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| 23. |
- Bellavia, Andrea, et al.
(författare)
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Sleep Duration and Survival Percentiles Across Categories of Physical Activity
- 2014
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Ingår i: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 179:4, s. 484-491
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Tidskriftsartikel (refereegranskat)abstract
- The association between long sleep duration and death is not fully understood. Long sleep is associated with low physical activity, which is a strong predictor of death. Our aim was to investigate the association between sleep duration and death across categories of total physical activity in a large prospective cohort of Swedish men and women. We followed a population-based cohort of 70,973 participants (37,846 men and 33,127 women), aged 45-83 years, from January 1998 to December 2012. Sleep duration and physical activity levels were assessed through a questionnaire. We evaluated the association of interest in terms of mortality rates by estimating hazard ratios with Cox regression and in terms of survival by evaluating 15th survival percentile differences with Laplace regression. During 15 years of follow-up, we recorded 14,575 deaths (8,436 men and 6,139 women). We observed a significant interaction between sleep duration and physical activity in predicting death (P < 0.001). Long sleep duration (>8 hours) was associated with increased mortality risk (hazard ratio = 1.24; 95% confidence interval: 1.11, 1.39) and shorter survival (15th percentile difference = -20 months; 95% confidence interval: -30, -11) among only those with low physical activity. The association between long sleep duration and death might be partly explained by comorbidity with low physical activity.
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| 24. |
- Bellavia, Andrea, et al.
(författare)
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Using Laplace Regression to Model and Predict Percentiles of Age at Death When Age Is the Primary Time Scale
- 2015
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Ingår i: American Journal of Epidemiology. - : OXFORD UNIV PRESS INC. - 0002-9262 .- 1476-6256. ; 182:3, s. 271-277
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Tidskriftsartikel (refereegranskat)abstract
- Increasingly often in epidemiologic research, associations between survival time and predictors of interest are measured by differences between distribution functions rather than hazard functions. For example, differences in percentiles of survival time, expressed in absolute time units (e.g., weeks), may complement the popular risk ratios, which are unitless measures. When analyzing time to an event of interest (e.g., death) in prospective cohort studies, the time scale can be set to start at birth or at study entry. The advantages of one time origin over the other have been thoroughly explored for the estimation of risks but not for the estimation of survival percentiles. In this paper, we analyze the use of different time scales in the estimation of survival percentiles with Laplace regression. Using this regression method, investigators can estimate percentiles of survival time over levels of an exposure of interest while adjusting for potential confounders. Our findings may help to improve modeling strategies and ease interpretation in the estimation of survival percentiles in prospective cohort studies.
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| 26. |
- Bengtsson, Tommy, et al.
(författare)
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Social Class and Excess Mortality in Sweden During the 1918 Influenza Pandemic
- 2018
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Ingår i: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 187:12, s. 2568-2576
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Tidskriftsartikel (refereegranskat)abstract
- There is no consensus in the literature about the role of socioeconomic factors on influenza mortality during the 1918 pandemic. While some scholars have found that social factors were important, others have not. In this study, we analyzed differences in excess mortality by social class in Sweden during the 1918 pandemic. We analyzed individual-level mortality of the entire population aged 30–59, by combining information from death records with census data on occupation. Social class was measured by an occupation-based class scheme. Excess mortality during the pandemic was measured as mortality relative to the same month the year before. Social class differences in mortality were modeled using a complementary log-log model, adjusting for potential confounding at the family, the residential (urban/rural) and the county levels. Our findings indicated notable class differences in excess mortality but no perfect class gradient. Class differences were somewhat larger for men than for women.
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