Assessment And Model Guided Cancer Screening Promotion Protocol: Formative Study

Objectives This trial tries to provide a novel intervention package for promoting CS utilization and web-based electronic system. Specific objectives including: 1) A cancer risk assessment model; 2) A cancer screening will influence factors model; 3)A set of cancer screening promotion protocol; 4) Web-based electronic system match with the model and protocol above.Method Cancer risk assessment model adopts two steps combination strategy: the first step is to collect the data of cancer incidence risk factors based on community residents who involved in cancer screening at Hefei by fast and details structural questionnaire; the second step is to calculate individual cancer risk score, it’s the sum of risk score of each type cancer calculated by multiplication cancer risk value get from risk assessment model using multivariate regression and path analysis and incidence rate as weight using logistic-equations. Cancer screening will influence factors model adopts ‘classification index’ and multivariate regression analysis method as follow: designing the structured questionnaire based on health belief model; carring out face to face interview survey of Hefei community residents using cluster random sampling; calculating the screening requirements index and its5 dimensions cognitive and emotional index through expert consultationand multivariate regression analysis; building cancer screening will influence factors model with screening requirements index as dependent variable and 5 congnitive and emotional index, age, education, income as independent variable. Cancer screening promotion protocol designed by theoretical model and pilot revised method: firstly, selecting health belief model as guiding framework; designing preliminary counseling procedures by expert advice; revising the preliminary procedures via pilot and feedback. Web-based electronic system developed by Visual Studio 2008 and SQL Sever 2008, using the method of rapid prototyping design and optimization the system.Result 1) Cancer risk assessment model building: There are high goodness-of-fit(R>96%) between actural and prediction age specific incidence rate using 3 parameters logistic equation of all cancers and common cancers. Randomly select about a quarter of variables(a total of 51) from cancer risk assessment questionnaire which contains 194 variables.Univariate logistic regression analysis based on 1780 residents who have participated in cacner screening(residents with positive results for cases, with negative results for controls). Its results shows that there are 11 variables significantly associated with breast cancer incidence(0.604<OR<0.614 or 1.440<OR<5.022), 10 variables significantly associated with upper gastrointestinal tract intraepithelial neoplasia incidence(OR=0.650 or1.018<OR<1.502) and 2 variables significantly associated with colorectal cancer or colorectal cancer lesions(3.116<OR<3.286). 2) Cancer screening will influence factors model: A total of 1186 residents completed the survey. Multiple linear regression analysis shows that there are positive associations between education and screening will of 4 cancers(breast cancer, cervical cancer, lung cancer, digestive tract cancer)(0.109<β<0.154,P<0.05), negative associations between age and breast cancer sceening will(-0.108<β<-0.080,P<0.05) and positive associations between age and digestive tract cancer(β=0.033,P<0.05). 5 cancer screening cognitive-affective factors index including perceived risks at getting cancer,perceived seriousness of cancer, perceived effectiveness of cancer screening,perceived benefits from cancer screening and perceived difficulties to taking cancer screening have statistically significant correlations with 4 cancers screening will(0.044<β<0.245). The absolute values of partial regression coefficent of majority cognitive-affective factors index are bigger than age and education for cancer screening will. The sum of partial regression coefficent from 5 cancer screening cognitive-affective factors index for cancer screening will are 0.65-0.72, is 8-12 times of age for cancer screening will and 5-8 times of education for cancer screening will. 3) Cancer screening will influence factors model: Cancer screening promotion protocol contains almost 200 items consisted of initial and re-enforcement counselling, the former covers 5 steps, i.e., alerting risks/ harms,discussing efficacy/ benefits, anticipating barriers/problems, developing resources/efficacy and planning for next steps, the last also divide into 5 steps, i.e., reviewing efforts made, encouraging improvements, identifying problems, solving problems and planning next step. 4) Web-based electronic system: This study provides intelligence on line assistance software.Conclusions All the model and protocol is expected to increase cancer screening utilizing rate significantly. Parts of parameters and algorithm in the two stage cancer risk assessment have been implemented. Cancer screening will model has preliminarily established. Cancer screening promotion protocol is also preliminarily developed and proved effectively by multi-facotr analysis.Web-based system has been put into use by primary doctors. While the two stage cancer risk assessment model building work is in progress, especially the effect of counseling protocol is yet to be validated by field evaluation.