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Background: Women with early-stage ovarian cancer may be asymptomatic or present with nonspecific symptoms. We examined health care utilization prior to ovarian cancer diagnosis to assess whether women with higher utilization differed in their prognosis and outcomes compared to women with low utilization., Methods: Using Medicaid, Medicare, and New York State Cancer Registry data for ovarian cancer cases diagnosed in 2006-2015, we examined selected health care visits that occurred 1-6 months before ovarian cancer diagnosis. We used multivariable-adjusted logistic regression to estimate odds ratios (ORs) and 95% CIs for associations of sociodemographic factors with number of prediagnostic visits and number of visits with tumor characteristics, and Cox proportional hazards regression to examine differences in survival by number of visits., Results: Women with >5 vs 0 prediagnostic visits were statistically significantly less likely to be diagnosed with distant vs local stage disease (OR, 0.72; 95% CI, 0.54-0.96), and women with 3-5 or >5 vs 0 prediagnostic visits had better overall survival (hazard ratio [HR], 0.88; 95% CI, 0.80-0.96 and HR, 0.90; 95% CI, 0.83-0.98, respectively). In stratified analyses, the association with improved survival was observed only among cases with regional or distant stage disease., Conclusions: Women with high health care utilization prior to ovarian cancer diagnosis may have better prognosis and survival, possibly because of earlier detection or better access to care throughout treatment. Women and their health care providers should not ignore symptoms potentially indicative of ovarian cancer and should be persistent in following up on symptoms that do not resolve., (© 2024 National Cancer Registrars Association.)

Background: Women with early-stage ovarian cancer may be asymptomatic or present with nonspecific symptoms. We examined health care utilization prior to ovarian cancer diagnosis to assess whether women with higher utilization differed in their prognosis and outcomes compared to women with low utilization., Methods: Using Medicaid, Medicare, and New York State Cancer Registry data for ovarian cancer cases diagnosed in 2006-2015, we examined selected health care visits that occurred 1-6 months before ovarian cancer diagnosis. We used multivariable-adjusted logistic regression to estimate odds ratios (ORs) and 95% CIs for associations of sociodemographic factors with number of prediagnostic visits and number of visits with tumor characteristics, and Cox proportional hazards regression to examine differences in survival by number of visits., Results: Women with >5 vs 0 prediagnostic visits were statistically significantly less likely to be diagnosed with distant vs local stage disease (OR, 0.72; 95% CI, 0.54-0.96), and women with 3-5 or >5 vs 0 prediagnostic visits had better overall survival (hazard ratio [HR], 0.88; 95% CI, 0.80-0.96 and HR, 0.90; 95% CI, 0.83-0.98, respectively). In stratified analyses, the association with improved survival was observed only among cases with regional or distant stage disease., Conclusions: Women with high health care utilization prior to ovarian cancer diagnosis may have better prognosis and survival, possibly because of earlier detection or better access to care throughout treatment. Women and their health care providers should not ignore symptoms potentially indicative of ovarian cancer and should be persistent in following up on symptoms that do not resolve.

OBJECTIVE: To identify missed deaths in the New York State Cancer Registry database and correct the vital status code. METHODS: The SEER*DMS SQL data search feature was used to identify cases which were potentially miscoded based on key words in the pathology and remarks text section of the abstract and the vital status coded. RESULTS: The SEER*DMS SQL data search feature allowed for miscoded vital status cases to be easily identified and corrected in our database. CONCLUSIONS: Improving the quality of the data being used for analysis, despite the quantity of changes being made, will in time generate more accurate survival statistics for the state of New York.

