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The age-adjusted cancer rates are defined as the weighted average of the age-specific cancer rates, where the weights are positive, known, and normalized so that their sum is 1. Fay and Feuer developed a confidence interval for a single age-adjusted rate based on the gamma approximation. Fay used the gamma approximations to construct an F interval...
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Context 1
... of the lower and upper limits of μ i that fall above and below the true mean are between 457 and 543. In Figure 4, we plotted the lengths of the simulated intervals against the variance of w ij . From Figures 1–4, we observe that the modified gamma intervals have empirical coverage at least 95%, but slightly lower than the gamma intervals of Fay and Feuer, 4 the beta and normal intervals (with lower limits truncated at 0 if they were negative) also have empirical coverage probabilities very close to 95%, and their widths are lower than the gamma intervals. The coverage probabilities of the upper limits of both the beta and modified gamma intervals are identical and at least 97.5%, but slightly lower than the gamma intervals of Fay and Feuer. 4 The lower limits of the normal intervals are slightly more conservative than those for gamma, while the upper limits of the normal intervals are least conservative. The advantage of using modified gamma intervals over the gamma intervals is clear from Figure 3, wherein the gamma intervals show a coverage probability of around 97% as the variance w ij increases, the modified intervals show the coverage probability staying slightly higher than 95%. Overall, from these simulation studies, the gamma intervals of Fay and Feuer 4 are more conservative than the proposed gamma. The beta intervals are slightly more liberal than both the modified gamma and the gamma intervals of Fay and Feuer. 4 The normal intervals are more liberal than the beta intervals. In simulations, when the Poisson means were 0, as the observed d ij were 0, we set the simulated values of D ij to be equal to 0. This is because D ij are non-negative random variables with the means and variances equal and if the mean of a D is 0 then that ...
Citations
... This allowed us to compare thyroid cancer trends and incidence rates before and after the implementation and revisions of the ATA guidelines. We then computed incidence rate ratios (IRRs) and used the Tiwari method to calculate 95% CIs for the IRRs [29]. Lastly, we used Joinpoint Regression software to fit, assess, and compare thyroid cancer incidence and trends over time stratified by race, SES, geographic location, histology, tumor size, and tumor stage [30]. ...
Background: The past four decades have seen a steady increase in thyroid cancer in the United States (US). This study investigated the impact of the American Thyroid Association (ATA)’s revised cancer management guidelines on thyroid cancer incidence trends and how the trends varied by socioeconomic, histologic, geographic, and racial and ethnic characteristics from 2000 to 2020. Methods: We used data from the Surveillance, Epidemiology, and End Results (SEER) database to identify thyroid cancer cases diagnosed among US patients between 2000 and 2020. We employed joinpoint regression software to fit, assess, and compare thyroid cancer incidence trends over time stratified by socioeconomic status (SES), histologic type, geographic location, and race/ethnicity. Results: Between 2000 and 2009, there was an average annual increase of 5.8% in thyroid cancer incidence (average annual percent change (AAPC): 5.8, p < 0.05). Subsequently, there was a modest rise (AAPC: 1.1, p < 0.05) from 2010 to 2015, followed by a significant annual decrease of 4.8% from 2016 to 2020 (AAPC: −4.8, p < 0.05). The joinpoint regression models identified prominent inflection points around 2009 and 2015, aligning with the years of the ATA’s cancer management revisions. These intricate dynamics in thyroid cancer incidence trends from 2000 to 2020 were shaped by SES and histologic, geographic, and racial/ethnic factors. Conclusions: Thyroid cancer incidence trends over the past two decades can be partially explained by the changes in thyroid cancer screening and management recommendations. These findings underscore the importance of cancer management strategies and highlight the need for targeted interventions to address disparities in thyroid cancer incidence across minority demographic groups.
... Average annual rates for 2015-2019 per 100,000 population were age adjusted (using 19 age groups) to the 2000 US standard population by the direct method [13]. Corresponding 95% CIs were calculated as modified gamma intervals [14]. To determine differences between subgroups, rate ratios were calculated [15]. ...
