Age-Specific Variation in Adult Mortality Rates in Developed Countries

Mortality Characteristics in Sumadija District from 2010 – 2017

Mortality rate represents a number of deaths on a particular territory per time unit. There are general and specific mortalities.

The aims at analysing the characteristics of mortality in Sumadija District for the time period ranging from 2010 – 2017.

The study includes all death cases in the District of Sumadija in period 2010-2017, 34681 cases. The data are described and analysed with χ² test and linear trend.

The trend analysis does not indicate any significant variations during the given time span. The gender analysis reveals that there is a significantly higher number of deceased persons among male population (52.2%, 47.8%, р<0.05). The average age of the deceased females (76.2±13.4) was higher than the average age of males (73.8±14.1) (р<0.05). The cause-specific analysis shows that natural causes of death dominate absolutely (96.5%) over violent deaths (2.7%) and undetermined causes (0.8%). The distribution of death causes according to ICD 10 shows that the most frequent causes of death are heart and blood vessel diseases, respiratory and neoplasm diseases.

The life expectancy of the inhabitants of Sumadija District is increasing over time. There was a slight decrease in the mortality rates during the observed time period. The highest number of the deceased people is 65 or more years old. Men have higher mortality rates throughout their lives. Natural death and non-communicable diseases are dominant. The most common causes of death are heart and blood vessels diseases, in women, and respiratory and neoplasm diseases, in men.

Life-long body mass index trajectories and mortality in two generations

PURPOSE

To identify life-long body mass index (BMI) trajectories across two related generations and estimate their associated mortality risks and population attributable deaths.

METHODS

We use prospective cohort data from the Framingham Heart Study (1948-2011) original (4576 individuals, 3913 deaths) and offspring (3753 individuals, 967 deaths) cohorts and latent trajectory models to model BMI trajectories from age 31 to 80 years. Survival models are used to estimate trajectory-specific mortality risk.

RESULTS

We define seven BMI trajectories among original cohort and six among offspring cohort. Among original cohort, people who are normal weight at age 31 years and gradually move to overweight status in middle or later adulthood have the lowest mortality risk even compared to those who maintain normal weight throughout adulthood, followed by overweight stable, lower level of normal weight, overweight downward, class I obese upward, and class II/III upward trajectories. Mortality risks associated with obesity trajectories have declined across cohorts, while the prevalence of high-risk trajectories has increased.

CONCLUSIONS

The mortality impact of weight gain depends on an individual's BMI trajectory. Population attributable deaths associated with unhealthy weight trajectories have grown over generations because the prevalence has increased, offsetting the decline in trajectory-specific mortality risks.

Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices

Recognizing that health outcomes are influenced by and occur within multiple social and physical contexts, researchers have used multilevel modeling techniques for decades to analyze hierarchical or nested data. Cross-Classified Multilevel Models (CCMM) are a statistical technique proposed in the 1990s that extend standard multilevel modeling and enable the simultaneous analysis of non-nested multilevel data. Though use of CCMM in empirical health studies has become increasingly popular, there has not yet been a review summarizing how CCMM are used in the health literature. To address this gap, we performed a scoping review of empirical health studies using CCMM to: (a) evaluate the extent to which this statistical approach has been adopted; (b) assess the rationale and procedures for using CCMM; and (c) provide concrete recommendations for the future use of CCMM. We identified 118 CCMM papers published in English-language literature between 1994 and 2018. Our results reveal a steady growth in empirical health studies using CCMM to address a wide variety of health outcomes in clustered non-hierarchical data. Health researchers use CCMM primarily for five reasons: (1) to statistically account for non-independence in clustered data structures; out of substantive interest in the variance explained by (2) concurrent contexts, (3) contexts over time, and (4) age-period-cohort effects; and (5) to apply CCMM alongside other techniques within a joint model. We conclude by proposing a set of recommendations for use of CCMM with the aim of improved clarity and standardization of reporting in future research using this statistical approach.

Unobserved Population Heterogeneity and Dynamics of Health Disparities

BACKGROUND

A growing body of literature has reported widening educational health disparities across birth cohorts or time periods in the United States, but has paid little attention to the implication of mortality selection on the cohort trend in health disparities.

OBJECTIVE

This study investigates how changes in the variance of unobserved frailty over time may complicate the interpretation of cohort trends in health disparities and life expectancy.

METHODS

We use the microsimulation method to test the effect of mortality selection and further propose a counterfactual simulation procedure to estimate its contribution. Data used in the simulations are based on Panel Studies of Income Dynamics 1968-2013, National Health and Nutrition Examination Survey data 1999-2012, and National Health Interview Survey data 1986-2011.

RESULTS

Simulation shows that mortality selection may generate seemingly contradictory trends in health disparities and life expectancy across birth cohorts at the group and individual level. Life expectancy can change even when individual mortality curve is fixed. In the absence of a change in the causal effect of education on mortality at the individual level, an educational life expectancy gap can change across cohorts as a result of the change in frailty variance. Empirical analysis shows that mortality selection accounts for a sizeable amount of contribution to the widening educational life expectancy gap from the 1950s to 1960s birth cohorts in the United States.

CONTRIBUTION

We demonstrate mortality selection can complicate the cohort trend in health disparities and life expectancy and propose a counterfactual simulation method to evaluate its contribution.

Obesity-mortality link over the life course: the contribution of population compositional changes

A key uncertainty in the obesity-mortality association continues to be how this association changes over the life course. Prior studies tend to rely on cross-sectional design with static weight status taken at the time of the survey. This study tracks a cohort of individuals and employs lifelong body mass index information from the Framingham Heart Study original cohort (1948-2010). We focus on respondents who were younger than age 45 at time of their first survey (n = 2,176) and evaluate how the mortality risk associated with obesity changes over three age groups (below 45, 45-59, and 60 and above) and how population compositions may contribute to this pattern. We find the hazard ratio associated with obesity compared to normal weight decreases over three age groups, but this pattern is influenced by different ages of onset of obesity, inconsistency in the reference group (normal weight) over ages, and mortality selection effects. These factors explain away the decreasing effect of obesity (with onset before age 45) on mortality up to age 60; after age 60, the detrimental effect still declines, but to a much less degree. Later onset of obesity, however, is not significantly associated with excess mortality risks after age 60.