OBJECTIVES:To describe the prevalence of risk factors for women at high risk of fracture in a population-based sample of postmenopausal women who were not using hormone replacement therapy (HRT), to demonstrate how the estimated prevalence of women at high risk of future fracture is affected by the different criteria used for classification, and to characterize the populations identified and missed by each of the criteria. A key study objective was to compare the proportion of postmenopausal women at high risk of fracture in a managed care population using several different definitions of who is at high risk. DESIGN:The Osteoporosis Population-based Risk Assessment study, a randomized trial of three screening strategies. SETTING:Conducted at Group Health Cooperative in western Washington state. PARTICIPANTS:Women aged 60 to 79 who had not used HRT for at least 12 months were chosen at random. MEASUREMENTS:In one of the trial arms, 428 women had their bone mineral density (BMD) measured at the hip and spine (L1-L4) using dual energy x-ray absorptiometry. Minimum t scores and z scores at all sites were used for classification. Risk factors for fractures were assessed at the time of the BMD scan. RESULTS:Guidelines based on the Study of Osteoporotic Fractures classified 25.1% of the women as being at high risk of fracture, compared with 30.0% and 68.0% using World Health Organization (WHO) recommendations and National Osteoporosis Foundation guidelines, respectively. Classification based on low BMD alone (WHO) failed to include more than 50% of women who had already experienced a clinical fracture. CONCLUSIONS:Prevalence of women at high risk of fracture not using HRT varies notably depending on the criteria used for identification. The criteria used to identify women to target for primary and secondary prevention of osteoporotic fractures has major implications for population-based prevention strategies.

AIM: Financial incentives improve response to electronic health surveys, yet little is known about how unconditional incentives (guaranteed regardless of survey completion), conditional incentives, and various combinations of incentives influence response rates. We compared electronic health survey completion with two different financial incentive structures. METHODS: We invited women aged 30-64 years enrolled in a U.S. healthcare system and overdue for Pap screening to complete a web-based survey after receiving a mailed human papillomavirus (HPV) self-sampling kit in a pragmatic trial. HPV kit returners (n = 272) and non-returners (n = 1,083) were allocated to one of two different incentive structures: (1) Unconditional: $5 pre-incentive only (n = 653); (2) Combined: $2 pre-incentive plus $10 post-incentive conditional on completion (n = 702). Chi-square tests evaluated whether survey completion differed by incentive structure within kit return groups or was modified by kit return status. For each incentive-by-kit status group, the cost-per-survey response was calculated as: ([number invited*pre-incentive amount] + [number responses*post-incentive amount]) / number responses. RESULTS: Overall, survey response was higher in kit returners vs. kit non-returners (42.6% vs. 11.0%, P < 0.01), and survey response was higher in the combined (20.1%) vs. unconditional (14.4%) incentive group (P = 0.01). Kit return status did not modify the association between incentive type and survey response (P = 0.52). Among respondents, time to survey completion did not differ by incentive type among either kit returners or non-returners. Among returners, the cost-per-survey response was similar between groups ($13.57 unconditional; $14.15 combined); among non-returners, the cost was greater in the unconditional ($57.78) versus the combined ($25.22) group. CONCLUSION: A combined incentive can be cost-effective for increasing survey response in health services research, particularly in hard-to-reach populations.

The characteristics that differentiate long-term (> or = 10 years) hormone replacement therapy (HRT) users from short-term (<10 years) users and nonusers are not well documented. The epidemiology of long-term HRT use was investigated within a random sample survey of 703 women aged 50-80 years who were members of Group Health Cooperative (GHC) of Puget Sound. Women who had been menopausal for > or = 10 years comprised the study population. Long-term HRT users (29.4 percent) were compared with short-term (28.1 percent) and never users (42.5 percent). The authors examined the association between duration of HRT use and demographic characteristics, personal and family medical history, menopausal symptoms, information used in decision making, attitudes toward HRT, provider encouragement to use HRT, and GHC utilization. Compared with never users, the strongest correlates of long-term HRT use were having a hysterectomy before or after menopause, positive attitudes espousing the benefits of HRTs, and perceived provider encouragement to use HRT. Long-term HRT use was not associated with educational attainment, ethnicity, body mass index, health status, physical activity, or family medical history. Correlates commonly associated with HRT use, such as higher education, greater physical activity and functioning, and lower chronic disease comorbidity, did not significantly distinguish long-term from short-term users.

Background

It is not well understood whether breast density is a marker of cumulative exposure to estrogen or a marker of recent exposure to estrogen. The authors examined the relationship between bone mineral density (BMD; a marker of lifetime estrogen exposure) and breast density.

