studies involved focus group discussions, in-depth and semi-structured interviews (Table 2 ).

Patient and Public Involvement

Patients were not directly involved or recruited into this study. We reviewed published articles that investigated the barriers to cervical cancer screening uptake by women in LMICs. The results will be disseminated through a publicly available research report and a manuscript and in conferences and webinars. They will also be distributed through the WHO and the institutions involved in the project.

The individual studies included participants from rural and urban areas, women living with and without HIV, women in the general public, women attending antenatal services, university students, and healthcare workers. Four studies included men [ 33 , 60 , 63 , 87 ] and in two of the studies, they were partners of women participants [ 33 , 63 ] while in the others they were university male students. Thirteen studies included healthcare workers exclusively or with non-healthcare workers [ 13 , 14 , 24 , 35 , 37 , 41 , 47 , 54 , 55 , 73 , 82 , 85 , 86 ]. Eight studies included participants younger than 18 years old including one study that included girls from the age of 10 years together with older women [ 15 , 17 , 26 , 32 , 34 , 49 , 58 , 87 ] -36. In 17 studies, age details were not specified (Table 2 ). Frequently missing information was age of the participants, type of screening and when the study was conducted. The sample sizes of studies ranged from 15 participants [ 24 , 48 , 54 ] to 15,317 participants in a study that analysed secondary data [ 26 ].

Types of screening methods

Forty eight percent of studies were about Papanicolaou (pap) smears exclusively or in combination with other screening methods, 25% about visual inspection with acetic acid (VIA) or visual inspection with Lugol’s iodine (VILI), 5% on HPV screening (through self-sampling or using DNA based tests) exclusively or in combination with other screening methods, while a total of 30% of studies did not specify the type of screening method (Table 2 ).

Since most studies identified were descriptive or qualitative in design, we analysed and summarized the main findings using a descriptive narrative, based on themes identified as levels of the social ecological model [ 88 ]. During the thematic analysis six authors in groups of two grouped the barriers that were identified into five categories, as defined below.

Individual/personal level barriers – obstacles experienced at individual level

Cultural/traditional and religious barriers – cultural, traditional, and religious views, norms, and expectations

Social barriers – community and societal obstacles

Health system barriers – factors in the design, function and implementation of health systems that make it difficult for some individuals to access, use or benefit from care

Structural barriers– macroscale obstacles that affected some women disproportionately

These categories are not entirely distinct or mutually exclusive as factors in one category overlap and are influenced by those in other categories (Refer to Fig.  2 for a visual diagram depicting barriers across each level).

figure 2

Examples of barriers in the five categories

The barriers to uptake of cervical cancer are interconnected and operate across and within the various levels of the social ecological model. The following category levels include factors that contribute to barriers to cervical cancer screening, spanning the patient/individual level to the structural level. The studies reviewed included quantitative and qualitative input from both women and men (including patients, women from the community, male and female students, female teachers and male partners), as well as from the health service-level (including nurses, doctors, community health workers, policy makers, NGO staff and district coordinators). Information about the different categories of barriers that were identified across the articles included in this review are provided in Table  3 .

Individual/personal level barriers

Most studies reported individual or personal level barriers to screening. The most common individual level barriers were lack of knowledge and information about cervical cancer and cervical cancer screening, and its benefits, including women who did not understand the value of screening – i. e., health examination in the absence of symptoms or ill health [ 18 , 19 , 20 , 21 , 25 , 31 , 43 , 47 , 48 , 49 , 51 , 52 , 56 , 58 , 60 , 64 , 76 , 81 ]. Another commonly reported individual level barrier was fear of receiving positive screening results with many women believing that a cancer diagnosis was terminal [ 15 , 19 , 20 , 21 , 25 , 31 , 33 , 35 , 36 , 37 , 40 , 44 , 46 , 48 , 49 , 50 , 52 , 53 , 60 , 62 , 64 , 69 , 74 ].

Studies also reported that women had misconceptions about screening and the screening process. Women feared pain from the screening procedure and had misconceptions about possible harms such as contracting cancer, or damage to the uterus or cervix during screening [ 13 , 14 , 15 , 17 , 23 , 30 , 39 , 44 , 47 , 48 , 49 , 51 , 54 , 56 , 61 , 62 , 65 , 81 ]. In Nigeria, women reported being afraid of contracting infections from the screening equipment or from other sources within the health facility [ 35 , 40 ]. In Ethiopia, most women offered self-sampling for HPV thought that the process would be painful, while some feared using the Evalyn brush [ 14 ], and in South Africa, some women reported fear of concurrent HIV testing during screening for cervical cancer [ 16 ].

In 33% of studies conducted in Africa, Asia, and South America many women reported being embarrassed to be screened or to undergo pelvic examination [ 13 , 15 , 18 , 19 , 21 , 23 , 29 , 30 , 31 , 33 , 39 , 44 , 47 , 49 , 57 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 69 , 71 , 72 , 81 ]. Embarrassment was associated with the activity of going to a facility for screening, the pelvic examination itself, and being examined by a male or young healthcare worker [ 60 , 61 , 65 ].

Studies also reported that women, regardless of geography or employment status, faced competing priorities and responsibilities and thus often had limited time to attend screening [ 28 , 73 ].

Cultural/traditional /religious and social barriers

Cultural/religious/traditional, and social barriers were closely intertwined in the studies evaluated. Eleven studies reported that women were not screened because of religious or traditional reasons and prohibitions [ 14 , 15 , 17 , 22 , 25 , 26 , 33 , 35 , 49 , 52 , 72 ]. Two studies reported on possible clashes between western and traditional views of cervical cancer screening [ 48 , 80 ], and mistrust of western medicine and preference for traditional medicine was reported in Ghana, and South Africa [ 15 , 48 ]. In Ecuador, there were competing interpretations of health between healthcare workers and the community [ 80 ]. Some studies (21%) also reported that men disapproved of cervical cancer screening, with some refusing for their wives to be screened [ 17 , 18 , 25 , 27 , 29 , 30 , 33 , 38 , 43 , 49 , 53 , 54 , 57 , 58 , 67 , 74 , 76 ]. Other studies reported that women’s health issues, including sexual and reproductive health, were deprioritized and not awarded the same urgency as other health issues [ 12 , 82 ], while others reported that cervical cancer screening was viewed as a private and taboo topic (culturally embarrassing) not to be discussed, due to its connection to sexual and reproductive health [ 82 ].

Social barriers were related to community disapproval or negative community perceptions about the health system, the screening process, lack of peer support, and stigmatization of cervical cancer and the screening process [ 13 , 14 , 22 , 27 , 36 , 40 , 44 , 55 , 59 , 60 , 82 ]. In some studies stigma was related to cervical cancer being viewed as a terminal disease by some [ 15 , 23 ], while in others stigma was due to association with sexual transmission, with women attending screening sometimes assumed to be engaged in infidelity or promiscuity [ 22 , 33 ]. In South Africa where concurrent HV testing was offered, stigma was related to the association of cervical cancer with HIV infection [ 36 ].

Health system barriers

Heath system barriers included lack of capacity, poor organization of services, lack of knowledge about cervical cancer amongst healthcare workers, lack of promotion of screening, poor (negative and unfriendly) attitudes of healthcare workers when interacting with patients, and lack of public confidence in the health system. Lack of capacity included limited numbers of healthcare facilities in general, but especially in rural areas, few healthcare facilities providing screening services, limited staff, brief and rushed consultations, and shortage of equipment and materials which often led to women being referred for screening far from where they live resulting in costly, and lengthy screening and diagnostic pathways [ 17 , 18 , 19 , 24 , 25 , 30 , 35 , 37 , 39 , 41 , 44 , 47 , 48 , 52 , 53 , 54 , 55 , 58 , 61 , 74 , 82 , 85 , 87 , 89 ].

Capacity barriers also included reports of poor knowledge of cervical cancer among healthcare workers, poor technical skill to perform screening procedures, limited supervision leaving staff uncertain about technique, and limited specialized experts such as gynaecologists for guidance and management of some patients [ 15 , 25 , 54 , 77 ]. In Kenya and Ethiopia, clinic operating times and unavailability of services on weekends also limited screening uptake [ 13 , 51 ]. In studies conducted in Uganda and South Africa, women reported that lack of privacy in healthcare facilities was a barrier to screening [ 29 , 48 ], while in Malawi, Munthali et al., identified a lack of space for screening services in healthcare facilities as a barrier [ 47 ]. Lack of confidence in the health system was reported in Nigeria and Uganda [ 40 , 54 ].

Eleven studies, seven in Africa ( n  = 7), Asia ( n  = 3) and South America ( n  = 1) found that poor, negative and discriminatory attitudes of healthcare workers towards women discouraged women from screening [ 16 , 25 , 49 , 50 , 51 , 52 , 53 , 59 , 61 , 65 , 80 ]. A study conducted in Nigeria, reported that discrimination toward Muslim women hindered access to healthcare facilities and screening [ 40 ]. Two studies also found that communication and language barriers between women and healthcare workers left women with unanswered questions and limited screening uptake [ 15 , 80 ].

Long wait times in healthcare facilities were a barrier to screening in South Africa, Uganda, Kenya and China [ 16 , 26 , 43 , 48 , 50 , 53 , 61 ]. This may partly also explain why women reported competing priorities for their time (work and family responsibilities) when they considered attending screening services.

Several studies reported on policy and guideline implementation barriers. Studies in Uganda, Indonesia, Brazil, and China found poor organisation of the services with limited information available about screening services leaving women without information about screening sites, and procedures for booking screening appointments [ 48 , 57 , 61 , 83 ]. In Bolivia, healthcare workers reported that lack of dissemination of screening guidelines, and lack of educational campaigns and infrastructure for screening limited screening uptake [ 82 ]. In Oceania, screening guidelines were not implemented while Bulgaria had no screening policy [ 85 , 86 ]. In Argentina and China, the screening policy excluded unmarried women from free screening (in China), thus limiting screening for some women since out-of-pocket screening costs were frequently identified as a barrier to uptake [ 61 , 84 ]. Healthcare workers also often failed to promote, recommend or offer screening and related cervical cancer information during other consultations [ 18 , 38 , 43 , 62 , 71 , 74 ].

Structural barriers

Structural barriers were mainly related to geographic distance to screening facilities, associated travel costs, poor transport systems, and screening costs where screening was not a free service in the absence of health insurance. Screening costs were a barrier in all continents, with travels costs a barrier in Africa, Asia and South America [ 12 , 15 , 18 , 23 , 25 , 31 , 33 , 34 , 35 , 41 , 43 , 44 , 46 , 47 , 48 , 51 , 59 , 60 , 79 , 84 , 86 ]. Long waiting times were also associated with additional costs for meals, and this increased overall screening costs [ 55 ]. Women in rural areas were disproportionately affected by distance, and travel costs [ 10 , 16 , 44 , 76 , 78 ]. In South Africa, Uganda and Nigeria, additional barriers were crime (which hindered free and safe travel), poor road networks and unreliable and inconvenient transport schedules to screening facilities [ 10 , 44 , 54 ]. One study reported lack of infrastructure for women with disabilities [ 11 ]. Other structural issues included low levels of education and low socioeconomic status [ 27 , 32 , 34 ], common among women living in conditions of poverty or limited resources.

Underlying reasons for barriers to screening uptake

Based on the descriptive analysis of the main findings of the studies included in this review, we identified four underlying reasons for barriers to cervical cancer screening uptake that should be addressed when considering interventions and policies for remedial action. Firstly, poor or ineffective messaging about cervical cancer, screening and prevention evidenced by limited information and education about cervical cancer and screening as a preventive strategy and misconceptions about the cause of cervical cancer, and the screening process, is a key underlying reason for poor screening uptake. Many women are not aware of screening and its value, and there are many misconceptions about screening in many communities. Secondly, health systems are poorly resourced to provide screening, lack clear policies on cervical cancer and screening, or poorly implement any existing policies [ 48 , 57 , 60 , 61 , 82 , 85 , 86 ]. Thirdly, there is limited access to health care services more generally, because of lack of universal health coverage and affordability, a common feature in many LMICs and a notable barrier to screening uptake [ 15 , 18 , 25 , 28 , 31 , 34 , 35 , 41 , 46 , 48 , 51 , 59 , 61 , 85 ]. Women often must travel to facilities far from where they live for screening services, indicating limited access in many geographic areas which is worsened by transport and other additional costs [ 15 , 18 , 22 , 23 ].

