Abstract
Objectives:
A meta-analysis was conducted to examine the effectiveness of HPV self-sampling proposal on cervical cancer screening (CCS) uptake when compared with an invitation to have a clinician to collect the sample. Secondary outcomes were acceptability and preference of self-sampling compared to clinician-collected samples.
Methods:
The present systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies examining the CCS uptake comparing self-sampling over invitation to be sampled by an healthcare professional and examining the proportion of women accepting or preferring self-sampling vs. clinician-collected sampling were included. The CCS uptake was also explored according to strategy of self-samplers' distribution, collection device type and screening status. Peters' test and Funnel Plot inspection were used to assess the publication bias. Quality of the studies was assessed through Cochrane Risk of Bias and NIH Quality Assessment tools.
Results:
One hundred fifty-four studies were globally identified, and 482,271 women were involved. Self-sampling procedures nearly doubled the probability (RR: 1.8; 95% CI: 1.7–2.0) of CCS uptake when compared with clinician-collected samples. The opt-out (RR: 2.1; 95% CI: 1.9–2.4) and the door-to-door (RR: 1.8; 95% CI: 1.6–2.0) did not statistically significant differ (p = 1.177) in improving the CCS uptake. A higher relative uptake was shown for brushes (RR: 1.6; 95% CI: 1.5–1.7) and swabs (RR: 2.5; 95% CI: 1.9–3.1) over clinician-collected samples. A high between-studies variability in characteristics of sampled women was shown. In all meta-analyses the level of heterogeneity was consistently high (I2 > 95%). Publication bias was unlikely.
Conclusions:
Self-sampling has the potential to increase participation of under-screened women in the CCS, in addition to the standard invitation to have a clinician to collect the sample. For small communities door-to-door distribution could be preferred to distribute the self-sampler while; for large communities opt-out strategies should be preferred over opt-in. Since no significant difference in acceptability and preference of device type was demonstrated among women, and swabs and brushes exhibited a potential stronger effect in improving CCS, these devices could be adopted.
Introduction
Genital infection with human papillomaviruses (HPV) is the most common sexually transmitted infection in the world (1). In some women, HPV infection will persist over time, and if this goes undetected and untreated, it can lead to precancerous cervical lesions and possibly progress to cervical cancer (2). HPV causes about 8.6% of the cancers affecting women worldwide. In absolute terms, about 570, 000 cases/year are estimated, almost all attributable to the HPV16/18 genotypes (3).
The time from HPV infection to cervical cancer will usually take 10–20 years or longer, and leaves great opportunity for screening and early detection (4). Indeed, secondary prevention measures such as cervical cytology (Pap smear), visual inspection with acetic acid or HPV testing, have strongly contributed to the reduction of incidence and mortality of cervical cancer, by identifying those women at high risk (5, 6). However, the adherence to screening programs in some areas of the world remains very low due to the invasiveness of the test and the lack of confidence in its effectiveness. Therefore, it is quite evident that the relevance of this public health issue necessitates innovative early detection approaches (7, 8). HPV testing through self-collected specimens has gained attention for its potential to increase screening participation. Recent systematic reviews have shown that high-risk HPV (hrHPV) testing on self-sampled specimens has a similar accuracy to detect underlying cervical precancer when compared to cytology on clinician-obtained cervical smears and under the condition that validated polymerase chain reaction (PCR)–based HPV assays are used (9, 10). In addition, several systematic reviews of randomized trials in the context of population-based screening programs showed that offering hrHPV self-sampling to never-screened and under-screened women increased participation compared with inviting women to have samples taken by healthcare professionals (HCPs) (11–13).
In recent years, numerous studies have investigated the acceptability of self-sampling methods (10, 14–16). Studies have considered women's attitudes toward self-collection and found that women have a high acceptance of and positive attitudes toward the use of self-collected HPV testing (9–11, 15, 16). Skepticism toward self-sampling has emerged, and it is attributable mainly to the fear of not carrying out a correct self-sampling or toward its underrated diagnostic performance (17, 18). Since the last published meta-analysis (19), several studies have measured the effectiveness of self-sampling in increasing the HPV-screening uptake. Moreover, it remains unclear which type of self-sampler offers a better performance. Therefore, we conducted an updated review and meta-analysis on women's attendance in cervical cancer screening (CCS) comparing self-sampled to clinician-collected specimens was conducted to assess whether the strategy of self-samplers' distribution (direct mailing to home, door-to-door distribution, or availability in clinics/pharmacies) and the type of device (brush, swab, lavage, tampon) and the screening status (never- or under-screneed vs. general population) could act as predictors of CCS uptake. Finally, the overall percentage of women who considered self-sampling to be acceptable and who preferred it over collection performed by healthcare personnel was estimated.
Methods
The present systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (20). The need for obtaining institutional review board approval or patient informed consent was waived for this study because it is a review of publicly available data.
Protocol registration
This study was registered in the International Register of Systematic Reviews (PROSPERO 2021: CRD42021266637) and the protocol is available for download.
Eligibility criteria
Studies were eligible if the following criteria were met: (1) examining the CCS uptake comparing self-sampling over invitation to be sampled by an HCP; (2) reporting enough data to estimate an effect size (Odds- or Risk-Ratio) of CCS uptake; (3) examining the proportion of women accepting or preferring self-sampling vs. clinician-collected sampling; (4) the study population involved women ages 18–70 years both among the general population and among those who were never- or under-screened; (5) the study was in English and published by May, 2022.
Outcomes
The primary outcome was the CCS uptake comparing self-sampling with clinician-collected samples for HPV testing. The CCS uptake was also explored according to strategy of self-samplers' distribution, collection device type and screening status. Self-samplers' distribution strategies evaluated were door-to-door (i.e., self-samplers were directly distributed to women), opt-out (i.e., mailing self-sampling kits directly to women's home addresses) and opt-in (i.e., receiving an invitation to actively order the kit by phone, by ordinary mail, or by picking it up at the pharmacy or local clinics).
Secondary outcomes were acceptability and preference of self-sampling compared to clinician-collected samples. Acceptability was defined as a unique answer (yes/no) to questions like “Did you find self-sampling acceptable?”. Similarly to a previous meta-analysis, the proxy questions “Would you recommend self-sampling to a relative or friend of yours?” or “Would you be willing to use a self-sampler again in the future?” were taken into account (21). Studies in which acceptability was not reported as binary data but measured by a continuous or numerical ordinal variable (e.g., 0–10 scale) were not considered unless an acceptability cut off was established. With regard to the preference outcome, we considered studies in which, after using the self-sampler, women were asked whether they preferred self-sampling or clinician-collected samples for future HPV screening visits.
Data sources and search strategy
A detailed bibliographic literature search was conducted until May 2022. Two co-authors (GDG, FL) independently searched PubMed, Web of Science, Scopus, Cochrane Central and Google Scholar combinations of the following keywords/Medical Subject Headings (MeSH) terms: “HPV”, “Human Papillomavirus”, “self-sampler”, “self-sampling”, “self-test”, “self-testing”, “home-based testing”, “community-based test”, “acceptability”, “acceptance”, “willingness”, “uptake”, “participation”, “preference”. Electronic searches were supplemented by manual searches of the reference list of relevant articles. Both observational and randomized studies were searched. Gray literature was not considered.
Study selection
All articles retrieved from the systematic search were exported to the Mendeley reference manager (www.mendeley.com), wherein duplicates were sought and removed. Three authors (GDG, FL, AT) independently winnowed titles and abstracts of the candidate papers to make a first selection. Full-text of selected papers was read to assess their eligibility in terms of topics of interest and the target population. Disagreements were resolved through discussion with a third author (AB).
Relevant articles were reviewed in full if the study abstract met the inclusion criteria or if an article lacked sufficient information in the abstract to make an inclusion/exclusion judgement, to minimize errors of omission. Figure 1 summarizes the flow diagram of the literature search and the study selection process.
Figure 1
Data extraction
An electronic collection form was used to extract the following information for each study: first author, year of publication, country, type of device (brush, swab, tampon or lavage), screening status (never or under-screened or general population), study design (observational or randomized). Women defined as “never-screened”, “under-screened”, “non-attendee” or “non-responders” to regular screening invitations were classified as “under-screened”. The self-samplers' distribution strategy (i.e., door-to-door, opt-out or opt-in strategy) was also retrieved. Regarding studies on acceptability and preference, information about the setting in which self-sampling occurred (at home or in a clinic) was also extracted.
Quality assessment
Study quality was independently assessed by three authors (GDG, FL, AT) through the revised Cochrane Risk of Bias (RoB2). Tools for parallel and cluster-randomized trials or the National Institutes of Health (NIH). Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, depending on the study design (22, 23). The ratings (good, fair or poor methodological quality) assigned by each reviewer were compared and disagreements were discussed between the two reviewers. If consensus was not reached, a third reviewer (AB) arbitrated.
