The Impact of Covid-19 on Future Higher-Age Mortality

Covid-19 has predominantly affected mortality at high ages. It kills by inflaming and clogging the air sacs in the lungs, depriving the body of oxygen ‒ inducing hypoxia ‒ which closes down essential organs, in particular the heart, kidneys and liver, and causes blood clots (which can lead to stroke or pulmonary embolism) and neurological malfunction. Evidence from different countries points to the fact that people who die from Covid-19 are often, but not always, much less healthy than the average for their age group. This is true for England & Wales – the two countries we focus on in this study. The implication is that the years of life lost through early death are less than the average for each age group, with how much less being a source of considerable debate. We argue that many of those who die from coronavirus would have died anyway in the relatively near future due to their existing frailties or co-morbidities. We demonstrate how to capture this link to poorer-than-average health using a model in which individual deaths are ‘accelerated’ ahead of schedule due to Covid-19. The model structure and its parameterization build on the observation that Covid-19 mortality by age is approximately proportional to all-cause mortality. This, in combination with current predictions of total deaths, results in the important conclusion that, everything else being equal, the impact of Covid-19 on the mortality rates of the surviving population will be very modest. Specifically, the degree of anti-selection is likely to be very small, since the life expectancy of survivors does not increase by a significant amount over pre-pandemic levels. We also analyze the degree to which Covid-19 mortality varies with socio-economic status. Headline statistics suggest that the most deprived groups have been disproportionately affected by Covid-19. However, once we control for regional differences in mortality rates, Covid-19 deaths in both the most and least deprived groups are also proportional to the all-cause mortality of these groups. However, the groups in between have approximately 10-15% lower Covid-19 deaths compared with their all-cause mortality. We argue that useful lessons about the potential pattern of accelerated deaths from Covid-19 can be drawn from examining deaths from respiratory diseases, especially at different age ranges. We also argue that it is possible to draw useful lessons about volatility spikes in Covid-19 deaths from examining past seasonal flu epidemics. However, there is an important difference. Whereas the spikes in seasonal flu increase with age, our finding that Covid-19 death rates are approximately proportional to all-cause mortality suggests that any spike in Covid-19 mortality in percentage terms would be similar across all age ranges. Finally, we discuss some of the indirect consequences for future mortality of the pandemic and the ‘lockdown’ measures governments have imposed to contain it. For example, there is evidence that some surviving patients at all ages who needed intensive care could end up with a new impairment, such as organ damage, which will reduce their life expectancy. There is also evidence that many people in lockdown did not seek a timely medical assessment for a potential new illness, such as cancer, or deferred seeking treatment for an existing serious illness, with the consequence that non-Covid-19-related mortality rates could increase in future. Self-isolation during lockdown has contributed to an increase in alcohol and drug consumption by some people which might, in turn, reduce their life expectancy. If another consequence of the pandemic is a recession and/or an acceleration in job automation, resulting in long-term unemployment, then this could lead to so-called ‘deaths of despair’ in future. Other people, by contrast, might permanently change their social behaviour or seek treatments that delay the impact or onset of age-related diseases, one of the primary factors that make people more susceptible to the virus – both of which could have the effect of increasing their life expectancy. It is, however, too early to quantify these possibilities, although it is conceivable that these indirect consequences could have a bigger impact on future average life expectancy than the direct consequences measured by the accelerated deaths model

SADD: STOCHASTIC ANALYSIS OF DESTRUCTIVELY MEASURED DATA - POSSIBILITIES TO INCLUDE BIOLOGICAL VARIATIONS IN STATISTICAL ANALYSIS

Three techniques are presented to include the structural variation always present in measured data in statistical analysis. The methods are investigated and compared using cross sectional data, generated based on an exponential model as if gathered by destructive measuring methods. All three methods are based on optimising objective functions based on the data and the biological shift model. These objective functions are calculated for each separate measuring point in time either according the specific density function belonging to the model applied, or after conversion into biological shift factors (also according to the model applied) according to a Gaussian distribution. The procedures used need to be improved, embedded in the existing statistical framework and all available statistical expertise and skills need to be combined into robust procedures capable of analysing everyday data

A look at profiler performance

Since about 1974, Doppler radars operating in UHF and VHF ranges have been used increasingly to study atmospheric winds. Historically, large systems capable of obtaining data from high altitudes have focused attention on the mesosphere and stratosphere, rather than on the troposphere wherein abides most of the weather considered by most meteorologists. Research address some questions the meteorologist must logically ask first, viz., what is the actual performance capability of these systems, how accurate is the wind data of interest to meteorologists, and from what altitudes in the troposphere are the data reliably obtained

Smoking Behaviors and Abstinence in Low-Income Pregnant Women

Background: Despite efforts to educate individuals about the hazards of smoking, pregnant women continue to smoke. In the literature, there is less evidence about successful abstinence strategies for low-income women. The purpose of this pilot study was to assess smoking behaviors and factors that support smoking abstinence in low-income pregnant women. Methods: Using a longitudinal design, quantitative and qualitative data were collected from pregnant women at a low-income community prenatal clinic. Based on the Transtheoretical model, all subjects received information about the harmful effects of smoking and secondhand exposure, while current smokers were given a “quit kit” and contacted up to one year post-delivery to evaluate smoking behaviors. Results: All subjects (N = 135) ranged in age from 18 to 41; 75% were not married; 78% had household incomes \u3c $30,000; and the majority were African American (40%). Fifty-five (40.7%) never smoked while 77(57%) had a smoking history, of these 18(23%) were spontaneous quitters. Data indicated that 36% reported smoking during pregnancy, with the majority in pre-contemplation. After one year, 18% of current smokers quit. Conclusions: Without a specific plan, the majority were unable to successfully abstain. Rate of abstinence may have been further influenced because subjects began smoking at an early age and were unsuccessful at previous quit attempts. Providers must continue to educate pregnant women but also evaluate strategies that require few provider visits, are cost effective, focus on relapse prevention, and can successfully influence smoking abstinence in low-income pregnant women

Monte Carlo Study of Ordering and Domain Growth in a Class of fcc-Alloy Models

Ordering processes in fcc-alloys with composition A_3B (like Cu_3Au, Cu_3Pd, CoPt_3 etc.) are investigated by Monte Carlo simulation within a class of lattice models based on nearest-neighbor (NN) and second-neighbor (NNN) interactions. Using an atom-vacancy exchange algorithm, we study the growth of ordered domains following a temperature quench below the ordering spinodal. For zero NNN-interactions we observe an anomalously slow growth of the domain size L(t) \sim t^\alpha, where \alpha \sim 1/4 within our accessible timescales. With increasing NNN-interactions domain growth becomes faster and \alpha gradually approaches the value 1/2 as predicted by the conventional Lifshitz-Allen-Cahn theory.Comment: 6 pages, 4 figure