Decomposition of disparities in life expectancy with applications to administrative health claims and registry data.

Research shows that geographic disparities in life expectancy between leading and lagging states are increasing over time while racial disparities between Black and White Americans have been going down. In the 65+ age strata morbidity is the most common cause of death, making differences in morbidity and associated adverse health-related outcomes between advantaged and disadvantaged groups an important aspect of disparities in life expectancy at age 65 (LE65). In this study, we used Pollard's decomposition to evaluate the disease-related contributions to disparities in LE65 for two types of data with distinctly differing structures: population/registry and administrative claims. To do so, we analyzed Pollard's integral, which is exact by construction, and developed exact analytic solutions for both types of data without the need for numerical integration. The solutions are broadly applicable and easily implemented. Applying these solutions, we found that the largest relative contributions to geographic disparities in LE65 were chronic lower respiratory diseases, circulatory diseases, and lung cancer; and, to racial disparities: arterial hypertension, diabetes mellitus, and cerebrovascular diseases. Overall, the increase in LE65 observed over 1998-2005 and 2010-2017 was primarily due to a reduction in the contributions of acute and chronic ischemic diseases; this was partially offset by increased contributions of diseases of the nervous system including dementia and Alzheimer's disease.

Forecasting prevalence and mortality of Alzheimer's disease using the partitioning models.

OBJECTIVES: Health forecasting is an important aspect of ensuring that the health system can effectively respond to the changing epidemiological environment. Common models for forecasting Alzheimer's disease and related dementias (AD/ADRD) are based on simplifying methodological assumptions, applied to limited population subgroups, or do not allow analysis of medical interventions. This study uses 5 %-Medicare data (1991-2017) to identify, partition, and forecast age-adjusted prevalence and incidence-based mortality of AD as well as their causal components. METHODS: The core underlying methodology is the partitioning analysis that calculates the relative impact each component has on the overall trend as well as intertemporal changes in the strength and direction of these impacts. B-spline functions estimated for all parameters of partitioning models represent the basis for projections of these parameters in future. RESULTS: Prevalence of AD is predicted to be stable between 2017 and 2028 primarily due to a decline in the prevalence of pre-AD-diagnosis stroke. Mortality, on the other hand, is predicted to increase. In all cases the resulting patterns come from a trade-off of two disadvantageous processes: increased incidence and disimproved survival. Analysis of health interventions demonstrates that the projected burden of AD differs significantly and leads to alternative policy implications. DISCUSSION: We developed a forecasting model of AD/ADRD risks that involves rigorous mathematical models and incorporation of the dynamics of important determinative risk factors for AD/ADRD risk. The applications of such models for analyses of interventions would allow for predicting future burden of AD/ADRD conditional on a specific treatment regime.

Underlying mechanisms of change in cancer prevalence in older U.S. adults: contributions of incidence, survival, and ascertainment at early stages.

PURPOSE: To quantitatively evaluate contributions of trends in incidence, relative survival, and stage at diagnosis to the dynamics in the prevalence of major cancers (lung, prostate, colon, breast, urinary bladder, ovaries, stomach, pancreas, esophagus, kidney, liver, and skin melanoma) among older U.S. adults age 65 +. METHODS: Trend partitioning was applied to the Surveillance, Epidemiology, and End Results Program data for 1973-2016. RESULTS: Growth of cancer prevalence in older adults decelerated or even decreased over time for all studied cancers due to decreasing incidence and improving survival for most of cancers, with a smaller contribution of the stage at cancer diagnosis. Changes in the prevalence of cancers of the lung, colon, stomach, and breast were predominantly due to decreasing incidence, increasing survival and more frequent diagnoses at earlier stages. Changes in prevalence of some other cancers demonstrated adverse trends such as decreasing survival in localized and regional stages (urinary bladder and ovarian) and growing impact of late-stage diagnoses (esophageal cancer). CONCLUSION: While decelerating or decreasing prevalence of many cancers were due to a beneficial combination of decreasing incidence and increasing survival, there are cancers for which decelerating prevalence is due to lack of improvement in their stage-specific survival and/or increasing frequency of diagnosis at advanced stages. Overall, if the observed trends persist, it is likely that the burden associated with cancer prevalence in older U.S. adults will be lower  comparing to projections based on constant increasing prevalence have previously estimated.

