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Therefore, in Bogot\u00e1 a home-visit based program was instituted to increase screening uptake. However, around 40% of the visited women fail to attend their Pap smear test appointments. Using this program as a case study, this paper presents a methodology that combines machine learning methods, using routinely collected administrative data, with Champion\u2019s Health Belief Model to assess women\u2019s beliefs about cervical cancer screening. The aim is to improve the cost-effectiveness of behavioural interventions aiming to increase attendance for screening. The results presented here relate specifically to the case study, but the methodology is generic and can be applied in all low-income settings.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This is a cross-sectional study using two different datasets from the same population and a sequential modelling approach. To assess beliefs, we used a 37-item questionnaire to measure the constructs of the CHBM towards cervical cancer screening. Data were collected through a face-to-face survey (<jats:italic>N<\/jats:italic>\u2009=\u20091699). We examined instrument reliability using Cronbach\u2019s coefficient and performed a principal component analysis to assess construct validity. Then, Kruskal\u2013Wallis and Dunn tests were conducted to analyse differences on the HBM scores, among patients with different poverty levels. Next, we used data retrieved from administrative health records (<jats:italic>N<\/jats:italic>\u2009=\u200923,370) to fit a LASSO regression model to predict individual no-show probabilities. Finally, we used the results of the CHBM in the LASSO model to improve its accuracy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Nine components were identified accounting for 57.7% of the variability of our data. Lower income patients were found to have a lower Health motivation score (<jats:italic>p<\/jats:italic>-value\u2009&lt;\u20090.001), a higher Severity score (<jats:italic>p<\/jats:italic>-value\u2009&lt;\u20090.001) and a higher Barriers score (<jats:italic>p<\/jats:italic>-value\u2009&lt;\u20090.001). Additionally, patients between 25 and 30\u00a0years old and with higher poverty levels are less likely to attend their appointments (O.R 0.93 (CI: 0.83\u20130.98) and 0.74 (CI: 0.66\u20130.85), respectively). We also found a relationship between the CHBM scores and the patient attendance probability. Average AUROC score for our prediction model is 0.9.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>In the case of Bogot\u00e1, our results highlight the need to develop education campaigns to address misconceptions about the disease mortality and treatment (aiming at decreasing perceived severity), particularly among younger patients living in extreme poverty. Additionally, it is important to conduct an economic evaluation of screening options to strengthen the cervical cancer screening program (to reduce perceived barriers). More widely, our prediction approach has the potential to improve the cost-effectiveness of behavioural interventions to increase attendance for screening in developing countries where funding is limited.\n<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12905-022-01800-3","type":"journal-article","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T15:10:16Z","timestamp":1654614616000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Improving intervention design to promote cervical cancer screening among hard-to-reach women: assessing beliefs and predicting individual attendance probabilities in Bogot\u00e1, Colombia"],"prefix":"10.1186","volume":"22","author":[{"given":"David","family":"Barrera Ferro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Bayer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sally","family":"Brailsford","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honora","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,7]]},"reference":[{"key":"1800_CR1","doi-asserted-by":"publisher","first-page":"e191","DOI":"10.1016\/S2214-109X(19)30482-6","volume":"8","author":"M Arbyn","year":"2020","unstructured":"Arbyn M, Weiderpass E, Bruni L, de Sanjos\u00e9 S, Saraiya M, Ferlay J, et al. 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Pontificia Universidad Javeriana (Faculty of Engineering\u2019s Research and Ethics Committee: FID-19\u2013107), SDS (Ethics Committee for Health Research 2019EE47807) and the University of Southampton (Faculty of Social Sciences\u2019 Ethics and Research Committee ERGO ID 48583.A1) granted ethical approval for this study. Prior to data collection, basic information about the project was provided to all participants and informed consent was obtained. SDS provided anonymised data for this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"212"}}