Statistical analysis
As a primary analysis, the overall CCS uptake were pooled between distribution of self-samplers' and clinician-collected samples, using a DerSimonian and Laird random-effects model (24). Subgroup analyses were successively performed to assess whether differences in the CCS uptake were attributable to the self-samplers' distribution strategy, device type, women's screening status and study design (RCTs vs. observational). Relative Risks (RRs) were reported in the forest plots as measure of the effect size.
Secondary outcomes were analyzed by meta-analysis of proportions. Since outcome proportions were often higher than 80%, the confidence intervals were calculated through Freeman-Tukey double-arcsin transformation, and subsequently retro-transformed to avoid compression of standard errors and consequent biased results. The Wilson method was used to compute 95% Confidence Intervals (CIs). Subgroup analyses were performed to investigate whether brushes, swabs, tampons and lavages were equally accepted and whether the device category influenced the preference of self-sampling vs. outpatient sampling. A further subgroup analysis was performed to estimate the impact of the self-sampling setting (at home or in a clinic) on the acceptability or preference. Cochran's Q test was used to investigate overall differences between subgroups, while pair-wise comparisons (among self-samplers' distribution strategies and device types) were performed by contrasting meta-regression coefficients of models with one predictor only. I-squared consistency index was calculated to assess heterogeneity among studies. Peters' test and Funnel Plot inspection were used to assess the publication bias. To ensure the robustness of the results, subgroup analyses were repeated considering only RCTs. Data were analyzed by the statistical software STATA software, version 16.1 (25).
Results
Databases searches yielded a total of 2, 438 articles, 78 of which were duplicates. Inspection of titles and abstracts resulted in the deletion of 2, 034 articles. A total of 326 full-text articles were retrieved for full review, and 154 articles met the inclusion criteria and were included in the analyses.
Overall, 482,271 women were involved, and all five continents were represented. Fifty-one (33.1%) studies were carried out in low-middle-income countries.
All but one of the RCTs showed a low risk of bias (Table 1). On the contrary, 53 (58.9%) out of 90 quasi-experimental or cross-sectional studies exhibited a fair or low overall quality (Table 2).
Table 1
| First authors | Year | Risk of bias arising from the randomization process | Risk of bias due to deviations from the intended interventions (effect of assignment to intervention) | Risk of bias due to deviations from the intended interventions (effect of adhering to intervention) | Risk of bias due to missing outcome data | Risk of bias in measurement of the outcome | Risk of bias in selection of the reported result | Overall risk of bias judgment |
|---|---|---|---|---|---|---|---|---|
| Arrossi et al. (26) | 2015 | Some concerns | Some concerns | Low | Low | Low | Low | Low |
| Bais et al. (27) | 2007 | Low | Low | Low | Low | Low | Low | Low |
| Bosgraaf et al. (28) | 2014 | Low | Low | Low | Low | Low | Low | Low |
| Brewer et al. (29) | 2021 | Some concerns | Some concerns | Low | Low | Low | Low | Low |
| Broberg et al. (30) | 2014 | Some concerns | Low | Low | Low | Low | Low | Low |
| Cadman et al. (31) | 2015 | Low | Low | Low | Low | Low | Low | Low |
| Carrasquillo et al. (32) | 2018 | Low | Low | Low | Low | Low | Low | Low |
| Castle et al. (33) | 2019 | Some concerns | Some concerns | Low | Low | Low | Low | Low |
| Catarino et al. (34) | 2015 | Low | Low | Low | Low | Low | Low | Low |
| Darlin et al. (35) | 2013 | Some concerns | Low | Low | Low | Low | Some concerns | Low |
| Flores et al. (36) | 2021 | Low | Low | Low | Low | Low | Low | Low |
| Giorgi Rossi et al. (37) | 2011 | Low | Low | Low | Low | Low | Low | Low |
| Giorgi Rossi et al. (38) | 2015 | Low | Low | Low | Low | Low | Low | Low |
| Gizaw et al. (39) | 2019 | Low | Some concerns | Low | Low | Low | Low | Low |
| Gok et al. (40) | 2010 | Low | Low | Low | Low | Low | Low | Low |
| Gok et al. (41) | 2012 | Low | Low | Low | Low | Low | Some concerns | Low |
| Gustavsonn et al. (42) | 2018 | Low | Low | Low | Low | Low | Low | Low |
| Haguenor et al. (43) | 2014 | Low | Low | Low | Low | Low | Low | Low |
| Harper et al. (44) | 2002 | Low | Low | Low | Low | Low | Low | Low |
| Hellsten et al. (45) | 2021 | Low | Low | Low | Low | Low | Low | Low |
| Ivanus et al. (46) | 2018 | Low | Low | Low | Low | Low | Low | Low |
| Jalili et al. (47) | 2019 | Low | Low | Low | Low | Low | Low | Low |
| Karjalainen et al. (48) | 2016 | Low | Low | Low | Low | Low | Low | Low |
| Kellen et al. (49) | 2018 | high | Low | Low | Low | Low | Low | Low |
| Kitchener et al. (50) | 2018 | Low | Low | Low | Low | Low | Low | Low |
| Lazcano-Ponce et al. (51) | 2011 | Some concerns | Some concerns | Low | Low | Low | Some concerns | Some concerns |
| Lilliecreutz et al. (52) | 2020 | Low | Low | Low | Low | Low | Low | Low |
| Mac Donald et al. (53) | 2021 | Some concerns | Some concerns | Low | Low | Low | Low | Low |
| Modibbo et al. (54) | 2017 | Some concerns | Some concerns | Low | Low | Low | Some concerns | Low |
| Molokwu et al. (55) | 2018 | Low | Low | Low | Low | Low | Low | Low |
| Moses et al. (56) | 2015 | Low | Low | Low | Some concerns | Low | Low | Low |
| Murphy et al. (57) | 2016 | Low | Low | Low | Low | Low | Low | Low |
| Peeters et al. (58) | 2020 | Some concerns | Some concerns | Low | Low | Low | Low | Low |
| Polman et al. (59) | 2019 | Low | Low | Low | Low | Low | Low | Low |
| Racey et al. (16) | 2016 | Low | Low | Low | Some concerns | Low | Low | Low |
| Reques et al. (60) | 2021 | Some concerns | Low | Low | Some concerns | Low | Low | Low |
| Sancho-Garnier et al. (61) | 2013 | Some concerns | Some concerns | Low | Low | Low | Low | Low |
| Scarinci et al. (62) | 2021 | Low | Low | Low | Low | Low | Low | Low |
| Sewali et al. (63) | 2015 | Low | Low | Low | Low | Low | Low | Low |
| Sultana et al. (64) | 2016 | Low | Low | Low | Low | Low | Some concerns | Low |
| Szarewski et al. (65) | 2011 | Some concerns | Some concerns | Low | Low | Low | Low | Low |
| Tamalet et al. (66) | 2013 | Low | Low | Low | Low | Low | Low | Low |
| Tranberg et al. (67) | 2018 | Low | Low | Low | Low | Low | Low | Low |
| Van de Wijgert et al. (68) | 2006 | Low | Low | Low | Low | Low | Low | Low |
| Virtanen et al. (69) | 2011 | Some concerns | Low | Low | Low | Low | Low | Low |
| Virtanen et al. (70) | 2015 | Low | Low | |||||
| Viviano et al. (71) | 2017 | Low | Low | Low | Low | Low | Low | Low |
| Wikstrom et al. (72) | 2011 | Some concerns | Some concerns | Low | Low | Low | Low | Low |
| Winer et al. (73) | 2019 | Low | Low | Low | Low | Low | Low | Low |
| Wong et al. (74) | 2018 | Low | Low | Low | Low | Low | Low | Low |
| Wong et al. (75) | 2016 | Low | Low | Low | Low | Low | Low | Low |
| Yamasaki et al. (76) | 2019 | Low | Low | Low | Low | Low | Low | Low |
| Zehbe et al. (77) | 2016 | Some concerns | Low | Low | Low | Low | Low | Low |
Risk of bias of included RCTs assessed by Cochrane risk of bias tools.
