The Kaiser Permanente Research Bank Cancer Cohort: a collaborative resource to improve cancer care and survivorship.

BACKGROUND: The Kaiser Permanente Research Bank (KPRB) is collecting biospecimens and surveys linked to electronic health records (EHR) from approximately 400,000 adult KP members. Within the KPRB, we developed a Cancer Cohort to address issues related to cancer survival, and to understand how genetic, lifestyle and environmental factors impact cancer treatment, treatment sequelae, and prognosis. We describe the Cancer Cohort design and implementation, describe cohort characteristics after 5 years of enrollment, and discuss future directions. METHODS: Cancer cases are identified using rapid case ascertainment algorithms, linkage to regional or central tumor registries, and direct outreach to KP members with a history of cancer. Enrollment is primarily through email invitation. Participants complete a consent form, survey, and donate a blood or saliva sample. All cancer types are included. RESULTS: As of December 31, 2020, the cohort included 65,225 cases (56% female, 44% male) verified in tumor registries. The largest group was diagnosed between 60 and 69 years of age (31%) and are non-Hispanic White (83%); however, 10,076 (16%) were diagnosed at ages 18-49 years, 4208 (7%) are Hispanic, 3393 (5%) are Asian, and 2389 (4%) are Black. The median survival time is 14 years. Biospecimens are available on 98% of the cohort. CONCLUSIONS: The KPRB Cancer Cohort is designed to improve our understanding of treatment efficacy and factors that contribute to long-term cancer survival. The cohort's diversity - with respect to age, race/ethnicity and geographic location - will facilitate research on factors that contribute to cancer survival disparities.

Validation of natural language processing to determine the presence and size of abdominal aortic aneurysms in a large integrated health system.

OBJECTIVE: Previous studies of the natural history of abdominal aortic aneurysms (AAAs) have been limited by small cohort sizes or heterogeneous analyses of pooled data. By quickly and efficiently extracting imaging data from the health records, natural language processing (NLP) has the potential to substantially improve how we study and care for patients with AAAs. The aim of the present study was to test the ability of an NLP tool to accurately identify the presence or absence of AAAs and detect the maximal abdominal aortic diameter in a large dataset of imaging study reports. METHODS: Relevant imaging study reports (n = 230,660) from 2003 to 2017 were obtained for 32,778 patients followed up in a prospective aneurysm surveillance registry within a large, diverse, integrated healthcare system. A commercially available NLP algorithm was used to assess the presence of AAAs, confirm the absence of AAAs, and extract the maximal diameter of the abdominal aorta, if stated. A blinded expert manual review of 18,000 randomly selected imaging reports was used as the reference standard. The positive predictive value (PPV or precision), sensitivity (recall), and the kappa statistics were calculated. RESULTS: Of the randomly selected 18,000 studies that underwent expert manual review, 48.7% were positive for AAAs. In confirming the presence of an AAA, the interrater reliability of the NLP compared with the expert review showed a kappa value of 0.84 (95% confidence interval [CI], 0.83-0.85), with a PPV of 95% and sensitivity of 88.5%. The NLP algorithm showed similar results for confirming the absence of an AAA, with a kappa of 0.79 (95% CI, 0.799-0.80), PPV of 77.7%, and sensitivity of 91.9%. The kappa, PPV, and sensitivity of the NLP for correctly identifying the maximal aortic diameter was 0.88 (95% CI, 0.87-0.89), 88.8%, and 88.2% respectively. CONCLUSIONS: The use of NLP software can accurately analyze large volumes of radiology report data to detect AAA disease and assemble a contemporary aortic diameter-based cohort of patients for longitudinal analysis to guide surveillance, medical management, and operative decision making. It can also potentially be used to identify from the electronic medical records pre- and postoperative AAA patients "lost to follow-up," leverage human resources engaged in the ongoing surveillance of patients with AAAs, and facilitate the construction and implementation of AAA screening programs.

Demographic differences in the utilization of clinical and direct-to-consumer genetic testing.