Finally, gender norms that deprioritize the health needs of women both at institutional, community and household levels also underly poor screening uptake [ 13 , 20 , 22 , 25 , 30 , 33 , 34 , 35 , 41 , 47 , 48 , 51 , 60 , 64 , 65 , 74 ]. Patriarchal norms which value the needs of men and boys over women and girls are often upheld in institutions and communities, which shapes political will and decision-making regarding investment in women’s health and creates inequitable health and access to care for women [ 90 , 91 ]. In many studies, women reported a lack of partner approval, permission, or support, as well as religious, cultural, or traditional prohibitions as a barrier to uptake, indicating the breadth and depth of the impact of gender norms.

This review provides a broad overview of the barriers to uptake of cervical cancer screening in LMICs. The barriers were generally the same across countries and continents and different study designs, and are attributable to interacting individual, social, cultural, health system and structural factors.

At the individual level, lack of knowledge and information about cervical cancer, the screening process, and its value, were frequently reported. This suggests that failure to address the knowledge and information gaps, will likely continue to limit uptake even in the absence of other barriers. The literature also reports poor uptake among well-informed women, who reported other barriers rooted in societal religious, cultural health system and structural barriers [ 92 , 93 , 94 , 95 , 96 , 97 ]. Another common individual level barrier was fear which encompassed a wide range of issues. Limited information about the screening process (how it is done and by whom), may result in fear of what to expect. In Switzerland, women preferred to screen themselves using the self-HPV test kit since it reduced discomfort, embarrassment and maintained privacy compared to the traditional pap smear test [ 97 ]. Appropriate and careful introduction and scale up of such self-testing could expand screening in LMICs. Fear of the screening outcome could indicate anxiety around stigmatization, related to discrimination of women with cervical cancer. In a Ugandan study, cervical cancer patients were abandoned by their families, while in a Zambian study, cervical cancer was associated with shame [ 82 , 98 ]. Stigma has also been reported in high income countries. Muslim women in London were hesitant to screen due to embarrassment and fear because they were unmarried and did not want to send implicit messages about being sexually active [ 99 ]. Another study also in the United Kingdom found that cervical cancer screening was stigmatized because of its association with HPV, and the perception that it shows failure of women’s responsibility for their health [ 100 ]. This emphasizes the urgent need for strengthened information dissemination, attention to gender-related discrimination, and dispelling of myths, about cervical cancer.

Cultural/traditional, religious, and social barriers were identified across many studies in all continents, but mainly in Africa and Asia. Lack of spousal and or family support were key barriers, and these may be driven by misconceptions about cervical cancer and traditional, cultural, or religious beliefs about pelvic examination and cancers, and this has also been reported in high income countries [ 101 , 102 ]. Overlapping with cultural/traditional and religious barriers were other social factors including misconceptions and stigmatization of screening and cervical cancer, largely shaped by gender norms [ 14 , 26 , 33 , 48 , 58 ]. The impact of gender norms and inequality were common barriers. When men hold decision-making power, women and girls can have limited access to the social, economic and health resources necessary for their well-being [ 91 ]. At the household level, men often shape the logistical, educational, and psychosocial factors that directly affect women’s ability to access cervical cancer services. Women who are emotionally and financially supported by their families and partners are more likely to get screened. Conversely, family and partners can play a key role in stigmatizing, isolating, and prohibiting women from accessing screening.

Well-functioning health systems with accessible services are critical for successful and effective health programmes. We found significant gaps in cervical cancer screening services in the health systems of LMICs ranging from a lack of high-level elements such as policies and guidelines, poor referral systems, limited points of service, inadequate resources (human and equipment/materials), to local level factors including poor attitudes of healthcare workers. Poor attitudes and discrimination by healthcare workers while inexcusable may be fuelled by staff overload and challenging and constrained conditions [ 47 , 103 , 104 ], areas in need of urgent attention of policy makers and implementers.

Access to screening services was also hindered by geography and cost. Travel costs are significant for women with limited financial means. Women with low levels of education – who often have limited financial means – were less likely to be screened, hence, investing in women’s education in combination with other equity-promoting interventions is likely to improve uptake, given the known benefits of education.

Strengths and limitations

This review includes a wide range of studies (both qualitative, quantitative, and mixed method study designs) and grey literature published over the period 2010 and 2020, enabling an extensive investigation of barriers to cervical cancer screening in LMICs. However, a potential limitation is that studies may have been overlooked due to the search terms used. For example, if studies used terms other than “Vaginal Smears”, “Papanicolaou”, “pap smear”, “pap stain”, “pap test” or “vaginal smear” to describe this specific screening test, they may not be included in the search results. We also included studies where barriers to cervical screening uptake was not a primary objective, and this may limit generalizability of some findings. However, the common barriers were corroborated by many different studies, looking at multiple level barriers to screening in LMICs.

Recommendations

To increase screening uptake and support the elimination of cervical cancer as a public health problem in LMICs, there is a need for implementation of clear cervical cancer policies and guidelines with the prerequisite structures and resources required across diverse health settings. Countries should review their cervical cancer policies and related programs, and fully implement screening guidelines which prioritize structured screening, rather than rely on opportunistic screening that is patient driven. Policies – both within and beyond the health sector – should also actively account for and work to eliminate stigma and all forms of disadvantage and discrimination that shape inequities in communities and within the healthcare system. There is also need for education, information dissemination, engagement, and advocacy about cervical cancer at the community and health facility level. This creation of knowledge and awareness amongst community members and providers around how to proactively reduce barriers to care is crucial for ensuring more women receive screening, and is central to addressing misconceptions, myths, and fears that are prevalent in many communities. Education and awareness initiatives should be grounded in accessible language, driven by local and community contexts and needs, and meaningfully engage diverse groups of women, men, boys and girls as well as multiple sector stakeholders (including a community health worker component focused on women’s health and counselling). Policies that support the sexual and reproductive health and rights of women and girls should be strengthened and expanded and account for inequities in access to care for diverse groups of women. This can include culturally appropriate interventions with a dedicated focus on promoting women’s health, taking into account the social and financial needs of communities. Further priorities at the health facility level includes adequately addressing issues around staff-patient ratio, staff capacities and competencies, organization and integration of facility services, and health promotion efforts aimed at attracting community members for screening. To engage women and communities effectively and consistently, outreach efforts should be conducted in a manner that recognises the different contexts with regards to physical access, affordability, culture, tradition, and competence of health providers to provide high quality and friendly services. Community and religious leaders, non-governmental organizations (NGOs), women who have been screened, and other stakeholders need to reinforce and advocate the message that screening saves lives. This would be an important step in combatting the stigma related to cervical cancer screening.

Future research should focus on generating robust data on which groups are under-screened and why. This must account for the differential experiences of women across diverse categories (e.g., age, socioeconomic status, geography, disability, etc.) and look at the multiple level barriers that converge to create or reinforce barriers to health and screening.

This review highlights some of the key issues highlighted in the literature to date, but there remains a dearth of information as to the multi-level barriers to screening that women face across axes of inequity, including gender, age, income, migrant status, ability, etc.

Finally, the introduction of more modern screening approaches in LMICs should also be supported. It is better information, better resources, and input from women themselves, that can ground how barriers are addressed and how access is improved moving forward.

This review identified a wide range of barriers to cervical cancer screening in LMICs. Urgent implementation of clear policies and programs, supported by health system capacity to implement them is required to address these barriers. The policies should support the promotion of women and girls’ health and rights, and gender equality. In addition, community-wide information dissemination, engagement and advocacy, and targeted further research on barriers to care across diverse groups and contexts are needed to effectively address the inequitable burden of cervical cancer in LMICs. It is only in reducing the barriers to cervical cancer screening that so many women continue to face, that the aims of the WHO’s global strategy to eliminate cervical cancer as a public health problem will be fulfilled.

Availability of data and materials

The articles reviewed and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Drs Desmond Kuupeil, Monica A Mensa and Nonjabulo Gwalawo from the Faculty of Public Health Medicine at the University of KwaZulu-Natal for assisting with the literature searches.

This project was funded by the World Health Organization.

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VC and SM conceptualised the project. SM wrote the project protocol. AJ and TG contributed to the protocol. AJ, ZP lead the data searches. All authors screened and reviewed abstracts and articles and extracted data. AJ and ZP lead the analysis. SM and ZP lead writing the manuscript. All authors contributed to the manuscript and approved it for publication.

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VC, HG, PG, SP are employed by the World Health Organization which funded the project.

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Additional file 1: appendix 1..

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Petersen, Z., Jaca, A., Ginindza, T.G. et al. Barriers to uptake of cervical cancer screening services in low-and-middle-income countries: a systematic review. BMC Women's Health 22 , 486 (2022). https://doi.org/10.1186/s12905-022-02043-y

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DOI : https://doi.org/10.1186/s12905-022-02043-y

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  • Cervical cancer
  • Social ecological model
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thesis on cervical cancer

Thesis: Surviving Cervical Cancer: A History of Prevention, Early Detection, and Treatment

Editor's note:

Alexis Darby defended her thesis titled “Surviving Cervical Cancer: A History of Prevention, Early Detection, and Treatment,” in May 2019 in front of committee members Jane Maienschein, Carolina Abboud, and Karin Ellison, earning her a Bachelor’s degree from Barrett, the Honors College. https://repository.asu.edu/items/53339

Cervical cancer, which many physicians as of 2019 consider to be a success in terms of establishing widely used forms of early preventative and diagnostic technologies, experienced a reduction in incidence rates in women by over fifty percent between 1975 and 2016. Cervical cancer does not often present in women with symptoms until it has entered a later stage of the disease. Because of this fact, in the early twentieth century, physicians were often only able to diagnose cervical cancer when either the woman reported complaints or there was a visual confirmation of lesions on the cervix. The symptoms women often reported included vague abdominal pain, bleeding after sex, and abnormal amounts of vaginal discharge, all of which are non-specific symptoms, making it even harder for women to be diagnosed with cervical cancer.

This thesis answers the following question: How does the history of cervical cancer show that prevention helps reduce rates of cancer-related deaths among women? By studying the history of cervical cancer, people can understand how a cancer that was once one of the top killers of women in the US has declined to become one of the lowest through the establishment of and effective communication of early prevention and diagnostics, both among the general public and within the medical community itself. This thesis is organized based on key episodes which were pertinent to the history of cervical cancer, primarily within the United States and Europe. The episodes are organized in context of the shifts in thought regarding cervical cancer and include topics such as vaccine technologies like the Gardasil and Cervarix vaccines, social awareness movements that educated women on the importance of early detection, and analyses of the early preventative strategies and attempts at treating cervical cancer.

After analyzing eleven key episodes, the thesis determined that, through the narrative of early attempts to treat cervical cancer, shifting the societal thought on cancer, evolving the importance of early detection, and, finally, obtaining a means of prevention, the history of cervical cancer does demonstrate that the development of preventative strategies has resulted in reducing cancer-related deaths among women. Understanding what it took for physicians to evolve from simply detecting cervical cancer to being able to prevent it entirely matters because it can change the way we think about managing other forms of cancer.

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Improving cervical cancer screening rates: a scoping review of resources and interventions

  • Review article
  • Published: 18 August 2022
  • Volume 33 , pages 1325–1333, ( 2022 )

Cite this article

  • Madyson L. Popalis 1 ,
  • Sarah I. Ramirez 1 ,
  • Kelsey M. Leach 1 ,
  • Marni E. Granzow 1 ,
  • Kelsey C. Stoltzfus 1 &
  • Jennifer L. Moss   ORCID: orcid.org/0000-0002-3794-1344 1  

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Introduction

Cervical cancer mortality can be prevented through early detection with screening methods such as Pap and high-risk human papillomavirus (hrHPV) tests; however, only 81% of women aged 21–65 are up-to-date on screening. Many interventions to increase cervical cancer screening have been implemented, but there is limited understanding about which intervention components are most successful.