Methods

The authors conducted a cross-sectional analysis among 1800 postmenopausal women > or = 54 years. BMD data were taken from two population-based studies conducted in 1992-1993 (n = 1055) and in 1998-1999 (n = 753). The authors linked BMD data with breast density information collected as part of a mammography screening program. They used linear regression to evaluate the density relationship, adjusted for age, hormone therapy use, body mass index (BMI), and reproductive covariates.

Results

There was a small but significant negative association between BMD and breast density. The negative correlation between density measures was not explained by hormone therapy or age, and BMI was the only covariate that notably influenced the relationship. Stratification by BMI only revealed the negative correlation between bone and breast densities in women with normal BMI. There was no relationship in overweight or obese women. The same relationship was seen for all women who had never used hormone therapy, but it was not significant once stratified by BMI.

Conclusions

BMD and breast density were not positively associated although both are independently associated with estrogen exposure. It is likely that unique organ responses obscure the relationship between the two as indicators of cumulative estrogen exposure.

Objective

Effectively communicating information about the complex decisions that face women at midlife, including whether to use hormone replacement therapy (HRT), is an ongoing challenge. Although numerous decision-making tools exist, few have been evaluated. The objective of this study was to examine women's use of a workbook designed to promote informed HRT decision-making.

Design

We developed a workbook to prepare women to discuss HRT, osteoporosis, heart disease, and breast cancer with their providers. To evaluate the workbook, women aged 45-65 years were randomly assigned to one of three groups: (1) workbook plus baseline and 6-month surveys, (2) workbook and 6-month survey, or (3) no workbook with both surveys. Results are based on the responses of 580 women in groups 1 and 2 (response rate, 84.2%).

Results

At 6 months, 79% of women recalled receiving the workbook, of whom 51% read all or most of it, 35% skimmed or read part of it, and 14% did not read it. The percentages of women completing self-assessments were 55% osteoporosis; 56% heart disease; 58% breast cancer; 57% advantages and disadvantages of HRT; and 52% personal preferences about HRT. As a result of the workbook, 10% made an appointment with their providers, and 12% had a discussion about HRT with their providers. Use of the workbook was not associated with menopause symptoms, attitudes about or use of HRT, hysterectomy, or provider discussions about menopause and HRT.

Conclusion

This simple approach of using a mailed workbook holds promise as a successful mechanism to prepare women to discuss HRT and other related health issues with their providers.

Objectives

The purpose of this study was to compare hormone replacement therapy (HRT) prescribing frequency to provider characteristics, attitudes and beliefs about menopause and HRT.

Methods

There was a mailed survey of providers at a large staff-model HMO in Washington state. Participants included 250 family practice physicians, 22 gynecologists, and 13 women's health care specialists and nurse midwives (83% response rate). The primary outcome, "HRT prescribing frequency" (derived from automated pharmacy and visit data) was defined as: the total number of estrogen prescriptions written by the provider and filled by women aged 50-80 years during the 12 months prior to the survey, divided by the number of visits made to the provider by women aged 50-80 years during that same 12-month period. Covariates included provider characteristics and beliefs about menopause and HRT. Logistic regression was used to distinguish providers in the upper 40% versus the lower 60% of HRT prescribing frequency.

Results

Controlling for age and practice type, HRT prescribing frequency was lower among men than women providers (odds ratio [OR] 0.38, 95% confidence interval [CI] 0.21-0.65), higher among providers who agreed (vs. disagreed or neutral) that a convincing scientific case has been made that HRT prevents heart disease (OR 2.66, 95% CI 1.53-4.61), and higher among those in the upper tertile vs. lower tertiles of an HRT encouragement scale (OR 2.50, 95% CI 1.29-4.85).

Conclusions

Female providers and providers with positive attitudes toward HRT are the most likely to prescribe it.

As more artificial intelligence (AI)-enhanced mammography screening tools enter the clinical market, greater focus will be placed on external validation in diverse patient populations. In this viewpoint, we outline lessons learned from prior efforts in this field, the need to validate algorithms on newer screening technologies and diverse patient populations, and conclude by discussing the need for a framework for continuous monitoring and recalibration of these AI tools. Sufficient validation and continuous monitoring of emerging AI tools for breast cancer screening will require greater stakeholder engagement and the creation of shared policies and guidelines.

Background

The integration of bone density testing into well-designed fracture prevention programs that can be applied in populations has not been studied.