Background
The Food and Drug Omnibus Report Act, signed into law in 2022, requires industry sponsors to include diversity action plans in clinical study protocols. Defining reliable methodology for measures and benchmarks is critical to ensuring adequate and consistent representation of historically underrepresented patient populations in clinical trials.
Methods
We provide an Advancing Inclusive Research (AIR) Calculator, summary tables, and data query bank to support target setting for the development of diversity action plans and to take steps toward defining enrollment standards. The AIR Calculator uses data from the US Cancer Statistics database, which covers 100% of the US population. The database provides descriptive statistics for people diagnosed with 26 different cancers from 2015–2019 by cancer site, age at diagnosis, sex, and race and ethnicity, all stratified by stage at diagnosis (early, de novo metastatic, and combined). Descriptive characteristics include frequency counts, age-adjusted incidence rates, incidence rate ratios, and 95% CIs. Robustness test results are available in the data query bank by year of diagnosis.
Results
This resource offers insights into distributions of cancer in the US. The AIR Calculator allows users to calculate representative clinical study distributions based on the sponsor-designated study size.
Discussion
The AIR Calculator serves as a valuable resource for planning of clinical studies, but additional data analyses are necessary for a comprehensive understanding at the study level. Comprehensive data collection and alignment across industry are essential to ensure consistent, accurate, and transparent benchmarks in historically underrepresented patient populations and to track progress toward the goal of improving their representation in clinical research.
... The Tiwari method was used to calculate the 95% CIs for all rates. 20 We compared the two populations in incidence stratified by age, race, cancer stage, primary site, and histologic group. For stratification by race or tumor stage, the results were presented only for the subgroups with sufficient numbers for analysis. ...
Background
Soft tissue sarcoma (STS) is one of the most frequently diagnosed cancers among men younger than age 30 years and a leading cause of cancer death in men younger than age 40 years. The military may be more exposed to STS risk factors and have generally better health and health care access than the general population, which may relate to lower cancer risk and/or early detection. This study compared STS incidence between servicemen and men in the general U.S. population.
Methods
Data were from the Department of Defense’s Automated Central Tumor Registry (ACTUR) and the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program. Subjects were active‐duty servicemen in ACTUR and men in SEER aged 18–59 years diagnosed with STS from 1990 to 2013. Age‐adjusted rates, incidence rate ratios (IRR), and 95% CIs were calculated.
Results
STS incidence rates were lower in ACTUR than SEER overall (IRR = 0.86 [0.78‐0.93]), for 18‐ to 39‐year‐old men (IRR = 0.78 [0.70‐0.86]), by race (White: IRR = 0.85 [0.77‐0.95]; Black: IRR = 0.77 [0.63‐0.94]), for sites other than skin/connective/soft tissue (IRR = 0.49 [0.37‐0.63]), other specified histologies (IRR = 0.84 [0.71‐0.98]), and unspecified histology (IRR = 0.57 [0.38‐0.82]). Rates were lower in ACTUR for regional (IRR = 0.37 [0.28‐0.47]) and distant metastases (IRR = 0.58 [0.43‐0.76]), even when race and age stratified. However, rates were higher in ACTUR for 40‐ to 59‐year‐old men (IRR = 1.25 [1.04‐1.48]) and localized tumors (IRR = 1.16 [1.04‐1.29]).
Conclusion
Lower STS rates among servicemen may relate to better health and early detection and treatment of STS‐associated conditions within the military health system, which provides universal care. Higher rates among 40‐ to 59‐year‐old servicemen may result from greater cumulative military‐related exposures.
... 30 We obtained 95% confidence intervals using the Tiwari modification. 31 In our primary analyses, we used the SEER-estimated ageadjusted incidence rates, and their standard errors, within subgroups of individuals simultaneously defined by the categories of the factors of interest: US region, race/ethnicity, sex, distant vs localized or regional summary stage, and year at diagnosis. Some of these combinations resulted in small enough groups that fewer than 16 incident CRC cases were observed. ...