A forecasting model of disease prevalence based on the McKendrick-von Foerster equation.

A new model for disease prevalence based on the analytical solutions of McKendric-von Foerster's partial differential equations is developed. Derivation of the model and methods to cross check obtained results are explicitly demonstrated. Obtained equations describe the time evolution of the healthy and unhealthy age-structured sub-populations and age patterns of disease prevalence. The projection of disease prevalence into the future requires estimates of time trends of age-specific disease incidence, relative survival functions, and prevalence at the initial age and year available in the data. The computational scheme for parameter estimations using Medicare data, analytical properties of the model, application for diabetes prevalence, and relationship with partitioning models are described and discussed. The model allows natural generalization for the case of several diseases as well as for modeling time trends in cause-specific mortality rates.

Theory of partitioning of disease prevalence and mortality in observational data.

In this study, we present a new theory of partitioning of disease prevalence and incidence-based mortality and demonstrate how this theory practically works for analyses of Medicare data. In the theory, the prevalence of a disease and incidence-based mortality are modeled in terms of disease incidence and survival after diagnosis supplemented by information on disease prevalence at the initial age and year available in a dataset. Partitioning of the trends of prevalence and mortality is calculated with minimal assumptions. The resulting expressions for the components of the trends are given by continuous functions of data. The estimator is consistent and stable. The developed methodology is applied for data on type 2 diabetes using individual records from a nationally representative 5% sample of Medicare beneficiaries age 65+. Numerical estimates show excellent concordance between empirical estimates and theoretical predictions. Evaluated partitioning model showed that both prevalence and mortality increase with time. The primary driving factors of the observed prevalence increase are improved survival and increased prevalence at age 65. The increase in diabetes-related mortality is driven by increased prevalence and unobserved trends in time-periods and age-groups outside of the range of the data used in the study. Finally, the properties of the new estimator, possible statistical and systematical uncertainties, and future practical applications of this methodology in epidemiology, demography, public health and health forecasting are discussed.

Assessment of the interaction of hyperbaric N2, CO2, and O2 on psychomotor performance in divers.

Diving narcosis results from the complex interaction of gases, activities, and environmental conditions. We hypothesized that these interactions could be separated into their component parts. Where previous studies have tested single cognitive tasks sequentially, we varied inspired partial pressures of CO2, N2, and O2 in immersed, exercising subjects while assessing multitasking performance with the Multi-Attribute Task Battery II (MATB-II) flight simulator. Cognitive performance was tested under 20 conditions of gas partial pressure and exercise in 42 male subjects meeting U.S. Navy age and fitness profiles. Inspired nitrogen (N2) and oxygen (O2) partial pressures were 0, 4.5, and 5.6 ATA and 0.21, 1.0, and 1.22 ATA, respectively, at rest and during 100-W immersed exercise with and without 0.075-ATA CO2 Linear regression modeled the association of gas partial pressure with task performance while controlling for exercise, hypercapnic ventilatory response, dive training, video game frequency, and age. Subjects served as their own controls. Impairment of memory, attention, and planning, but not motor tasks, was associated with N2 partial pressures >4.5 ATA. Sea level O2 at 0.925 ATA partially rescued motor and memory reaction time impaired by 0.075-ATA CO2; however, at hyperbaric pressures an unexpectedly strong interaction between CO2, N2, and exercise caused incapacitating narcosis with amnesia, which was augmented by O2 Perception of narcosis was not correlated with actual scores. The relative contributions of factors associated with diving narcosis will be useful to predict the effects of gas mixtures and exercise conditions on the cognitive performance of divers. The O2 effects are consistent with O2 narcosis or enhanced O2 toxicity.

Trends and outcomes in the use of surgery and radiation for the treatment of locally advanced esophageal cancer: a propensity score adjusted analysis of the surveillance, epidemiology, and end results registry from 1998 to 2008.

