REDCap for Biocontainment Worker Symptom Monitoring.

The Ebola epidemic of 2014 demonstrated that outbreaks of high-consequence infectious diseases, even in remote parts of the world, can affect communities anywhere in the developed world and that every healthcare facility must be prepared to identify, isolate, and provide care for infected patients. The Nebraska Biocontainment Unit (NBU), located at Nebraska Medicine in Omaha, Nebraska, cared for 3 American citizens exposed in West Africa and confirmed with Ebola virus disease (EVD). Symptom monitoring of healthcare workers caring for these patients was implemented, which included twice daily contact to document the absence or presence of signs of fever or illness. This article describes the symptom monitoring experience of the NBU and local and state public health agencies. Based on lessons learned from that experience, we sought a more efficient solution to meet the needs of both the healthcare facility and public health authorities. REDCap, an open-source application used commonly by academic health centers, was used to develop an inexpensive symptom monitoring application that could reduce the burden of managing these activities, thus freeing up valuable time. Our pilot activities demonstrated that this novel use of REDCap holds promise for minimizing costs and resource demands associated with symptom monitoring while offering a more user-friendly experience for people being monitored and the officials managing the response.

Prognostic estimator of survival for patients with localized and extended pancreatic ductal adenocarcinoma.

The 18,352 pancreatic ductal adenocarcinoma (PDAC) cases from the Surveillance Epidemiology and End Results (SEER) database were analyzed using the Kaplan-Meier method for the following variables: race, gender, marital status, year of diagnosis, age at diagnosis, pancreatic subsite, T-stage, N-stage, M-stage, tumor size, tumor grade, performed surgery, and radiation therapy. Because the T-stage variable did not satisfy the proportional hazards assumption, the cases were divided into cases with T1- and T2-stages (localized tumor) and cases with T3- and T4-stages (extended tumor). For estimating survival and conditional survival probabilities in each group, a multivariate Cox regression model adjusted for the remaining covariates was developed. Testing the reproducibility of model parameters and generalizability of these models showed that the models are well calibrated and have concordance indexes equal to 0.702 and 0.712, respectively. Based on these models, a prognostic estimator of survival for patients diagnosed with PDAC was developed and implemented as a computerized web-based tool.

Breast cancer incidence in black and white women stratified by estrogen and progesterone receptor statuses.

BACKGROUND: There is increasing evidence that breast cancer is a heterogeneous disease presented by different phenotypes and that white women have a higher breast cancer incidence rate, whereas black women have a higher mortality rate. It is also well known that white women have lower incidence rates than black women until approximately age 40, when rate curves cross over and white women have higher rates. The goal of this study was to validate the risk of white and black women to breast cancer phenotypes, stratified by statuses of the estrogen (ER) and progesterone (PR) receptors. METHODOLOGY/PRINCIPAL FINDINGS: SEER17 data were fractioned by receptor status into [ER+, PR+], [ER-, PR-], [ER+, PR-], and [ER-, PR+] phenotypes. It was shown that in black women compared to white women, cumulative age-specific incidence rates are: (i) smaller for the [ER+, PR+] phenotype; (ii) larger for the [ER-, PR-] and [ER-, PR+] phenotypes; and (iii) almost equal for the [ER+, PR-] phenotype. Clemmesen's Hook, an undulation unique to women's breast cancer age-specific incidence rate curves, is shown here to exist in both races only for the [ER+, PR+] phenotype. It was also shown that for all phenotypes, rate curves have additional undulations and that age-specific incidence rates are nearly proportional in all age intervals. CONCLUSIONS/SIGNIFICANCE: For black and white women, risk for the [ER+, PR+], [ER-, PR-] and [ER-, PR+] phenotypes are race dependent, while risk for the [ER+, PR-] phenotype is almost independent of race. The processes of carcinogenesis in aging, leading to the development of each of the considered breast cancer phenotypes, are similar in these racial groups. Undulations exhibited on the curves of age-specific incidence rates of the considered breast cancer phenotypes point to the presence of several subtypes (to be determined) of each of these phenotypes.

Extension of cox proportional hazard model for estimation of interrelated age-period-cohort effects on cancer survival.