Background
Colorectal cancer (CRC) incidence rates have been decreasing in the United States (US), but there is limited information about differences in these improvements among individuals from different racial and ethnic subgroups across different regions of the US.
Methods
Data from the National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology, and End Results (SEER) databases were used to examine trends in CRC incidence from 2001 to 2020 using a population-based retrospective cohort study. We obtained annual estimates of CRC incidence and used meta-regression analyses via weighted linear models to identify main effects and interactions that explained differences in CRC incidence trends among groups defined by race/ethnicity and US region while also considering CRC stage and sex. To summarize overall trends over time in incidence rates for specific racial and ethnic groups within and across US regions, we obtained average annual percentage change (AAPC) estimates.
Results
The greatest differences in CRC incidence trends were among groups defined by race/ethnicity and US region. Non-Hispanic Black (NHB) persons had the largest declines in CRC incidence, with AAPC estimates ranging from −2.27 (95% CI: −2.49 to −2.06) in the South to −3.03 (95% CI: −3.59 to −2.47) in the West, but had higher-than-average incidence rates at study end. The AAPC estimate for American Indian/Alaska Native (AIAN) persons suggested no significant change over time (AAPC: −0.41, 95% CI: −2.51 to 1.73).
Conclusion
CRC incidence trends differ among racial/ethnic groups residing in different US regions. Notably, CRC incidence rates have not changed noticeably for AIAN persons from 2001-2020. These findings highlight the importance of reinvigorating collaborative efforts to develop geographic and population-specific screening and preventative approaches to reduce the CRC burden experienced by Native American communities and members of other minoritized groups.
... High enclave + low nSES was selected as the reference group as each of these categories had the lowest incidence rate of invasive breast cancer for AANHPI females. We used the NCI's SEER*Stat software (version 8.4.2) to compute AAIR per 100,000 population and IRR and 95% confidence intervals (Tiwari modification) [20,21]. Subgroup analyses were conducted for localized and advanced disease. ...
Few studies have examined whether the incidence rates of invasive breast cancer among Asian American, Native Hawaiian, and Pacific Islander (AANHPI) populations differ by the neighborhood social environment. Thus, we examined associations of ethnic enclave and neighborhood socioeconomic status (nSES) with breast cancer incidence rates among AANHPI females in California.
A total of 14,738 AANHPI females diagnosed with invasive breast cancer in 2008–2012 were identified from the California Cancer Registry. AANHPI ethnic enclaves (culturally distinct neighborhoods) and nSES were assessed at the census tract level using 2007–2011 American Community Survey data. Breast cancer age-adjusted incidence rates and incidence rate ratios (IRRs) were estimated for AANHPI ethnic enclave, nSES, and their joint effects. Subgroup analyses were conducted by stage of disease.
The incidence rate of breast cancer among AANHPI females living in lowest ethnic enclave neighborhoods (quintile (Q)1) were 1.21 times (95% Confidence Interval (CI) 1.11, 1.32) that of AANHPI females living highest ethnic enclave neighborhoods (Q5). In addition, AANHPI females living in highest vs. lowest SES neighborhoods had higher incidence rates of breast cancer (Q5 vs. Q1 IRR = 1.30, 95% CI 1.22 to 1.40). The incidence rate of breast cancer among AANHPI females living in low ethnic enclave + high SES neighborhoods was 1.32 times (95% CI 1.25, 1.39) that of AANHPI females living in high ethnic enclave + low SES neighborhoods. Similar patterns of associations were observed for localized and advanced stage disease.
For AANHPI females in California, incidence rates of breast cancer differed by nSES, ethnic enclave, when considered independently and jointly. Future studies should examine whether the impact of these neighborhood-level factors on breast cancer incidence rates differ across specific AANHPI ethnic groups and investigate the pathways through which they contribute to breast cancer incidence.