We conducted a scoping review of existing literature and available resources for cervical cancer screening interventions to identify gaps in the research. We used t tests and correlations to identify associations among intervention components and effect sizes.

Out of nine studies, the mean overall effect size for interventions was 11.3% increase in Pap testing for cervical cancer screening (range =  − 4–24%). Interventions that included community health workers or one-on-one interaction had the biggest effect size ( p  < 0.05). No associations with effect size were noted for literacy level, number of intervention components, or targeting by race/ethnicity.

Conclusions

Future interventions may include educational sessions with community health workers or one-on-one patient interaction to improve cervical cancer screening. Further research is needed to establish effect sizes for large-scale interventions and hrHPV screening interventions.

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Acknowledgments

Funding for this project came from K22 CA225705 (PI: Moss) and an Institutional Research Grant, IRG-17-175-04, from the American Cancer Society (PI: Moss). In addition, the project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UL1 TR002014 and UL1 TR00045. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Popalis, M.L., Ramirez, S.I., Leach, K.M. et al. Improving cervical cancer screening rates: a scoping review of resources and interventions. Cancer Causes Control 33 , 1325–1333 (2022). https://doi.org/10.1007/s10552-022-01618-2

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A systematic review of economic evaluations of cervical cancer screening methods

  • Thatohatsi Sefuthi   ORCID: orcid.org/0000-0002-7738-2444 1 &
  • Lungiswa Nkonki 2  

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The aims of this systematic review were to (1) identify primary- and model-based economic evaluations of cervical cancer screening methods and to (2) provide a contextual summary of valuation outcomes associated with three types of cervical cancer screening tests: visual inspection with acetic acid, human papillomavirus deoxyribonucleic acid, and Papanicolaou smear.

Introduction

Cervical cancer screening is an important public health priority with the potential to improve the detection of precancerous lesions in high-risk females for early intervention and disease prevention. Test performance and cost-effectiveness differ based on the specific screening method used across different platforms. There is a need to appraise existing economic evaluations of cervical cancer screening methods.

This review considered primary-based and model-based full economic evaluations of cervical cancer screening methods. The evaluation methods of interest included cost-effectiveness analysis, cost-utility analysis, cost-minimization analysis, cost–benefit analysis, and cost-consequence analysis. We searched Scopus, PubMed, National Health Economic Evaluation Database (NH EED), Cochrane, and the Health Economic Evaluation Database for full economic evaluations of cancer screening methods. No formal date restrictions were applied. Model-based and primary-based full economic evaluations were included. A critical appraisal of included studies was performed by the main investigator, while a second independent reviewer assessed critical appraisal findings for any inconsistencies. Data were extracted using a standardised data extraction tool for economic evaluations. The ultimate outcomes of costs, effectiveness, benefits, and utilities of cervical cancer screening modalities were extracted from included studies, analysed, and summarised.

From a total of 671 screened studies, 44 studies met the study inclusion criteria. Forty-three studies were cost-effectiveness analyses, one study reported both cost-utility and cost-effectiveness outcomes, and another study reported cost utilities of cervical cancer screening methods only. Human papillomavirus (HPV) DNA testing was reported as a dominant stand-alone screening test by 14 studies, while five studies reported visual inspection with acetic acid (VIA) as a dominant stand-alone screening test. Primary HPV screening strategies were dominant in 21 studies, while three studies reported cytology-based screening strategies as the dominant screening method.

Conclusions

Existing evidence indicates that HPV-based and VIA testing strategies are cost-effective, but this is dependent on setting. Our review suggests the limited cost-effectiveness of cytology-based testing, which may be due in part to the need for specific infrastructures and human resources.

Systematic review registration

PROSPERO CRD42020212454 .

Peer Review reports

Cervical cancer is a common malignancy and a leading cause of cancer-related mortality worldwide [ 1 ]. Cervical cancer is an essential contributor to the disease burden in sub-Saharan Africa, with an estimated 75,000 new cases documented each year and approximately 50,000 new deaths recorded annually [ 2 ]. Countries in western, middle, and southern Africa are hardest hit by cervical cancer-related deaths, with world age-standardised mortality rates of 23.0%, 21.1.%, and 20.0%, respectively [ 3 ]. The economic burden of cervical cancer is substantial. For example, a study by Wu et al. (2020) reported that, in the Henan province of China, costs associated with cervical cancer, from diagnosis to 1 year after discharge, ranged from US $8,066 to 22,888 per patient [ 4 ].

Cervical cancer is caused by infection with high-risk serotypes of the human papillomavirus (HPV) [ 5 ]. Infection with HPV can lead to the development of precancerous lesions and malignancy if left untreated [ 6 ]. Since neoplastic transformation can take years or even decades to occur, early detection and treatment of precancerous lesions provide a vital intervention opportunity [ 7 ]. The World Health Organization (WHO) has identified cervical cancer as a potentially eliminable form of cancer [ 7 ]. However, cervical cancer remains underdiagnosed in clinical settings, particularly in developing countries [ 8 ]. Evidence indicates that adequate screening reduces cervical cancer-related deaths [ 9 ]. In the global strategy for cervical cancer elimination, the WHO estimates that cervical cancer can be eliminated within this century, if, by 2030: (a) 90% of girls are fully vaccinated with the HPV vaccine by age 15, (b) 70% of women are screened with using a high-performance test at 35 years of age and 45 years of age, and (c) 90% of women with precancer are treated and 90% of women with invasive cancer managed [ 10 ]. However, an HPV vaccine global market study reported that, as of 2021, only 13% of girls are fully vaccinated and protected from cervical cancer [ 11 ]. Such data underscores the need to maintain high cervical cancer screening rates in eligible populations.

Screening for cervical cancer can be performed using unaided visual inspection with acetic acid (VIA), assisted cytological (e.g. a Papanicolaou (Pap) smear), and molecular (e.g. HPV DNA testing) methods [ 12 ,  13 ]. A Pap test is a liquid cytology-based test that analyses cervix cells [ 14 ]. Unaided VIA is carried out by observing cervix cell colour changes in response to acetic acid exposure [ 15 ]. These screening methods differ in their diagnostic value, accuracy, and associated costs to both the user and healthcare system [ 16 ].

Health economic evaluations [ 17 ] are comparative analyses of alternative courses of action regarding their costs and consequences [ 18 ]. They provide a framework to assist decision-makers in providing much-needed interventions based on available clinical evidence leveraged against the cost to the healthcare sector [ 19 ].

Economic evaluations from limited-resource settings like India [ 20 ] and South Africa [ 21 ] suggest that VIA is the most cost-effective primary screening test for cervical cancer. On the other hand, studies carried out in high-income countries such as Canada suggested that HPV DNA testing is the most cost-effective screening method, perhaps due in part to the ability and willingness of the country to pay for its routine adoption [ 22 ].

However, health economic evaluations focused on cervical cancer screening are limited by their use of different methodologies, and generalisation across prior studies is often not possible. The lack of consistent methods highlights the need for a methodical approach to exploring systematic differences across various economic evaluations.

We conducted an initial search of common research databases (PROSPERO, Medline, Cochrane, JBI) to identify prior studies which reviewed cervical cancer screening health economic evaluations. At least three previous systematic reviews [ 23 , 24 , 25 ] have provided evidence supporting the cost-effectiveness of cervical cancer screening. However, Nahvijou et al. (2014) [ 26 ] limited their systematic review to cost-effectiveness analyses of cervical cancer screening methods. In 2015, Mendes et al. [ 25 ] used mathematical models to evaluate the impact of cervical cancer screening strategies. Although critical insights were gleaned from this review, restricting the study type to mathematical modelling resulted in excluding primary-based economic evaluations. In their more recent review, Mezei et al. (2017) [ 24 ] also limited their review to cost-effectiveness analyses, focusing on lower- to middle-income countries.

Furthermore, the authors selected only model-based economic evaluations for review, thus excluding a large body of economic evaluation evidence from randomised controlled trials and primary cost-effectiveness studies. The authors did not carry out an appraisal of the methodological quality of the studies, which reduced the validity of the results. Lastly, the authors focus on the cost-effectiveness of screening methods. The present review builds on the findings reported by Nahvijou et al. (2014), Mendes et al. (2015), and Mezei et al. (2017) by evaluating all full economic evaluation methods, including cost-utility, cost–benefit, cost-minimisation, and cost-consequence analysis.

The aim of the present review was to critically appraise cervical cancer screening methods towards the improvement of precancerous lesion detection from a societal perspective, i.e. encompassing perspectives from the patient and their family members, healthcare providers, and third-party payers, and society at large.

We conducted [ 27 ] a preliminary search of PROSPERO, Medline, the Cochrane Database of Systematic Reviews, and the Joanna Briggs Institute (JBI) Database of Systematic Reviews and Implementation Reports. We found no current or underway systematic reviews on the topic. The study protocol was registered in PROSPERO under the registration number: CRD42020212454.

Review question

From the societal perspective, what evidence does full economic evaluations provide to support the use of specific cervical cancer screening methods to improve the detection of precancerous cervical lesions in women?

Inclusion criteria

Participants.

The participants of interest were women eligible to be screened for cervical cancer. Eligibility criteria differed between countries.

Intervention(s)

We reviewed studies exploring the cost-effectiveness of three different cervical cancer screening methods, i.e. HPV testing, VIA, and cytological testing. Information on costs and outcomes was sought for the screening methods implemented as a stand-alone intervention and within the context of a broader strategy or intervention, where cervical screening was combined with HPV vaccination.

Comparator(s)

This review considered studies which compared the three primary methods amongst themselves and/or compared to no screening.

The review considered studies which included the following outcomes: costs, effectiveness, benefits, and utilities. These measures include uptake, coverage, incremental cost-effectiveness ratios, cost per quality-adjusted life year (QALY), and cost per disability-adjusted life year (DALY). Outcomes were extracted from the included studies.

The review focused on full economic evaluations of cervical cancer screening methods performed without considering sociocultural, geographic, or ethnic factors.

Types of studies

The review considered primary- and model-based full economic evaluations of cervical cancer screening methods.

The review was conducted using the JBI methodology for systematic reviews of economic evaluation evidence [ 27 ].

Search strategy

The principal investigator (TS) performed a formal screening of the available academic literature from 07 September, 2020, to 18 January, 2021, across selected databases of interest (PubMed, Scopus, Cochrane, and the National Health Economic Evaluation and Health Economic Evaluation Databases). Other researchers duplicated all searches and screening of suitable studies to ensure a unanimous selection of appropriate economic evaluations for this review. The search terms used were “economic evaluation” and cervical cancer screening (see Additional file 1 : Appendix I). All logical synonyms and iterations of these search combinations were considered depending on the database and information source. The reference lists of selected studies were also screened to identify article citations of possible interest for the present research. Inclusion criteria were as follows: (1) studies published in English and (2) studies which considered female patients screened for cervical cancer using visual (VIA), cytological (Papanicolaou smear), or molecular (HPV DNA testing) methods. Exclusion criteria were as follows: (1) studies not available in English and (2) other systematic reviews and meta-analyses. We applied no date restrictions.

All relevant citations identified using these criteria were collated and uploaded into a Microsoft Excel template, and duplicates were removed. Two independent researchers then screened titles and abstracts. Suitable studies were retrieved, and their citation details were imported into the JBI System for the Unified Management, Assessment, and Review of Information (JBI SUMARI) (JBI, Adelaide, Australia) [ 27 ]. The full-text versions of eligible studies were assessed. The reasons for the exclusion of studies were also documented and reported. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow diagram was used to illustrate the flow of information through the different phases of the present review [ 28 ].

