EDITORIAL - United States: François Lenglet explains why life expectancy continues to decline

In his column, François Lenglet discusses the reasons for the decline in life expectancy among the American population.

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The life expectancy of Americans is in sharp decline. Between 2019 and 2021, she lost almost three years , to reach 76.1 years. This figure is the lowest of the developed countries, it is less than China, it is barely more than Mexico. This veritable tumble has no precedent for a century and the First World War. If the Covid is one of the explanations for this fall, the drop in life expectancy is in fact the symptom of a country in accelerated decline because life expectancy is an indicator of the well-being of a society. .

Considering the different ethnicities, the Indians suffered the most striking fall. They have lost seven years in three years, to regain their level of 1944, at 65 years old. The Blacks then the Whites come next. Hispanics manage to do better, stabilizing below the age of 80. Asians are the best with a life expectancy above 80 years. As a result, there is more than 15 years of difference in life expectancy between an American of Asian origin and a native American, that is to say an Indian.

Social background plays a role in life expectancy. The differences between the ethnic groups overlap in part with the differences in the level of education. Those with higher education live an average of 6.3 years longer than those without.

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What are the causes of this decline among Americans?

The United States is going through a period of social disintegration . In addition to gun deaths (suicide or murder) and excess road deaths, the spread of drugs, opiate medications in particular, hit the working classes of the white population. This part suffered a downgrade due to the collapse of industry in the center of the country, resulting in a massacre of overdoses. They are the ones Nobel laureate in economics Angus Deaton has called the " dead of despair ".

The Covid also played. In particular over the year 2020. But he was selective in hitting the unskilled and the poor, those who were already in mediocre conditions. This part of the population is mostly untreated, affected by obesity, diabetes and/or hypertension. 40% of Americans are obese .

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Unskilled populations have little access to the healthcare system. If the level of education plays so much on life expectancy, it is because it is generally correlated with a job that offers protective health insurance . As for obesity, linked to junk food and poverty, American leaders no longer dare to stigmatize it, for fear of being criticized for anti-fat racism. And among Republicans, the reluctance is all the stronger as their electorate comes largely from rural areas, which are much more affected by obesity than the cities.

This two-part article provides an overview of the global burden of atherothrombotic cardiovascular disease. Part I initially discusses the epidemiologic transition which has resulted in a decrease in deaths in childhood due to infections, with a concomitant increase in cardiovascular and other chronic diseases; and then provides estimates of the burden of cardiovascular (CV).

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Diseases with specific focus on the developing countries. Next, we summarize key information on risk factors for cardiovascular disease (CVD) and indicate that their importance may have been underestimated. Then, we describe overarching factors influencing variations in CVD by ethnicity and region and the influence of urbanization. Part II of this article describes the burden of CV disease by specific region or ethnic group, the risk factors of importance, and possible strategies for prevention.

For most populations, the last century has witnessed the most dramatic improvements in health in history. Life expectancy at birth has increased from a global average of 46 years in 1950 to 66 years in 1998.1 The health status and disease profile of human societies have historically been linked to the level of their economic development and social organization. With industrialization, the major causes of death and disability, in the more advanced societies, have shifted from a predominance of nutritional deficiencies and infectious diseases, to those classified as degenerative [chronic diseases such as cardiovascular disease (CVD),

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Cancer, and diabetes]. This shift has been termed “the epidemiologic transition.”2 At any given time, different countries in the world or even different regions within a country are at different stages of the epidemiologic transition. This transition can occur not only between different disease categories (eg, deaths from childhood diarrhea and malnutrition giving way to adult chronic diseases), but also within a specific disease category (eg, rheumatic heart disease of the young giving way to chronic coronary artery diseases of middle age or valve calcification, degeneration, and heart failure of the elderly.

