Improving intervention design to promote cervical cancer screening among hard-to-reach women: assessing beliefs and predicting individual attendance probabilities in Bogotá, Colombia

Abstract

Background Despite being a preventable disease, cervical cancer continues to be a public health concern, affecting mainly lower and middle-income countries. Therefore, in Bogotá 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’s Health Belief Model to assess women’s 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. Methods 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 (N = 1699). We examined instrument reliability using Cronbach’s coefficient and performed a principal component analysis to assess construct validity. Then, Kruskal–Wallis 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 (N = 23,370) 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. Results 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 (p-value < 0.001), a higher Severity score (p-value < 0.001) and a higher Barriers score (p-value < 0.001). Additionally, patients between 25 and 30 years old and with higher poverty levels are less likely to attend their appointments (O.R 0.93 (CI: 0.83–0.98) and 0.74 (CI: 0.66–0.85), 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. Conclusion In the case of Bogotá, 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.

Impact of virtual education based on health belief model on cervical cancer screening behavior in middle‐aged women: A quasi‐experimental study

“…HBM specifically focuses on the adoption of preventive health behaviors 22 and emphasizes that the adoption of these behaviors is dependent on individual perceptions and beliefs regarding the fear of illness or health problems, as well as understanding and evaluating the benefits and barriers to the desired behavior. 6 , 23 According to HBM, if individuals perceive the risk and susceptibility to a disease such as cervical cancer, the severity of that disease and its consequences, and the benefits of desired preventive behavior, and feel fewer barriers to performing the correct behavior and have enough confidence in their ability to overcome those barriers, they will adopt preventive behavior. In addition, having cues to action that trigger people to change behavior can help them to do screening behavior.…”

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

“…Cervical cancer is the fourth most frequent cancer in women worldwide, with an estimated 604,000 new cases and 342,000 deaths in 2020 [16], with low-and middle-income countries accounting for approximately 90% of the cases and deaths [17][18][19][20]. For early diagnosis of cervical cancer, its screening using cervical cytology specimens is performed based on the Bethesda System [21].…”