Economic evaluation outcomes of interest

Full economic evaluation methods of interest included cost-effectiveness (CEA), cost utilities (CUA), cost–benefit (CBA), cost-minimization and cost-consequence (CC). Measures of interest included ICERS of cost/year lives saved (YLS), cost/death averted, cost/CIN2 detected, cost/QALY gained, cost/life-year (LY), marginal cost/case detected, and cost/life-year gained (LYG). Since the focus was on economic evaluations of global screening methods, no specific sociodemographic or cultural factors were considered outcomes of interest.

Information sources

Searched databases included Scopus, HEED, NHEED, Cochrane Library, and PubMed.

Assessment of methodological aspects of the study

The methodological quality of suitable studies was scored using the JBI standardised critical appraisal instrument [ 27 ] as well as Drummond’s checklist for assessing economic evaluations [ 19 ], which may be found in Additional file 1 : Appendix III. Model-based studies were appraised using a model assessment checklist developed by Phillips et al. [ 29 ], which may be found in Additional file 1 : Appendix IV.

An independent reviewer assessed critical appraisal findings for any discrepancies. We resolved disagreements were resolved through discussion. Primary-based studies were included if they scored over 5 points in the appraisal, while model-based studies were included if they scored ten and above.

Data extraction

One reviewer extracted data from studies selected for inclusion in the review using the standardised data extraction tool from JBI SUMARI. A second independent reviewer assessed extracted data for inconsistencies and discrepancies. The JBI SUMARI tool was augmented by a data extraction tool developed by Wijnen et al. [ 30 ]. Extracted information included (1) descriptive data about cervical cancer screening studies, including study perspective, geographical setting, and study population characteristics, as well as study methods; (2) resource use results, cost and measures of cost-effectiveness, cost utility, cost–benefit, cost minimisation, and cost consequence; and (3) conclusions about factors which drive (impede) the cost-effectiveness of cervical cancer screening. Incremental cost-effectiveness ratios (ICERS) were converted to international dollars using the base year of 2020. Original costs were converted to the local currency of the study market using market exchange rate data [ 31 ]. Adjustment for inflation was carried out by multiplying ICERs by a GDP deflator obtained from the World Bank.

Data synthesis

Extracted data were analysed and summarised to respond to the review question using the JBI Dominance Ranking Matrix (DRM). Data analysis considered the collected data on study features, results, and authors’ conclusions about the contextual factors that drove or impeded cost-effectiveness. The DRM has three potential outcomes for the cost of intervention of interest against the health outcome(s) of interest:

Strong dominance is characterised by decisions distinctly favouring either the intervention or comparator from a cost or clinical effectiveness standpoint.

In weak dominance, data favours either costs or effectiveness.

Non-dominance is characterised by a less effective or more costly intervention.

The analysis also summarised data on the characteristics, results, and authors about the circumstances in which the intervention was likely to have a higher (or less) cost–benefit, cost utility, or cost consequence.

Study inclusion

From a total of 671 titles and citations screened following the removal of duplicates ( n  = 16), 80 abstracts were screened, and 74 studies were selected for full-text screening. Following the exclusion of ineligible studies (Fig.  1 ), 44 studies were included in this review.

figure 1

PRISMA flow diagram: search results, study selection, and inclusion process

In general, studies that were excluded during full-text selection compared health technologies beyond the scope of the research question. Additional file 1 : Appendix IV documents studies ineligible following the full-text review.

Methodological quality: primary-based studies

Primary-based studies were scored against eleven questions from the JBI standardised critical appraisal instrument [ 25 ] and Drummond’s checklist for assessing economic evaluations. All ( n  = 7) primary-based studies scored 11 out of 11 on the appraisal questions, except for a study by Jin et al. (2016), which had partially provided the relevant costs and outcomes for identified alternatives and had partially valued costs and consequences. Figure  2 summarises the scores of studies measured against the appraisal checklist.

figure 2

Methodological quality appraisal of primary studies

Methodological quality: model-based studies

Using a model assessment checklist developed by Phillips et al. [ 28 ], 37 studies were scored and assessed against twenty-two questions. The checklist assessed and categorised specific model elements like the present, unclear, or absent. All ( n  = 37) studies had a statement of the decision problem or objective and a statement of scope or perspective. The rationale for the model structure was provided by 97% ( n  = 36) of the studies.

Model structural assumptions were provided by 95% ( n  = 35) of the studies. All ( n  = 37) studies reported intervention strategies or comparators and the types of models they used. The model time horizon was reported by 73% ( n  = 27) of the studies, and 97% ( n  = 36) reported model disease states or pathways. Cycle length was present in 43% ( n  = 16) studies, absent in 38% ( n  = 14) studies, and unclear in 19% ( n  = 7) studies. In total, 97% ( n  = 36) studies reported both data identification and modelling elements, while 3% ( n  = 1) did not report on these elements. Baseline data was reported by 95% ( n  = 35) of the studies and was absent in 5% ( n  = 2) of the studies. Treatment effects were reported in 97% ( n  = 36) of the studies, while one treatment effects were absent in 3% ( n  = 1) of the studies. Intervention costs were reported by 97% ( n  = 36) of studies and were absent in 3% ( n  = 1) of studies. In addition, 97% ( n  = 36) of the studies reported quality-of-life weights. Data incorporation into models was reported in 97% ( n  = 36) of studies and was absent in 3% ( n  = 1) of studies.

The assessment of methodological uncertainty was reported in 78% ( n  = 29) of the studies, while 22% ( n  = 8) did not report having assessed methodological uncertainty. The studies reported structural uncertainty of models by 57% ( n  = 21), while 43% ( n  = 16) did not report structural uncertainty. Heterogeneity uncertainty was reported by 14% ( n  = 5) of studies, while 86% ( n  = 32) of the studies did not account for heterogeneity uncertainty.

The assessment of parameter uncertainty was reported in 78% ( n  = 29) of studies and was absent in 19% ( n  = 7) of the studies. It was unclear whether parameter uncertainty had been assessed in 3% ( n  = 1) of the studies. Approximately, 97% ( n  = 36) of the study models demonstrated internal consistency, while internal consistency was unclear in 3% ( n  = 1) of the studies. Models were externally consistent in 89% ( n  = 33) of the studies, while model external consistency was unclear in 11% ( n  = 4) of the studies. Figure  2  and Table 1 summarises the study scores.

Critical appraisal of results

All 44 initial studies identified were selected for inclusion in the review. Primary-based studies met the decision rules to include studies which scored above 5 using the checklist. All 37 model-based studies were included. We made an executive decision to include one study by Campos et al. (2012) [ 32 ], which had not met the decision rule since data about the model had been reported in a supplementary file.

Characteristics of included studies

Studies were available in English and published between 2004 and 2021 (Additional file 1 : Appendix V). Thirty-eight studies (88%) were model based and thus focused on hypothetical female cohorts as eligible participants. Studies were conducted across different locations, including South Africa, India, Greece, Lebanon, and Nicaragua. Although studies assumed various names to characterise perspectives, perspectives can be broadly categorised into three modalities, i.e. payer, patient, and societal perspectives. A total of 14 (33%) studies assumed a societal approach, while 18 (42%) studies used a payer perspective. The main characteristics of the studies included in the review are reported in Additional file 1 : Appendix IV.

Main findings

The most common economic evaluations examined cost-effectiveness ( n  = 43; 97%), followed by cost utility ( n  = 2.5%). A total of 20 (45%) cost-effectiveness studies reported singular screening methods as dominant, while 26 cost-effectiveness studies reported screen and treatment strategies as dominant.

Economic evaluation findings from cost-effectiveness studies

Due to significant methodological and structural heterogeneity, results were not suitable for meta-analysis, which was further impeded by varying study designs, methodology, and outcome reporting formats. For example, no model-based studies shared the same modelling assumptions. Table 2 details the dominant stand-alone screening technologies and strategies reported in cost-effectiveness analysis studies. VIA was the dominant screening method in five studies, while HPV DNA testing was reported as the dominant screening strategy in 14 studies. No study reported cytological testing as a dominant stand-alone screening methodology for cervical cancer.

Table 3 outlines the screening strategies which were reported as dominant. Twenty-one studies reported HPV DNA-based screening strategies as dominant, and three studies reported cytology-based screening strategies as dominant. Within the context of screening strategies, no studies reported VIA-based screening strategies as dominant.

Table 4 outlines outcome measures associated with dominant screening methods and strategies. Estimated outcomes used in the cost-effectiveness analyses were as follows: ICERS of cost/year lives saved (YLS), cost/death averted, cost/CIN2 detected, cost/life year (LY), marginal cost/case detected, and cost/life-year gained (LYG). Studies which analysed both cost-effectiveness and cost utility included cost/QALY gained as an outcome measure. Costs were reported in international dollars, using the base year of 2020.

Economic evaluation findings from cost-utility studies

Guerrero et al. [ 72 ] compared VIA to Pap smear screening implemented alone or with HPV vaccination at different coverages. Outcome measures were ICERS in the form of cost/QALY gained and reduction in cervical cancer. VIA was associated with the highest dominance and cost-saving in various coverage scenario analyses, with ICERS ranging from dominant to 1443 USD. VIA augmented by HPV vaccination of pre-adolescent girls was reported to be dominant at a coverage of 80%, with an ICER of US $783. Zhao et al. (2019) performed a cost-effectiveness analysis of cervical cancer screening methods, augmented by a utility analysis. The authors found that careHPV testing every 5 years had the highest cost-utility ratio (1,783.8 Yuan/year) [ 41 ].

We critically appraised economic evaluation studies of cervical cancer screening methods ( n  = 44). In total, 44 studies (100%) supported the cost-effectiveness of cervical cancer screening. Our results suggested that primary HPV DNA testing strategies are cost-effective in several settings. VIA may be cost-effective in some environments, including rural areas, but not in others. Similarly, cost-utility findings comparing cytology and VIA often describe that VIA has higher utility. These findings are echoed by Mezei et al. (2017). After performing a systematic review of the cost-effectiveness of cervical cancer screening methods in LMICs, they concluded that HPV testing and VIA were the most cost-effective screening methods [ 24 ]. Pap testing is frequently dominated by HPV testing and VIA but is cost-effective in co-testing and triaging. Our results also suggest that cervical cancer screening modalities are most effective when applied within a broader context of treatment and intervention. This would include consideration of the health economics of cervical cancer in addition to evidence for the effectiveness of different established modalities. Our review further suggests that sample collection, screening sequence and algorithms, and coverage are essential.

One factor that influences the cost-effectiveness of cervical cancer screening modalities is sample collection. Mezei et al. [ 52 ] compared self-collection followed by clinic-based VIA triage to clinic-based collection and triage in HPV-positive females in Uganda. The reduction in cervical cancer incidence and ICERs (USD/YLS) was used as cost-effectiveness measures. The use of Monte Carlo modelling allowed the authors to show that self-collection was more cost-effective than clinic-based VIA triage-based ICER outcomes. Using cytology-based screening as a comparator, Vassilakos et al. [ 51 ] also reported that offering HPV self-testing is more cost-effective compared to cytology and associated with a reduction in cervical cancer cases and cancer-related mortality. Both authors correlate a critical gain to HPV self-testing is increased population coverage.

The method sequence could also affect cervical cancer screening cost-effectiveness. Jin et al. [ 56 ] compared the three screening methods for cervical cancer of interest in this review and found significant differences in their diagnostic accuracy. Co-testing was identified as more accurate but also less cost-effective. These findings echo those reported by Campos et al. [ 55 ], who compared different methods and interventions in their lifetime risk reduction and ICERS (USD/YLS). These measures found HPV testing with intervention to be more cost-effective compared to cytology-based strategies. Using the Nicaraguan cost-effectiveness threshold (GDP per capita of US $2090), HPV cryotherapy remained comparatively cost-effective, with an ICER of US $320/YLS [ 55 ].