Genetic testing has increased over the last decade due to growth in the number of clinical and direct-to-consumer (DTC) tests. However, there is uncertainty about how increased DTC genetic testing affects disparities. Between November 2017 and February 2018, a nationwide electronic survey on experiences with genetic testing was conducted among adult Kaiser Permanente members. Logistic regression was used to calculate adjusted odds ratios and 95% confidence intervals comparing receipt of clinical and DTC genetic testing between groups by race and ethnicity. Invitations were sent to 57,331 members, and 10,369 surveys were completed. 22% of respondents had received genetic testing (17% DTC and 5% provider-ordered). Non-Hispanic Whites were more likely than other groups to have clinical genetic testing but were similar to Hispanics and non-Hispanic Blacks in rates of DTC genetic testing. Among those who received any health-related genetic test, 10% reported abnormal results. Of these, non-Hispanic Whites were more likely than other racial/ethnic groups to speak to a medical professional about abnormal results. Results suggest that racial/ethnic disparities in the use of clinical genetic services persist. Additional research is needed to identify lessons learned from DTC genetic testing that may increase equity in the use of clinical genetic services.

Perceptions of the appropriateness of care in California adult intensive care units.

INTRODUCTION: Increased demand for expensive intensive care unit (ICU) services may contribute to rising health-care costs. A focus on appropriate use may offer a clinically meaningful way of finding the balance. We aimed to determine the extent and characteristics of perceived inappropriate treatment among ICU doctors and nurses, defined as an imbalance between the amount or intensity of treatments being provided and the patient's expected prognosis or wishes. METHODS: This was a cross-sectional study of doctors and nurses providing care to patients in 56 adult ICUs in California between May and August 2013. In total, 1,363 doctors and nurses completed an anonymous electronic survey. RESULTS: Thirty-eight percent of 1,169 respondents (95% confidence interval (CI) 35% to 41%, 51.1% of physicians and 35.8% of nurses) identified at least one patient as receiving inappropriate treatment. Respondents most commonly reported that the amount of treatment provided was disproportionate to the patient's expected prognosis or wishes-325 out of 429 (76%, 95% CI 72% to 80%)--and that treatment was 'too much' in 93% of cases. Factors associated with perceived inappropriateness of treatment were the belief that death in their ICU is seen as a failure (odds ratio (OR) 5.75, 95% CI 2.28 to 14.53, P = 0.000), profession (doctors more than nurses) (OR 2.50, 95% CI 1.58 to 3.97, P = 0.000), lack of collaboration between doctors and nurses (OR 1.84, 95% CI 1.21 to 2.80, P = 0.004), intent to leave their job (OR 1.73, 95% CI 1.18 to 2.55, P = 0.005), and the perceived responsibility to control health-care costs (OR 1.57, 95% CI 1.05 to 2.33, P = 0.026). Providers supported formal communication training (90%, 95% CI 88% to 92%) and mandatory family meetings (89%, 95% CI 87% to 91%) as potential solutions to reduce the provision of inappropriate treatment. CONCLUSIONS: Doctors and nurses working in California ICUs frequently perceive treatment to be inappropriate. They also identified measures that could reduce the provision of inappropriate treatment.

Exploring public attitudes towards approaches to discussing costs in the clinical encounter.

