Modeling absolute differences in life expectancy with a censored skew-normal regression approach

Health, work, and contributing factors on life satisfaction: A study in Norway before and during the COVID-19 pandemic

Background

The COVID-19 outbreak has posed considerable challenges for people's health, work situations and life satisfaction. This article reports on a study of the relationship between self-reported health and life satisfaction before and during the COVID-19 pandemic in Norway, and examines the role of work in explaining the health-life satisfaction relationship.

Method

The study was based on survey data collected from 3185 Norwegian employees in 2019 and 3002 employees in 2020. Propensity score matching techniques were used to assess the mediating effects of work situations and income loss on the health-life satisfaction relationship. Skew-t regression models were further applied to estimate changes in life satisfaction before and during the pandemic, as well as to explore different underlying mechanisms for the health-life satisfaction association.

Results

The study found a negative association between ill health and life satisfaction. Compared to the healthy population, people with poor health were more likely to experience worsened work situations. A negative work situation is further associated with lower life satisfaction, and the pandemic aggravated life satisfaction for those who had worsened work situations. When exploring central contributing factors for life satisfaction, we found that health-related risks and work-life balance played predominant roles in predicting life satisfaction before the pandemic, while different types of household structure were among the most important predictors of life satisfaction during the pandemic.

Conclusion

A reduction in life satisfaction is explained by ill health, but different underlying mechanisms facilitated people's life satisfaction before and during the pandemic. While work situation and health risks were important predictors for life satisfaction in 2019, worries about more unstable work situations and less access to family support accentuated worsened life satisfaction in 2020. The findings suggest the necessity of labour market interventions that address the security and maintenance of proper and predictable work situations, especially in these more uncertain times.

Selection bias in studies of human reproduction-longevity trade-offs

A shorter lifespan as a potential cost of high reproductive effort in humans has intrigued researchers for more than a century. However, the results have been inconclusive so far and despite strong theoretical expectations we do not currently have compelling evidence for the longevity costs of reproduction. Using Monte Carlo simulation, it is shown here that a common practice in human reproduction-longevity studies using historical data (the most relevant data sources for this question), the omission of women who died prior to menopausal age from the analysis, results in severe underestimation of the potential underlying trade-off between reproduction and lifespan. In other words, assuming that such a trade-off is expressed also during reproductive years, the strength of the trade-off between reproduction and lifespan is progressively weakened when women dying during reproductive ages are sequentially and non-randomly excluded from the analysis. In cases of small sample sizes (e.g. few hundreds of observations), this selection bias by reducing statistical power may even partly explain the null results commonly found in this field. Future studies in this field should thus apply statistical approaches that account for or avoid selection bias in order to recover reliable effect size estimates between reproduction and longevity.

Calibration adjustments to address bias in mortality analyses due to informative sampling-a census-linked survey analysis in Switzerland

BACKGROUND

Sampling bias, like survey participants' nonresponse, needs to be adequately addressed in the analysis of sampling designs. Often survey weights will be calibrated on specific covariates related to the probability of selection and nonresponse to get representative population estimates. However, such calibrated survey (CS) weights are usually constructed for cross-sectional results, but not for longitudinal analyses. For example, when the outcome of interest is time to death, and sampling selection is related to time to death and censoring, sampling is informative. Then, unweighted or CS weighted inferential statistical analyses may be biased. In 2010, Switzerland changed from a decennial full enumeration census to a yearly registry-based (i.e., data from harmonised community registries) and a survey-based census system. In the present study, we investigated the potential bias due to informative sampling when time to death is the outcome of interest, using data from the new Swiss census system.

METHODS

We analysed more than 6.5 million individuals aged 15 years or older from registry-based census data from years 2010 to 2013, linked with mortality records up to end of 2014. Out of this population, a target sample of 3.5% was sampled from the Swiss Federal Statistical Office (SFSO) in a stratified yearly micro census. The SFSO calculated CS weights to enable representative population estimates from the micro census. We additionally constructed inverse probability (IP) weights, where we used survival information in addition to known sampling covariates. We compared CS and IP weighted mortality rates (MR) and life expectancy (LE) with estimates from the underlying population. Additionally, we performed a simulation study under different sampling and nonresponse scenarios.

RESULTS

We found that individuals who died in 2011, had a 0.67 (95% CI [0.64-0.70]) times lower odds of participating in the 2010 micro census, using a multivariable logistic regression model with covariates age, gender, nationality, civil status, region and survival information. IP weighted MR were comparable to estimates from the total population, whereas CS weighted MR underestimated the population MR in general. The IP weighted LE estimates at age 30 years for men were 50.9 years (95% CI [50.2-51.6] years), whereas the CS weighted overestimated LE by 2.5 years. Our results from the simulation study confirmed that IP weighted models are comparable to population estimates.

CONCLUSION

Mortality analyses based on the new Swiss survey-based census system may be biased, because of informative sampling. We conclude that mortality analyses based on census-linked survey data have to be carefully conducted, and if possible, validated by registry information to allow for unbiased interpretation and generalisation.

Comparison of non-parametric methods for ungrouping coarsely aggregated data

BACKGROUND

Histograms are a common tool to estimate densities non-parametrically. They are extensively encountered in health sciences to summarize data in a compact format. Examples are age-specific distributions of death or onset of diseases grouped in 5-years age classes with an open-ended age group at the highest ages. When histogram intervals are too coarse, information is lost and comparison between histograms with different boundaries is arduous. In these cases it is useful to estimate detailed distributions from grouped data.

METHODS

From an extensive literature search we identify five methods for ungrouping count data. We compare the performance of two spline interpolation methods, two kernel density estimators and a penalized composite link model first via a simulation study and then with empirical data obtained from the NORDCAN Database. All methods analyzed can be used to estimate differently shaped distributions; can handle unequal interval length; and allow stretches of 0 counts.

RESULTS

The methods show similar performance when the grouping scheme is relatively narrow, i.e. 5-years age classes. With coarser age intervals, i.e. in the presence of open-ended age groups, the penalized composite link model performs the best.

CONCLUSION

We give an overview and test different methods to estimate detailed distributions from grouped count data. Health researchers can benefit from these versatile methods, which are ready for use in the statistical software R. We recommend using the penalized composite link model when data are grouped in wide age classes.