Background: Intrarecord edits on site-sex combinations are a standard tool to identify errors in the coding of sex in cancer registry data. However, the percentage of sex-specific cancers, like cervix, is low (20 percent of total invasive cases). Visual review and follow-back to improve the quality of the sex coding is labor intensive and typically only performed as a special project on subsets of data. The New York State Cancer Registry (NYSCR) created an edit for identifying potential sex misclassification in cancer registry data and has made its components available for use through the North American Association of Central Cancer Registries (NAACCR). The edit uses the most popular male and female first names based on decade of birth to identify potentially miscoded cases. This paper provides a summary of 3 independently conducted assessments of the sex edit at the central cancer registry level and includes a focus on misclassification of sex for breast cancer., Methods: The sex edit was applied in 3 state cancer registries: Alabama, Alaska, and Florida. Alabama applied the edit to their entire database for 1996-2004 (N = 190,614) and compared the results to external databases available to most cancer registries. Alaska applied the edit to their entire database (N = 46,645) and were able to compare the results to 2 unique, state-based databases (Alaska Permanent Fund Dividend database and State Troopers database). Florida applied the sex edit to a sample of sites (n = 953,074) with particular attention to breast cancer. RESULTS for breast cases were compared to results from an a priori quality control project on Florida male breast cancer cases. Using the Florida data, issues specific to male breast cancer were evaluated., Results: In Alabama, 45 percent of 977 cases flagged as potentially miscoded sex were determined to be miscodes. In Alaska, 19 percent of 88 cases flagged as potentially miscoded sex were determined to be miscodes but the percent of miscoded cases identified by the edit more than doubled in the most recent years of data. For the Florida male breast cancer comparison, the sex edit correctly identified 729 of 903 cases known to be miscoded (81 percent) and was unable to assign a potential sex on the remaining 174 cases-but did not incorrectly flag any cases as miscodes., Implications: The sex edit is a useful tool for identifying cases that require further review to confirm the reported sex code is correct. However, it only assesses 69 percent to 84 percent of cases based on name and, of those flagged, only 19 percent to 45 percent are true misclassifications. But for breast cancer, a site with a skewed male to female ratio, the verified misclassification rate was 100 percent of the male breast cancer cases flagged as potential females. The proper application of the sex edit can improve the quality of the sex variable and can greatly reduce the impact of miscoded sex on gender-skewed sites like male breast cancer.

Timeliness is one of the key indicators of cancer surveillance data quality, as delayed reporting of cases results in an underestimation of the cancer rate in a population. The purpose of this paper is to assess temporal trends in reporting delay by cancer site from 1999-2010. Using data from the Surveillance, Epidemiology, and End Results (SEER) 9 cancer registries and the New York State Cancer Registry, I calculated short-, medium-, and long-term delay for the most common cancer sites for each year and identified the linear trend. Nearly all sites showed a decrease in delay over the period, and many showed a statistically significant decrease. The decrease in delay was more pronounced in the New York State data. These findings reflect long-term improvement in the timeliness of cancer reporting, but there remains room for improvement. Leukemia and myeloma are especially problematic, as these sites are heavily dependent on reporting by private physicians.

Background: Multiple dates of diagnosis are often received from different reporting sources at a central cancer registry. Resolving these inconsistencies can be a labor-intensive task. To our knowledge, no algorithms for the consolidation of diagnosis dates have been published. We present such an algorithm here., Methods: The algorithm uses a "take the best" heuristic approach, incorporating the reported dates of diagnosis, class of case, service type (a New York-specific item similar to type of reporting source), and the date of first contact. The algorithm was evaluated by comparing results to those obtained with manual review by experienced certified tumor registrars (CTRs)., Results: From a sample of 209,907 tumors with multiple diagnosis dates reported to the New York State Cancer Registry (NYSCR), the algorithm determined a single date for 94.7 percent of these, with the balance designated for manual review. Of a sample of 636 tumors that were manually reviewed to evaluate the algorithm, the algorithm obtained the same year as the CTRs for 621 tumors (97.6 percent), the same month and year for 572 tumors (89.9 percent) and the same month, year, and day for 518 tumors (81.4 percent). There was much lower agreement between the manually derived dates and the originally consolidated dates., Conclusion: The algorithm presented here is accurate, efficient, and reliable, and hopefully will help the cancer registry community move toward standard practices for record consolidation.

Incidence rates for many cancer sites are strongly correlated with area measures of socioeconomic conditions such as poverty rate. Analyzing such measures at the county scale produces misleading results by masking enormous within-county variations. The census tract is a more suitable scale for assessing the relationship between cancer and socioeconomics. The North American Association of Central Cancer Registries (NAACCR) developed a census tract-level poverty indicator variable which was included as an optional item in its 2010 Call for Data. This variable does not allow the identification of individual census tracts as long as the county of diagnosis is not known. It is expected that this data item will be made available to researchers in future releases of the CINA Deluxe file.