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.

[Development of a Series of Neutralizing Nanobodies against SARS-CoV-2 Spike Protein].

Countering the spread of new respiratory infections and reducing the damage they cause to society requires efficient strategies for rapidly developing of targeted therapeutics, such as monoclonal antibodies. Nanobodies, defined as variable fragments of heavy-chain camelid antibodies, have a set of characteristics that make them particularly convenient for this purpose. The speed at which the SARS-CoV-2 pandemic spread confirmed that the key factor in the development of therapeutics is obtaining highly effective blocking agents as soon as possible, as well as the diversity of epitopes to which these agents bind. We have optimized the selection process of blocking nanobodies from the genetic material of camelids and obtained a panel of nanobody structures with affinity to Spike protein in the lower nanomolar and picomolar ranges and with high binding specificity. The subset of nanobodies that demonstrate the ability to block the interaction between the Spike protein and the cellular ACE2 receptor was selected in experiments in vitro and in vivo. It has been established that the epitopes bound by the nanobodies are located in the RBD domain of the Spike protein and have little overlap. The diversity of binding regions may allow a mixture of nanobodies to retain potential therapeutic efficacy towards new Spike protein variants. Furthermore, the structural features of nanobodies, particularly their compact size and high stability, indicate the possibility of their utilization in the form of aerosols.

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.

[CD47 receptor as a primary target for cancer therapy].

Recently, a number of new highly efficient antibody-based anticancer therapeutics have emerged. These receptor-binding antibodies have beneficial toxicity profiles associated with relatively mild side effects. Therefore, the search for novel surface proteins that are present on cancer cells and play important metabolic or defensive roles has intensified. Additionally, the therapeutic stimulation of patient's immune system in order to aim its components, specifically, phagocytes and cytotoxic T-lymphocytes, at tumor cells is gaining traction. This review is focused on the CD47 surface receptor, a ubiquitously expressed molecule, which could nevertheless serve as a therapeutic target due to its ability to simultaneously stimulate both natural and adaptive immune response.

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.

[Role of PDLIM4 and c-Src in Breast Cancer Progression].

High heterogeneity is characteristic of oncology diseases, often complicating the choice of optimal anticancer treatment. One cancer type may combine tumors differing in histogenesis, genetic lesions, and mechanism of cell transformation. Differences in the mechanism of cell malignant transformation result in specifics of cancer cell metabolism and sensitivity to various agents, including anticancer treatments. Hence, the molecular subtype of a tumor is essential to know for choosing the optimal therapeutic strategy. The review considers the role actin-associated proteins and tyrosine kinases, in particular, PDLIM4 and Src kinase, play in the formation of pathological signaling pathways.

[Oncotoxic proteins in cancer therapy: mechanisms of action].

Cancer therapeutics based on protein biomolecules that exhibit selective toxic of inhibiting effects towards tumor cells without affecting normal tissue, are gaining extensive attention in cancer research. This heterogenous group of proteins consists of several subgroups, among them, are engineered cancer antigen-specific antibodies that suppress tumor growth by blocking proliferation-inducing receptors, or by direct action of a covalently attached toxin. Another subgroup of anticancer proteins that also represents promising potential therapeutic agents is oncotoxic proteins that can selectively trigger proapoptotic signaling in cancer cells. The oncotoxic proteins target such commonly disturbed processes in tumor calls as enhanced cell proliferation, altered cell-cycle control, deficient apoptotic response, inhibited mitochondrial respiration and activated glycolysis. The introduction of oncotoxic proteins to the clinic might substantially widen and upgrade modern arsenal of anticancer therapeutics.

Small-molecule RETRA suppresses mutant p53-bearing cancer cells through a p73-dependent salvage pathway.

Identification of unique features of cancer cells is important for defining specific and efficient therapeutic targets. Mutant p53 is present in nearly half of all cancer cases, forming a promising target for pharmacological reactivation. In addition to being defective for the tumor-suppressor function, mutant p53 contributes to malignancy by blocking a p53 family member p73. Here, we describe a small-molecule RETRA that activates a set of p53-regulated genes and specifically suppresses mutant p53-bearing tumor cells in vitro and in mouse xenografts. Although the effect is strictly limited to the cells expressing mutant p53, it is abrogated by inhibition with RNAi to p73. Treatment of mutant p53-expressing cancer cells with RETRA results in a substantial increase in the expression level of p73, and a release of p73 from the blocking complex with mutant p53, which produces tumor-suppressor effects similar to the functional reactivation of p53. RETRA is active against tumor cells expressing a variety of p53 mutants and does not affect normal cells. The results validate the mutant p53-p73 complex as a promising and highly specific potential target for cancer therapy.

The risk of radiation-induced cerebrovascular disease in Chernobyl emergency workers.

The paper deals with estimating radiation risks of non-cancer diseases of the circulatory system among the Chernobyl emergency workers based on data from the Russian National Medical and Dosimetric Registry. The results for the cohort of 61,017 people observed between 1986 and 2000 are discussed. These are essentially updated results for the similar cohort that was studied by authors earlier in 1986-1996. Newly discovered is the statistically significant dose risk of ischemic heart disease [ERR Gy = 0.41, 95% CI = (0.05; 0.78)]. Confirmation is provided for the existence of significant dose risks for essential hypertension [ERR Gy = 0.36, 95% CI = (0.005; 0.71)] and cerebrovascular diseases [ERR Gy = 0.45, 95% CI = (0.11; 0.80)]. In 1996-2000, the assessed ERR Gy for cerebrovascular diseases was 0.22 with 95% CI = (-0.15; 0.58). Special consideration is given to cerebrovascular diseases in the cohort of 29,003 emergency workers who arrived in the Chernobyl zone during the first year after the accident. The statistically significant heterogeneity of the dose risk of cerebrovascular diseases is shown as a function of the duration of stay in the Chernobyl zone: ERR Gy = 0.89 for durations of less than 6 wk, and ERR Gy = 0.39 on average. The at-risk group with respect to cerebrovascular diseases are those who received external radiation doses greater than 150 mGy in less than 6 wk [RR = 1.18, 95% CI = (1.00; 1.40)]. For doses above 150 mGy, the statistically significant risk of cerebrovascular diseases as a function of averaged dose rate (mean daily dose) was observed: ERR per 100 mGy d = 2.17 with 95% CI = (0.64; 3.69). The duration of stay within the Chernobyl zone itself, regardless of the dose factor, had little influence on cerebrovascular disease morbidity: ERR wk = -0.002, with 95% CI = (-0.004; -0.001). The radiation risks in this large-scale cohort study were not adjusted for recognized risk factors such as excessive weight, hypercholesterolemia, smoking, alcohol consumption, and others.

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.