Objectives

We sought to compare the outcomes of 3 strategies for allocating bone density testing within an HMO-based fracture prevention program.

Research design

Women were randomly sampled and allocated to one of 3 groups: (1) a universal group, in which all were offered bone mineral density (BMD) testing (1986 contacted; 415 participated); 2) the SCORE group, in which women scoring > or = 7 on the SCORE questionnaire were invited for BMD testing (1940 contacted; 576 participated); and (3) the Study of Osteoporotic Fracture (SOF)-based group, in which women with > or = 5 hip fracture risk factors were invited for BMD testing (5342 contacted; 2176 participated).

Subjects

Women aged 60-80 not taking hormone therapy or osteoporosis medication were included.

Measures

Outcomes ascertained during 33 months of follow-up in all women contacted included initiation of osteoporosis treatment and hip and total fracture rates. Outcomes evaluated among all participants included changes in fracture risk factors, osteoporosis knowledge, and satisfaction with the program.

Results

Osteoporosis treatment rates did not differ among all women contacted but were slightly higher among trial participants in the universal and SCORE groups (21.1% and 20.2%, respectively; versus 16.7% in the SOF-based group (P value versus universal = 0.04). Among all women contacted, fracture rates were lowest in the universal group (74.11/1000) and differed significantly compared with the SCORE (99.44/1000; P = 0.009) and SOF-based groups (91.77/1000;P = 0.02). Knowledge about osteoporosis risk factors was highest in the universal group and lowest in the SOF-based group (P < 0.01).

Conclusions

The degree to which BMD testing was offered to women in a fracture prevention program significantly affected total fracture rates, change in some fracture risk factors, and knowledge about risk factors.

Objectives

To describe differences in osteoporosis risk factors and rates of fracture and antiresorptive therapy use in women who did and did not participate in an osteoporosis screening program.

Setting

Group Health Cooperative, a health maintenance organization in western Washington state.

Participants

A total of 9,268 women (aged 60-80) who were not using any antiresorptive therapy were invited to participate in an osteoporosis screening program. This study compares the 35% who participated with the 65% who did not.

Design

This observational cohort study of women invited to participate in a randomized, controlled trial of an osteoporosis screening program provided all participants with personalized feedback on their risk of osteoporosis. Some participants also received bone density testing. Automated administrative data were used to examine differences between participants and nonparticipants in fracture outcomes and medication initiation before and after invitation.

Results

Baseline fracture rates did not differ between participants and nonparticipants. After age adjustment, nonparticipants had a higher hip fracture rate (14.1 vs 8.3 per 1,000) and a lower rate of initiating any antiresorptive therapy (10.3 vs 17.9 per 100) than participants after an average of 28 to 29 months of follow-up.

Conclusion

Participants had reduced hip fracture rates and increased initiation of antiresorptive therapy compared with nonparticipants. It was not possible to determine whether participating in the screening program, unmeasured confounding, or selection bias accounted for differences in hip fracture or therapy initiation rates. These results suggest that women who do not participate in osteoporosis screening should be pursued to identify individuals who could benefit from primary and secondary osteoporosis prevention.

PURPOSE: The aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography. METHODS: A systematic review was performed across five databases (Embase, PubMed, IEEE Explore, Engineer Village, and arXiv) through December 10, 2020. Studies that used screening examinations from real-world settings to externally validate AI algorithms for mammographic cancer detection were included. The main outcome was diagnostic accuracy, defined by area under the receiver operating characteristic curve (AUC). Performance was also compared between radiologists and either stand-alone AI or combined radiologist and AI interpretation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. RESULTS: After data extraction, 13 studies met the inclusion criteria (148,361 total patients). Most studies (77% [n = 10]) evaluated commercially available AI algorithms. Studies included retrospective reader studies (46% [n = 6]), retrospective simulation studies (38% [n = 5]), or both (15% [n = 2]). Across 5 studies comparing stand-alone AI with radiologists, 60% (n = 3) demonstrated improved accuracy with AI (AUC improvement range, 0.02-0.13). All 5 studies comparing combined radiologist and AI interpretation with radiologists alone demonstrated improved accuracy with AI (AUC improvement range, 0.028-0.115). Most studies had risk for bias or applicability concerns for patient selection (69% [n = 9]) and the reference standard (69% [n = 9]). Only two studies obtained ground-truth cancer outcomes through regional cancer registry linkage. CONCLUSIONS: To date, external validation efforts for AI screening mammographic technologies suggest small potential diagnostic accuracy improvements but have been retrospective in nature and suffer from risk for bias and applicability concerns.