Several studies included in this review underscored the importance of screening coverage. In Lebanon, results from a model-based cost-effectiveness analysis indicated that using cytology as a screening modality with a shift from the current 20% coverage to at least 50% would reduce cervical cancer incidence considerably [ 38 ]. More gains would be achieved if HPV testing was used as a screening modality, at 50% coverage, resulting in a 23.4% reduction in the incidence of cervical cancer [ 38 ]. Modulating coverage for different strategies (50–80%) tend to favour the cost-effectiveness of HPV-based screening strategies [ 38 ].

Several study limitations should be noted. None of the included studies which used models and simulations accounted for uncertainty associated with heterogeneity, and few accounted for model structural uncertainty. Consequently, internal or external model consistency could not be guaranteed. Several model-based studies used the same model Campos et al. [ 71   46   34 ]. Consequently, study findings are not disparate. Lastly, critical appraisal and data extraction were performed by one reviewer. However, this limitation was offset by critical appraisal and extracted data being assessed for inconsistencies by another independent reviewer.

In conclusion, our review supports the general cost-effectiveness of HPV testing and VIA as screening strategies for cervical cancer. Compared to HPV testing and VIA, cytology testing is the least cost-effective. Future studies would do well to examine the health economics of cervical cancer screening, with emphasis on the test performance of different screening modalities. Furthermore, parameters such as the order of screening methods, and its relationship to the screening intervention, screening coverage, screening modality, and the number of screening visits, could have important implications for care. The ultimate success of cervical cancer screening and treatment could depend on a broader perspective in deciding which strategy is most appropriate for the individual patient and context.

Study implications for practice, policymakers, and future researchers

This review sought to synthesise available evidence on cervical cancer screening methods and strategies to achieve optimal precancerous lesion detection and thus avert cervical cancer. Given the significant heterogeneity of studies included in our review, study results could not be pooled and were not suitable for meta-analyses, a limitation common to economic evaluation systematic reviews. This limitation underscores the need to develop and further standardise economic reporting. An interim measure which researchers can apply is sub-set group analysis, i.e. aim to pool and compare studies similar in setting, participants, and outcomes. Ultimately, researchers should keep in mind that health economic reviews are not intended to provide conclusive recommendations for routine practice but rather to guide policymakers in developing optimised strategies for testing and intervention [ 27 ].

Review findings have demonstrated the multi-faceted nature required to achieve optimal screening strategies. An extension of existing research might show the need for clinicians to offer due consideration to the individual and public health costs of cervical cancer screening. HPV and VIA screening might be more appropriate screening options for clinicians. A combined approach might also prove feasible, and clinicians might need to consider the order in which screening is performed in order to maximise cost-effectiveness. Furthermore, a large body of models and simulations targeted towards cervical cancer screening evaluation exist. Countries intending to introduce more relevant and improved cancer strategies can leverage the existing body of knowledge by learning from documented best practices.

Recommendations for research

Few studies have discussed how HPV vaccination could inform decisions on screening reduction, which is vital as several countries seek to roll out HPV vaccination. It will be essential to know what bearing this will have on cervical cancer screening programmes to minimise inefficiencies. Further research would do well to determine what treatment options are associated with ideal clinical and economic value.

Abbreviations

Visual inspection with acetic acid

Human papillomavirus deoxyribonucleic acid

Papanicolaou smear

World Health Organization

Human immunodeficiency virus

Human papillomavirus

Health Economics Resource Centre

Budget impact analysis

Loop electrosurgical excision procedure

Incremental cost-effectiveness ratio

Cervical intraepithelial neoplasia

President’s Emergency Plan for AIDS Relief

National Health Economic Evaluation Database

Health Economic Evaluation Database

Joanna Briggs Institute Dominance Ranking Matrix

Age-standardised mortality rates

Joanna Briggs Institute

Gross domestic product

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Acknowledgements

The authors would like to gratefully acknowledge colleagues from the Stellenbosch University Division of Health Systems and Public Health and the Division of Epidemiology and Biostatistics for their contribution and support. This review is submitted as a part of the Master of Philosophy Degree in Health Systems and Public Health at the Stellenbosch University.

This work was supported by a grant from the Harry Crossley Foundation. The Harry Crossley Foundation did not play a role in data collection and analysis or interpreting the results.

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Appendix I : Search strategy. Appendix II : Data extraction instrument. Appendix III : JBI standardised tool and Drummond's Checklist . Appendix IV : Phillip et al Checklist for Model-Based Studies. Appendix V : Studies excluded on full text. Appendix VI : Characteristics of Included Studies. Table: Characteristics of Included Studies - Economic Evaluation Form. Appendix VII : Abstract Checklist.

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Sefuthi, T., Nkonki, L. A systematic review of economic evaluations of cervical cancer screening methods. Syst Rev 11 , 162 (2022). https://doi.org/10.1186/s13643-022-02017-z

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thesis on cervical cancer

FACILITATORS ENABLING SUSTAINABLE CERVICAL CANCER CONTROL PROGRAMS IN LOW- AND MIDDLE-INCOME COUNTRIES: STRENGTHENING HEALTH SYSTEMS IN ZAMBIA

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thesis on cervical cancer

  • Affiliation: Gillings School of Global Public Health, Department of Health Policy and Management
  • By 2030, an estimated 75% of cancer deaths will occur in low- and middle-income countries (LMICs) (Bray et al., 2018). Health systems in LMICs will need sustainable strengthening to meet the growing burden. In 2020, the World Health Organization (WHO) launched the 2020-2030 Global Strategy Towards the Global Elimination of Cervical Cancer (WHO, 2019). Screening, treatment, and early detection have proven effective in controlling cervical cancer. While sustainability of these interventions has not been extensively studied, identified barriers and facilitators to sustainability map closely to the six WHO building blocks for health systems strengthening. This linkage suggests that designing sustainable cervical cancer programs may help strengthen the broader health system. This study strives to identify the facilitators of sustainable cervical cancer screening and treatment programs that strengthen healthcare systems in an LMIC. Using a sequential mixed methods qualitative approach, the study’s aims and methods included: 1) describe the barriers and facilitators to sustaining cervical cancer programs in LMICs using a literature review and key informant interviews; 2) conduct a qualitative case study in Zambia, a country with a demonstrated sustainable cervical cancer program, using key informant interviews, document review and triangulation of data sources, to determine how the cervical cancer program strengthens the Zambian health system; and 3) develop a conceptual framework that links the identified facilitators to sustainable cervical cancer screening programs in LMICs (results from Aims 1-3) to the WHO health systems framework using a synthesis of results from aims 1 and 2. The results of this study provide insights into the facilitators and barriers of cervical cancer program sustainability, and ways that the cervical cancer program in Zambia strengthens the local health system. The findings build on the available evidence and inform a framework that provides guidance to countries as they are implementing cervical cancer screening and treatment programs. This research further highlights the need for future research, specifically implementation science, to inform future scale-up and sustainability of cervical cancer interventions.
  • Health Systems Strengthening
  • Sustainability
  • Implementation
  • Cancer Control
  • Cervical Cancer
  • Public health
  • https://doi.org/10.17615/kjmc-mg63
  • Dissertation
  • In Copyright - Educational Use Permitted
  • Stover, Angela M
  • Greene, Sandra
  • Herrington Jr., James E
  • Kapambwe, Sharon
  • Citonje Msadabwe-Chikuni, Susan
  • Doctor of Public Health
  • University of North Carolina at Chapel Hill Graduate School

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thesis on cervical cancer

Analytical Methods

A rapid immunoassay for dual-mode detection of hpv16 and hpv18 dna based on au@pdpt nanoparticles.

Cervical cancer (CC) remains one of the most severe global health challenges toward women primarily due to persistent infection with high-risk human papillomavirus (HPV) subtypes, particularly with HPV16 and HPV 18. Effective detection of these high-risk HPV strains is crucial for CC prevention. Current screening programs for HPV DNA include PCR and in situ hybridization, which are accurate and sensitive. However, these approaches demand a high level of expertise, along with expensive instruments and consumables, thus hindering their widespread use. Therefore, there is a compelling demand to develop an efficient, straightforward, and cost-effective method. Here, we proposed a lateral flow immunoassay (LFIA) method based on Au@PdPt nanoparticles for simultaneous detection and genotyping of HPV16 and HPV18 within 15 min. This innovative approach allows for qualitative assessment by the naked eye and enables semi-quantitative detection through a smartphone. In this work, under optimal conditions, the qualitative visual limits of detection (vLOD) for HPV16 and HPV18 reached 0.007 nM and 0.01 nM, respectively, which were 32-fold for HPV16 and 20-fold for HPV18 more sensitive than the conventional AuNPs-LFIA. Meanwhile, the semi-quantitative limits of detection (qLOD) of HPV16 and HPV18 were 0.05 nM and 0.02 nM, respectively, for semi-quantitative detection. In conclusion, our formulated approach represents a significant step forward in HPV detection and genotyping with the potential to enhance accessibility and effectiveness in the early diagnosis of CC at the point of care and beyond.

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thesis on cervical cancer

H. Xiao, W. Chen, M. Lin, S. Jiang, X. Cui and S. Zhao, Anal. Methods , 2024, Accepted Manuscript , DOI: 10.1039/D3AY02307A

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What to Know About the HPV Vaccine and Cancer Prevention

New research shows many eligible people are not getting the shots.

A pair of hands wearing blue gloves puts a bandage on the arm of a patient.

By Dani Blum

Nearly 20 years after the first vaccine against human papillomavirus became available, many eligible Americans still are not getting the shot — even though it provides powerful protection against the leading cause of cervical cancer and a strong risk factor for anal cancer.

HPV is the most common sexually transmitted infection in the United States, and while most infections are asymptomatic and clear up on their own within two years, a small number persist and can cause cancer. HPV causes nearly all cases of cervical cancer, and can also lead to penile, anal, oral, vulvar and vaginal cancers .

The HPV vaccine, delivered as two or three doses, can significantly cut the risk of infection. It “is really one of the most effective vaccines we have,” said Dr. Lauri Markowitz, the HPV team lead in the Centers for Disease Control and Prevention’s division of viral diseases. But uptake remains stubbornly low: A report released by the C.D.C. this month showed that in 2022, only 38.6 percent of children ages 9 to 17 had received at least one dose of the HPV vaccine. Other new research suggests that HPV vaccination rates stalled in the wake of the coronavirus pandemic.

A study published this week laid out some of the primary reasons cited by parents in the United States who don’t plan to vaccinate their children against HPV, including safety concerns, a lack of knowledge about the vaccine and a belief that it isn’t necessary.

“We are still facing an uphill battle from what I would call inappropriate messaging or incomplete messaging when the vaccine rolled out about why this is so important,” said Karen Knudsen, chief executive of the American Cancer Society.

How does the vaccine work?

The HPV vaccine fools the body into thinking it has come into contact with the virus, marshaling antibodies in defense. Those antibodies can help clear the virus and prevent infection if someone is later exposed, which can happen through oral, anal and vaginal sex .

The vaccine offers protection from the types most likely to cause cervical and anal cancers and genital warts. Since the vaccine was introduced in 2006, infections with the types of HPV that cause most HPV-related cancers and genital warts have fallen by 88 percent among teen girls and by 81 percent among young adult women, according to the C.D.C.

One reason doctors are so enthusiastic about the vaccine is that it is one of very few tools to combat HPV: Condoms do not entirely prevent transmission, and there is no treatment for the virus itself. Researchers believe HPV is responsible for more than 90 percent of cervical and anal cancers and a majority of vaginal, vulvar, and penile cancers.

Who should get it? And how?

Children can be vaccinated starting at age nine. The C.D.C. recommends the vaccine for all preteens from the age of 11 or 12 and anyone up to age 26. It’s most effective before people are exposed to the virus, and “the assumption is that most people have started having sexual intercourse by age 26,” said Dr. Ban Mishu Allos, an associate professor of medicine at Vanderbilt University Medical Center.

The vaccine may still provide some benefit for people over age 26, and is approved up until age 45. The C.D.C. says that people between the ages of 27 and 45 might get the vaccine after talking to their doctors about their risk for new HPV infections.