BACKGROUND: Patients' willingness to discuss costs of treatment alternatives with their physicians is uncertain. OBJECTIVE: To explore public attitudes toward doctor-patient discussions of insurer and out-of-pocket costs and to examine whether several possible communication strategies might enhance patient receptivity to discussing costs with their physicians. DESIGN: Focus group discussions and pre-discussion and post-discussion questionnaires. PARTICIPANTS: Two hundred and eleven insured individuals with mean age of 48 years, 51 % female, 34 % African American, 27 % Latino, and 50 % with incomes below 300 % of the federal poverty threshold, participated in 22 focus groups in Santa Monica, CA and in the Washington, DC metro area. MAIN MEASUREMENTS: Attitudes toward discussing out-of-pocket and insurer costs with physicians, and towards physicians' role in controlling costs; receptivity toward recommended communication strategies regarding costs. KEY RESULTS: Participants expressed more willingness to talk to doctors about personal costs than insurer costs. Older participants and sicker participants were more willing to talk to the doctor about all costs than younger and healthier participants (OR = 1.8, p = 0.004; OR = 1.6, p = 0.027 respectively). Participants who face cost-related barriers to accessing health care were in greater agreement than others that doctors should play a role in reducing out-of-pocket costs (OR = 2.4, p = 0.011). Participants did not endorse recommended communication strategies for discussing costs in the clinical encounter. In contrast, participants stated that trust in one's physician would enhance their willingness to discuss costs. Perceived impediments to discussing costs included rushed, impersonal visits, and clinicians who are insufficiently informed about costs. CONCLUSIONS: This study suggests that trusting relationships may be more conducive than any particular discussion strategy to facilitating doctor-patient discussions of health care costs. Better public understanding of how medical decisions affect insurer costs and how such costs ultimately affect patients personally will be necessary if discussions about insurer costs are to occur in the clinical encounter.

Physician cost profiling--reliability and risk of misclassification.

BACKGROUND: Insurance products with incentives for patients to choose physicians classified as offering lower-cost care on the basis of cost-profiling tools are increasingly common. However, no rigorous evaluation has been undertaken to determine whether these tools can accurately distinguish higher-cost physicians from lower-cost physicians. METHODS: We aggregated claims data for the years 2004 and 2005 from four health plans in Massachusetts. We used commercial software to construct clinically homogeneous episodes of care (e.g., treatment of diabetes, heart attack, or urinary tract infection), assigned each episode to a physician, and created a summary profile of resource use (i.e., cost) for each physician on the basis of all assigned episodes. We estimated the reliability (signal-to-noise ratio) of each physician's cost-profile score on a scale of 0 to 1, with 0 indicating that all differences in physicians' cost profiles are due to a lack of precision in the measure (noise) and 1 indicating that all differences are due to real variation in costs of services (signal). We used the reliability results to estimate the proportion of physicians in each specialty whose cost performance would be classified inaccurately in a two-tiered insurance product in which the physicians with cost profiles in the lowest quartile were labeled as "lower cost." RESULTS: Median reliabilities ranged from 0.05 for vascular surgery to 0.79 for gastroenterology and otolaryngology. Overall, 59% of physicians had cost-profile scores with reliabilities of less than 0.70, a commonly used marker of suboptimal reliability. Using our reliability results, we estimated that 22% of physicians would be misclassified in a two-tiered system. CONCLUSIONS: Current methods for profiling physicians with respect to costs of services may produce misleading results.

Comparing Kaiser Permanente Members to the General Population: Implications for Generalizability of Research.

Introduction Insight into the characteristics of populations from which research samples are drawn is essential to understanding the generalizability of research findings. This study characterizes the membership of Kaiser Permanente and compares members to the population of the communities in which they live. Methods This study is a descriptive comparison of population distributions for Kaiser Permanente members vs the general population within counties in which Kaiser Permanente operates. Kaiser Permanente data on demographics, membership, geographically linked census data, and chronic condition prevalence were compared with community data drawn from the US Census and the Behavioral Risk Factor Surveillance System. Results Overall, Kaiser Permanente members were older (50% aged 40 or older compared to 45.8% of the general population) and more likely to be female (51.8% vs 50.5% of the general population). Distribution by race and ethnicity was similar for all Regions combined but varied somewhat within Regions. Distribution by neighborhood-linked income, education, and social vulnerability was similar between Kaiser Permanente and the community. Prevalence of 6 of 7 chronic conditions was higher in the community than in Kaiser Permanente, with differences ranging from 0.5% for depression to 7.7% for hyperlipidemia. Conclusion The demographic characteristics of Kaiser Permanente members are similar to the general population within each of the Kaiser Permanente Regions. Overall, the size and diversity of the Kaiser Permanente membership offers an effective platform for research. This approach to comparing health system members with the larger community provides valuable context for interpreting real-world evidence, including understanding the generalizability of research and of measures of system performance.