We examined outcomes and trends in surgery and radiation use for patients with locally advanced esophageal cancer, for whom optimal treatment isn't clear. Trends in surgery and radiation for patients with T1-T3N1M0 squamous cell or adenocarcinoma of the mid or distal esophagus in the Surveillance, Epidemiology, and End Results database from 1998 to 2008 were analyzed using generalized linear models including year as predictor; Surveillance, Epidemiology, and End Results doesn't record chemotherapy data. Local treatment was unimodal if patients had only surgery or radiation and bimodal if they had both. Five-year cancer-specific survival (CSS) and overall survival (OS) were analyzed using propensity-score adjusted Cox proportional-hazard models. Overall 5-year survival for the 3295 patients identified (mean age 65.1 years, standard deviation 11.0) was 18.9% (95% confidence interval: 17.3-20.7). Local treatment was bimodal for 1274 (38.7%) and unimodal for 2021 (61.3%) patients; 1325 (40.2%) had radiation alone and 696 (21.1%) underwent only surgery. The use of bimodal therapy (32.8-42.5%, P = 0.01) and radiation alone (29.3-44.5%, P < 0.001) increased significantly from 1998 to 2008. Bimodal therapy predicted improved CSS (hazard ratios [HR]: 0.68, P < 0.001) and OS (HR: 0.58, P < 0.001) compared with unimodal therapy. For the first 7 months (before survival curve crossing), CSS after radiation therapy alone was similar to surgery alone (HR: 0.86, P = 0.12) while OS was worse for surgery only (HR: 0.70, P = 0.001). However, worse CSS (HR: 1.43, P < 0.001) and OS (HR: 1.46, P < 0.001) after that initial timeframe were found for radiation therapy only. The use of radiation to treat locally advanced mid and distal esophageal cancers increased from 1998 to 2008. Survival was best when both surgery and radiation were used.

[The mechanism of the antitumor effect of total/subtotal radiotherapy with non-tumoricidal doses of radiation].

The myelodepression at therapy for solid malignancies is considered as a mediating element of common antineoplastic activity on the basis of ability of stem cells of hemapoietic system to participate in regeneration of the various tissues of the body, including tumor. The equivalence of the therapeutic benefit mediated by both mild myelodepression due to total/subtotal radiation exposure and conventional chemotherapy with cytotoxic drugs is argued.

The quadratic hazard model for analyzing longitudinal data on aging, health, and the life span.

A better understanding of processes and mechanisms linking human aging with changes in health status and survival requires methods capable of analyzing new data that take into account knowledge about these processes accumulated in the field. In this paper, we describe an approach to analyses of longitudinal data based on the use of stochastic process models of human aging, health, and longevity which allows for incorporating state of the art advances in aging research into the model structure. In particular, the model incorporates the notions of resistance to stresses, adaptive capacity, and "optimal" (normal) physiological states. To capture the effects of exposure to persistent external disturbances, the notions of allostatic adaptation and allostatic load are introduced. These notions facilitate the description and explanation of deviations of individuals' physiological indices from their normal states, which increase the chances of disease development and death. The model provides a convenient conceptual framework for comprehensive systemic analyses of aging-related changes in humans using longitudinal data and linking these changes with genotyping profiles, morbidity, and mortality risks. The model is used for developing new statistical methods for analyzing longitudinal data on aging, health, and longevity.

[Variation of mortality risk among cancer patients and individualized therapy].

To investigate the deviation of survival curves from monotonous shape, 17 curves for 6 most common cancer sites of 87,205 patients (aged 30-89) were selected from The U.S. Surveillance, Epidemiology and End Results (SEER) Register database. The curves were analyzed using monotonous exponential and harmonic functions which allowed a corresponding estimation of quasi-sinusoidal disturbances of monthly death hazard. Detectable fluctuations of the latter were established in the 0-40--month life span zone depending on tumor site and age at diagnosis.

Cumulative index of health disorders as an indicator of aging-associated processes in the elderly: results from analyses of the National Long Term Care Survey.

