Chronic illness and poverty in The Netherlands

Agt, Heleen van

doi:10.1093/eurpub/10.3.197

Cited by 24 publications

(19 citation statements)

References 9 publications

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“…Severe chronic disease has been shown to be associated with poverty [38][39][40][41]. Welfare systems aim to provide security and help all individuals to achieve "life goals" -regardless of health status.…”

Section: Multiple Sclerosis and Work Disabilitymentioning

confidence: 99%

Sources and level of income among individuals with multiple sclerosis compared to the general population: A nationwide population-based study

et al. 2015

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Section: Multiple Sclerosis and Work Disabilitymentioning

confidence: 99%

Sources and level of income among individuals with multiple sclerosis compared to the general population: A nationwide population-based study

et al. 2015

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“…All occupation measures were classified according to the Erikson, Goldthorpe and Portocarero (EGP) scheme (Erikson et al, 1983) into four levels: manual workers, selfemployed, lower-grade professionals (routine non-manual work, middle management, skilled non-manual work) and higher-grade professionals (higher levels of management, academia). These classifications have been shown to discriminate socioeconomic differences in health and other health-related behaviors among the Dutch population (Bosma et al, 1999;van de Mheen et al, 1999;van Agt et al, 2000;van Lenthe et al, 2000).…”

Section: Socioeconomic Positionmentioning

confidence: 99%

Dietary intakes of adults in the Netherlands by childhood and adulthood socioeconomic position

et al. 2004

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Objective: To determine whether there are socioeconomic differences in the intakes of total fat, fatty acids and fruit among adults in the Netherlands using childhood (parental) and participant's own socioeconomic position (SEP). Furthermore, to quantify the independent effects of childhood and adulthood SEP on dietary behavior in adulthood. Design: Cross-sectional study among participants in the GLOBE study. Subjects: A total of 2512 men and women, aged 25-78 y, living in a region in the southeast Netherlands. Methods: Dietary intakes were collected by an interviewer-administered quantitative food frequency questionnaire. Average daily intakes of total energy, total, saturated, monounsaturated, polyunsaturated fat and fruit were calculated. The highest educational level was used to classify the participant's adulthood SEP. Parental indicators (mother's education and father's occupation when participants were 12 y of age) were used to classify childhood SEP. Results: Males with lower levels of education had moderately higher energy intakes than their more educated counterparts, but did not differ in their intakes of total fat, fatty acids and fruit. Among females, the least educated groups had marginally higher intakes of total and monounsaturated fat than the most educated group, and were less likely to consume fruit. For most of these significant differences, the participant's own education demonstrated independent effects that were consistent with chronic disease inequalities. A small residual effect of mother's education was also demonstrated for intakes of some nutrients for males and females, and for fruit consumption among females. However, the effect size of mother's education was rather small and not always consistent with disease inequalities. Conclusions:The results imply that socioeconomic disparities in intakes of some dietary factors may contribute to inequalities in chronic disease. Adulthood SEP potentially has a more direct influence on dietary intake inequalities than childhood SEP.

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“…The WHO indicated that 4 out of every 5 people with chronic illnesses were in developing countries and that 3 of every 5 global mortalities are caused by chronic illness [35]. This implies that there is an association between poverty and chronic illness, social deprivation and mortality and money and good or bad health [36]. The current work found that the most influential factor influencing illness rate in Jamaica is poverty followed by unemployment and GDP per capita growth.…”

Section: Discussionmentioning

confidence: 75%

Under-Five Mortality, Health and Selected Macroeconomic Variables: The Children behind the Digits

Bourne¹

2012

Epidemiol

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IntroductionThe study of mortality dates back to the seventeenth century [1][2][3]. Graunt's [1] work on the 'Bill of mortality' sets a premise for the establishment of mathematical modeling in the area of mortality. In the nineteenth century, Gompertz [4] developed a mathematical formula that estimated mortality at different ages. Gompertz's [4] theory established that mortality increases at geometric progression at a particular age and forwarded that this can be represented by a mortality risk function µ(x) = α. e Makeham's [6] function that it does not hold true at older ages and that it overly estimated mortality at older ages (80+ years) [7,8], the work has laid the foundation upon which many studies have been framed [9]. The life tables is one of the creations that emerged from mortality statistics [10,11], which was used to indicate life expectancy or health of a population.The literature speaks to decreasing mortality at younger ages, Bourgeois-Pichat [12,13] proposed disaggregating infant mortality in endogenous and exogenous (accidents or infections) components and fitted this by the formula:, where a is a constant denoting the endogenous process, cumulative death in the cohort by age n (in days). Modifications are well documented in the literature to Bourgeois-Pichat's [13] work, which guide contemporary studies in the area. Examining child health, using infant and child mortality rates in Peru, Paxson and Schady [14] found that infant mortality increased during economic crisis and that infant and child mortality followed a collinear pattern over the studied period (1978-to-1999). Paxson and Schady [14] like early scholar fitted infant mortality rates with linear models for each year of birth: M it = α + X it βit + ε it , where M it is child born in year t to mother I died in the first year of life, X it is maternal characteristics (level of schooling, age, area of residence) and the error term, ε it .The earlier pioneers are still guiding the directions of contemporary scholarships. In 1992, Waldmann [15] assessing infant mortality and income, used log (infant mortality) = β log (Nonrich Income) + γRich Share. He modified the early model as follows: log (infant mortality) = β 0 + β 1 log (Poor Income) + β 2 log(Middle Income) + γRich Share + δYear 1970. Using the aforementioned model, Waldmann [15] found a positive correlation between log(Poor income) and log infant mortality and a negative one for log middle income and log infant mortality. AbstractBackground: Mortality is filled with studies that evaluated infant mortality, child mortality, and income distribution and mortality, but no single research in the English-speaking Caribbean has wholly examined child mortality, inflation, infant mortality, poverty and economic crisis as well as modeling those phenomena.

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Chronic illness and poverty in The Netherlands

Cited by 24 publications

References 9 publications

Sources and level of income among individuals with multiple sclerosis compared to the general population: A nationwide population-based study

Sources and level of income among individuals with multiple sclerosis compared to the general population: A nationwide population-based study

Dietary intakes of adults in the Netherlands by childhood and adulthood socioeconomic position

Under-Five Mortality, Health and Selected Macroeconomic Variables: The Children behind the Digits

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