CareTrack: assessing the appropriateness of health care delivery in Australia.

OBJECTIVE: To determine the percentage of health care encounters at which a sample of adult Australians received appropriate care (ie, care in line with evidence-based or consensus-based guidelines). DESIGN, SETTING AND PARTICIPANTS: Computer-assisted telephone interviews and retrospective review of the medical records (for 2009-2010) of a sample of at least 1000 Australian adults to measure compliance with 522 expert consensus indicators representing appropriate care for 22 common conditions. Participants were selected from households in areas of South Australia and New South Wales chosen to be representative of the socioeconomic profile of Australians. Health care encounters occurred in health care practices and hospitals with general practitioners, specialists, physiotherapists, chiropractors, psychologists and counsellors. MAIN OUTCOME MEASURE: Percentage of health care encounters at which the sample received appropriate care. RESULTS: From 15 292 households contacted by telephone, 7649 individuals agreed to participate, 3567 consented, 2638 proved eligible, and 1154 were included after gaining the consent of their health care providers. The adult Australians in this sample received appropriate care at 57% (95% CI, 54%-60%) of 35 573 eligible health care encounters. Compliance with indicators of appropriate care at condition level ranged from 13% (95% CI, 1%-43%) for alcohol dependence to 90% (95% CI, 85%-93%) for coronary artery disease. For health care providers with more than 300 eligible encounters each, overall compliance ranged from 32% to 86%. CONCLUSIONS: Although there were pockets of excellence and some aspects of care were well managed across health care providers, the consistent delivery of appropriate care needs improvement, and gaps in care should be addressed. There is a need for national agreement on clinical standards and better structuring of medical records to facilitate the delivery of more appropriate care.

The distillation method: A novel approach for analyzing randomized trials when exposure to the intervention is diluted.

OBJECTIVE: To introduce a novel analytical approach for randomized controlled trials that are underpowered because of low participant enrollment or engagement. DATA SOURCES: Reanalysis of data for 805 patients randomized as part of a pilot complex care intervention in 2015-2016 in a large delivery system. In the pilot randomized trial, only 64.6% of patients assigned to the intervention group participated. STUDY DESIGN: A case study and simulation. The "Distillation Method" capitalizes on the frequently observed correlation between the probability of subjects' participation or engagement in the intervention and the magnitude of benefit they experience. The novel method involves three stages: first, it uses baseline covariates to generate predicted probabilities of participation. Next, these are used to produce nested subsamples of the randomized intervention and control groups that are more concentrated with subjects who were likely to participate/engage. Finally, for the outcomes of interest, standard statistical methods are used to re-evaluate intervention effectiveness in these concentrated subsets. DATA EXTRACTION METHODS: We assembled secondary data on patients who were randomized to the pilot intervention for one year prior to randomization and two follow-up years. Data included program enrollment status, membership data, demographics, utilization, costs, and clinical data. PRINCIPAL FINDINGS: Using baseline covariates only, Generalized Boosted Regression Models predicting program enrollment performed well (AUC 0.884). We then distilled the full randomized sample to increasing levels of concentration and reanalyzed program outcomes. We found statistically significant differences in outpatient utilization and emergency department utilization (both follow-up years), and in total costs (follow-up year two only) at select levels of population concentration. CONCLUSIONS: By offering an internally valid analytic framework, the Distillation Method can increase the power to detect effects by redefining the estimand to subpopulations with higher enrollment probabilities and stronger average treatment effects while maintaining the original randomization.

"A Gift to My Family for Their Future": Attitudes about Genetic Research Participation.

