Occupational Injuries in Germany: Population-Wide National Survey Data Emphasize the Importance of Work-Related Factors

Abstract

Unintentional injuries cause much of the global mortality burden, with the workplace being a common accident setting. Even in high-income economies, occupational injury figures remain remarkably high. Because risk factors for occupational injuries are prone to confounding, the present research takes a comprehensive approach. To better understand the occurrence of occupational injuries, sociodemographic factors and work- and health-related factors are tested simultaneously. Thus, the present analysis aims to develop a comprehensive epidemiological model that facilitates the explanation of varying injury rates in the workplace. The representative phone survey German Health Update 2010 provides information on medically treated occupational injuries sustained in the year prior to the interview. Data were collected on sociodemographics, occupation, working conditions, health-related behaviors, and chronic diseases. For the economically active population (18–70 years, n = 14,041), the 12-month prevalence of occupational injuries was calculated with a 95% confidence interval (CI). Blockwise multiple logistic regression was applied to successively include different groups of variables. Overall, 2.8% (95% CI 2.4–3.2) of the gainfully employed population report at least one occupational injury (women: 0.9%; 95% CI 0.7–1.2; men: 4.3%; 95% CI 3.7–5.0). In the fully adjusted model, male gender (OR 3.16) and age 18–29 (OR 1.54), as well as agricultural (OR 5.40), technical (OR 3.41), skilled service (OR 4.24) or manual (OR 5.12), and unskilled service (OR 3.13) or manual (OR 4.97) occupations are associated with higher chances of occupational injuries. The same holds for frequent stressors such as heavy carrying (OR 1.78), working in awkward postures (OR 1.46), environmental stress (OR 1.48), and working under pressure (OR 1.41). Among health-related variables, physical inactivity (OR 1.47) and obesity (OR 1.73) present a significantly higher chance of occupational injuries. While the odds for most work-related factors were as expected, the associations for health-related factors such as smoking, drinking, and chronic diseases were rather weak. In part, this may be due to context-specific factors such as safety and workplace regulations in high-income countries like Germany. This assumption could guide further research, taking a multi-level approach to international comparisons.

Occupation as a Proxy for Job Exposures? Routine Data Analysis Using the Example of Rehabilitation

“…The Job Exposure Index is based on job-exposure matrices. Job-exposure matrices are well established to reflect job exposures for various research questions including the association of job exposures to subjective health, injuries, early retirement, lifestyle factors, and health behaviour [4,12,14,15]. It has been shown for single exposures and diseases that the use of job-exposure matrices can provide similar results as the individual assessment of work-related exposures [42][43][44].…”

“…Job exposures are associated with various health-related outcomes such as injuries, morbidity, mortality, sick leave and reduction in earning capacity [1][2][3][4][5][6][7]. They can be differentiated into physical job exposures like harmful working environments (noise, dirt, heat, cold, toxic or allergic substances, carrying heavy weights, e. g.) and psychosocial job exposures like time pressure, low job autonomy, shift work or frequent overtime [8][9][10].…”

Hacia una mejor prevención de los accidentes laborales mortales en Portugal

“…Según los resultados de los estudios de Kirschenbaum, Oigenblick y Goldberg (2000), y Santana et al (2003), el sexo no es una variable predictiva de los accidentes laborales. En cambio, algunas investigaciones recientes de Zangirolani et al (2008) y de Rommel et al (2016) indican que los trabajadores varones tienen una probabilidad significativamente mayor de ser víctimas de accidentes en el lugar de trabajo. Zangirolani et al (2008) atribuyen la alta tasa de accidentalidad a la mayor concentración de riesgos en ocupaciones realizadas predominantemente por hombres.…”

“…La edad es otra variable cuya correlación con el número de accidentes laborales o con la tasa de accidentalidad se verifica comúnmente. Si bien Kirschenbaum, Oigenblick y Goldberg (2000) observaron que la edad no era un predictor de los accidentes relacionados con el trabajo, Rommel et al (2016) establecieron una correlación estadísticamente significativa entre determinados grupos etarios (por ejemplo, de 18 a 29 años) y el número de accidentes laborales. En consecuencia, podemos formular la siguiente hipótesis: H2 -La probabilidad de que los trabajadores sean víctimas de accidentes laborales mortales se correlaciona con su edad.…”

“…Los datos se analizan mediante regresión logística, un método empleado en diversos estudios sobre accidentes laborales (Kirschenbaum, Oigenblick y Goldberg, 2000;Lima et al, 1999;Rommel et al, 2016), así como en otros ámbitos cuando la variable dependiente es una variable categórica binaria, al margen de si las independientes son de carácter cualitativo o cuantitativo. Se trata de un modelo especialmente adecuado y útil para estudiar la incidencia en términos probabilísticos de una de las dos posibilidades de una variable binaria (en este caso, el carácter mortal de un accidente laboral), evaluando el poder explicativo, el nivel de significación y el signo (positivo o negativo) de cada una de las variables independientes, así como para cuantificar sus efectos, ceteris paribus, en el logit y en el cociente de probabilidad (Andrews et al, 1981;Ferreira, 2013;Marôco, 2014).…”