... Confidence intervals for standardized incidence measures were constructed using the Tiwari modified gamma method, which is recommended when performing direct standardization on sparse data. 39,40 Multivariable negative binomial regression was used to evaluate the associations of year, province/territory, age group, and sex with the incidence rate of OHCA patients admitted to the hospital. This modeling approach was selected over simple Poisson regression due to the significant overdispersion of count data, as confirmed through a Lagrange Multiplier test (X 2 (1)=16.04, ...
... Ratios, with 95% confidence intervals (CIs), were calculated using the Tiwari 2006 revision in SEER*Stat 8.4.2 (NCI). 20 To assess trends in CMM incidence rates among elderly individuals in the United States from 1987 to 2016, this study used the Joinpoint Regression Model. For population-based trends in cancer incidence and mortality rates, the logarithmic linear model is typically used. ...
Background and objectives
The prevalence and fatality rates of cutaneous malignant melanoma (CMM) have been rising, particularly among the elderly. This study analyzes CMM incidence trends in the United States elderly population from 1987 to 2016 to inform prevention and management strategies.
Methods
Using incidence data from the Surveillance, Epidemiology, and End Results database spanning 1989 to 2008, we calculated the age-adjusted standardized population incidence rates for CMM in elderly individuals. The Joinpoint software was employed to estimate annual percent change and analyze trends in CMM incidence among elderly individuals from 1987 to 2016.
Results
The study included 56,997 elderly CMM patients from eight Surveillance, Epidemiology, and End Results registries, of whom 36,726 were male (64.4%). The age-adjusted CMM incidence rate from 2012 to 2016 was 0.99 per 1,000, a 2.8-fold increase from 1987–1991 (95% confidence interval: 2.7–2.9). Incidence rates increased with age and birth cohort, peaking at 1.53 per 1,000 males and 0.59 per 1,000 females aged 85+ during 2012–2016. Birth cohort effects also showed a continuous increase.
Conclusions
This study reveals a substantial increase in CMM incidence rates among the elderly from 1987 to 2016, particularly between 2012 and 2016. Incidence rates escalated with age and birth cohort, with the highest rates observed in individuals aged 85 and older.
... [24]. The Tiwari technique was used to estimate the ASIRs based on the 2000 US standard population and the accompanying 95% confidence intervals (CIs) [28] using the SEER*Stat version 8.4.1.2 [24]. ...
Background
Gastric cancer ranks among the top cancers in terms of both occurrence and death rates in the United States (US). Our objective was to provide the incidence trends of gastric cancer in the US from 2000 to 2020 by age, sex, histology, and race/ethnicity, and to evaluate the effects of the COVID-19 pandemic.
Methods
We obtained data from the Surveillance, Epidemiology, and End Results 22 program. The morphologies of gastric cancer were classified as adenocarcinoma, gastrointestinal stromal tumor, signet ring cell carcinoma, and carcinoid tumor. We used average annual percent change (AAPC) and compared pairs using parallelism and coincidence. The numbers were displayed as both counts and age-standardized incidence rates (ASIRs) per 100000 individuals, along with their corresponding 95% confidence intervals (CIs).
Results
Over 2000–2019, most gastric cancers were among those aged ≥55 years (81.82%), men (60.37%), and Non-Hispanic Whites (62.60%). By histology, adenocarcinoma had the highest incident cases. During the COVID-19 pandemic, there was a remarkable decline in ASIRs of gastric cancer in both sexes and all races (AAPC: -8.92; 95% CI: -11.18 to -6.67). The overall incidence trends of gastric cancer were not parallel, nor identical.
Conclusions
The incidence of gastric cancer shows notable variations by age, race, and sex, with a rising trend across ethnicities. While the overall incidence has declined, a noteworthy increase has been observed among younger adults, particularly young Hispanic women; however, rates decreased significantly in 2020.
... 25 The 95% confidence intervals were estimated using Tiwari's method. 26 United States 2000 standard populations by 5-year age groups were used for computing age-adjusted rates. 27 The initially reported case count for a given diagnosis year tends to be smaller than the updated counts later reported for that year, which produces bias in estimating cancer incidence trends. ...