Table 2
| First authors | Year | Research question clearly stated | Study population clearly specified and defined | Participation rate of eligible persons at least 50% | Eligibility criteria applied uniformly to all participants | Sample size justification, power description, or variance and effect estimates provided | Different level of exposure | Exposure clearly defined | Outcome measures clearly defined, valid, reliable and implemented consistently across all study participants | Key potential confounding variables measured and statistically adjusted | Overall quality |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Agorastos et al. (78) | 2005 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Aiko et al. (79) | 2017 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Allende et al. (80) | 2019 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Anderson et al. (81) | 2017 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Anhang et al. (82) | 2006 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Bansil et al. (83) | 2014 | Yes | Yes | No | Yes | No | No | Yes | Yes | No | Poor |
| Barbee et al. (84) | 2010 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Behnke et al. (85) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Berner et al. (86) | 2013 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Brewer et al. (87) | 2019 | Yes | Yes | No | Yes | No | Yes | Yes | Yes | No | Fair |
| Broquet et al. (88) | 2015 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Castell et al. (89) | 2014 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Catarino et al. (90) | 2015 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Chatzistamatiou et al. (14) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Chatzistamatiou et al. (91) | 2017 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Chou et al. (92) | 2016 | Yes | Yes | No | Yes | No | No | Yes | Yes | No | Poor |
| Crofts et al. (93) | 2015 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Crosby et al. (94) | 2015 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Dannecker et al. (95) | 2004 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| de Melo Kuil et al. (96) | 2017 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Good |
| Delerè et al. (97) | 2011 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Des marais et al. (98) | 2019 | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | No | Good |
| Desai et al. (99) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Duke et al. (100) | 2015 | Yes | Yes | No | Yes | No | No | Yes | Yes | No | Poor |
| Dutton et al. (101) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Dzuba et al. (102) | 2002 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Esber et al. (103) | 2018 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Galbraith et al. (104) | 2014 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Goldstein et al. (105) | 2020 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Good |
| Gottschlich et al. (106) | 2019 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Gottschlich et al. (15) | 2017 | Yes | Yes | No | Yes | No | No | Yes | Yes | Yes | Fair |
| Guan et al. (107) | 2012 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Haile et al. (108) | 2019 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Hinten et al. (109) | 2017 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Igidbashian et al. (110) | 2011 | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | No | Good |
| Ilangovan et al. (111) | 2016 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Islam et al. (112) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Jones et al. (113) | 2012 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Jones et al. (114) | 2008 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Katanga et al. (115) | 2021 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Ketalaars et al. (116) | 2017 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Good |
| Khanna et al. (117) | 2007 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Khoo et al. (12) | 2021 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Kilfoyle et al. (118) | 2018 | Yes | Yes | No | Yes | No | No | Yes | Yes | Yes | Fair |
| Kohler et al. (13) | 2019 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Landy et al. (119) | 2022 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Laskow et al. (120) | 2017 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Good |
| Litton et al. (121) | 2013 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Lorenzi et al. (122) | 2019 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Ma'som et al. (123) | 2016 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Madhivanan et al. (124) | 2021 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Good |
| Mahande et al. (125) | 2021 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Good |
| Malone et al. (126) | 2020 | Yes | Yes | No | Yes | No | No | Yes | Yes | No | Poor |
| Mandigo et al. (127) | 2015 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Mao et al. (128) | 2017 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Maza et al. (129) | 2018 | Yes | Yes | Yes | Yes | Yes | No | No | Yes | No | Fair |
| McLarty et al. (130) | 2019 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Mremi et al. (131) | 2021 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Murchland et al. (11) | 2019 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | Good |
| Nakalembe et al. (132) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Nelson et al. (133) | 2015 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Nobbenhuis et al. (134) | 2002 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Obiri-Yeboah et al. (135) | 2017 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Good |
| Oranratanaphan et al. (136) | 2014 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Good |
| Pantano et al. (137) | 2021 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Penaranda et al. (138) | 2015 | Yes | Yes | No | Yes | No | No | Yes | Yes | No | Poor |
| Reiter et al. (139) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Rosenbaum et al. (140) | 2014 | Yes | Yes | No | Yes | Yes | No | Yes | Yes | No | Fair |
| Sechi et al. (141) | 2022 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | Good |
| Sellors et al. (142) | 2000 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Shin et al. (143) | 2019 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Silva et al. (144) | 2017 | Yes | Yes | Yes | Yes | No | No | No | Yes | No | Poor |
| Surriabre et al. (145) | 2017 | Yes | Yes | Yes | Yes | No | No | No | Yes | No | Poor |
| Swanson et al. (146) | 2018 | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | Good |
| Szarewski et al. (147) | 2007 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Taku et al. (148) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Tan et al. (149) | 2021 | Yes | Yes | No | Yes | No | No | Yes | Yes | No | Poor |
| Tiiti et al. (150) | 2021 | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | Yes | Good |
| Torrado Garcia et al. (151) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Torres et al. (152) | 2018 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Trope et al. (153) | 2013 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Van Baars et al. (154) | 2012 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Virtanen et al. (155) | 2014 | Yes | Yes | No | Yes | No | No | Yes | Yes | No | Poor |
| Waller et al. (17) | 2006 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Wang et al. (156) | 2020 | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No | Good |
| Wedisinghe et al. (157) | 2022 | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | No | Good |
| Wikstrom et al. (158) | 2007 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
| Winer et al. (159) | 2016 | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | Yes | Good |
| Wong et al. (160) | 2020 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Good |
| Zehbe et al. (161) | 2011 | Yes | Yes | Yes | Yes | No | No | Yes | Yes | No | Fair |
Risk of bias of included observational studies assessed by NIH Quality assessment tool for observational cohort and cross-sectional studies.
Cervical cancer screening uptake
Forty-nine (31.8%) of studies included measured CCS uptake (Table 3); 46 (93.9%) were RCTs and 3 (5.1%) were quasi-experimental studies. Regarding characteristics of the studied population, 40 studies (81.6%) were focused on under-screened women, while 9 (18.4%) involved the general population. Cervical brushes were used in 21 (42.9%) studies, swabs in 20 (40.8%) studies and lavages in 7 (14.3%) studies. In 3 (6.1%) studies, the type of device was not reported. In 2 (4.1%) studies, both a brush and a lavage were proposed to the participants. In 12 (24.5%) studies self-samplers were directly distributed to women (door-to-door), and the opt-out and opt-in strategies were used in 30 (61.2%) and 10 (20.4%) studies, respectively. In 7 (14.3%) studies both opt-out and opt-in strategies were examined.
Table 3
| First authors | Year | Country | Sample size | Design | Area | Sample age | Country economic status | Social subgroup | Screening status | Device type | Control | Intervention | Control arm size | Experimental arm size |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Arrossi et al. (26) | 2015 | Argentina | 7, 650 | Cluster randomized clinical trial | Urban and rural | 40–49# | MIC | – | Under-screened | Brush | Door-to-door recommendation to have a clinician-collected sample | Door-to-door distribution of self-samplers by HCPs | 4, 018 | 3, 632 |