BACKGROUND: Broad participation in genetic research is needed to promote equitable advances in disease treatment and prevention. OBJECTIVES: The objective of the study was to assess motivations for, and concerns about, genetic research participation. METHODS: The Genetics in Research and Health Care Survey was sent in winter 2017-2018 to 57,331 adult Kaiser Permanente (KP) members from 7 US regions to assess attitudes about genetic testing in health care and research. The survey included an open-ended question on why members would or would not participate in genetic research. Open text responses to this question were coded in the qualitative analysis software Dedoose and analyzed using a thematic analysis approach. Code summaries were organized by major themes, subthemes, and exemplary quotes. RESULTS: Of the 10,369 participants who completed the survey, 2,645 (25%) provided a comment describing reasons they would or would not participate in research involving genetic testing. Respondents who provided a text comment were 64% female, 49% non-Hispanic (NH) White, 17% Asian/Pacific Islander, 20% Hispanic, and 14% NH Black. The primary themes identified were (1) altruism; (2) decision-making and planning; (3) data use; and (4) data security. These major themes were consistent across each race and ethnic group. CONCLUSIONS: To promote broad participation in genetic research, it is important that recruitment materials address the primary motivators for genetic research participation, including altruism and the potential use of results for personal decision-making. Study materials should also address concerns about possible misuse of genetic information and fears over potential data breaches.

Population-Based Incidence of New Ampicillin, Cephalexin, Cefaclor, and Sulfonamide Antibiotic "Allergies" in Exposed Individuals with and without Preexisting Ampicillin, Cephalexin, or Cefaclor "Allergies".

BACKGROUND: There is a theoretical concern, unconfirmed by population-based challenge data, that clinically significant, immunologically mediated hypersensitivity occurs among ß-lactams sharing side chains. OBJECTIVE: To determine the population-based allergy incidence associated with the use of ß-lactams sharing exact R1 side chains (ampicillin, cephalexin, and cefaclor [ACC]), with or without a current ACC allergy or a sulfonamide antibiotic allergy for comparison. METHODS: All courses of ACC and trimethoprim-sulfamethoxazole used by any Kaiser Permanente California members in 2017 and 2018, with follow-up through January 2019, were identified along with their preexisting antibiotic allergy status and all new antibiotic-specific allergies reported within 30 days of course initiation. RESULTS: A total of 1,167,713 courses of ACC were administered to individuals with no sulfonamide antibiotic or ACC allergy and 4,771 new ACC allergies (0.41%) were reported. Moreover, 130,032 courses of ACC were administered to individuals with a sulfonamide antibiotic allergy and no ACC allergy, and 904 new ACC allergies (0.70%) were reported. There were 5,958 courses of ACC administered to individuals with an ACC allergy, 2,341 who also had sulfonamide antibiotic allergy, and 52 new ACC allergies (0.87%) were reported. CONCLUSIONS: The incidence of new ACC allergy reports is minimally and non-specifically increased among individuals with a preexisting ACC or sulfonamide antibiotic allergy compared to the baseline incidence in the population. This argues against clinically significant, immunologically mediated cross-reactivity among ß-lactams sharing exact side chains in individuals with preexisting but unconfirmed ß-lactam allergy. Any previously reported, even unrelated antibiotic allergy appears to be a risk factor for reporting a new antibiotic allergy.

The impact of health plan physician-tiering on access to care.

BACKGROUND: In an attempt to improve quality and control costs, health plans are creating tiered products that encourage enrollees to seek care from "high-value" physicians. However, tiered products may limit access to care because patients may have to travel unreasonable distances to visit the nearest high-value physician. OBJECTIVE: To assess geographic access to high-value physicians, particularly for disadvantaged populations. DESIGN: Cross-sectional observational study. PARTICIPANTS: Physicians and adult patients in Massachusetts. MAIN MEASURES: Travel time from census block centroid to nearest physician address under two scenarios: patients can see (1) any physician or (2) only high-value physicians. KEY RESULTS: Among the physicians, 768 (20.9%) primary care physicians (PCPs), 225 (26.6%) obstetricians/gynecologists, 69 (10.3%) cardiologists, and 31 (6.0%) general surgeons met the definition of high-value. Statewide mean travel times to the nearest PCP, obstetrician/gynecologist, cardiologist, or general surgeon under the two scenarios (any physician vs. only high-value physicians) were 2.8 vs. 4.8, 6.0 vs. 7.2, 7.0 vs. 12.4, and 6.6 vs. 14.8 minutes, respectively. Across the four specialties, between 89.4%-99.4% of the population lived within 30 minutes of the nearest high-value physician. Rural populations had considerably longer travel times to see high-value physicians, but other disadvantaged populations generally had shorter travel times than comparison groups. CONCLUSIONS: Most patients in Massachusetts are likely to have reasonable geographic access to high-value physicians in tiered health plans. However, local demographics, especially rural residence, should be taken into consideration when applying tiered health plans broadly. Future work should investigate whether patients can and will switch to receive care from high-value physicians.