For countries in the earliest stage of development, the predominant circulatory diseases are rheumatic heart disease, those due to other infections, and nutritional deficiency–related disorders of the heart muscle. Geographic regions experiencing this phase include Sub-Saharan Africa (SSA) and the rural areas of South America and South Asia (SA). During the second stage,

As infectious disease burdens are reduced and nutrition improves, diseases related to hypertension, such as hemorrhagic stroke and hypertensive heart disease, become more common. Regions experiencing this phase include China and other Asian countries. During the third stage, as life expectancy continues to improve, high-fat diets, cigarette smoking, and sedentary lifestyles become more common.

Noncommunicable diseases then predominate, with the highest mortality caused by atherosclerotic CVD, most frequently ischemic heart disease and atherothrombotic stroke, especially at ages below 50 years. This phase is found in urban India,4 Latin America, and the former socialist countries. For most developing and middle-income countries, the increased incidence of CVD adds to the continuing.

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Burden of infectious, nutritional, and perinatal diseases, which has been termed the “double-burden” (Table 2).1 During the fourth stage, increased efforts to prevent, diagnose, and treat ischemic heart disease and stroke are able to delay these diseases to more advanced ages. The regions that have reached this fourth stage include Western Europe, North America (excluding some parts of Mexico), Australia, and New Zealand.

Ecological, case-control, and cohort studies in many populations have identified a number of markers which are associated with either an increased or a decreased risk of CVD. Whether or not these associations are causal is decided by applying several criteria. These include the strength and consistency of association, temporal relationship, dose-response relationship, biologic plausibility, experimental evidence, and very importantly, concordant evidence from randomized human trials when available. It is the coherence of information from several different types of studies which has led to our body of knowledge and provides persuasive evidence of the causal link of several risk factors with CVD.

It has been suggested that conventional risk factors only explain about half of the variance in coronary heart disease.14 However, there may be several reasons why the role of conventional risk factors like tobacco, cholesterol, and high blood pressure may have been substantially underestimated.

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Regression Dilution Bias

Earlier cohort studies frequently included only single (as opposed to multiple) measures of a given risk factor. Single measures have a large variance and as a result, are only moderately correlated with subsequent measurements of the same risk factor in the same population. Therefore, relating a single (as opposed to multiple) measure of a risk factor to an outcome leads to substantial underestimation of the strength of association.15 The degree to which the regression dilution bias leads to underestimation of an association is illustrated as follows: without correction for the regression dilution bias, a 10% difference in cholesterol level was considered to be associated with a 20% difference in coronary heart disease (CHD) rates; after correction, a 10% difference in cholesterol level is estimated to lead to a 30% difference in CHD rates.16 The regression dilution bias has a similar and substantial impact on the estimates of risk associated with specific differences in blood pressure, glucose levels, etc.

The effect of several risk factors for vascular disease may take several years to fully manifest. Cohort studies of relatively short duration may therefore only identify a part of this effect, while extended studies may uncover a larger effect. For example, the study of cigarette smoking in British doctors revealed a stronger relative risk in the 40 year followup than in the initial 20-year followup.17 Similarly, trials of lipid lowering interventions have demonstrated only a modest impact in preventing CHD during the first year or two of the trials, but there was a substantially greater impact in the fourth and fifth years,18,19 with the possibility that larger differences may have been evident with more prolonged intervention.

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Dichotomous Categorization of Risk Factors

Often, studies have categorized cholesterol as “normal” or “abnormal”, blood pressure as “hypertensive” or “normotensive” or glucose as “diabetic” or “nondiabetic”. However, the risks associated with these and other risk factors operate on a continuum. Arbitrary categories tend to ignore the substantial risks contributed by these factors below the clinical threshold, ie, even within the so called normal range.20–22

Inability to Recognize or Quantify Exposures in the Unexposed Group

The inability to quantify exposure to a risk factor in the individuals classified as unexposed leads to an underestimation of the differences in exposure, between those exposed to a risk factor compared with those unexposed, as in the case of tobacco smoke. For example, nonsmokers, in most studies, include passive smokers.23 The risks of the active smoker, compared with the true nonsmoker are likely to be underestimated when passive smokers are included in the latter category.

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