| Bais et al. (27) | 2007 | Netherlands | 2, 830 | Randomized clinical trial | Urban | 30–50§ | HIC | – | Under-screened | Brush | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home | 284 | 2, 546 |
| Brewer et al. (29) | 2021 | New Zeland | 3, 553 | Randomized clinical trial | Urban and rural | 44# | HIC | Indigenous Māori, Pacific and Asian women | Under-screened | Swab | Invitation letter proposing a clinician-collected sample | Intervention 1: invitation letter proposing a self-sample at local hospital Intervention 2: self-samplers mailed to home | 512 | Intervention 1: 1, 574 Intervention 2: 1, 467 |
| Broberg et al. (30) | 2014 | Sweden | 8, 800 | Randomized clinical trial | Urban and rural | 46.8** | HIC | – | Under-screened | Brush | Control 1: reminder letter proposing a clinician-collected sample Control 2: reminder letter and reminder phone call proposing a clinician-collected sample | Self-samplers mailed to home | Control 1: 4, 000 Control 2: 4, 000 | 800 |
| Cadman et al. (31) | 2015 | England | 6, 000 | Randomized clinical trial | Urban and rural | 40.0* | HIC | – | Under-screened | Swab | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home | 3, 000 | 3, 000 |
| Carrasquillo et al. (32) | 2018 | USA | 601 | Randomized clinical trial | Urban and rural | 48.7* | HIC | Ethnic minorities in South-Florida. Haitian, hispanic and black women | Under-screened | Swab | Control 1: outreach programme by HCPs proposing a clinician-collected sample Control 2: facilitated navigation by HCPs to have a clinician-collected sample | Health education programme with door-to-door distribution of self-samplers or facilitated navigation to Pap smear offered by HCWs | Control 1: 182 Control 2: 212 | 207 |
| Castle et al. (33) | 2019 | Brazil | 483 | Randomized clinical trial | Urban | 42.5** | MIC | – | Under-screened | Brush | Door-to-door proposal to have a clinician-collected sample | Intervention 1: door-to-door choice between self-sampling and Pap-testing by HCWs Intervention 2: door-to-door distribution of self-samplers by HCWs | 160 | Intervention 1: 162 Intervention 2: 161 |
| Castle et al. (162) | 2011 | USA | 119 | Quasi-experimental trial | Rural | 42.5** | HIC | Underserved women in the Mississippi Delta | Under-screened | Brush | Voucher for free and facilitated clinician-collected sample | Health education programme and door-to-door distribution of self-samplers by HCWs | 42 | 77 |
| Darlin et al. (35) | 2013 | Sweden | 1, 500 | Randomized clinical trial | Urban and rural | 50.3** | HIC | – | Under-screened | Swab | Invitation and recall letter proposing a clinician-collected sample | Self-samplers mailed to home | 500 | 1, 000 |
| Duke et al. (100) | 2015 | Canada | 6, 057 | Quasi-experimental trial | Rural | 45–49† | HIC | – | General population | Swab | Control 1: Promotion campaign and invitation letter proposing a clinician-collected sample Control 2: invitation letter proposing a clinician-collected sample | HPV screening promotion campaign and self-samplers available at public locations (i.e., hair salons, pharmacies) | Control 1:2, 761 Control 2: 1, 536 | 1, 760 |
| Elfström et al. (163) | 2019 | Sweden | 8, 000 | Randomized clinical trial | Urban and rural | 47.0* | HIC | – | Under-screened | Swab | Invitation letter proposing a clinician-collected sample | Intervention 1: invitation to order a self-sampler through an online application Intervention 2: self-samplers mailed to home | 2, 000 | Intervention 1: 2, 000 Intervention 2: 2, 000 Intervention 3: 2, 000 |
| Enerly et al. (164) | 2016 | Norway | 3, 393 | Randomized clinical trial | Urban | 35–49† | HIC | – | Under-screened | Brush/Lavage | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home | 2, 593 | 800 |
| Giorgi Rossi et al. (37) | 2011 | Italy | 2, 473 | Randomized clinical trial | Urban and rural | 25–64§ | HIC | – | Under-screened | Lavage | Control 1: reminder letter proposing a clinician-collected sample (HPV test) Control 2: reminder letter proposing a clinician-collected sample (PAP test) | Intervention 1: invitation to order a self-sampler by phone-call Intervention 2: self-samplers mailed to home | Control 1: 616 Control 2: 619 | Intervention 1: 622 Intervention 2: 616 |
| Giorgi Rossi et al. (38) | 2015 | Italy | 14, 041 | Randomized clinical trial | Urban and rural | 30–64§ | HIC | – | Under-screened | Lavage | Recall letter proposing a clinician-collected sample | Intervention 1: self-samplers mailed to home Intervention 2: self-samplers available at local pharmacies | 5, 012 | Intervention 1: 4, 516 Intervention 2: 4, 513 |
| Gizaw et al. (39) | 2019 | Ethiopia | 2, 356 | Cluster randomized clinical trial | Urban and rural | 30–34† | LIC | – | Under-screened | Brush | Community education programme proposing a clinician-collected sample | Community health education programme and invitation to self-sample at local hospital | 1, 143 | 1, 213 |
| Gok et al. (41) | 2012 | Netherlands | 26, 409 | Randomized clinical trial | Urban and rural | 39–43† | HIC | – | Under-screened | Brush | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home | 264 | 26, 145 |
| Gok et al. (40) | 2010 | Netherlands | 28, 073 | Randomized clinical trial | Urban and rural | 30–60§ | HIC | – | Under-screened | Lavage | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home with previous notification | 281 | 27, 792 |
| Gustavsonn et al. (42) | 2018 | Sweden | 36, 390 | Randomized clinical trial | Urban and rural | 39.5** | HIC | – | Under-screened | Brush | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home | 18, 393 | 17, 997 |
| Haguenor et al. (43) | 2014 | France | 5, 998 | Randomized clinical trial | Urban and rural | 51.1* | HIC | – | Under-screened | Swab | Control 1: invitation letter proposing a clinician-collected sample Control 2: reminder letter and phone call proposing a clinician-collected sample | Self-samplers mailed to home | Control 1:1, 999 Control 2: 2, 000 | 1, 999 |
| Hellsten et al. (45) | 2021 | Sweden | 29, 604 | Randomized clinical trial | Urban and rural | 37.8** | HIC | – | General population | Swab | Invitation letter proposing a clinician-collected sample | Self-samplers mailed to home | 14, 839 | 14, 765 |
| Ivanus et al. (46) | 2018 | Slovenia | 26, 556 | Randomized clinical trial | Urban and rural | 49.8* | HIC | – | Under-screened | Not Reported | Reminder letter proposing a clinician-collected sample | Intervention 1: self-samplers mailed to home Intervention 2: self-samplers available at local pharmacies | 2, 600 | Intervention 1: 9, 556 Intervention 2: 14, 400 |
| Jalili et al. (47) | 2019 | Canada | 1, 052 | Randomized clinical trial | Urban and rural | 42.6** | HIC | – | Under-screened | Brush | Invitation letter proposing a clinician-collected sample | Self-samplers mailed to home | 523 | 529 |
| Kellen et al. (49) | 2018 | Belgium | 35, 895 | Randomized clinical trial | Urban and rural | 50–54† | HIC | – | Under-screened | Brush | Control 1: reminder letter proposing a clinician-collected sample Control 2: reminder letter and phone call proposing a clinician-collected sample | Intervention 1: invitation to order a self-sampler by phone-call or email Intervention 2: self-samplers mailed to home | Control 1: 8, 849 Control 2: 8, 830 | Intervention 1: 9, 098 Intervention 2: 9, 118 |
| Kitchener et al. (50) | 2018 | UK | 8, 849 | Cluster randomized clinical trial | Urban and rural | Not available | HIC | – | Under-screened | Brush and lavage | Control 1: invitation letter proposing a clinician-collected sample Control 2: nurse navigators proposing a clinician-collected sample Control 3: timed-appointment to have a clinician-collected sample | Intervention 1: self-samplers mailed to home Intervention 2: self-samplers available on request | Control 1: 3, 782 Control 2: 1, 007 Control 3: 1, 629 | Intervention 1: 1, 141 Intervention 2: 1, 290 |
| Landy et al. (119) | 2022 | UK | 784 | Randomized clinical trial | Urban | 55–59† | HIC | – | General population | Swab | Invitation letter proposing a clinician-collected sample | Invitation letter proposing a clinician-collected sample or a self-sampler mailed to home | 391 | 393 |
| Lazcano-Ponce et al. (51) | 2011 | Mexico | 22, 102 | Randomized clinical trial | Urban and rural | 35–39† | MIC | – | General population | Brush | Door-to-door education programme proposing a clinician-collected sample | Health education programme and door-to-door distribution of self-samplers by HCWs | 12, 731 | 9, 371 |
| Lilliecreutz et al. (52) | 2020 | Sweden | 9, 752 | Randomized clinical trial | Urban and rural | 30–64§ | HIC | – | Under-screened | Swab | Control 1: phone call proposing a clinician-collected sample Control 2: invitation letter proposing a clinician-collected sample | Self-samplers mailed to home | Control 1: 3, 146 Control 2: 3, 538 | 3, 068 |
| Mac Donald et al. (53) | 2021 | New Zealand | 1, 539 | Cluster randomized clinical trial | Urban and rural | 40–49† | HIC | – | Under-screened | Swab | Texting, email, letter or phone call proposing a clinician-collected sample | Self-samplers offered during a clinical visit | 806 | 733 |
| Modibbo et al. (54) | 2017 | Nigeria | 400 | Randomized clinical trial | Urban and rural | 40.8* | MIC | – | General population | Swab | Invitation letter proposing a clinician-collected sample | Self-samplers mailed to home | 200 | 200 |