Depression and medication adherence in the treatment of chronic diseases in the United States: a meta-analysis.

OBJECTIVE: To conduct a meta-analysis of the association between depression and medication adherence among patients with chronic diseases. Poor medication adherence may result in worse outcomes and higher costs than if patients fully adhere to their medication regimens. DATA SOURCES: We searched the PubMed and PsycINFO databases, conducted forward searches for articles that cited major review articles, and examined the reference lists of relevant articles. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS, AND INTERVENTIONS: We included studies on adults in the United States that reported bivariate relationships between depression and medication adherence. We excluded studies on special populations (e.g., substance abusers) that were not representative of the general adult population with chronic diseases, studies on certain diseases (e.g., HIV) that required special adherence protocols, and studies on interventions for medication adherence. STUDY APPRAISAL AND SYNTHESIS METHODS: Data abstracted included the study population, the protocol, measures of depression and adherence, and the quantitative association between depression and medication adherence. Synthesis of the data followed established statistical procedures for meta-analysis. RESULTS: The estimated odds of a depressed patient being non-adherent are 1.76 times the odds of a non-depressed patient, across 31 studies and 18,245 participants. The association was similar across disease types but was not as strong among studies that used pharmacy records compared to self-report and electronic cap measures. LIMITATIONS: The meta-analysis results are correlations limiting causal inferences, and there is some heterogeneity among the studies in participant characteristics, diseases studied, and methods used. CONCLUSIONS: This analysis provides evidence that depression is associated with poor adherence to medication across a range of chronic diseases, and we find a new potential effect of adherence measurement type on this relationship. Although this study cannot assess causality, it supports the importance that must be placed on depression in studies that assess adherence and attempt to improve it.

The effect of different attribution rules on individual physician cost profiles.

BACKGROUND: Some health plans profile physicians on the basis of their relative costs and use these profiles to assign physicians to cost categories. Physician organizations have questioned whether the rules used to attribute costs to a physician affect the cost category to which that physician is assigned. OBJECTIVE: To evaluate the effect of 12 different attribution rules on physician cost profiles. DESIGN: Under each of the 12 attribution rules, a cost profile was created for the physicians in the aggregated claims database and the physicians were assigned to a cost category (high cost, average cost, low cost, or low sample size). The attribution rules differed by unit of analysis, signal for responsibility, number of physicians who can be assigned responsibility, and threshold value for assigning responsibility. SETTING: Four commercial health plans in Massachusetts. PATIENTS: 1.1 million adults continuously enrolled in 4 commercial health plans in 2004 and 2005. MEASUREMENTS: Percentage of all episodes assigned to any physician and percentage of costs billed by a physician that were included in his or her own profile were calculated under each rule. The cost category assignments from a commonly used default rule were compared with those from each of the other 11 attribution rules and the rate of disagreement was calculated. RESULTS: Percentage of episodes that could be assigned to a physician varied substantially across the 12 rules (range, 20% to 69%), as did the mean percentage of costs billed by a physician that were included in that physician's own cost profile (range, 13% to 60%). Depending on the alternate rule used, between 17% and 61% of physicians would be assigned to a different cost category than that assigned by using the default rule. LIMITATION: Results might differ if data from another state or from Medicare were used. CONCLUSION: The choice of attribution rule affects how costs are assigned to a physician and can substantially affect the cost category to which a physician is assigned. PRIMARY FUNDING SOURCE: U.S. Department of Labor.