You can ask your primary care doctor or local health centers for the vaccine. Most insurance plans fully cover it through age 26. Children and adolescents who are uninsured or underinsured can get the shots for free through the Vaccines for Children program. After age 26, insurance may not fully cover the shot, which can cost hundreds of dollars per dose. Merck, which makes the HPV vaccine Gardasil 9, has a patient assistance program for eligible people.

Why are vaccination rates still low?

Researchers believe much of the hesitation stems from a key misunderstanding: “More people perceive it as a sexually transmitted infection prevention vaccine, as opposed to a cancer prevention vaccine,” said Kalyani Sonawane, an associate professor of public health sciences at the M.U.S.C. Hollings Cancer Center and an author of the new paper on parental attitudes toward HPV vaccination.

Dr. Sonawane’s research has also found that many parents are concerned about side effects. But doctors say many people do not experience side effects, and for those that do, the issues are generally mild and can include arm soreness, nausea, dizziness or, in some cases, fainting.

Doctors urge parents to vaccinate their children before they’re likely to become sexually active, which gives some parents pause, said Dr. Monica Woll Rosen, an obstetrician-gynecologist at the University of Michigan Medical School.

“You’re doing something to prevent them from getting cancer in 30 years,” she said, “and the disconnect might be too large for some people to really wrap their heads around.”

Dani Blum is a health reporter for The Times. More about Dani Blum

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A Model for Predicting Cervical Cancer Using Machine Learning Algorithms

Associated data.

Data of Cervical cancer Availability Statement: dataset was obtained from the open-access Cervical cancer (Risk Factors) Data Set database of Cervical Cancer Risk Factors for Biopsy and are available at https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29 (accessed on 24 March 2022).

A growing number of individuals and organizations are turning to machine learning (ML) and deep learning (DL) to analyze massive amounts of data and produce actionable insights. Predicting the early stages of serious illnesses using ML-based schemes, including cancer, kidney failure, and heart attacks, is becoming increasingly common in medical practice. Cervical cancer is one of the most frequent diseases among women, and early diagnosis could be a possible solution for preventing this cancer. Thus, this study presents an astute way to predict cervical cancer with ML algorithms. Research dataset, data pre-processing, predictive model selection (PMS), and pseudo-code are the four phases of the proposed research technique. The PMS section reports experiments with a range of classic machine learning methods, including decision tree (DT), logistic regression (LR), support vector machine (SVM), K-nearest neighbors algorithm (KNN), adaptive boosting, gradient boosting, random forest, and XGBoost. In terms of cervical cancer prediction, the highest classification score of 100% is achieved with random forest (RF), decision tree (DT), adaptive boosting, and gradient boosting algorithms. In contrast, 99% accuracy has been found with SVM. The computational complexity of classic machine learning techniques is computed to assess the efficacy of the models. In addition, 132 Saudi Arabian volunteers were polled as part of this study to learn their thoughts about computer-assisted cervical cancer prediction, to focus attention on the human papillomavirus (HPV).

1. Introduction

Human life is plagued with difficulties because it is difficult to predict when problems arise. In general, women usually experience several difficulties in their lifetime. One of the most critical ailments they may face is cervical cancer, which causes many problems [ 1 ]. The elevated mortality age of uterine cancer is due to women’s lack of knowledge about the importance of early detection [ 2 ]. Cervical cancer is a dangerous cancer, which threatens women’s health worldwide, and its early signs are relatively difficult to detect [ 3 ]. It is responsible for damaging deep tissues of the cervix and can gradually reach other areas of the human body, such as the lungs, liver, and vagina, which can increase the difficulties involved [ 4 ]. However, while cervical cancer is a slow-growing malignancy, precancerous advances have made early detection, prevention, and therapy possible. Cervical cancer has been reduced in most nations over past decades as detection technologies have improved. This year, 4290 people are predicted to die from cervical cancer [ 5 ]. The fatality rate has dropped by roughly half since the mid-1970s, thanks in part to enhanced screening, which has resulted in the early identification of cervical cancer. The death rate has reduced from over 4% per year in 1996–2003 to less than 1% in 2009–2018 [ 6 ]. The pre-invasive stages of cervical cancer of the uterus last for a long time. Screening tests can provide successful treatment of precancerous-stage lesions, so that cancer can be prevented. Nonetheless, it has been determined that the death rate in underdeveloped nations is exceptionally high, since they do not benefit from state-provided preventive strategies, such as free immunization programs and national assessment programs.

When the cervix’s human papillomavirus (HPV) infection is left untreated, cervical cancer develops [ 7 ]. Because it causes neoplastic development, the human papillomavirus (HPV) is the most common infectious agent in cervical cancer. The improper proliferation of cervical cancer cells and the multiplication of abnormal cells as a result of a malignant phase is referred to as neoplastic progression [ 8 ]. The healthcare industry regularly generates massive amounts of data that can be used to extract information for forecasting future sickness based on a patient’s treatment history and health data. Furthermore, these areas can be enhanced by leveraging crucial data in healthcare. Machine learning helps individuals process vast amounts of complex medical data in healthcare and then analyze it for therapeutic insights. Doctors can then use this information to provide medical care. As a result, patient satisfaction can be improved when machine learning (ML) is employed in healthcare.

Cervical cancer is one of the most common malignancies among women worldwide. Recently, many studies have been conducted on cervical cancer using modern techniques that provide prediction in the early stage. Using machine learning has contributed to early prediction [ 9 ]. Therefore, the most important causes of this disease among female populations are lack of awareness, lack of access to resources and medical centers, and the expense of undergoing regular examination in some countries [ 10 ]. Machine learning has improved the performance of analyses and the generation of accurate patient data. One researcher [ 11 ] employed text mining, machine learning, and econometric tools to determine which core and enhanced quality attributes and emotions are more relevant in forecasting clients’ satisfaction in different service scenarios. Their paper presents findings related to health product marketing and services, and proposes an automated and machine-learning-based technique for generating insights. It also aids healthcare/health product e-commerce managers improve the design and execution of e-commerce services. Moreover, the importance of continuous quality improvement in the performance of machine learning algorithms from a health care management and management information technologies point of view is demonstrated in this paper by describing different kinds of machine learning algorithms and analyzing healthcare data utilizing machine learning algorithms [ 12 ]. This study identified algorithms that are better suited for the categorization of negative and positive cervical cancer for clinical use. Cervical cancer can be diagnosed with the help of such algorithms. Deep learning has shown a significant impact on health and medical imaging, which helps evaluate the diagnostic accuracy of deep learning (DL) algorithms in identifying pathologies in medical imaging [ 13 ].

The objectives of this study are as follows:

  • To analyze and classify cervical cancer using machine learning algorithms that will help doctors accurately diagnose the cancer.
  • To identify the correlations between the parameters that are likely to be responsible for cervical cancer.
  • To conduct a survey that identifies women’s concerns about cervical cancer, and provides a message to the readers as well as the research community.

Section 2 provides a literature review, Section 3 describes the research methodology, and Section 4 includes the results and discussion.

2. Literature Review

This section provides the literature selection criteria (LSC) and the papers that have been collected to review the literature from all the databases. The literature selection criteria (LSC) section shows how we selected related papers based on the selection criteria, after collecting the articles from the databases. Looking at papers published between 2010 and November 2020, this research has explored several electronic databases, such as Institute of Electrical and Electronics Engineers (IEEE) Xplore, PubMed, National Center for Biotechnology Information (NCBI), Springer, Google Scholar, and Elsevier. Based on the selected articles, the literature review is provided in detail below.

Literature Selection Criteria

The advantage of selection criteria is that it is possible to work according to a plan, especially when downloading the papers. According to the time duration set, articles can be searched, and fake journals can be skipped. In terms of search criteria, the research paper must be a conference paper or journal article, and it must use a machine-learning-based model or program intended solely for cervical cancer prediction. In addition, the following conditions must be met:

  • Purposes must be included in the research paper.
  • The time frame being surveyed is from 2010 to 30 November 2021. It is important to analyse the previous studies’ insights
  • We do not include any research work that has not yet been printed, or is not peer reviewed.

In [ 14 ], the authors conducted a survey-based study on cervical cancer detection, including performance analysis to determine the accuracy of various distinctive types of architecture in an artificial neural network (ANN), where the ANN was used for identifying cancerous, normal, and abnormal cells. The authors of [ 15 ] used cervigram images to illustrate a method of screening cervical cancer with the oriented local histogram technique (OLHT), which can increase edges, and the dual-tree complex wavelet transform (DT-CWT), which can help achieve multi-resolution images. Using a UCI data repository and six machine learning (ML) classifiers, ref. [ 16 ] proposed a model that can predict the exact level of cervix infection. Data pre-processing was carried out with physician verification to extract some features and to perform validation. To complete the study, 10-fold cross-validation is utilized to assess the performance of the suggested model. Another key study was published in [ 16 ], which used machine learning classifiers (SVM, QUEST, C&R tree, and MLP). The investigation examined distinct metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC). The QUEST parameters were 95.55%, 90.48%, 100%, and 95.20%, respectively. This research proposed a federated learning method for machinery malfunction diagnostics to address the data island problem. Each participant’s model training is implemented on a local level, and a self-supervised learning scheme is provided to improve learning performance [ 17 ].

Five different machine learning algorithms are used by [ 18 ], including random forest, KNN, C5.0, SVM, and RPart. After finishing the training and evaluating the performance of all of the classifiers (C5.0, RF, RPART, SVM, and KNN), the best options in terms of accuracy were investigated, showing values of 97%, 96.9%, 96%, 88%, and 88%. Machine learning (ML) algorithms such as decision tree, random forest, and logistic regression were used in conjunction with the voting model. In [ 19 ], cervical cancer was detected using a dataset containing four target parameters (biopsy, cytology, Schiller, and Hinselmann), as well as 32 risk factors, collected from the University of California (UCI). Machine learning (ML) algorithms were applied, including the the decision tree and decision jungle approaches. The study observed that the decision tree algorithm showed a higher value (98.5%). In another study using the Microsoft Azure ML tool, an appropriate data mining technique was developed from the boosted decision tree, decision forest, and decision jungle algorithms to detect cervical cancer [ 20 ]. The models’ performances were measured in terms of accuracy, area under the receiver operating characteristic (AUROC) curve, specificity, and sensitivity, with 10-fold cross-validation applied to the outputs to improve the decision tree algorithm’s performance, reaching a value of 97.8% on the AUROC curve. The authors of [ 21 ] presented a survey-based study on cervical cancer prevention from the perspective of women in Bug, IRI, and Mayuge in Eastern Uganda, using a questionnaire to collect data from 900 women aged 25 to 49 years. After measuring and scoring the women’s knowledge and statements about cervical cancer treatment, the data was analyzed using Stata 12.0 software. After doing bivariate and multivariate analysis, the authors discovered that 794 women, or roughly 88.2%, had heard of the condition. A majority of 557 women (70.2%) acquired their information from the radio, while a minority of 120 women (15.1%) got their information from health care organizations.