| Moses et al. (56) | 2015 | Uganda | 500 | Randomized clinical trial | Urban | 39.1* | LIC | – | General population | Swab | Door-to-door appointment with HCWs proposing a clinician-collected sample | Door-to-door distribution of self-samplers by HCWs | 250 | 250 |
| Murphy et al. (57) | 2016 | USA | 94 | Randomized clinical trial | Urban | 48.7* | HIC | HIV-positive women | Under-screened | Brush | clinician-collected sample proposed during a clinical visit | Self-samplers offered during a clinical visit | 31 | 63 |
| Peeters et al. (58) | 2020 | Belgium | 88 | Randomized clinical trial | Urban and rural | 45–54† | HIC | – | Under-screened | Brush | Face-to-face general practitioner advice for a clinician-collected sample | Self-samplers offered face-to-face by general practitioner | 43 | 45 |
| Polman et al. (59) | 2019 | Netherlands | 16, 361 | Randomized clinical trial | Urban and rural | 45.6** | HIC | – | General population | Brush | Invitation letter proposing a clinician-collected sample | Self-samplers mailed to home | 8, 168 | 8, 193 |
| Racey et al. (16) | 2016 | Canada | 818 | Randomized clinical trial | Rural | 51.2** | HIC | – | Under-screened | Swab | Control 1: no intervention (opportunistic screening of women previously invited to have a clinician-collected sample) Control 2: invitation letter proposing a clinician-collected sample | Self-samplers mailed to home | Control 1: 152 Control 2: 331 | 335 |
| Reques et al. (60) | 2021 | France | 687 | Randomized clinical trial | Urban | 41.0* | HIC | Underprivileged women (sex workers, slum dwellers) | Under-screened | Not Reported | clinician-collected sample proposed during a clinical visit in a community setting | Self-samplers offered during a medical consultation in a community setting | 304 | 383 |
| Sancho-Garnier et al. (61) | 2013 | France | 18, 730 | Randomized clinical trial | Urban | 40–44† | HIC | Women belonging to lower socio-economic groups | Under-screened | Swab | Reminder letter proposing clinician-collected sample proposed during a clinical visit | Self-samplers mailed to home | 9, 901 | 8, 829 |
| Scarinci et al. (62) | 2021 | USA | 335 | Cluster randomized clinical trial | Rural | 43.0* | HIC | – | Under-screened | Brush | Door-to door invitation to have a clinician-collected sample | Door-to-door choice between self-sampling and Pap-screening | 170 | 165 |
| Sewali et al. (63) | 2015 | USA | 63 | Randomized clinical trial | Urban | 55.1* | HIC | Somali immigrant women in Minnesota | Under-screened | Brush | Door-to door invitation to have a clinician-collected sample | Door-to-door distribution of self-samplers | 31 | 32 |
| Sultana et al. (64) | 2016 | Australia | 8, 160 | Randomized clinical trial | Urban and rural | 40–49† | HIC | – | Under-screened | Swab | Invitation letter proposing a clinician-collected sample | Self-samplers mailed to home | 1, 020 | 7, 140 |
| Szarewski et al. (65) | 2011 | England | 3, 000 | Randomized clinical trial | Urban | 48.0* | HIC | – | Under-screened | Swab | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home | 1, 500 | 1, 500 |
| Tamalet et al. (66) | 2013 | France | 8, 081 | Randomized clinical trial | Urban | 45–54† | HIC | – | General population | Swab | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home | 4, 314 | 3, 767 |
| Tranberg et al. (67) | 2018 | Denmark | 9, 791 | Randomized clinical trial | Urban and rural | 40–49† | HIC | – | Under-screened | Brush | Reminder letter proposing a clinician-collected sample | Intervention 1: self-samplers mailed to home Intervention 2: invitation (email, phone, text message) to order a self-sampler | 3, 262 | Intervention 1: 3, 265 Intervention 2: 3, 264 |
| Virtanen et al. (69) | 2011 | Finland | 1, 0014 | Randomized clinical trial | Urban | 42.2** | HIC | – | Under-screened | Lavage | Reminder letter proposing a clinician-collected sample | Intervention 1: self-samplers mailed to home after further invitation to Pap screening Intervention 2: self-samplers mailed to home with no further invitation letter | 6, 302 | Intervention 1: 1, 315 Intervention 2: 2, 397 |
| Virtanen et al. (70) | 2015 | Finland | 7, 552 | Quasi-experimental trial | Urban | 45–49† | HIC | – | Under-screened | Lavage | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home | 7, 397 | 155 |
| Viviano et al. (71) | 2017 | Switzerland | 667 | Randomized clinical trial | Urban | 42.2** | HIC | – | Under-screened | Swab | Invitation letter proposing a clinician-collected sample | Self-samplers mailed to home | 331 | 336 |
| Wikstrom et al. (72) | 2011 | Sweden | 4, 060 | Randomized clinical trial | Urban | 39–60§ | HIC | – | Under-screened | Brush | Invitation letter proposing a clinician-collected sample | Self-samplers mailed to home (2, 000) | 2, 060 | 2, 000 |
| Winer et al. (73) | 2019 | USA | 19, 851 | Randomized clinical trial | Urban | 50–54† | HIC | – | Under-screened | Not Reported | Invitation letter proposing a clinician-collected sample | Self-samplers mailed to home | 9, 891 | 9, 960 |
| Yamasaki et al. (76) | 2019 | Japan | 249 | Randomized clinical trial | Rural | 40–49† | HIC | Women living on the remote Goto island | Under-screened | Brush | Reminder letter proposing a clinician-collected sample | Self-samplers mailed to home | 124 | 125 |
| Zehbe et al. (77) | 2016 | Canada | 1, 002 | Cluster randomized clinical trial | Rural | 25–69§ | HIC | – | General population | Swab | Community educational programme proposing a clinician-collected sample | Self-samplers mailed to home | 598 | 404 |
Characteristics of the included studies assessing cervical cancer screening (CCS) uptake comparing self-sampling with clinician-collected samples for HPV testing.
Sample age reported as
mean,
weighted mean,
median,
##weighted median,
median age group or
range.
Country economic status reported as: HIC, high income country; MIC, middle income Country; LIC, low income country.
Overall, self-sampling procedures nearly doubled the probability (RR: 1.9; 95% CI: 1.8–2.0) of CCS uptake when compared with clinician-collected samples (Figure 2).
Figure 2
Self-samplers' distribution strategy
With regard to self-sampler distribution strategy, the opt-out (RR: 2.1; 95% CI: 1.9–2.4) and the door-to-door (RR: 1.8; 95% CI: 1.6–2.0) did not statistically significant differ (p = 1.177) in improving the CCS uptake. In contrast, the opt-in (RR: 1.4; 95% CI: 1.2–1.7) showed a significantly lower efficacy than the opt-out strategy (p = 0.001); no statistically significant difference was displayed with respect to door-to-door distribution (p = 0.093) (Figure 3). The pooled analyses restricted to RCTs showed a statistically significant difference in improving CCS uptake between opt-out (RR: 2.2; 95% CI: 2.0–2.5) and door-to-door strategies (RR: 1.7; 95% CI: 1.5–2.0) (p = 0.048) and between the latter and the opt-in strategy (RR: 1.4; 95% CI: 1.1–1.7) (p = 0.048).
Figure 3
Device type
Figure 4 showed the RR of CCS uptake for HPV testing by self-sampler type. The results of those analyses showed a higher relative uptake for vaginal lavages (RR: 1.2; 95% CI: 1.1–1.5), brushes (RR: 1.6; 95% CI: 1.5–1.7) and swabs (RR: 2.5; 95% CI: 1.9–3.1) over clinician-collected samples. The analyses compared swabs and brushes and brushes and lavages showed a statistically significant difference (p = 0.004 and p < 0.001, respectively). When the analyses were restricted to RCTs, a pooled RR estimate of 2.7 (95% CI: 2.0–3.7) for swabs, 1.6 (95% CI: 1.5–1.7) for brushes and 1.3 (95% CI: 1.1–1.5) for lavages, were shown. Similarly, both the swabs-brushes (p < 0.001) and the brushes-lavages (p = 0.009) comparisons displayed a statistically significant difference.
Figure 4
Screening status
In the meta-analysis of studies reporting screening status, the overall RR was >1.00 indicating a potential effect of self-sampling in improving CCS uptake both among under-screened women (RR: 2.1; 95% CI: 1.9–2.3) and general population (RR: 1.4; 95% CI: 1.2–1.7) compared to clinician collected samples, and the difference was statistically significant (p < 0.001). Similarly, the efficacy of self-sampling was significantly higher (p = 0.015) when only RCTs were kept in the analysis, in both groups [under-screened women (RR: 2.1; 95% CI: 1.9–2.4) and general population (RR: 1.6; 95% CI: 1.3–1.9)].
Heterogeneity and publication bias
The level of heterogeneity was consistently high (I2 > 95%) in the overall and subgroup analyses. Publication bias was unlikely, as suggested by Peters' test (p = 0.06) (Figure 5).
Figure 5
Secondary outcomes
Characteristics of the included studies assessing acceptability and preference of self-sampling vs. clinician-collected samples were displayed in Table 4. One-hundred and eight (70.1%) studies measured at least one secondary outcome: 12 (11.1%) of them were RCTs, 68 (63.0%) were cross-sectional studies and 28 (25.9%) had a quasi-experimental design. Seventy-two (66.7%) considered under-screened women, the rest involved the general population. Twenty-eight (25.9%) studies assessed acceptability and in 52 (48.2%) studies women were asked for preference. Both, acceptability and preference, were assessed in 28 (25.9%) studies. In 64 (59.3%) studies self-sampling occurred in a clinical setting, in 39 (36.1%) it occurred at home, and in 4 studies (3.7%) it occurred in both settings. The setting was not reported in one study.
