Do General Practitioners Influence the Uptake of Breast Cancer Screening?

Abstract

Objectives —: To investigate the relative importance of patient and general practice characteristics in explaining variations between practices in the uptake of breast cancer screening. Design —: Ecological study examining variations in breast cancer screening rates among 131 general practices using routine data. Setting —: Merton, Sutton, and Wandsworth Family Health Services Authority, which covers parts of inner and outer London. Main outcome measure —: Percentage of eligible women aged 50–64 who attended for mammography during the first round of screening for breast cancer (1991–1994). Results —: Of the 43 063 women eligible for breast cancer screening, 25 826 (60%) attended for a mammogram. Breast cancer screening rates in individual practices varied from 12·5% to 84·5%. The estimated percentage list inflation for the practices was the variable most highly correlated with screening rates ( r= −0·69). There were also strong negative correlations between screening rates and variables associated with social deprivation, such as the estimated percentage of the practice population living in households without a car ( r= −0·61), and with variables that measured the ethnic make-up of practice populations, such as the estimated percentage of people in non-white ethnic groups ( r= −0·60). Screening rates were significantly higher in practices with a computer than in those without (59·5% v 53·9%, difference 5·6%, 95% confidence interval 1·1 to 10·2%). There was no significant difference in screening rates between practices with and without a female partner; with and without a practice nurse; and with and without a practice manager. In a forward stepwise multiple regression model that explained 58% of the variation in breast cancer screening rates, four factors were significant independent predictors (at P = 0·05) of screening rates: list inflation and people living in households without a car were both negative predictors of screening rates, and chronic illness and the number of partners in a practice were both positive predictors of screening rates. The practice with the highest screening rate (84·5%) contacted all women invited for screening to encourage them to attend for their mammogram and achieved a rate 38% higher than predicted from the regression model. Breast cancer screening rates were on average lower than cervical cancer screening rates (mean difference 14·5%, standard deviation 12·0%) and were less strongly associated with practice characteristics. Conclusions —: The strong negative correlation between breast cancer screening rates and list inflation shows the importance of accurate age-sex registers in achieving high breast cancer screening rates. Breast cancer screening units, family health services authorities, and general practitioners need to collaborate to improve the accuracy of the age-sex registers used to generate invitations for breast cancer screening. The success of the practice with the highest screening rate suggests that practices can influence the uptake of breast cancer screening among their patients. Giving general practitioners a greater role in breast cancer screening, either by offering them financial incentives or by giving them clerical support to check prior notification lists and contact non-attenders, may also help to increase breast cancer screening rates.