BACKGROUND: We employ an approach based on the elaborated frailty index (FI), which is capable of taking into account variables with mild effect on the aging, health and survival outcomes, and investigate the connections between the FI, chronological age and the aging-associated outcomes in the elderly. METHODS: Cross-sectional analysis of pooled data from the National Long Term Care Survey (NLTCS) assessing health and functioning of the U.S. elderly in 1982, 1984, 1989, 1994, and 1999. RESULTS: Distributions of frequency, residual life span, mortality rate, and relative risk of death are remarkably similar over age and FI. Coefficients of correlation between FI and age are low both for males (0.127, p<.01) and females (0.221, p<.01). The FI-specific age patterns show deceleration at advanced ages. The FI can provide order of magnitude better resolution in estimating mean remaining life span compared to age. Males have smaller FI than females while males' mortality risks are higher. For short-time horizons, the FI and age are largely independently associated with mortality risks. CONCLUSIONS: The FI: (i) can be considered as an adequate sex-specific indicator of the aging-associated processes in the elderly, (ii) can characterize these processes independently of age, and (iii) is a better characteristic of the aging phenotype than chronological age.

Modeling the effects of biologically incorporated radionuclides and chronic low-dose ionizing radiation exposure on both cancer and chronic non-cancer diseases.

Efforts to model the health effects of low-dose ionizing radiation (IR) have often focused on cancer. Meanwhile, significant evidence links IR and age-associated non-cancer diseases. Modeling of such complex processes, which are not currently well understood, is a challenging problem. In this paper we briefly overview recent successful attempts to model cancer on a population level and propose how those models may be adapted to include the impact of IR and to describe complex non-cancer diseases. We propose three classes of models which we believe are well suited for the analysis of the health effects in human populations exposed to low-dose IR. These models use biostatistical/epidemiological techniques and mathematical formulas describing the biological mechanisms of the impact of IR on human health. They can combine data from multiple sources and from distinct levels of biological/population organization. The proposed models are intrinsically multivariate and non-linear and capture the dynamic aspects of health change.

Population models for the health effects of ionizing radiation.

In this paper we review recently-developed extension frailty, quadratic hazard, stochastic process, microsimulation, and linear latent structure models, which have the potential to describe the health effects of human populations exposed to ionizing radiation. We discuss the most common situations for which such models are appropriate. We also provide examples of how to estimate the parameters of these models from datasets of various designs. Carcinogenesis models are reviewed in context of application to epidemiologic data of population exposed to ionizing radiation. We also discuss the ways of how to generalize stochastic process and correlated frailty models for longitudinal and family analyses in radiation epidemiology.

Demographic analysis and modeling of human populations exposed to ionizing radiation.

The health effects of ionizing radiation on human populations are often analyzed using epidemiological statistical methods. Because of the complexity of the health consequences of ionizing radiation and the prolonged period during which the consequences emerge, we propose to evaluate these health effects using mathematical models that are based on the best theoretical reasoning and prior biological evidence about disease mechanisms. We believe this will improve the ability of the model to identify health effects and reduce erroneous inferences.

Modeling nonlinear effects in longitudinal survival data: implications for the physiological dynamics of biological systems.

Despite the wealth of longitudinal data on the health dynamics of human populations, information on covariates (risk factors) changes in those studies has not been systematically and fully exploited. In this work we use the 46-year follow-up of the Framingham Heart Study to analyze dynamics of these risk factors in survival models that go far beyond the standard linear dynamic formulation. We focus on improving the inferences about the physiology of human aging processes and its plasticity and on modeling state trajectories for individuals considering the effect of nonlinear interactions among covariates. We find that using standard statistical methods to construct models describing the age dependence of health status might give rise to surprising results with highly "diluted" dynamics, but with significantly improved statistical criteria. It is found that problems with the dynamics are a consequence of the intrinsic nonlinear nature of these models. We show that evolution of the risk factors measured in the Framingham study is more complicated for females than for males (i.e., female health status is more sensitive to nonlinear interactions among risk factors). We suggest that this is due to the rapid rate of decline of estrogen production after menopause.