Heather J. Baer

Health risk appraisal tools may be useful for identifying individuals who would benefit from lifestyle changes and increased surveillance. We evaluated the validity of the Your Disease Risk tool (YDR) for estimating relative risk of coronary heart disease (CHD) among middle-aged women. We included 55,802 women in the Nurses’ Health Study who completed a mailed questionnaire about risk factors in 1994 and had no history of heart disease at that time. Participants were followed through 2004 for the occurrence of CHD. We estimated each woman’s 10-year relative risk of CHD using YDR, and we compared the estimated YDR relative risk category (ranging from “very much below average” to “very much above average”) to the observed relative risk for each category using logistic regression. We also examined the discriminatory accuracy of YDR using concordance statistics (c-statistics). There were 1165 CHD events during the 10-year follow-up period. Compared to the “about average” category, the observed age-adjusted relative risk was 0.43 (95 % confidence interval: 0.33, 0.56) for the “very much below average” category and 2.48 (95 % confidence interval: 1.68, 3.67) for the “very much above average” category. The age-adjusted c-statistic for the model including the YDR relative risk category was 0.71 (95 % confidence interval: 0.69, 0.72). The model performed better in younger than older women. The YDR tool appears to have moderate validity for estimating 10-year relative risk of CHD in this population of middle-aged women. Further research should aim to improve the tool’s performance and to examine its validity in other populations.

Introduction: Overweight and obesity in childhood and adolescence are associated with reduced breast cancer risk, independent of adult body mass index (BMI). These associations may be mediated through breast density. Methods: We prospectively examined associations of early life body fatness with adult breast density measured by MRI in 182 women in the Dietary Intervention Study in Children (DISC) who were ages 25–29 at follow-up. Height, weight, and other factors were measured at baseline (ages 8–10) and annual clinic visits through adolescence. We used linear mixed-effects models to quantify associations of percent breast density and dense and non-dense breast volume at ages 25–29 with quartiles of age-specific youth body mass index (BMI) Z-scores, adjusting for clinic, treatment group, current adult BMI, and other well-established risk factors for breast cancer and predictors of breast density. Results: We observed inverse associations between age-specific BMI Z-scores at all youth clinic visits and percent breast density, adjusting for current adult BMI and other covariates (all p values p trend p values <0.01), whereas there were no clear associations with non-dense breast volume. Conclusions: These results support the hypothesis that body fatness during childhood and adolescence may play an important role in premenopausal breast density, independent of current BMI, and further suggest direct or indirect influences on absolute dense breast volume. Clinical Trials Registration Number: NCT00458588; April 9, 2007

Primary care providers often fail to identify patients who are overweight or obese or discuss weight management with them. Electronic health record-based tools may help providers with the assessment and management of overweight and obesity. We describe the design of a trial to examine the effectiveness of electronic health record-based tools for the assessment and management of overweight and obesity among adult primary care patients, as well as the challenges we encountered. We developed several new features within the electronic health record used by primary care practices affiliated with Brigham and Women’s Hospital in Boston, MA. These features included (1) reminders to measure height and weight, (2) an alert asking providers to add overweight or obesity to the problem list, (3) reminders with tailored management recommendations, and (4) a Weight Management screen. We then conducted a pragmatic, cluster-randomized controlled trial in 12 primary care practices. We randomized 23 clinical teams (“clinics”) within the practices to the intervention group (n = 11) or the control group (n = 12). The new features were activated only for clinics in the intervention group. The intervention was implemented in two phases: the height and weight reminders went live on 15 December 2011 (Phase 1), and all of the other features went live on 11 June 2012 (Phase 2). Study enrollment went from December 2011 through December 2012, and follow-up ended in December 2013. The primary outcomes were 6-month and 12-month weight change among adult patients with body mass index ≥25 who had a visit at one of the primary care clinics during Phase 2. Secondary outcome measures included the proportion of patients with a recorded body mass index in the electronic health record, the proportion of patients with body mass index ≥25 who had a diagnosis of overweight or obesity on the electronic health record problem list, and the proportion of patients with body mass index ≥25 who had a follow-up appointment about their weight or were prescribed weight loss medication. We encountered challenges in our development of an intervention within the existing structure of an electronic health record. For example, although we decided to randomize clinics within primary care practices, this decision may have introduced contamination and led to some imbalance of patient characteristics between the intervention and control practices. Using the electronic health record as the primary data source reduced the cost of the study, but not all desired data were recorded for every participant. Despite the challenges, this study should provide valuable information about the effectiveness of electronic health record-based tools for addressing overweight and obesity in primary care.

