The Demography of Health and Healthcare

Pol, Louis G.; Thomas, Richard K.

doi:10.1007/978-90-481-8903-8

Cited by 28 publications

(19 citation statements)

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“…However, he also found that early adversity triggered a range of social transitions, including earlier marriages and antisocial behavior (Elder 1998). This is representative of a social trajectory model (STM) because he argued that the mechanism is sociobehavioral; Depression-era children possibly carry scars from that period that are not strictly physiological (Pol and Thomas 2002). In other words, it is not (necessarily 2 ) that malnutrition or stress cause long-lasting physiological damage but rather that early hardship causes people to engage in riskier behaviors.…”

Section: Social Trajectory Modelsmentioning

confidence: 98%

Cognitive Disparities: The Impact of the Great Depression and Cumulative Inequality on Later-Life Cognitive Function

Hale

2017

Demography

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Population aging has driven a spate of recent research on later-life cognitive function. Greater longevity increases the lifetime risk of memory diseases that compromise the cognitive abilities vital to well-being. Alzheimer's disease, thought to be the most common underlying pathology for elders' cognitive dysfunction (Willis and Hakim 2013), is already the sixth leading cause of death in the United States (Alzheimer's Association 2016). Understanding social determinants of pathological cognitive decline is key to crafting interventions, but evidence is inconclusive for how social factors interact over the life course to affect cognitive function. I study whether early-life exposure to the Great Depression is directly associated with later-life cognitive function, influences risky behaviors over the life course, and/or accumulates with other life-course disadvantages. Using growth curve models to analyze the Health and Retirement Study, I find that early-life exposure to the Great Depression is associated with fluid cognition, controlling for intervening factors-evidence for a critical period model. I find little support for a social trajectory model. Disadvantage accumulates over the life course to predict worse cognitive function, providing strong evidence for a cumulative inequality model.

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Section: Social Trajectory Modelsmentioning

confidence: 98%

Cognitive Disparities: The Impact of the Great Depression and Cumulative Inequality on Later-Life Cognitive Function

Hale

2017

Demography

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“…According to American mortality statistics, it is held that get married is regarded as an advantage of being healthier. Nevertheless, after adjustment for age, the mortality rate for the married is lower and life expectancy is higher than for unmarried (17). Limited studies have been conducted on the effect of marital status on mortality, and even lesser on cancer mortality.…”

Section: Cancer Mortality and Socio-demographic Factorsmentioning

confidence: 99%

Risk of dying from cancer by socio-demographic indicators in the Slovak Republic

Kubák¹,

Štefko²,

Barták³

et al. 2017

Cent Eur J Public Health

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SUMMARYAim: Cancer mortality distribution was investigated by detailed neoplasms groups, age, sex, marital status of deceased, and regions in the Slovak Republic, and examined how these determinants influence the odds of dying due to cancer.Methods: A retrospective analysis of cancer mortality statistics registered in the Slovak Republic during the years 1996-2014. For this time period, data was available only on the underlying subgroups of cancer deaths, place of death, age, year, sex, and marital status. Binary logistic regression was applied for odds of dying calculation influenced by these socio-demographic factors.Results: The most common are deaths from malignant neoplasms of digestive organs in males as well as females. The biggest difference among both genders is recognized in malignant neoplasms of lip, oral cavity and pharynx, where deaths among males are on average 7.9 times higher in comparison to females. As for place of death the Bratislava region reports the highest level of cancer mortality stated at 25.22% of all deaths, on the contrary the Banská Bystrica region reports only 21.40% of all deaths. Age has a negative influence on odds of dying due to neoplasms compared to all other causes of death by 1.7%. In all regions compared to the reference Bratislava region, the odds of dying from neoplasms are lower. Being female diminishes the odds of dying due to neoplasms by 25.7% compared to males. Yearly the relative ratio of dying from neoplasms increases with respect to all other causes of death. When single people are set as the reference category, the relation of the probability of death from cancer to the probability of death due to other causes of death is higher for married, divorced and widowed persons.Conclusions: The results should be taken into account when comparing risk of dying due to cancer among people with the mentioned sociodemographic characteristics. Health policy makers should consider place of death and cancer types while planning hospital care units.

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“…Additionally, more educated people should die later than people with lower educational level. This is partly explained by healthier lifestyles and more appropriate use of the healthcare services (Soltes & Gavurova, 2014;Rogalewicz, 2015;Zavadil et al, 2015), what could be apparent in both prevalence of various diseases, mortality differences and effective hospital care delivery system (Pol & Thomas, 2013;Nováková & Šoltés, 2016;Bem et al, 2015;Siedlecki et al, 2015). However, our analyses did not confirm these assumptions.…”

Section: Discussionmentioning

confidence: 99%

“…Some behavioural factors are completely modifiable, like alcohol and tobacco consumption, obesity, physical activity; partly modifiable are socioeconomic characteristics, e.g. family income, education, occupational status, marital status; conversely, others are non-modifiable, such as age, sex, ethnicity (Pol & Thomas, 2013). One of the many factors that may play a role is educational level which used to be marked as an indicator of social and economic status.…”

Section: Introductionmentioning

confidence: 99%

Educational Attainment and Cardiovascular Disease Mortality in the Slovak Republic

Gavurová

Vagašová

Gróf

2017

Economics &Amp; Sociology

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ABSTRACT. This paper devotes to the development analysis of cardiovascular disease mortality rate by sex, age, education, and leading causes of deaths during the period of 1996-2014 in the Slovak Republic. Survival analysis and Cox proportional hazard model were conducted to estimate the impact of sex and education level on the probability of death due to cardiovascular diseases at different age. According to our results, standardised mortality rates decreased by an average of 31.5% for both sexes. The leading causes of death were hearth failure and cardiomyopathy for persons under 30 years of age. The myocardial infarction, chronic ischemic heart disease and atherosclerosis were the most common causes of death for adults, as well as seniors. Women represented a lower level of hazard rate than men and primary education group reported the lowest level of hazard rate in comparison to the other education groups.

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The Demography of Health and Healthcare

Cited by 28 publications

References 0 publications

Cognitive Disparities: The Impact of the Great Depression and Cumulative Inequality on Later-Life Cognitive Function

Cognitive Disparities: The Impact of the Great Depression and Cumulative Inequality on Later-Life Cognitive Function

Risk of dying from cancer by socio-demographic indicators in the Slovak Republic

Educational Attainment and Cardiovascular Disease Mortality in the Slovak Republic

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