Background
The COVID‐19 pandemic had a significant impact on cancer screening and treatment, particularly in 2020. However, no single study has comprehensively analyzed its effects on cancer incidence and disparities among groups such as race/ethnicity, socioeconomic status (SES), persistent poverty (PP), and rurality.
Methods
Utilizing the recent data from the United States National Cancer Institute's Surveillance, Epidemiology, and End Results Program, we calculated delay‐ and age‐adjusted incidence rates for 13 cancer sites in 2020 and 2015–2019. Percent changes (PCs) of rates in 2020 compared to 2015–2019 were measured and compared across race/ethnic, census tract‐level SES, PP, and rurality groups.
Results
Overall, incidence rates decreased from 2015–2019 to 2020, with varying PCs by cancer sites and population groups. Notably, NH Blacks showed significantly larger PCs than NH Whites in female lung, prostate, and colon cancers (e.g., prostate cancer: NH Blacks −7.3, 95% CI: [−9.0, −5.5]; NH Whites: −3.1, 95% CI: [−3.9, −2.2]). Significantly larger PCs were observed for the lowest versus highest SES groups (prostate cancer), PP versus non‐PP groups (prostate and female breast cancer), and all urban versus rural areas (prostate, female breast, female and male lung, colon, cervix, melanoma, liver, bladder, and kidney cancer).
Conclusions
The COVID‐19 pandemic coincided with reduction in incidence rates in the U.S. in 2020 and was associated with worsening disparities among groups, including race/ethnicity, SES, rurality, and PP groups, across most cancer sites. Further investigation is needed to understand the specific effects of COVID‐19 on different population groups of interest.
... For each simulation, we calculated (i) survival rate, (ii) age-adjusted survival rate, and (iii) aggregate comorbidity-adjusted life expectancy (in years). Due to COVID's severe age-associated mortality and the distribution of age within racial groups, survival rates for sub-populations were age-adjusted, and confidence intervals for both raw and age-adjusted rates were calculated using the modified Gamma method of Tiwari et al. [25,26] Raw life expectancy was calculated for each subject from the corresponding National Vital Statistics System (NVSS) life tables for their age, sex and race. The impact of comorbidities on life expectancy was estimated by applying the adjustments previously calculated by Cho et al. for Medicare recipients without a history of cancer [27]. ...
Introduction
Arguments over the appropriate Crisis Standards of Care (CSC) for public health emergencies often assume that there is a tradeoff between saving the most lives, saving the most life-years, and preventing racial disparities. However, these assumptions have rarely been explored empirically. To quantitatively characterize possible ethical tradeoffs, we aimed to simulate the implementation of five proposed CSC protocols for rationing ventilators in the context of the COVID-19 pandemic.
Methods
A Monte Carlo simulation was used to estimate the number of lives saved and life-years saved by implementing clinical acuity-, comorbidity- and age-based CSC protocols under different shortage conditions. This model was populated with patient data from 3707 adult admissions requiring ventilator support in a New York hospital system between April 2020 and May 2021. To estimate lives and life-years saved by each protocol, we determined survival to discharge and estimated remaining life expectancy for each admission.
Results
The simulation demonstrated stronger performance for age-sensitive protocols. For a capacity of 1 bed per 2 patients, ranking by age bands saves approximately 29 lives and 3400 life-years per thousand patients. Proposed protocols from New York and Maryland which allocated without considering age saved the fewest lives (~13.2 and 8.5 lives) and life-years (~416 and 420 years). Unlike other protocols, the New York and Maryland algorithms did not generate significant disparities in lives saved and life-years saved between White non-Hispanic, Black non-Hispanic, and Hispanic sub-populations. For all protocols, we observed a positive correlation between lives saved and life-years saved, but also between lives saved overall and inequality in the number of lives saved in different race and ethnicity sub-populations.
Conclusion
While there is significant variance in the number of lives saved and life-years saved, we did not find a tradeoff between saving the most lives and saving the most life-years. Moreover, concerns about racial discrimination in triage protocols require thinking carefully about the tradeoff between enforcing equality of survival rates and maximizing the lives saved in each sub-population.
