Incidence and Survival for Patients Diagnosed With Breast, Colorectal, and Lung Cancer in an Integrated System.

INTRODUCTION: Documenting trends in cancer incidence and survival is a national priority. This study estimated age- and sex-adjusted incidence and 5-year relative survival among patients with cancer diagnosed within Kaiser Permanente compared to Surveillance, Epidemiology, and End Results (SEER) estimates. METHODS: The cohort included Kaiser Permanente health plan members diagnosed with breast (BC), colorectal (CRC), or lung cancer (LC) between January 1, 1999 and December 31, 2018. Incidence was computed as age-adjusted rates per 100,000 member-years. SEER*Stat was used to compute 5-year relative survival. RESULTS: Kaiser Permanente BC incidence rates were persistently higher than SEER from 2004 (126.5 [95% confidence interval (CI) = 123.2-129.9] vs 122.6 [95% CI = 121.3-123.2]) through 2013 (132.06 [95% CI = 129.5-135.7] vs 126.7 [95% CI = 125.9-127.5]). Kaiser Permanente CRC and LC incidence rates were lower than SEER for all years except 2008, showing a spike in CRC incidence (51.5 [95% CI = 49.9-53.0] vs 46.1 [95% CI = 45.7-46.4]). Kaiser Permanente BC, CRC, and LC survival estimates for all stages were higher than SEER. CONCLUSIONS: Incidence rates for all-stage and localized-stage BC were consistently higher for Kaiser Permanente than for SEER. CRC and LC rates were lower. Kaiser Permanente survival rates were consistently higher than for SEER. The strengths of these findings are associated with the ability to capture "gold-standard" cancer registry data on defined Kaiser Permanente populations. However, findings should be interpreted cautiously given differences in the underlying populations and secular and regional differences between Kaiser Permanente and SEER. The Kaiser Permanente population is younger and more racially diverse than SEER aggregate populations, and Kaiser Permanente members are insured with access to preventive care (eg, smoking cessation programs, cancer screening).

Data Quality Assessment and Multi-Organizational Reporting: Tools to Enhance Network Knowledge.

OBJECTIVE: Multi-organizational research requires a multi-organizational data quality assessment (DQA) process that combines and compares data across participating organizations. We demonstrate how such a DQA approach complements traditional checks of internal reliability and validity by allowing for assessments of data consistency and the evaluation of data patterns in the absence of an external "gold standard." METHODS: We describe the DQA process employed by the Data Coordinating Center (DCC) for Kaiser Permanente's (KP) Center for Effectiveness and Safety Research (CESR). We emphasize the CESR DQA reporting system that compares data summaries from the eight KP organizations in a consistent, standardized manner. RESULTS: We provide examples of multi-organization comparisons from DQA to confirm expectations about different aspects of data quality. These include: 1) comparison of direct data extraction from the electronic health records (EHR) and 2) comparison of non-EHR data from disparate sources. DISCUSSION: The CESR DCC has developed codes and procedures for efficiently implementing and reporting DQA. The CESR DCC approach is to 1) distribute DQA tools to empower data managers at each organization to assess their data quality at any time, 2) summarize and disseminate findings to address data shortfalls or document idiosyncrasies, and 3) engage data managers and end-users in an exchange of knowledge about the quality and its fitness for use. CONCLUSION: The KP CESR DQA model is applicable to networks hoping to improve data quality. The multi-organizational reporting system promotes transparency of DQA, adds to network knowledge about data quality, and informs research.

A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks.

OBJECTIVE: To compare rule-based data quality (DQ) assessment approaches across multiple national clinical data sharing organizations. METHODS: Six organizations with established data quality assessment (DQA) programs provided documentation or source code describing current DQ checks. DQ checks were mapped to the categories within the data verification context of the harmonized DQA terminology. To ensure all DQ checks were consistently mapped, conventions were developed and four iterations of mapping performed. Difficult-to-map DQ checks were discussed with research team members until consensus was achieved. RESULTS: Participating organizations provided 11,026 DQ checks, of which 99.97 percent were successfully mapped to a DQA category. Of the mapped DQ checks (N=11,023), 214 (1.94 percent) mapped to multiple DQA categories. The majority of DQ checks mapped to Atemporal Plausibility (49.60 percent), Value Conformance (17.84 percent), and Atemporal Completeness (12.98 percent) categories. DISCUSSION: Using the common DQA terminology, near-complete (99.97 percent) coverage across a wide range of DQA programs and specifications was reached. Comparing the distributions of mapped DQ checks revealed important differences between participating organizations. This variation may be related to the organization's stakeholder requirements, primary analytical focus, or maturity of their DQA program. Not within scope, mapping checks within the data validation context of the terminology may provide additional insights into DQA practice differences. CONCLUSION: A common DQA terminology provides a means to help organizations and researchers understand the coverage of their current DQA efforts as well as highlight potential areas for additional DQA development. Sharing DQ checks between organizations could help expand the scope of DQA across clinical data networks.