The authors of [ 22 ] analyzed various machine learning approaches used from 2006 to 2017 to diagnose cervical cancer. In this research, a comparison was made using existing relevant works based on cervical cancer medical data, to determine the benefits and drawbacks of different approaches. Most studies had used unbalanced medical image datasets. The survey also mentioned employing deep learning to predict cervical cancer. Furthermore, the goal of [ 23 ] was to see how well the Cox proportional hazard regression model and the deep learning neural network model predicted survival in cervical cancer patients. A dataset from the University of California, Irvine, was used in the study [ 23 ], which included age, number of pregnancies, contraceptive use, smoking habits, and chronological records of sexually transmitted infections (STDs). The study’s essential purpose was to use Hinslemann screening methods to predict cervical cancer. With 10-fold validation, a data mining strategy was used with the boosted decision tree, decision forest, and decision jungle approaches. Moreover, on the AUROC (area under receiver operating characteristic) curve, the boosted decision tree method achieved a forecast precision of 98%. The best example of using electronic health record (EHR) data to predict cervical cancer is [ 24 ]. Four machine learning classifiers were used to predict cancer. The random forest algorithm produced the best results, with an AUC (area under the curve) of 0.97 one day before diagnosis, up from 0.70 a year before diagnosis. The primary purpose of [ 25 ] was to create a method that can anticipate the early effects of radiation on bone metastases in cervical cancer patients. The researchers employed class imbalance learning (CIL) in data mining to tackle the challenge of an imbalanced dataset. To deal with the issue of imbalanced data categorization, many models, such as ant-miner, RIPPER, Ridor, PART, ADTree, C4.5, ELM, and weighted ELM, with the synthetic minority over-sampling approach (SMOTE) were used. The study aimed to assist in the early detection of cervical cancer. The study showed the use of machine learning in defining a data validation mechanism to improve the performance of cervical cancer prediction. The study also suggested genetic assistance as an optional strategy to enhance the validity of the prediction. Additionally, [ 26 ] has presented a method based on machine learning approaches for identifying cardiac disease. Classification algorithms were used to construct the system. The model suggested a conditional mutual information feature selection method to overcome the feature selection problem. Feature selection methods are utilized to improve classification accuracy and shorten the time it takes to develop a classification system.

Furthermore, the fundamental purpose of [ 27 ] was to examine how big data analytics and machine-learning-based approaches may be used for diabetes. The results demonstrate that the proposed machine-learning-based system might score as high as 86% on the diagnostic accuracy of DL. Health specialists and other stakeholders collaborated to create classification models that would assist in diabetes prediction and the design of prevention measures. Based on the findings, the authors review the literature on machine models and propose an intelligent framework for diabetes prediction. Anther study has been conducted [ 28 ] where a methodology for heart disease was developed using the UCI repository dataset and healthcare monitors to estimate the public’s risk of heart disease. In addition, classification algorithms were employed to classify patient data to detect cardiac disease, such as doosted decision tree and decision forest. The classification was performed using data from the benchmark dataset during the training phase. At the testing stage, accurate patient data was used to determine whether illness existed. The results demonstrate that the proposed model based on machine learning could score as high as 92% on the diagnostic accuracy of DL. Comparative analysis of existing research are provided in Table 1 .

Comparative analysis of existing research.

Based on the above review, it can be stated that several traditional algorithms have been used to predict cervical cancer; still, the models do not achieve a satisfactory level, because the selection of important features is the most crucial part of machine learning, and the authors have not specified how the chosen features were selected. In addition, just using traditional deep learning algorithms does not indicate that a model is suitable for practical implementation in the healthcare sector; rather, model customization is required to remove the overfitting and make it faster for a clinical application. Nonetheless, this research has come up with an effective solution. Various state-of-the-art techniques are used in this study to take this research to a satisfactory level and assist doctors in diagnosing cervical disease.

3. Methodology

The proposed research methodology is classified into several segments: research dataset, data preprocessing, predictive model selection (PMS), and training method. Figure 1 depicts an architectural diagram of the proposed research; by looking at Figure 1 , it can be clearly observed that the architectural diagram has been separated into four phases, because the model presented in this research performs some essential tasks in each stage. Details on research data collection are described in the Research Dataset section. The Data Preprocessing section mentions how to remove noise from the dataset and make it useful for feeding in machine learning. The type of predictive model selected to predict cervical cancer in this research is shown in the PMS portion. The requisites for model training are shown in the Training Methods section. Finally, we design the platform to provide an overall pipeline of cervical cancer prediction using the Python programming language. This research implements an algorithm that is better suited for the categorization of negative and positive cervical cancer diagnoses for clinical use. Cervical cancer can be diagnosed with the help of algorithms including decision tree, logistic regression, support vector machine (SVM), K-nearest neighbours (KNN), adaptive boosting, dradient boosting, random forest, and XGBoost. The sequence and consequences are presented in the following sections.

An external file that holds a picture, illustration, etc.
Object name is sensors-22-04132-g001.jpg

Proposed research model for classifying cervical cancer.

The proposed ML-based model is depicted in Figure 1 . The training data will be fed to the system at the beginning of the model training. Then, ML algorithms are adopted. After that, model input data and new input data are applied to the scheme to train the architecture properly. Finally, prediction is performed on the newly accumulated data.

3.1. Research Dataset

The UCI repository contributed to the dataset “Cervical Cancer Risk Factors for Biopsy” [ 29 ]. The collection contains information about 858 people’s activities, demographics, and medical history. Multiple missing values occur in this dataset for hospital patients as a result of several patients declining to answer questions due to privacy concerns [ 30 ]. The collection has 858 instances, each with 32 properties. The dataset includes 32 variables and the histories of 858 female patients [ 30 ]. The dataset includes 32 variables and the histories of 858 female patients, including factors such as age, IUD, smokes, STDs, and so on. The research dataset’s attributes are provided in Table 2 .

Attributes of the research dataset.

3.2. Data Preprocessing

Data preprocessing is divided into three sections, which are as follows: data cleaning, data transformation, and data reduction. Data preprocessing is critical since it directly impacts project success. Data impurity occurs when attributes or attribute values contain noise or outliers, and redundant or missing data [ 30 ]. We have removed the missing values and outliers from this dataset. The data transformation stage is kept in place to change the data into suitable forms for the mining process. This research combines normalization, attribute selection, discretization, and concept hierarchy generation. When dealing with a huge amount of data, analysis becomes more difficult when the data dimension is large. The data reduction approach is employed in this research to overcome this. It seeks to improve storage efficiency, while lowering the cost of data storage and processing. We have applied the dimension reduction technique because it is another useful technique that can be used to mitigate overfitting in machine learning models. For that, we have applied the principal component analysis (PCA) technique.

3.3. Predictive Model Selection (PMS)

Several machine learning classification algorithms have been used in the PMS, namely support vector machine (SVM), decision tree classifier (DTC), random forest (RF), logistic regression (LR), gradient boosting (GB), XGBoost, adaptive boosting (AB), and K-nearest neighbor (KNN). This section has highlighted some of the algorithms that have achieved a satisfactory level of accuracy on the adopted research dataset. Thus, we have illustrated the theoretical interpretation of these algorithms in the following subsections.

3.3.1. Decision Tree (Dt)

Both classification and regression problems can be solved with the classification and regression tree or CART algorithm, which is also called the DT. The DT looks a lot like the branches of a tree, which is why the word ‘tree’ is included in its name. The decision tree starts from the ‘root node’ just as the tree starts from the root. From the root node, the branches of this tree spread through different decision conditions; such nodes are called decision nodes (and called leaf nodes after making a final decision).

3.3.2. Random Forest (Rf)

Ensemble learning enhances model performance by using multiple learners. RF is also a kind of ensemble learning. Following the RF bagging method reduces the chances of results being affected by outliers. This works well for both categorical and continuous data. Datasets do not need to be scaled, and the higher the number of learners, the more computational resources are required for complex models. In this algorithm, the decision is made by voting. Such an algorithm is called ensemble learning. Random forests are made up of many trees or shrubs. Just as there are many trees in the forest, random forests also have many decision trees. The decision that most trees make is considered the final decision.

3.3.3. Adaptive Boosting (AB)

The adaptive boosting technique creates a powerful learner by combining the knowledge of a number of weak learners. In this scenario, every single weak learner utilizes the exact same input, often known as a training set. Every initial input or piece of training data is given the same amount of importance. The responsibility for correcting the incorrect predictions made by the first weak learner is passed on to the next weak learner, who is given greater weight on the predictions made by the first weak learner and is turned over to the next weak learner. As a result, the errors that the second weak learner made in its predictions are passed on to the following weak learner in the same fashion, but with increased weight. The same process is continued until the number of inaccurate forecasts is reduced to a manageable level. In the end, a powerful learner is developed via the combined efforts of all the weak learners. In this way, the amount of inaccuracy in the forecast is reduced.

3.3.4. Support Vector Machine (SVM)

The support vector machine algorithm can be used for classification and regression problems. However, SVMs are quite popular for relatively complex types of small or medium classification datasets. In this algorithm, data points are separated by a hyperplane, and the kernel determines what the hyperplane will look like. If we plot multiple variables in a normal scatter plot, in many cases, that plot cannot separate two or more data classes. The kernel of an SVM is a significant element, which can convert lower-dimensional data into higher-dimensional space, and thus differentiate between types [ 31 ]. The following equations are used in the case of SVM (1) and (2) [ 32 ]:

In this case, w is the (possibly normalized) average vector to the hyperplane. These two specific hyperplanes bound the “margin” in the region or area, and the maximum hyperplane lies halfway between them. These hyperplanes can be defined by equations using a normalized or standardized dataset.

Therefore, the width or the margin of the two hyperplanes for data classification can be written as follows:

3.4. Radial Basis Function (RBF) Kernel Support Vector Machine (SVM)

The support vector machine (SVM) performs well on linear and nonlinear data. This method of classifying nonlinear data includes the radial base function. Putting data in the function space relies heavily on the kernel function [ 33 ]. When plotting many variables in a typical scatter plot, it is often impossible to distinguish between various sets of data. An SVM’s kernel is a technique for transforming lower-dimensional input into higher-dimensional space and identifying different classes. In addition, the radial basis function is a nonlinear function. The support vector machine’s most popular feature is its ability to classify objects automatically. Infinite-dimensional space can be mapped to any input with this kernel.

After utilizing Equation (1), we can obtain the following:

By applying Equation (3) in (4), we get a new function, where N represents the trained data.

Gradient Boosting

The gradient boosting algorithm also follows the sequential ensemble learning method. Through loss optimization, weak learners gradually become better than previous weak learners. For example, the second weak learner is better than the first, the third weak learner is better than the second, and so on. As the weak learner periodicity increases, the amount of error in the model decreases, and the model becomes a stronger learner. The gradient boosting algorithm works relatively well for regression-type problems [ 34 ].

The difference between gradient boosting and adaptive boosting is that in adaptive boosting, error is gradually reduced by updating the weight of the wrong predictive samples. In gradient boosting, the loss function is optimized, and each loss is optimized [ 35 ]. The amount of error also decreases. To optimize this loss function, each weak learner changes its alternative weak learner model, so that the next weak learner is better than the previous one. Gradient boosting consists of three components: weak learner, loss function optimization, and additive model. The following Equations (6)–(11) show the working procedure of the gradient boosting algorithm mathematically [ 36 ]:

  • “Reconfigure the function estimate with a constant value” f ^ ( x ) = f ^ 0 , f ^ 0 = γ , γ ∈ ℝ , f ^ 0 = arg min γ ∑ i = 1 n L ( y i , γ ) (6)
  • “For each iteration “t = 1,…,T”:”   Compute   pseudo - residuals   r t , r i t = − [ ∂ L ( y i , f ( x i ) ) ∂ f ( x i ) ] f ( x ) = f ^ ( x )   for   i = 1 , … , n (7)

Include the latest function g t ( x ) (it can be any model, but here we are applying decision trees) as regression on pseudo-residuals.

“Determine optimal coefficient “ ρ _ t ” at “ g _ t ( x )” about the initial loss function”

“Improve current approximation”

  • 3. The ultimate GBM model will be the addition of the elementary constant and the entire following function update: f ^ ( x ) = ∑ i = 0 T f ^ ( x ) (11)

4. Result Analysis

This section is categorized into four parts: empirical consequence report (ECP), exploratory cervical data analysis (ECDA), computational complexity analysis (CCA), comparative analysis, and survey data analysis (SDA).

4.1. Empirical Consequence Report (ECP)

The accuracy of predictions from the classification algorithms is estimated by applying a classification report. The report demonstrates the precision, recall, and f1 score of the key classification metrics on a per-class basis. By using true positive (TP), false positive (FP), true negative (TN), and false negative (FN), the metrics are computed [ 37 ]. Table 3 demonstrates the classification reports of the several traditional machine learning algorithms where the precision, recall, and F1 scores are denoted by “P”, “R”, and “F1”. Precision is the ratio of the model’s correct positive estimates to the total (correct and incorrect) positive estimates; recall is the ratio of being able to predict positive as positive; and F1 is the weighted average of precision and recall (this score considers both false positives and false negatives). A classification report has been included in the table, where 0 means negative class and 1 means positive class.

