Epidemiologic Determinants of Dynamics in Heart Failure Prevalence and Mortality in Older U.S. Adults

Recent declines in heart failure (HF) prevalence and increases in mortality among older adults in the US suggest the need for research to investigate the relative contribution of the epidemiological determinants of these two processes to their historical and current trends. Study data were derived from a 5% sample of Medicare beneficiaries, 1991-2017. Partitioning analysis was used to decompose age-adjusted prevalence and incidence-based mortality (IBM) into their constituent components. HF prevalence trend decomposition demonstrated three phases: (a) Decelerated Increasing Prevalence (1994-2006) mainly driven by decreasing incidence, overpowering increasing survival, (b) Accelerated Declining Prevalence (2007-2014) and (c) Decelerated Declining Prevalence (2015-2017), mainly driven by declining incidence, overpowering declining survival. For HF IBM four phases were identified: (a) Decelerated Increasing Mortality (1994-2001) with declining incidence and increasing survival driving deceleration, (b) Accelerated Declining Mortality (2002-2012), (c) Decelerated Declining Mortality (2013-2016), mainly driven by declining incidence, overpowering declining survival, and (d) Accelerated Increasing Mortality (2017) mainly driven by declining survival, overpowering declining incidence. Study findings suggest that the recent decade-long decline in HF prevalence and 15-year decline in HF mortality mainly reflected decreasing incidence, while the most recent increase in mortality was due to declining survival, which may be associated with the Hospital Readmission Reduction Program. If current trends of incidence and survival persist, HF prevalence and mortality are forecasted to grow, suggesting that actions to reduce HF risk factors and improve treatment and management of HF after diagnosis are warranted.

Emergence and variability of influence exerted by weather and climatic factors on life expectancy in the russian federation taking into account differentiation of rf regions as per socioeconomic and sanitary-epidemiologic determinants

The present research focuses on estimating influence exerted by weather and climatic factors on life expectancy (LE) in the Russian Federation taking into account socioeconomic and sanitary-epidemiologic determinants. To estimate influence exerted by this factor on LE, a mathematic model was applied; the model was based on neuron networks and allowed taking into account emergence and variability of influence exerted on changes in LE by a set of heterogeneous factors including weather and climatic ones. It was established that over 2010–2018 climate changed in most RF regions as there was a growth in average monthly temperatures (temperature deviated from its long-term average monthly values by +1.2ºС in July, and by +1,5ºС in January),and changes in precipitations (deviations amounted to -1.9% in July and +13.0% in January). It was established that «average monthly temperature in July» exerted the greatest direct influence on LE; thus, if this parameter grows by 1%, it results in additional 1.7 days of LE. «Average precipitations quantity in January» turned out to be the most significant factor leading to a decrease in LE; a 1% growth in this parameter resulted in LE decrease by 0.12 days. It was shown that mathematical expectancy of LE loss variability in RF regions obtained basing on 85 scenarios of weather and climatic conditions ranged from -4.2 days to 348.7 days. Overall in the RF climate-associated losses in LE taken as weighted average as per population number amounted to 191.7 days. It was established that climate-associated losses in LE were authentically lower in North Caucasian regions than in regions located in temperate zone with Atlantic-continental and continental climate (by 1.6 and 1.8 times accordingly). We also comparatively analyzed losses in LE due to influence exerted by climate in RF regions distributed into different groups (clusters) as per socioeconomic parameters; the analysis revealed authentic differences between the second and the fourth cluster (p=0.01), and between the third and the fourth ones (p=0.006). We didn’t reveal any authentic differences in climate-associated losses in LE among clusters as per sanitary-epidemiologic parameters.

Emergence and variability of influence exerted by weather and climatic factors on life expectancy in the russian federation taking into account differentiation of rf regions as per socioeconomic and sanitary-epidemiologic determinants

The present research focuses on estimating influence exerted by weather and climatic factors on life expectancy (LE) in the Russian Federation taking into account socioeconomic and sanitary-epidemiologic determinants. To estimate influence exerted by this factor on LE, a mathematic model was applied; the model was based on neuron networks and allowed taking into account emergence and variability of influence exerted on changes in LE by a set of heterogeneous factors including weather and climatic ones. It was established that over 2010–2018 climate changed in most RF regions as there was a growth in average monthly temperatures (temperature deviated from its long-term average monthly values by +1.2ºС in July, and by +1,5ºС in January),and changes in precipitations (deviations amounted to -1.9% in July and +13.0% in January). It was established that «average monthly temperature in July» exerted the greatest direct influence on LE; thus, if this parameter grows by 1%, it results in additional 1.7 days of LE. «Average precipitations quantity in January» turned out to be the most significant factor leading to a decrease in LE; a 1% growth in this parameter resulted in LE decrease by 0.12 days. It was shown that mathematical expectancy of LE loss variability in RF regions obtained basing on 85 scenarios of weather and climatic conditions ranged from -4.2 days to 348.7 days. Overall in the RF climate-associated losses in LE taken as weighted average as per population number amounted to 191.7 days. It was established that climate-associated losses in LE were authentically lower in North Caucasian regions than in regions located in temperate zone with Atlantic-continental and continental climate (by 1.6 and 1.8 times accordingly). We also comparatively analyzed losses in LE due to influence exerted by climate in RF regions distributed into different groups (clusters) as per socioeconomic parameters; the analysis revealed authentic differences between the second and the fourth cluster (p=0.01), and between the third and the fourth ones (p=0.006). We didn’t reveal any authentic differences in climate-associated losses in LE among clusters as per sanitary-epidemiologic parameters.

Epidemiology and Risk Factors for Kidney Cancer

The purpose of this narrative review is to summarize evidence of the epidemiology of and risk factors for kidney cancer with a focus on renal cell carcinoma in adults. The etiology of kidney cancer is largely unknown and the main epidemiologic determinants are large geographic and temporal variations in incidence rates. Established risk factors include tobacco smoking, body size, and history of hypertension and chronic kidney disease. Other suspected risk factors require additional investigation, as do the underlying biologic mechanisms that are responsible for disease occurrence. Opportunities to prevent kidney cancer include targeting modifiable risk factors—for example, smoking abstinence/cessation and body weight control—as well as interventions along the diagnostic pathway to improve early diagnosis. Molecular epidemiology, including, but not limited to, metabolomics and tumor genomics, are new areas of research that promise to play important roles in identifying some of the underlying causes of kidney cancer.

Epidemiologic Determinants Affecting Cigarette Smoking Cessation: A Retrospective Study in a National Health System (SSN) Treatment Service in Rome (Italy)

This retrospective study aims to evaluate epidemiologic characteristics of patients attending stop smoking courses, based on group therapy, testing their influence on smoking cessation in univariate and multivariate model. A total of 123 patients were included in this study. Mean age was 53 (). Sixty-seven percent were women. At the end of the courses 66% of patients stopped smoking, after 12 months only 39% remained abstinent. Patients younger than 50 years statistically tended to continue smoking 6 months ( 1.49, C.I. 95%: 1.06–2.44) and 12 months ( 1.37, C.I. 95%: 1.02–2.52) after the end of the courses. A low self-confidence in quitting smoking was significantly related to continuing tobacco consumption after 6 months ( 1.84, C.I. 95%: 1.14–2.99). Low adherence to therapeutic program was statistically associated to maintenance of tobacco use at 6 months ( 1.76, C.I. 95%: 1.32–2.35) and 12 months ( 1.45, C.I. 95%: 1.11–1.88). This association was confirmed at 6 months in the analysis performed on logistic regression model ().