Rate of Second and Third Trimester Weight Gain and Preterm Delivery Among Underweight and Normal Weight Women.

Objectives Low gestational weight gain (GWG) in the second and third trimesters has been associated with increased risk of preterm delivery (PTD) among women with a body mass index (BMI) < 25 mg/m(2). However, few studies have examined whether this association differs by the assumptions made for first trimester gain or by the reason for PTD. Methods We examined singleton pregnancies during 2000-2008 among women with a BMI < 25 kg/m(2) who delivered a live-birth ≥28 weeks gestation (n = 12,526). Women received care within one integrated health care delivery system and began prenatal care ≤13 weeks. Using antenatal weights measured during clinic visits, we interpolated GWG at 13 weeks gestation then estimated rate of GWG (GWGrate) during the second and third trimesters of pregnancy. We also estimated GWGrate using the common assumption of a 2-kg gain for all women by 13 weeks. We examined the covariate-adjusted association between quartiles of GWGrate and PTD (28-36 weeks gestation) using logistic regression. We also examined associations by reason for PTD [premature rupture of membranes (PROM), spontaneous labor, or medically indicated]. Results Mean GWGrate did not differ among term and preterm pregnancies regardless of interpolated or assumed GWG at 13 weeks. However, only with GWGrate estimated from interpolated GWG at 13 weeks, we observed a U-shaped relationship where odds of PTD increased with GWGrate in the lowest (OR 1.36, 95 % CI 1.10, 1.69) or highest quartile (OR 1.49, 95 % CI 1.20, 1.85) compared to GWGrate within the second quartile. Further stratifying by reason, GWGrate in the lowest quartile was positively associated with spontaneous PTD while GWGrate in the highest quartile was positively associated with PROM and medically indicated PTD. Conclusions Accurate estimates of first trimester GWG are needed. Common assumptions applied to all pregnancies may obscure the association between GWGrate and PTD. Further research is needed to fully understand whether these associations are causal or related to common antecedents.

Associations of Gestational Weight Gain with Preterm Birth among Underweight and Normal Weight Women.

Studies report increased risk of preterm birth (PTB) among underweight and normal weight women with low gestational weight gain (GWG). However, most studies examined GWG over gestational periods that differ by term and preterm which may have biased associations because GWG rate changes over the course of pregnancy. Furthermore, few studies have specifically examined the amount and pattern of GWG early in pregnancy as a predictor of PTB. Within one integrated health care delivery system, we examined 12,526 singleton pregnancies between 2000 and 2008 among women with a body mass index <25 kg/m(2), who began prenatal care in the first trimester and delivered a live-birth >28 weeks gestation. Using self-reported pregravid weight and serial measured antenatal weights, we estimated GWG and the area under the GWG curve (AUC; an index of pattern of GWG) during the first and second trimesters of pregnancy (≤28 weeks). Using logistic regression adjusted for covariates, we examined associations between each GWG measure, categorized into quartiles, and PTB (<37 weeks gestation). We additionally examined associations according to the reason for PTB by developing a novel algorithm using diagnoses and procedure codes. Low GWG in the first and second trimesters was not associated with PTB [aOR 1.11, (95% CI 0.90, 1.38) with GWG <8.2 kg by 28 weeks compared to pregnancies with GWG >12.9]. Similarly, pattern of GWG was not associated with PTB. Our findings do not support an association between GWG in the first and second trimester and PTB among underweight and normal weight women.

A standardized relative resource cost model for medical care: application to cancer control programs.

Medicare data represent 75% of aged and permanently disabled Medicare beneficiaries enrolled in the fee-for-service (FFS) indemnity option, but the data omit 25% of beneficiaries enrolled in Medicare Advantage health maintenance organizations (HMOs). Little research has examined how longitudinal patterns of utilization differ between HMOs and FFS. The Burden of Cancer Study developed and implemented an algorithm to assign standardized relative costs to HMO and Medicare FFS data consistently across time and place. Medicare uses 15 payment systems to reimburse FFS providers for covered services. The standardized relative resource cost algorithm (SRRCA) adapts these various payment systems to utilization data. We describe the rationale for modifications to the Medicare payment systems and discuss the implications of these modifications. We applied the SRRCA to data from four HMO sites and the linked Surveillance, Epidemiology, and End Results-Medicare data. Some modifications to Medicare payment systems were required, because data elements needed to categorize utilization were missing from both data sources. For example, data were not available to create episodes for home health services received, so we assigned costs per visit based on visit type (nurse, therapist, and aide). For inpatient utilization, we modified Medicare's payment algorithm by changing it from a flat payment per diagnosis-related group to daily rates for diagnosis-related groups to differentiate shorter versus longer stays. The SRRCA can be used in multiple managed care plans and across multiple FFS delivery systems within the United States to create consistent relative cost data for economic analyses. Prior to international use of the SRRCA, data need to be standardized.