Classification report of the machine learning algorithms for classifying cervical cancer.

To obtain the classification report [ 38 ], the following Equations (12)–(15) are used.

P: The relationship between the accurate positive estimate generated by the model and the overall (correct and inaccurate) positive estimate. It is articulated as:

Recall/sensitivity: Positivity is represented by the ratio of accurate to inaccurate predictions. It is written in mathematical notation as follows:

F1: This is the harmonic mean of precision and recall, and it provides a more accurate estimate of the amount of misclassification cases than the accuracy metric. It can be expressed numerically as:

Accuracy: It is the measure of all the instances correctly predicted. It is given as:

The mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and R-squared (R2) are frequently used to measure a model’s effectiveness in terms of regression analysis. The accuracy of gradient boosting and XGBoost is obtained with the performance metrics, as shown in Table 3 . The MAE illustrates the commonality of clearly distinguishing between specific and predicted values within the dataset. Similarly, the MAE shows the traditional square difference between main and anticipated standards. The RMSE also computes the standard deviation of the residuals. Finally, the R-squared (R2) represents the fraction of variation inside the variable quantity defined by the regression toward the mean model [ 38 ]. We have interpreted different algorithms with the corresponding evaluation matrices. From the finding in Table 3 , the highest classification scores have been achieved with random forest (RF), decision tree (DT), and adaptive boosting. In contrast, XGBoost provides a higher level of regularization for the gradient boosting algorithm. Advanced regularization (L1 and L2) is utilized in XGBoost to increase model generalization. In terms of performance, XGBoost is superior to the gradient boosting algorithm. Its training is quite fast, and it may be dispersed across numerous clusters if necessary. Because we need to determine the distinction between a classification model, XGBoost, and gradient boosting, we have separated these models into a separate table ( Table 4 ) to survey the accuracy measurements of each of them, and found the highest accuracy of 100 with gradient boosting.

Accuracy measurement of gradient boosting and XGradient boosting.

4.2. Exploratory Cervical Data Analysis (ECDA)

Figure 2 shows the correlation graph. Correlation describes how two or more variables are connected [ 39 ]. These variables may be input data features used to forecast our target variable. Correlation is a mathematical method used to evaluate how one variable moves or shifts in relation to another. It informs us about the intensity of the relationship between the two variables. It is a bivariate analysis measure that defines the relationship between various variables [ 39 ]. Moreover, finding the correlation is significant in cervical analysis because essential factors can be identified by finding the relationship between each variable. Two characteristics (variables) may be positively correlated with one another.

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Correlations between different variables of cervical cancer.

In the same way, two features (variables) can be negatively correlated with one another. This implies that as the value of one variable rises, the other variable(s) falls. On the other hand, if one variable’s value increases or decreases, but the value of the other variable(s) does not, this indicates no correlation. The correlations are illustrated in Figure 2 .

Figure 3 and Figure 4 visualize the count measurement regarding the number of pregnancies, the number of sexual partners, and age, and a comparison between biopsy and number of pregnancies. The cervix is the uterus’s lower, narrowest portion. It creates a canal that leads to the vaginal opening. Cervical biopsies can be performed in a variety of ways. As shown in Figure 4 , it is evident that a relationship between biopsy and pregnancy exists, but occasionally fluctuates.

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Count measurement in terms of the number of pregnancies, number of sexual partners, and age.

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Visualization of comparison between biopsy and number of pregnancies.

4.3. Computational Complexity Analysis (CCA)

Machine learning computational complexity is a quantitative examination of the possibilities for effective computer learning [ 40 ]. It is focused on successful and general learning algorithms and works within recently deployed machine inference models based on computational complexity theory. We conducted a complexity analysis of various classic algorithms because these types of algorithms have previously been utilized to identify cervical cancer. Researchers confront numerous challenges regarding algorithm selection, so determining the computational complexity before creating a model is critical. Table 5 shows a short summary of different algorithms, indicating the complexity analysis of regression, dataset training, and prediction.

Computational complexity of machine learning algorithms.

4.4. Validation

This research has applied cross-validation, which is a method that examines the research model to achieve better residuals [ 41 ]. The problem with validation is that it does not indicate how good data will be when it is used to make new estimates for a new result. The better solution to this problem is not applying the entire dataset when we run data training, which requires removing some of the data before training starts. Then, when we finish training with the data, we can use the data removed in the assessment to show how the model fits on “new’’ data. We have applied five-fold cross validation, and we did a resampling method that uses different portions of the data to test and train a model on various iterations. This model achieved satisfactory performance, and as the data size is not large, we aim to apply these validation indicators in the next phase as our research is still ongoing.

4.5. Survey Data Analysis (SDA)

Another part of our research is conducting survey data analysis. To determine how many people are aware of cervical cancer, we have completed survey questionnaires based on the aim of this research. In this research, a stratified sampling technique has been used; stratified sampling is a similar or homogenous group-based sampling method [ 42 ]. Our priority for this survey was to analyze the number of women who are less aware of cervical cancer. It is certainly true that many women often feel too shy to talk about the mentioned diseases with their parents, so in this research, we have highlighted this issue, so that essential steps can be taken to raise awareness. In addition, the core biopsy test is significant, and many are not familiar with this test. This was the primary reason for taking a survey and analyzing the data. All members of the same group usually have the same characteristics; such groups are called strata. Table 6 shows some major survey questions (number of responses: N = 132; 94.69% answered all questions correctly).

Some major survey questions for investigating cervical cancer.

Figure 5 illustrates the number of responses in terms of awareness of human papillomavirus (HPV). By looking at Figure 5 , it can be clearly seen that 31% of the participants are not aware of HPV, while 62% are aware of the virus. Only 7% of respondents were unsure.

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Number of responses regarding the awareness of human papillomavirus (HPV).

In addition, Figure 6 compares the responses in terms of whether or not the rate of being affected by cervical cancer is becoming higher than before. It is noticeable that 73% of participants agreed with this statement, while 17% disagreed. A minority of participants (around 10%) were unsure.

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Survey responses regarding whether or not the rate of being affected by cervical cancer is becoming higher than before.

Figure 7 and Figure 8 compare the proportions of biopsy tests and awareness levels in rural vs. urban areas. A total of 132 responses were recorded during the survey. Of these, 26% of all participants had not yet undergone a biopsy test, while 6% of participants were unsure. According to the survey, those who live in cities are more aware (71%) than those in rural areas (21%). Another 8% of participants said both are equivalently aware of cervical cancer.

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Total percentage of individuals who have undergone a biopsy test or another cervical cancer (uterus)-related test before.

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The awareness level in rural and urban areas regarding cervical cancer.

5. Discussion

Based on the findings of this research, it can be stated that the objectives of this paper have been achieved. Its research methodology was enriched with a set of algorithms including decision tree (DT), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), adaptive boosting, gradient boosting, random forest (RF), and XGBoost. The research has reached a satisfactory result for both predictions and classification. This investigation also observed that the DT and RF algorithms were used in conjunction with the Microsoft Azure machine learning (ML) method to achieve a proper data mining technique for predicting cervical cancer. The study has further noticed that the performances of the traditional algorithms used in previous research are comparatively low. It is important to use data scaling, conduct missing value removal, and select a suitable algorithm in the case of disease analysis and prediction. Still, previous research has not shown the details of this pipeline. It is a matter of great concern that this work has not been accomplished much in previous research using gradient boosting algorithms. Since the gradient boosting algorithm also follows the sequential ensemble learning method, the wave learners gradually get better than their previous wave learners through this method of loss optimization.

It is essential to point out that the researchers did not restrict their effort to simply developing the model; rather, they also validated and evaluated the model’s performance. Several validation strategies, including ROC-AUC, confusion matrix, and cross-validation, were applied by the researchers, and the researchers found that the efficacy with respect to cervical cancer is adequate. In addition, the current research investigated the most important predictors and the algorithms that are most frequently utilized for the purpose of cervical cancer prediction. During the preprocessing phase, some aspects of the patients’ samples, such as the length of time they drank alcohol and their HIV and HSV2 infection status, revealed that factors whose samples had undergone modest variations could not be considered accurate predictors. Fewer predictors may need to be analyzed in subsequent studies because of the potential importance of a given characteristic for the community or the patient’s social status. This may make it easier to conduct the research more quickly. However, with the help of this machine learning model, women have the opportunity to benefit from knowing more about cervical cancer and what effect it has on the human body. This study will focus on women in order to identify which symptoms or parameters are important for identifying for cervical cancer, as well as the causes and effects of these symptoms and parameters.

This study has further performed a survey with 132 participants in Saudi Arabia to explore cervical cancer awareness, focusing on the human papillomavirus (HPV). This data is mainly gathered to identify individuals’ thoughts and comments regarding HPV and cervical cancer. By conducting survey-based data analysis, the study has evaluated and rated the women’s awareness and behaviors regardings cervical cancer care. It is notable that the authors did not address why HPV is responsible for cervical cancer; also, the survey did not show how much women knew about the biopsy test.

While working with the proposed models and algorithms, a number of limitations have been observed. First of all, the DT algorithm is very unstable, which means that a slight change in the data will significantly change the layout of the best decision tree. It is insufficiently reliable. With similar data, several other predictors perform better. Second, this study faced massive problems while dealing with the dataset, because numerous data have been enumerated and interpreted in the data pre-processing stage. The model will provide an optimum result only if a considerable number of data-processing techniques have been adopted. Third, the survey data have been preserved to apply machine learning to conduct sentiment analysis regarding cervical cancer, but in this study, the researchers could not accommodate different data-processing techniques to apply the ML models.

6. Conclusions

Early detection increases the likelihood of successful treatment in the pre-cancer and cancer stages. Being aware of any signs and symptoms of cervical cancer can also aid in avoiding diagnostic delays. This research has focused on cervical cancer using conventional machine learning (ML) principles and several traditional machine learning algorithms, such as decision tree (DT), logistic regression (LR), support vector machine (SVM), and K-nearest neighbors (KNN). In terms of cervical cancer prediction, the highest classification score of 100% has been achieved with the random forest (RF), decision tree (DT), adaptive boosting, and gradient boosting algorithms. In contrast, 99% accuracy has been found with SVM. The results of these algorithms are applied to identify the most relevant predictors. We have received satisfactory accuracy compared to the support vector machine algorithm. The findings of this study revealed that the SVM model could be used to find the most important predictors. As the number of essential predictors for analysis decreases, the computational cost of the proposed model decreases. The disease can be predicated more accurately with the use of machine learning. Furthermore, boosting patients’ personal health and socio-cultural status can lead to cervical cancer prevention.

In addition, this research conducted a survey in Saudi Arabia, with 250 participants, to learn their thoughts in response to the investigation of cervical cancer; risk factors have also been identified through some data analyses. In the future, this research will experiment with many datasets, analyze various deep learning algorithms and their computational complexity, and show a pipeline that can extract more important insights through statistical analysis in further research.

Acknowledgments

The authors are thankful to the Dean of Scientific Research at Najran University for funding this work under the Research Groups Funding program, grant code (NU/RG/SERC/11/8).

Funding Statement

The funding for this work was provided by the Research Groups Funding program, grant code (NU/RG/SERC/11/8).

Author Contributions

Conceptualization, N.A.M. and A.A.; methodology, N.A. and A.A.; software, A.A. and N.A.M.; validation, N.A.M. and A.A.; formal analysis, N.A.M. and A.A.; investigation, A.A.; resources, N.A.M. and A.A.; writing—original draft preparation, N.A.M. and A.A.; writing—review and editing, N.A. and A.A.; supervision, N.A.M. and A.A.; project administration, N.A.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

IMAGES

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