In the frame of the Cox proportional hazard (PH) model, a novel two-step procedure for estimating age-period-cohort (APC) effects on the hazard function of death from cancer was developed. In the first step, the procedure estimates the influence of joint APC effects on the hazard function, using Cox PH regression procedures from a standard software package. In the second step, the coefficients for age at diagnosis, time period and birth cohort effects are estimated. To solve the identifiability problem that arises in estimating these coefficients, an assumption that neighboring birth cohorts almost equally affect the hazard function was utilized. Using an anchoring technique, simple procedures for obtaining estimates of interrelated age at diagnosis, time period and birth cohort effect coefficients were developed.As a proof-of-concept these procedures were used to analyze survival data, collected in the SEER database, on white men and women diagnosed with LC in 1975-1999 and the age at diagnosis, time period and birth cohort effect coefficients were estimated. The PH assumption was evaluated by a graphical approach using log-log plots. Analysis of trends of these coefficients suggests that the hazard of death from LC for a given time from cancer diagnosis: (i) decreases between 1975 and 1999; (ii) increases with increasing the age at diagnosis; and (iii) depends upon birth cohort effects.The proposed computing procedure can be used for estimating joint APC effects, as well as interrelated age at diagnosis, time period and birth cohort effects in survival analysis of different types of cancer.

Estimation of hazard functions in the log-linear age-period-cohort model: application to lung cancer risk associated with geographical area.

An efficient computing procedure for estimating the age-specific hazard functions by the log-linear age-period-cohort (LLAPC) model is proposed. This procedure accounts for the influence of time period and birth cohort effects on the distribution of age-specific cancer incidence rates and estimates the hazard function for populations with different exposures to a given categorical risk factor. For these populations, the ratio of the corresponding age-specific hazard functions is proposed for use as a measure of relative hazard. This procedure was used for estimating the risks of lung cancer (LC) for populations living in different geographical areas. For this purpose, the LC incidence rates in white men and women, in three geographical areas (namely: San Francisco-Oakland, Connecticut and Detroit), collected from the SEER 9 database during 1975-2004, were utilized. It was found that in white men the averaged relative hazard (an average of the relative hazards over all ages) of LC in Connecticut vs. San Francisco-Oakland is 1.31 +/- 0.02, while in Detroit vs. San Francisco-Oakland this averaged relative hazard is 1.53 +/- 0.02. In white women, analogous hazards in Connecticut vs. San Francisco-Oakland and Detroit vs. San Francisco-Oakland are 1.22 +/- 0.02 and 1.32 +/- 0.02, correspondingly. The proposed computing procedure can be used for assessing hazard functions for other categorical risk factors, such as gender, race, lifestyle, diet, obesity, etc.

A Generalized Beta model for the age distribution of cancers: application to pancreatic and kidney cancer.

The relationships between cancer incidence rates and the age of patients at cancer diagnosis are a quantitative basis for modeling age distributions of cancer. The obtained model parameters are needed to build rigorous statistical and biological models of cancer development. In this work, a new mathematical model, called the Generalized Beta (GB) model is proposed. Confidence intervals for parameters of this model are derived from a regression analysis. The GB model was used to approximate the incidence rates of the first primary, microscopically confirmed cases of pancreatic cancer (PC) and kidney cancer (KC) that served as a test bed for the proposed approach. The use of the GB model allowed us to determine analytical functions that provide an excellent fit for the observed incidence rates for PC and KC in white males and females. We make the case that the cancer incidence rates can be characterized by a unique set of model parameters (such as an overall cancer rate, and the degree of increase and decrease of cancer incidence rates). Our results suggest that the proposed approach significantly expands possibilities and improves the performance of existing mathematical models and will be very useful for modeling carcinogenic processes characteristic of cancers. To better understand the biological plausibility behind the aforementioned model parameters, detailed molecular, cellular, and tissue-specific mechanisms underlying the development of each type of cancer require further investigation. The model parameters that can be assessed by the proposed approach will complement and challenge future biomedical and epidemiological studies.

A novel approach for analysis of the log-linear age-period-cohort model: application to lung cancer incidence.

A simple, computationally efficient procedure for analyses of the time period and birth cohort effects on the distribution of the age-specific incidence rates of cancers is proposed. Assuming that cohort effects for neighboring cohorts are almost equal and using the Log-Linear Age-Period-Cohort Model, this procedure allows one to evaluate temporal trends and birth cohort variations of any type of cancer without prior knowledge of the hazard function. This procedure was used to estimate the influence of time period and birth cohort effects on the distribution of the age-specific incidence rates of first primary, microscopically confirmed lung cancer (LC) cases from the SEER9 database. It was shown that since 1975, the time period effect coefficients for men increase up to 1980 and then decrease until 2004. For women, these coefficients increase from 1975 up to 1990 and then remain nearly constant. The LC birth cohort effect coefficients for men and women increase from the cohort of 1890-94 until the cohort of 1925-29, then decrease until the cohort of 1950-54 and then remain almost unchanged. Overall, LC incidence rates, adjusted by period and cohort effects, increase up to the age of about 72-75, turn over, and then fall after the age of 75-78. The peak of the adjusted rates in men is around the age of 77-78, while in women, it is around the age of 72-73. Therefore, these results suggest that the age distribution of the incidence rates in men and women fall at old ages.