Impact of diabetes and modifiable risk factors on pancreatic cancer survival in a population-based study after adjusting for clinical factors.

PURPOSES: Our study aimed to examine the impact of diabetes, smoking and BMI on pancreatic cancer survival in a population-based setting by adjusting both sociodemographic and clinical factors and measuring their attributable risk. METHODS: Data on pancreatic adenocarcinoma patients diagnosed in 2011-2017 were acquired from the Louisiana Tumor Registry. Diabetes, smoking, height, and weight were abstracted from medical records and linked with Hospital Inpatient Discharge Data to enhance the completeness of the diabetes data. The Cox regression model was used to assess effect sizes of diabetes, smoking, and BMI on cancer-specific survival and survival rate. The partial population attributable risk was employed to measure the attributable risk of these risk factors. RESULTS: Of the 3,200 eligible patients, 34.6% were diabetics, 23.9% were current smokers, and 52.3% had BMI ≥ 25 kg/m2. After adjusting for sociodemographic and clinical factors, diabetic patients had an increased cancer-specific death risk of 15% (95% CI, 1.06-1.25), 36% (95% CI, 1.19-1.44) for current smokers, and 24% (95% CI, 1.00-1.54) for patients with a BMI ≥ 40 when compared to their counterparts. Diabetic current smokers had significantly lower 2- and 3-year adjusted cancer-specific survival rates, 13.1% and 10.5%, respectively. By eliminating diabetes and modifiable risk factors, an estimated 16.6% (95% CI, 6.9%-25.9%) of the cancer-specific deaths could be avoided during a nine-year observational period between 2011 and 2019. CONCLUSIONS: Diabetes and smoking contributed substantially to the reduction of pancreatic cancer survival even after controlling for sociodemographic and clinical factors; however, BMI ≥ 35 was observed to increase risk of mortality among stage III-IV patients only.

Influence of socioeconomic status and hospital type on disparities of lymph node evaluation in colon cancer patients.

BACKGROUND: A minimum of 12 dissected lymph nodes (LNs) has been recommended as a consensus guideline for resections in colon cancer patients. This study assessed the influence of both socioeconomic status (SES) and hospital type on compliance with this colon LN dissection guideline and examined the time trend for ≥12 LNs dissected. METHODS: Stage I to III incident colon cancer cases diagnosed from 1996 to 2007 were obtained from the Louisiana Tumor Registry. A composite census tract-level SES score was created to serve as a surrogate for individual-level SES. Hospitals performing colon resections were categorized into 5 groups according to the Commission on Cancer Accreditation Program. Multiple logistic regression analyses were used. RESULTS: Of 10,460 colon cancer cases diagnosed during the study period, 43.9% had ≥12 LNs dissected. Patients residing in less affluent SES areas were less likely to receive a dissection of ≥12 nodes than those residing in more affluent areas. SES was no longer significant after adjusting for race, sex, age, stage, grade, anatomic subsite, diagnosis year, and hospital type. In contrast, hospital type was significantly associated with the number of LNs dissected, even after adjusting for other factors. Patients diagnosed from 2002 to 2007 were twice as likely (95% confidence interval, 1.84-2.17) to have ≥12 LNs dissected than those diagnosed from 1996 to 2001 after adjustment. CONCLUSIONS: In Louisiana, hospital type is an independent significant predictor of adequate LN evaluation for colon cancer. Training and education are needed to reduce this disparity in the facilities with consistently lower LN yield in their dissections.

Using NAPIIA to improve the accuracy of Asian race codes in registry data.

BACKGROUND: Misclassification of race/ethnicity, particularly for Asians and American Indians, has been an issue existing in cancer registry data for years. Over the past 10 years, the Asian population has increased noticeably in both the US and in Louisiana. Therefore, accurate recording of Asian races/ethnicities in cancer registry databases has become essential for disparity research. The objectives of this study were to demonstrate that using the North American Association of Central Cancer Registries (NAACCR) Asian/Pacific Islander Identification Algorithm (NAPIIA) could improve the coding accuracy of Asian ethnicities and to identify sources for manually verifying race/ethnicity. METHODS: We selected cases diagnosed in years 1995 to 2008 with first race (NAACCR item 160) coded to any Asians, other race, unknown race, or non-Asian race with birthplace in an Asian country. We then converted these races to Asian, Not Otherwise Specified (Asian NOS) race code 96 and applied NAPIIA on the records. The resultant Asian races/ethnicities assigned by NAPIIA were then compared to the original race. When the NAPIIA-assigned Asian codes were different from the original race, the cases were manually reviewed. Kappa statistic test was used to measure the interobserver agreement; sensitivity and positive predictive value (PPV) were used to assess the degree of discrepancy for each Asian racial/ethnic subgroup separately. RESULTS: Of 2,147 cases run through the NAPIIA, 22.3% (479) were identified with coding discrepancies. Overall, the agreement on Asian subgroups between the original and NAPIIA-assigned was almost perfect (Kappa = 0.8682). When NAPIIA-assigned race and manually reviewed race were compared, the Vietnamese subgroup had the highest consistent rate (95%). Of the 237 cases where the original race was coded to Asian NOS, 93.7% were verified as having a more specific race/ethnicity. Slightly over 98% of deceased patients found in Louisiana online death certificate database had specific race/ethnicity information. CONCLUSIONS: NAPIIA is an excellent tool to assist cancer registries in improving the coding accuracy of Asian subgroups and enhancing the data quality by reducing cases with Asian NOS and unknown race. The death certificate is a great source for identifying and/or verifying race/ethnicity based on several factors including patient's race code and place of birth as well as the parent's names and place of birth.