Social determinants of disability pension: a 10-year follow-up of 62 000 people in a Norwegian county population

Krokstad, Steinar; Johnsen, Roar; Westin, Steinar

doi:10.1093/ije/31.6.1183

Cited by 212 publications

(284 citation statements)

References 30 publications

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“…The education level is known as an individual [51][52][53][54][55][56] These models were adjusted for the proportion of people aged 65+ and population density (log-transformed ). * : Adjusted for the proportion of people aged 65+, population density (log-transformed ), per capita income, and ordinary income/expenditure balance ratio : Partial regression coefficient SE: standard error was to generate hypotheses regarding the factors that determine the differences in health expectancy in Japan as a preliminary step for further research, these limitations are insignificant.…”

Section: Selection Of Independent Variablesmentioning

confidence: 99%

Factors Explaining Disability-free Life Expectancy in Japan: the Proportion of Older Workers, Self-reported Health Status, and the Number of Public Health Nurses

Kondo

Mizutani

Minai

et al. 2005

Journal of Epidemiology

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Statistical analysisFirst, for descriptive observations and screening purposes, distributions of the selected variables were studied using histograms. Log-transformation was performed for the skewed variables of population density and unemployment rate. Then, correlation coefficients between DFLE65s of men and women and independent variables were calculated. For the correlation analyses, Pearson's product moment correlation coefficient or Spearman's rank correlation coefficient were used for the independent variables of normal or non-normal distribution, respectively. An independent variable with one absolute value of the correlation coefficient of 0.20 or more was selected as a factor that was potentially associated with the DFLE65s. The variables that were insignificant or irrelevant with regard to DFLE65 were excluded (e.g., the percentage of paved roads or electric energy consumption). Further, the linearity of each association with independent variables and DFLE65s were checked by analyzing scatter charts, and its heteroscedasticity was assessed by observing a residual plot. Finally, multivariate linear regression analyses were performed using the 24 selected variables. The proportion of older people (aged 65 years or more) and the population density (log-transformed) were included in the regression models as potential confounding factors. These data were included because it could theoretically affect other explanatory variables although the correlation between the two indicators and DFLE65s was not significantly strong. In fact, many explanatory variables were associated with them (e.g., Pearson's correlation coefficients of the number of public health nurses (PHNs) per 100,000 population were -0.81 for log-transformed population density and 0.80 for the proportion of older people).In Pearson's correlation analysis and regression analysis, Aomori prefecture data was excluded as an outlier because its DFLE65s for both men (14.0 years) and women (17.3 years) were lower than that of the average of 47 prefectures (mean standard deviation [SD] = 15.2 0.4 in men and 18.5 0.4 in women) by three SDs. In addition, residual analyses showed that the inclusion of Aomori prefecture data in the regression analysis deteriorated the linearity and equality of variance of the regression between DFLE65s and explanatory variables.Self-reported health status was determined based on the question, "What do you think of your current health status?" in a Comprehensive Survey of the Living Conditions of People on Health and Welfare. 21 The following five alternatives were provided as answers: good, relatively good, normal, relatively bad, and bad. In our study, the proportion of good self-reported health that was obtained by combining the proportion of "good" and "relatively good" self-reported health was used for analysis.All the variables used contained no missing data. All p values are two tailed. All data analyses were performed using the SAS

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Section: Selection Of Independent Variablesmentioning

confidence: 99%

Factors Explaining Disability-free Life Expectancy in Japan: the Proportion of Older Workers, Self-reported Health Status, and the Number of Public Health Nurses

Kondo

Mizutani

Minai

et al. 2005

Journal of Epidemiology

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“…Those undertaken have shown positive associations between the award of disability pension and 'mentally strenuous work', as in a Finnish study by Krause et al (1997) and with low job control (studies from Denmark and Norway) (Krokstad et al 2002;Claussen and Dalgard 2009;Friis et al 2008;Christensen et al 2008). However, to our knowledge, only two studies have employed the Job Content Questionnaire (JCQ) in this context.…”

Section: Introductionmentioning

confidence: 99%

Can high psychological job demands, low decision latitude, and high job strain predict disability pensions? A 12-year follow-up of middle-aged Swedish workers

Canivet

Choi

Karasek

et al. 2012

Int Arch Occup Environ Health

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“…Tras varias simulaciones se establecieron diferentes puntos de corte. En relación a la duración de la IT, varios estudios observan un aumento en la probabilidad de desarrollar una IP al incrementar la duración de la IT [13][14][15][16][17][18] . Este tiempo de duración de la IT se agrupó en tres categorías con su respectiva puntuación: menos de 4 meses (0 puntos), entre 4 y 6 meses (1 punto) y más de 6 meses (2 puntos).…”

Section: Materials Y Métodosunclassified

Estimación de la carga de enfermedad por incapacidad laboral permanente en España durante el período 2009-2012

López

Duran

Alonso

et al. 2014

Rev. Esp. Salud Publica

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Los autores declaran no tener conflictos de intereses. RESUMENFundamentos: La incapacidad laboral permanente (IP) supone una importante carga social y económica. El objetivo de este estudio fue estimar la carga de enfermedad debida a la IP en España.Métodos: A partir de la Muestra Continua de Vidas Laborales (MCVL) se imputó como diagnóstico de la IP (2009-2012) el de la incapacidad temporal (IT) iniciada en 2009 en los individuos que cumplieron dos criterios: larga duración de la IT y corta duración del período entre la IT y la IP. Para los casos con diagnóstico imputado se calculó la carga de enfermedad por IP a partir de los años potenciales de vida laboral perdidos (APVLP), definidos como la diferencia entre la edad de jubilación (65 años) y la edad de inicio de la IP.Resultados: Según la MCVL, 163.135 (13,6%) personas sufrieron de IT en 2009, de las cuáles a 4.738 (0,39%) se le reconoció una IP entre 2009-2012. Se pudo establecer el diagnóstico de IP en 3.073 (64,9%) casos. Las causas más frecuentes de IP fueron los trastornos musculo-esqueléticos (27,4%) y mentales (11,6%) que, asimismo, produjeron mayor cantidad de APVLP por IP: 11.086 (26,5%) y 7.052 (16,9%), respectivamente. Los trastornos mentales destacaron por producir IP a edades más jóvenes (mediana=49 años). Conclusiones:Los trastornos mentales y los musculo-esqueléticos representaron la mayor carga de enfermedad en individuos en situación de IP.

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Social determinants of disability pension: a 10-year follow-up of 62 000 people in a Norwegian county population

Abstract: Even for a medically based DP, low socioeconomic status, low level of education and occupational factors might be strong determinants when compared to medical factors alone. These non-medical determinants are usually not addressed by individual based health or rehabilitation programmes.

Cited by 212 publications

References 30 publications

Factors Explaining Disability-free Life Expectancy in Japan: the Proportion of Older Workers, Self-reported Health Status, and the Number of Public Health Nurses

Factors Explaining Disability-free Life Expectancy in Japan: the Proportion of Older Workers, Self-reported Health Status, and the Number of Public Health Nurses

Can high psychological job demands, low decision latitude, and high job strain predict disability pensions? A 12-year follow-up of middle-aged Swedish workers

Estimación de la carga de enfermedad por incapacidad laboral permanente en España durante el período 2009-2012

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