Letter from JAMA Internal Medicine — Documentation and Diagnosis of Overweight and Obesity in Electronic Health Records of Adult Primary Care Patients

Primary care clinicians can play an important role in identifying individuals at increased risk of cancer, but often do not obtain detailed information on family history or lifestyle factors from their patients. We evaluated the feasibility and effectiveness of using a web-based risk appraisal tool in the primary care setting. Five primary care practices within an academic care network were assigned to the intervention or control group. We included 15,495 patients who had a new patient visit or annual exam during an 8-month period in 2010–2011. Intervention patients were asked to complete a web-based risk appraisal tool on a laptop computer immediately before their visit. Information on family history of cancer was sent to their electronic health record (EHR) for clinicians to view; if accepted, it populated coded fields and could trigger clinician reminders about colon and breast cancer screening. The main outcome measure was new documentation of a positive family history of cancer in coded EHR fields. Secondary outcomes included clinician reminders about screening and discussion of family history, lifestyle factors, and screening. Among eligible intervention patients, 2.0 % had new information on family history of cancer entered in the EHR within 30 days after the visit, compared to 0.6 % of eligible control patients (adjusted odds ratio = 4.3, p = 0.03). There were no significant differences in the percent of patients who received moderate or high risk reminders for colon or breast cancer screening. Use of this tool was associated with increased documentation of family history of cancer in the EHR, although the percentage of patients with new family history information was low in both groups. Further research is needed to determine how risk appraisal tools can be integrated with workflow and how they affect screening and health behaviors.

Early life body size and circulating levels of IGF-1 and IGFBP-3 have been linked to increased risks of breast and other cancers, but it is unclear whether these exposures act through a common mechanism. Previous studies have examined the role of IGF-1 and IGFBP-3 genetic variation in relation to adult height and body size, but few studies have examined associations with birthweight and childhood size.

Few studies have examined multiple risk factors for mortality or formally compared their associations across specific causes of death. The authors used competing risks survival analysis to evaluate associations of lifestyle and dietary factors with all-cause and cause-specific mortality among 50,112 participants in the Nurses’ Health Study. There were 4,893 deaths between 1986 and 2004: 1,026 from cardiovascular disease, 931 from smoking-related cancers, 1,430 from cancers not related to smoking, and 1,506 from all other causes. Age, body mass index at age 18 years, weight change, height, current smoking and pack-years of smoking, glycemic load, cholesterol intake, systolic blood pressure and use of blood pressure medications, diabetes, parental myocardial infarction before age 60 years, and time since menopause were directly related to all-cause mortality, whereas there were inverse associations for physical activity and intakes of nuts, polyunsaturated fat, and cereal fiber. Moderate alcohol consumption was associated with decreased mortality. A model that incorporated differences in the associations of some risk factors with specific causes of death had a significantly better fit compared with a model in which all risk factors had common associations across all causes. In the future, this new model may be used to identify individuals at increased risk of mortality.

Body fatness at young ages may be related to breast cancer risk independently of adult adiposity. The authors conducted a prospective analysis among 188,860 women (7,582 breast cancer cases) in the Nurses’ Health Study (1988–2004) and Nurses’ Health Study II (1989–2005) who recalled their body fatness at ages 5, 10, and 20 years using a 9-level pictogram (level 1: most lean; level 9: most overweight). Body fatness at young ages was inversely associated with risk of both premenopausal and postmenopausal breast cancer (per 1-unit increase in adolescent body fatness, relative risk (RR) = 0.88 and RR = 0.91, respectively; P_trend < 0.0001). Among all women, the RR for adolescent body fatness of level 6.5 or higher versus level 1 was 0.57 (per 1-unit increase, RR = 0.90; P_trend < 0.0001) and was unaffected by adjustment for current body mass index. The association was stronger for women with birth weights under 8.5 pounds (P_interaction = 0.04) and stronger for estrogen receptor-negative tumors than for estrogen receptor-positive tumors (per 1-unit increase, RR = 0.86 and RR = 0.92, respectively; P_{heterogeneity} = 0.03). Body fatness at young ages has a strong and independent inverse relation to breast cancer risk throughout life.

BACKGROUND: Lobules in normal breast tissue can be classified based on their degree of development, which may affect their susceptibility to carcinogenesis. However, few epidemiologic studies to date have addressed this.METHODS: The authors examined the association between lobule type and subsequent breast cancer risk in a nested case‐control study of benign breast disease (BBD) and breast cancer within the Nurses' Health Studies (200 cases, 915 controls). Benign breast biopsy slides were reviewed by pathologists, and normal terminal duct lobular units were classified as having no type 1 lobules, mixed lobule types, or predominant type 1 and no type 3 lobules. Logistic regression was used to compute odds ratios (ORs) and 95% confidence intervals (CIs) for the association between lobule type and breast cancer risk.RESULTS: Women with predominant type 1 and no type 3 lobules (54 cases, 321 controls) had a decreased risk of breast cancer compared with those with no type 1 lobules or mixed lobule types (OR=0.63; 95% CI, 0.44‐0.91), although this was attenuated after adjustment for histologic category of BBD (OR=0.71; 95% CI, 0.49‐1.02). Having predominant type 1 lobules and no type 3 lobules was associated with a similar risk reduction for all categories of BBD (nonproliferative: OR=0.73 [95% CI, 0.36‐1.50]; proliferative without atypia: OR=0.80 [95% CI, 0.47‐1.35]; and atypical hyperplasia: OR=0.61 [95% CI, 0.28‐1.35]).CONCLUSIONS: These results provided preliminary evidence that lobule type may be an important marker of breast cancer risk in women with BBD. Cancer 2009. © 2009 American Cancer Society.