Table 4
| First authors | Year | Country | Design | Screening status | Age | Country economic status | Area | Social subgroup | Device type | Sampling setting | Total responders (acceptability) | Total responders (preference) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Abdullah et al. (165) | 2018 | Malesia | Cross-sectional | General population | 40.6* | MIC | Urban and rural | – | Brush | Clinic | 164 | 164 |
| Agorastos et al. (78) | 2005 | Greece | Quasi-experimental trial | Under-screened | 44* | HIC | Urban and rural | – | Brush | Clinic | – | 379 |
| Aiko et al. (79) | 2017 | Japan | Quasi-experimental trial | Under-screened | 40–49† | HIC | Urban | – | Brush | Home | – | 127 |
| Allende et al. (80) | 2019 | Bolivia | Cross-sectional | Under-screened | 20–49§ | MIC | Urban and rural | – | Brush | Clinic | – | 221 |
| Anderson et al. (81) | 2017 | USA | Cross-sectional | General population | 44# | HIC | Urban and rural | Low-income women from North Carolina | Brush | Home | 227 | – |
| Anhang et al. (82) | 2006 | USA | Cross-sectional | Under-screened | 35–44† | HIC | Urban | – | Swab | Clinic | – | 172 |
| Avian et al. (166) | 2022 | Italy | Quasi-experimental trial | General population | 40–49† | HIC | Urban and rural | – | Swab | Clinic | – | 1, 032 |
| Bansil et al. (83) | 2014 | India, Nicaragua, Uganda | Cross-sectional | Under-screened | 44* | MIC | Urban and rural | – | Brush | Clinic | – | 3, 464 |
| Barbee et al. (84) | 2010 | USA | Cross-sectional | Under-screened | 18–70§ | HIC | Urban and rural | Haitian immigrant women residing in Little Haiti | Swab | Home | 245 | 245 |
| Behnke et al. (85) | 2020 | Ghana | Cross-sectional | Under-screened | 41* | MIC | Rural | – | Brush | Clinic | – | 52 |
| Berner et al. (86) | 2013 | Cameroon | Quasi-experimental trial | Under-screened | 39# | MIC | Urban and rural | – | Swab | Clinic | – | 217 |
| Bosgraaf et al. (28) | 2014 | Netherlands | Randomized clinical trial | General population | 44.5* | HIC | Urban | – | Brush and Lavage | Clinic | – | 9, 360 |
| Brewer et al. (87) | 2019 | New Zealand | Quasi-experimental trial | General population | 30–69§ | HIC | Urban and rural | – | Lavage and Swab | Clinic | – | 44 |
| Broquet et al. (88) | 2015 | Madagascar | Cross-sectional | General population | 42, 5## | LIC | Urban and rural | – | Swab | Clinic | 300 | 300 |
| Castell et al. (89) | 2014 | Germany | Cross-sectional | Under-screened | 53# | HIC | Urban and rural | – | Lavage | Home | 108 | – |
| Catarino et al. (34) | 2015 | Switzerland | Randomized clinical trial | General population | 42# | HIC | Urban | – | Brush and Swab | Clinic | – | 126 |
| Catarino et al. (90) | 2015 | Switzerland | Cross-sectional | General population | 43.6* | HIC | Rural | – | Swab | Home | 130 | – |
| Chatzistamatiou et al. (14) | 2020 | Greece | Cross-sectional | Under-screened | 45# | HIC | Rural | – | Swab | Clinic | – | 12, 376 |
| Chatzistamatiou et al. (91) | 2017 | Greece | Cross-sectional | General population | 44# | HIC | Rural | – | Brush | Clinic | 339 | 334 |
| Chaw et al. (167) | 2022 | Brunei | Cross-sectional | Under-screened | 45# | HIC | Urban | – | Swab | Clinic | 97 | 97 |
| Chou et al. (92) | 2016 | Taiwan | Cross-sectional | General population | 48# | HIC | Urban | – | Brush | Home | 282 | – |
| Crofts et al. (93) | 2015 | Cameroon | Cross-sectional | Under-screened | 43# | MIC | Rural | – | Swab | Clinic | – | 86 |
| Crosby et al. (94) | 2015 | USA | Cross-sectional | Under-screened | 40.2* | HIC | Rural | Rural appalachian women | Swab | Home | – | 400 |
| Dannecker et al. (95) | 2004 | Germany | Cross-sectional | Under-screened | 42* | HIC | Urban | – | Brush | Clinic | 333 | 318 |
| de Melo Kuil et al. (96) | 2017 | Brasil | Quasi-experimental trial | Under-screened | 25–45† | MIC | Urban and rural | – | Lavage | Clinic | – | 160 |
| Delerè et al. (97) | 2011 | Germany | Cross-sectional | Under-screened | 25.7## | HIC | Urban | – | Lavage | Home | – | 156 |
| Des marais et al. (98) | 2019 | USA | Quasi-experimental trial | Under-screened | 45# | HIC | Urban | – | Brush | Clinic and Home | 188 | – |
| Desai et al. (99) | 2020 | Nigeria | Cross-sectional | Under-screened | 35–39† | MIC | Urban and rural | – | Brush | Clinic | – | 9, 065 |
| Duke et al. (100) | 2015 | Canada | Quasi-experimental trial | Under-screened | 45–49† | HIC | Rural | – | Swab | Home | 168 | – |
| Dutton et al. (101) | 2020 | Australia | Cross-sectional | General population | 35–39† | HIC | Rural | Aboriginal community | Swab | Home | 200 | – |
| Dzuba et al. (102) | 2002 | Mexico | Quasi-experimental trial | Under-screened | 43* | MIC | Urban and rural | – | Swab | Clinic | – | 1, 067 |
| Esber et al. (168) | 2018 | Malawi | Cross-sectional | General population | 33** | LIC | Rural | – | Swab | Clinic | 199 | 199 |
| Flores et al. (36) | 2021 | Mexico | Randomized clinical trial | General population | 43.8* | MIC | Urban | – | Brush | Clinic | 500 | – |
| Galbraith et al. (104) | 2014 | USA | Cross-sectional | Under-screened | 40–49† | HIC | Urban and rural | Women living in a situation of economic hardship | Brush | Home | 211 | 211 |
| Giorgi Rossi et al. (37) | 2011 | Italy | Randomized clinical trial | General population | 25–64§ | HIC | Urban and rural | – | Lavage | Home | – | 139 |
| Goldstein et al. (105) | 2020 | China | Quasi-experimental trial | General population | 35–65§ | HIC | Rural | – | Swab | Clinic | 600 | 600 |
| Gottschlich et al. (106) | 2019 | Thailand | Cross-sectional | Under-screened | 50.44* | MIC | Urban and rural | – | Swab | Clinic | 267 | 219 |
| Gottschlich et al. (15) | 2017 | Guatemala | Cross-sectional | Under-screened | 34.5* | MIC | Urban and rural | Indigenous community | Swab | Home | 178 | – |
| Guan et al. (107) | 2012 | China | Cross-sectional | Under-screened | 41# | HIC | Rural | – | Brush | Clinic | – | 174 |
| Guerra Rodriguez et al. (169) | 2022 | Mexico | Cross-sectional | General population | 26* | MIC | Urban | – | Brush | Clinic | 60 | 60 |
| Haile et al. (108) | 2019 | Ethiopia | Quasi-experimental trial | Under-screened | 32* | LIC | Urban | – | Brush | Clinic | 83 | 83 |
| Harper et al. (44) | 2002 | USA | Randomized clinical trial | Under-screened | 37.7* | HIC | Urban | – | Swab and Tampon | 67 | – | |
| Hinten et al. (109) | 2017 | Holland | Cross-sectional | Under-screened | 56# | HIC | Urban | Renal transplant recipients women | Brush | Clinic | – | 157 |
| Igidbashian et al. (110) | 2011 | Italy | Quasi-experimental trial | Under-screened | 38# | HIC | Urban | – | Brush and Lavage | Clinic | – | Lavage: 76 Brush: 96 |
| Ilangovan et al. (111) | 2016 | USA | Cross-sectional | Under-screened | 52* | HIC | Urban | Latina and Haitian patients | Swab | Clinic | 120 | 120 |
| Islam et al. (112) | 2020 | Kenia | Quasi-experimental trial | Under-screened | 39# | MIC | Urban | Sex Workers | Brush | Clinic | – | 399 |
| Jones et al. (113) | 2012 | United States | Quasi-experimental trial | General population | 45# | HIC | Urban | – | Lavage | Clinic | – | 197 |
| Jones et al. (114) | 2008 | Netherlands | Cross-sectional | Under-screened | 35# | HIC | Urban | – | Lavage | Home | – | 91 |
| Karjalainen et al. (48) | 2016 | Finland | Randomized clinical trial | Under-screened | 40–49† | HIC | Urban and rural | – | Brush and Lavage | Clinic | – | Lavage: 161 Brush: 159 |
| Katanga et al. (115) | 2021 | Tanzania | Quasi-experimental trial | Under-screened | 41* | LIC | Urban | – | Brush | Home | – | 416 |
| Ketelaars et al. (116) | 2017 | Netherlands | Quasi-experimental trial | Under-screened | 43.4* | HIC | Urban | – | Brush | Clinic | – | 2, 131 |
| Khanna et al. (117) | 2007 | USA | Quasi-experimental trial | Under-screened | 32* | HIC | Urban | – | Brush | Clinic | – | 499 |
| Khoo et al. (12) | 2021 | Malaysia | Cross-sectional | Under-screened | 35–45§ | MIC | Urban | – | Swab | Clinic | 725 | 725 |
| Kilfoyle et al. (118) | 2018 | USA | Cross-sectional | General population | 44# | HIC | Urban and rural | Low-income women from North Carolina | Brush | Home | – | 221 |
| Kohler et al. (13) | 2019 | Botswana | Cross-sectional | Under-screened | 45* | MIC | Urban and rural | – | Swab | Clinic | 104 | 105 |
| Landy et al. (119) | 2022 | UK | Cross-sectional | General population | 55–59† | HIC | Urban | – | Brush | Clinic | – | 170 |
| Laskow et al. (120) | 2017 | El Salvador | Cross-sectional | General population | 40.7* | MIC | Rural | – | Brush | Home | 41 | – |
| Litton et al. (121) | 2013 | USA | Cross-sectional | Under-screened | 35.4** | HIC | Rural | African American women living in the Mississippi Delta | Swab | Home | – | 516 |
| Lorenzi et al. (122) | 2019 | Brasile | Cross-sectional | Under-screened | 36.2* | MIC | Urban | – | Brush | Clinic | – | 116 |
| Madhivanan et al. (124) | 2021 | India | Cross-sectional | Under-screened | 39# | MIC | Rural | – | Brush | Clinic | 118 | 118 |
| Mahande et al. (125) | 2021 | Tanzania | Cross-sectional | General population | 35.6* | LIC | Urban and rural | – | Swab | Home | 350 | – |
| Malone et al. (126) | 2020 | USA | Cross-sectional | General population | 40–49† | HIC | Urban | – | Swab | Home | – | 117 |
| Mandigo et al. (127) | 2015 | Haiti | Cross-sectional | General population | 18–50§ | LIC | Rural | – | Not Reported | Home | 485 | – |
| Mao et al. (128) | 2017 | USA | Cross-sectional | Under-screened | 35.7* | HIC | Urban | – | Swab | Home | – | 1, 759 |
| Ma'som et al. (123) | 2016 | Malaysia | Cross-sectional | Under-screened | 38# | MIC | Urban | – | Brush | Clinic | – | 803 |
| Maza et al. (129) | 2018 | El Salvador | Cross-sectional | General population | 42.86* | MIC | Rural | – | Not Reported | Home | 1, 867 | – |
| McLarty et al. (130) | 2019 | USA | Cross-sectional | Under-screened | 49# | HIC | Urban | – | Tampon | Home | – | 55 |
| Molokwu et al. (55) | 2018 | USA | Randomized clinical trial | Under-screened | 46.4* | HIC | Urban and rural | Border dwelling hispanic women | Swab | Home | – | 107 |
| Mremi et al. (131) | 2021 | Tanzania | Cross-sectional | General population | 35–44† | LIC | Urban and rural | – | Swab | Home | 1, 108 | – |
| Murchland et al. (11) | 2019 | Guatemala | Cross-sectional | Under-screened | 33.9** | MIC | Rural | – | Swab | Home | 760 | – |
| Nakalembe et al. (132) | 2020 | Uganda | Cross-sectional | Under-screened | 34# | LIC | Rural | – | Brush | Clinic | 1, 316 | – |
| Nelson et al. (133) | 2015 | USA | Quasi-experimental trial | Under-screened | 24.1** | HIC | Rural | – | Swab | Home | – | 62 |
| Ngu et al. (170) | 2022 | Hong Kong | Quasi-experimental trial | Under-screened | 43# | HIC | Urban | – | Swab | Home | 295 | – |
| Nobbenhuis et al. (134) | 2002 | Holland | Quasi-experimental trial | General population | 35* | HIC | Urban | – | Lavage | Clinic | – | 56 |
| Obiri-Yeboah et al. (135) | 2017 | Ghana | Quasi-experimental trial | Under-screened | 44.1* | MIC | Urban | – | Brush | Home | – | 194 |
| Oranratanaphan et al. (136) | 2014 | Thailand | Quasi-experimental trial | Under-screened | 40.6* | MIC | Urban | – | Brush | Clinic | – | 100 |
| Pantano et al. (137) | 2021 | Brazil | Cross-sectional | Under-screened | 49.4* | MIC | Urban and rural | – | Brush | Home | 405 | 313 |
| Penaranda et al. (138) | 2015 | USA | Cross-sectional | Under-screened | 48.2* | MIC | Urban and rural | Border dwelling women | Swab | Clinic | 118 | 106 |
| Polman et al. (59) | 2019 | Holland | Randomized clinical trial | Under-screened | 43.7* | HIC | Urban and rural | – | Brush | Clinic | – | 1, 662 |
| Racey et al. (16) | 2016 | Canada | Randomized clinical trial | General population | 51.2** | HIC | Rural | – | Swab | Home | 68 | – |
| Reiter et al. (139) | 2020 | USA | Cross-sectional | General population | 46, 7* | HIC | Urban | – | Tampon | Home | 79 | 79 |
| Rosenbaum et al. (140) | 2014 | El Salvador | Cross-sectional | Under-screened | 41–59† | MIC | Rural | – | Brush | Clinic | – | 518 |
| Sellors et al. (142) | 2000 | USA | Quasi-experimental trial | Under-screened | 31.5* | HIC | Urban | – | Brush | Home | 127 | – |
| Shin et al. (143) | 2019 | Korea | Cross-sectional | Under-screened | 20–49† | HIC | Urban | – | Swab | Clinic | 728 | – |
| Sechi et al. (141) | 2022 | Italy | Quasi-experimental trial | Under-screened | 39, 5* | HIC | Urban | – | Swab | Clinic | 40 | – |
| Silva et al. (144) | 2017 | Portugal | Cross-sectional | Under-screened | 26* | HIC | Urban | – | Not Reported | Not Reported | 303 | 276 |
| Sormani et al. (171) | 2022 | Cameroon | Cross-sectional | General population | 40.6# | MIC | Urban | – | Swab | Clinic | 2, 196 | 2, 201 |
| Surriabre et al. (145) | 2017 | Bolivia | Cross-sectional | Under-screened | 25–59§ | MIC | Urban and rural | – | Not Reported | Clinic | – | 201 |
| Swanson et al. (146) | 2018 | Kenya | Cross-sectional | General population | 36* | MIC | Rural | – | Tampon | Home | 255 | – |
| Szarewski et al. (147) | 2007 | UK | Quasi-experimental trial | Under-screened | 32## | HIC | Urban | – | Swab | Clinic | – | 702 |
| Taku et al. (148) | 2020 | South Africa | Cross-sectional | Under-screened | 44## | MIC | Rural | – | Brush | Clinic | 737 | 720 |
| Tan et al. (149) | 2021 | Malesia | Quasi-experimental trial | General population | 40.5* | MIC | Urban and rural | – | Brush | Clinic | 10 | 10 |
| Tiiti et al. (150) | 2021 | Sud Africa | Cross-sectional | General population | 36.8* | MIC | Urban and rural | – | Brush and Swab | Clinic | 526 | 526 |
| Torrado Garcia et al. (151) | 2020 | Colombia | Cross-sectional | Under-screened | 46.5# | MIC | Urban | Women belonging to the low socioeconomic stratum | Brush | Clinic | 420 | 420 |
| Torres et al. (152) | 2018 | Brasile | Cross-sectional | Under-screened | 26–36† | MIC | Rural | – | Brush | Home | – | 412 |
| Trope et al. (153) | 2013 | Thailand | Cross-sectional | Under-screened | 25–60§ | MIC | Rural | – | Swab | Clinic | 388 | 388 |
| Van Baars et al. (154) | 2012 | Netherlands | Cross-sectional | Under-screened | 40* | HIC | Urban | – | Brush | Clinic | 127 | – |
| Van de Wijgert et al. (68) | 2006 | South Africa | Randomized clinical trial | Under-screened | 29.9* | MIC | Urban | – | Swab and Tampons | Clinic | – | Swab: 222 Tampon: 228 |
| Virtanen et al. (155) | 2014 | Finland | Cross-sectional | General population | 40–49† | HIC | Urban and rural | – | Lavage | Home | 809 | 889 |
| Waller et al. (17) | 2006 | UK | Quasi-experimental trial | Under-screened | 34.2* | HIC | Urban | – | Swab | Clinic | – | 902 |
| Wang et al. (156) | 2020 | USA | Cross-sectional | Under-screened | 50# | HIC | Urban | HIV positive women | Brush | Clinic and Home | 61 | – |
| Wedisinghe et al. (157) | 2022 | Scotland | Quasi-experimental trial | General population | 51.9** | HIC | Rural | – | Brush | Clinic and Home | 272 | – |
| Wikstrom et al. (158) | 2007 | Sweden | Cross-sectional | General population | 35–44† | HIC | Urban and rural | – | Swab | Home | – | 91 |
| Winer et al. (159) | 2016 | USA | Cross-sectional | Under-screened | 43* | HIC | Rural | – | Swab | Clinic and Home | 318 | 306 |
| Wong et al. (74) | 2018 | Hong Kong | Randomized clinical trial | Under-screened | 38.2* | HIC | Urban | Sex workers | Swab | Clinic | – | 68 |
| Wong et al. (160) | 2020 | Hong Kong | Cross-sectional | General population | 39* | HIC | Urban | – | Brush | Home | – | 124 |
| Wong et al. (75) | 2016 | Hong Kong | Randomized clinical trial | Under-screened | 50.9* | HIC | Urban | – | Swab | Clinic | 351 | 392 |
| Zehbe et al. (161) | 2011 | Canada | Cross-sectional | Under-screened | 25–39† | HIC | Rural | Women belonging to the First Nation community | Swab | Clinic | 47 | 48 |
Characteristics of the included studies assessing acceptability and preference of self-sampling vs. clinician-collected samples.
Sample age reported as
mean,
weighted mean,
median,
weighted median,
median age group or
range.
Country economic status reported as: HIC, high income country; MIC, middle income country; LIC, low income country.
Acceptability
Meta-analyses examining the proportion of women who found self-sampling acceptable, showed a very high pooled estimate (95%; 95% CI: 94–97%) (Figure 6). No differences (p = 0.420) were found among acceptability of brushes (93%; 95% CI: 90–96%), swabs (96%; 95% CI: 93–98%), lavages (98%; 95% CI: 95–100%) and tampons (97%; 95% CI: 92–100%). Moreover, the percentage of women who self-reported acceptance of self-sampling at home (96%; 95% CI: 93–98%) overlapped with acceptance of self-sampling in a clinical setting (96%; 95% CI: 94–98%). In all meta-analyses high heterogeneity (I2> 95%) was observed.
Figure 6
Preference
Sixty-six percent (95% CI: 62–70%) of women preferred self-sampling procedures vs. clinician-collected samples (Figure 7). No significant difference (p = 0.850) was shown when brushes (67%; 95% CI: 58–74%), swabs (65%; 95% CI: 59–70%), lavages (68%; 95% CI: 60–76%) and tampons (77%; 95% CI: 31–100%) were compared. Finally, the preference of women for self-sampling was almost equal (p = 0.841) when it was performed at home (66%; 95% CI: 57–74%), or in a clinical setting (67%; 95% CI: 62–71%). The level of heterogeneity was high (I2> 95%).
Figure 7
Discussion
The findings of the present meta-analysis provide a summary of the implementation options of self-sampling for HPV testing. Since the COVID-19 pandemic has had an enormous impact on CCS attendance, self-sampling could offer a unique opportunity for catch-up screening and will play an important role in improving the global coverage of CCS. Indeed, the World Health Organization strongly recommends the use of self-sampling for HPV screening to contribute to reaching a coverage of 70% by 2030 and eliminate HPV correlated diseases in the next decades (172). Considering that for an intervention to be effective it must be broadly accepted, evidence about women's acceptability for CCS comparing self-sampled with clinician-collected specimens is also provided.
The findings of the present meta-analysis showed that self-sampling for HPV testing is an effective tool to reach women in the context of organized CCS programs. Indeed, women were nearly twice as likely to use CCS services through self-sampling as compared with clinician-based sampling. Considering that the option of cervical precancer detection from self-collected samples showed similar clinical accuracy for hrHPV testing as clinician-collected samples (9, 173, 174), this result increases evidence in support of incorporating self-sampling into organized screening programs to better respond to the disruption of CCS programs after the COVID-19 pandemic. Moreover, the meta-analyses split into sub-groups according to dissemination strategies, suggested that a door-to-door approach, in which an HCP visits women at home to inform on CCS and offer a self-sampling HPV test kit, has almost doubled the CCS uptake by seven-fold. However, it has to be pointed out that the door-to-door approach has been mainly investigated in low-resource settings or for reaching under-screened women in high-resource settings. The findings showed an even higher likelihood of attending CCS for the opt-out approach (i.e., mailing of self-collection devices to women's homes without them taking the initiative), compared with controls (i.e., invitation letters sent home, reminding phone calls or suggestions from the HCP to be screened in the local hospital or from a gynecologist). In high-resource settings, research has focused on an alternative invitation scenario (opt-in strategy) in which women request a self-collection kit that is mailed to home or pick it up at pharmacy or clinic. The analyses showed that the opt-in approach reached a high CCS uptake when compared to mailing a reminder letter proposing a clinician-collected samples, although lower than response rates to the opt-out and door-to-door approaches. It should be noted that the opt-in approach has the advantage to be less expensive, especially on a national level. Bring together, these results confirm recent literature. In particular, the meta-analysis by Yeh et al., found that opt-out strategy increased CCS participation (RR: 2.27; 95% CI: 1.89–2.71) (19), and Arbyn et al. found similar results when comparing opt-out self-samplers distribution with a reminder letter/advice from HCP to have a clinician to collect the sample (9).
In the relevant studies, several types of devices to collect exfoliated cells of the cervicovaginal duct for HPV-DNA detection were employed. It should be noted that the distribution of brush- and swab-based devices were associated with significantly higher uptake when compared with invitation to be sampled by a clinician. The latter result deserves attention since, as previously demonstrated, the type of HPV self-sampling device may play an important role in women's acceptability and preference of a CCS strategy (87, 110). The findings of the present meta-analysis highlighted high pooled acceptability and overall preference of self-sampling compared to clinician-based sampling, downsizing potential concerns about self-sampling (e.g., worry of not being able to correctly carry out the sampling), as previously described (17, 175, 176). The finding that especially non-attender women preferred self-sampling to clinician-based sampling for future CCS programs deserves attention, for its potential to increase participation in primary CCS. High acceptability and preference of self-sampling have the potential to improve CCS uptake and its effects on incidence and mortality from cervical cancer. Acceptability of self-sampling demonstrated advantages from both public health and individual patient perspective (177). Proper communication of the self-sampling process to women needs to be realized to address eventual women's concerns and emphasizes that most women are able to successfully obtain an adequate sample or deliver self-sampling by HCPs who can explain the process face-to-face.
In contrast to the findings of Nishimura et al., who documented that swabs were preferred by women when compared with other devices (10) no differences in acceptability regarding the type of self-sampling devices were found.
Contextual factors are essential in real life decision-making: when referring to a small community, offering a door-to-door device could be the most preferable strategy. Differently, when a high number of women have to be reached, mailing the device could represent a cost-effective alternative. Regarding the type of self-sampler device, a pilot investigation could be useful before introducing a large-scale use of self-samplers, as suggested by Arbyn et al. (9). Moreover, elements to consider in order to improve CCS uptake are cultural, religious and socio-economic characteristics of the target community (55, 178, 179). A study carried out on Nigerian women showing that individuals with greater spirituality were less likely to carry out self-sampling (180). Similarly, a systematic review focusing on Islamic women shows that cervical cancer prevention still represents a considerable taboo among them and this can lead to under-screening (181). Further, additional aspects that can interfere with the effectiveness of a self-sampling campaign are the perceived costs and time required for being screened (178, 179, 182). The costs and the need to inform women about the importance of being screened are pivotal among migrants and minorities (183). In the authors' opinion, the use of prepaid and pre-addressed envelopes, the absence of costs for women, the presence of clear and detailed instructions in the self-sampling kits and continuous education about the importance of CCS, could be decisive factors to maximize the uptake.
Strengths and limitations
To the best of our knowledge no recent meta-analysis measuring the effect of self-sampling, across different distribution strategies, type of devices and screening status has been conducted, and the present results could be pivotal to provide practical suggestions for the organization of CCS program. Further strengths consist of the considerable number of subjects included, and the analysis of the recently published results of RCTs.
As above-mentioned, a possible limitation of this meta-analysis is the high heterogeneity, likely attributable to the wide socio-cultural diversity of the samples of women enrolled. Consequently, the results must be interpreted with caution highlighting the need to consider potential factors underlying the success of a self-sampling CCS campaign. Other limitations are the lack of search in the gray literature and the exclusion of all findings reported in languages different than English.
Conclusions
Self-sampling has the potential to increase participation of under-screened women in the CCS, in addition to the standard invitation to have a clinician to collect the sample. For small communities door-to-door distribution could be preferred to distribute the self-sampler; while for large communities opt-out strategies should be preferred over opt-in. Finally, since no significant difference in acceptability and preference of device type was demonstrated among women, and swabs exhibited a potential stronger effect in improving CCS, these devices could be adopted primarily over tampons and lavages.
Statements
Data availability statement
The original contributions presented in the study are included in the article material, further inquiries can be directed to the corresponding author.
Author contributions
FL participated in the conception and design of the study, contributed to the data collection, and wrote the first draft of the article. GD participated in the conception and design of the study, collected the data, performed the data analysis, contributed to analysis interpretation, and wrote the first draft of the article. AT contributed to the data collection and to the data analysis. AB designed the study, was responsible for the data collection and interpretation, wrote the article, and was guarantor for the study. All authors take responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the manuscript for publication.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Abbreviations
CCS, cervical cancer screening; CI, Confidence Interval; HCPs, Healthcare professionals; HPV, Human Papillomavirus; hrHPV, high-risk HPV; RR, Relative Risk; RCT, randomized controlled trial.
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Summary
Keywords
human papillomavirus, cervical cancer screening, self-sampling, uptake, acceptability, preference, systematic review, meta-analysis
Citation
Di Gennaro G, Licata F, Trovato A and Bianco A (2022) Does self-sampling for human papilloma virus testing have the potential to increase cervical cancer screening? An updated meta-analysis of observational studies and randomized clinical trials. Front. Public Health 10:1003461. doi: 10.3389/fpubh.2022.1003461
Received
26 July 2022
Accepted
15 November 2022
Published
08 December 2022
Volume
10 - 2022
Edited by
Suneela Garg, University of Delhi, India
Reviewed by
Dana Kristjansson, Norwegian Institute of Public Health (NIPH), Norway; Heidi E. Jones, City University of New York, United States; Madhu Gupta, Post Graduate Institute of Medical Education and Research (PGIMER), India
Updates
Copyright
© 2022 Di Gennaro, Licata, Trovato and Bianco.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Francesca Licata f.licata@unicz.it
This article was submitted to Public Health Policy, a section of the journal Frontiers in Public Health
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