Why has life expectancy increased in the last 100 years give at least 3 reasons?

Life Expectancy

Anticipated life expectancy is sometimes used to gauge the utility of care for older patients. Convenient tools to approximate the length of life have been developed and validated.198 CVD therapies may then be considered in relation to the predicted longevity, with the intention of only using therapies when there is sufficient longevity for a meaningful effect. These decisions must be informed by awareness of each therapy's anticipated lag time to benefit as well as any potential lag time to harm. It is important to clarify which therapies are likely to relieve symptoms and which may extend life. Therapies providing symptom improvement can be useful even if longevity is limited (e.g., balloon aortic valvuloplasty), whereas those that primarily prolong survival times (e.g., statin therapy) might only have value for those with significant life expectancy. This approach is also limited by the fact that many therapies have multiple effects. Whereas statins take about 2 years to yield likely survival benefits for CHD, they can reduce claudication more rapidly.29

Introduction

Life expectancy is the average number of years a person in a population could expect to live after age x. It is the life table parameter most commonly used to compare the survival experience of populations. The age most often selected to make comparisons is 0.0 (i.e., birth), although, for many substantive and policy analyses, other ages such as 65+ and 85+ are more relevant and may be used (e.g., for determining person-years of Medicare and Social Security benefit entitlement). To calculate life expectancy at age x (ex), age-specific mortality and population counts are needed to determine the age-specific mortality rates (i.e., the qx) and survival probabilities (lx) used in life table computations. Life expectancy is determined by multiplying the sequence of the probabilities of survival at each age to determine the proportion of a population expected to survive to age x. The number of persons expected to be alive in each single year of age category after age x is summed to determine the total number of years left to be lived after the index age (Lx). The total number of person-years to be lived after age x divided by the expected number of survivors to that age yields the life expectancy at age x.

Life Expectancy

The patient's longevity plays an important role in considering intervention, particularly in the case of asymptomatic patients. In NASCET, ECST, and ACAS, only patients who had a life expectancy of 5 years were eligible for enrollment.34-37 In the symptomatic studies,34,35 patients in the medical arm with most recurrent events occurred within the first 1 to 3 months of the initial symptom. In the asymptomatic studies, the risk of stroke in the medical arms was considerably less: 11% in ACAS and 11. 8% ACST at 5 years.37,38 These observations suggest that symptomatic patients with high-grade stenosis can be reasonably considered for intervention even when their life expectancy is only 2 to 3 years. However, asymptomatic patients should be expected to survive at least 3 to 5 years to derive significant benefit in stroke reduction from CEA. Of interest, long-term results from Carotid Revascularization Endarterectomy Versus Stenting Trial (CREST) indicate that patients in that trial had an estimated life expectancy approximating that of the population at large, indicating that patients in this trial were well selected for intervention.67

Conclusion

Life expectancy can be understood to be equal to the area under a survival curve regardless of its shape. A gain in life expectancy associated with adopting one health strategy over another (or of being in one exposure group vs another) is the area between the respective survival curves. In order to put a given gain into proper perspective, it is necessary to understand the baseline risk in the control group and the proportion of people who are likely to benefit from the intervention. It is certainly a misconception to view gains in life expectancy as increments of time tacked onto the end of a fixed life span. Life expectancy can be estimated from empirical data by a variety of methods that each have strengths and weaknesses.

Comorbid Conditions, Function, and Life Expectancy

With advancing age and declines in reserve, older adults experience high rates of chronic illness and often related functional decline. Eighty percent of those over age 65 years have at least one chronic illness, and 50% have two or more comorbid conditions. Some of these conditions contribute directly to increased rates of mortality, including the leading causes of death among older adults—heart disease, cancer, stroke, lung disease, and Alzheimer’s disease. Many common diseases, however, primarily threaten function and result in disability and institutionalization. Arthritis, hearing loss, and vision impairment are all important problems in this respect. The presence of multiple comorbid conditions compounds the disabling effects of individual diseases and further complicates management. In the era of evidence and guidelines, a clinician caring for a patient with several common chronic conditions, such as diabetes mellitus, coronary artery disease, and osteoporosis, may feel compelled to prescribe six or seven medications to remain in compliance with current recommendations. This practice can result in “polypharmacy” (described later), adding significant cost to the patient with limited accounting for risks, benefits, and individual preferences. In addition to considering the discrete management of individual diseases, care of the older adult requires assessment of the overall impact of treatment on symptoms, function, and life expectancy. To address this common clinical challenge, the American Geriatrics Society maintains publication of guiding principles on care of older persons with multiple comorbid conditions that highlights the importance of accounting for complex interactions between conditions, risks, and benefits of various treatment options, overall prognosis, and patient goals and preferences.

Function, defined formally by Reuben et al., is “a person’s ability to perform tasks and fulfill social roles across a broad range of complexity”—more succinctly, self-care capacity. Assessing this ability provides the clinician with a means of understanding the impact of illness, assessing quality of life, identifying care needs, and estimating progress and prognosis. Comprehensive assessment of function should include questions about self-care capacity as well as objective measures of cognition and mobility (see later sections for details about the latter two). Self-care capacity is most often divided into basic, instrumental, and advanced ADLs. Basic ADLs include those actions that maintain personal health and hygiene, including transferring, bathing, toileting, dressing, and eating. Instrumental ADLs (IADLs) include activities necessary for living independently, specifically driving or using public transportation, cooking, shopping, managing medications and finances, using the telephone (or other communication device), and doing housework. Advanced ADLs include social or occupational functions associated with activities such as hobbies, employment, or caregiving. Approximately 30% of adults over age 65 and 78% of those over age 85 have difficulty with IADLs or one or more basic ADLs. Predictably, as the incidence of disability rises, so does the rate of dependence and placement in skilled facilities. Long-term care in skilled facilities increases from 2% among those aged 65 to 74 to 14% among those older than 85 years. Impairment in ADLs is also associated with an increased risk of falls, depression, and death in the affected elder. Among older adults the assessment of self-care capacity provides key health status information independent of age and comorbid conditions.

Worldwide Variations in Life Expectancy

Life expectancy is the average age at death for a particular population group. Note that life expectancies are also determined for each age group, usually in 10-year increments.

Life expectancies are available for 225 populations ranging from China to the tiny sovereign states of Andorra and San Marino. As shown in Table 1, life expectancies at birth for males and females combined range from 33.23 years for Swaziland to 83.52 years for Andorra (Central Intelligence Agency, 2007).

Consistent with findings that a good environment and SES promote longevity, most of the longest living populations are found in highly developed populations with superior sanitation, education, and health care. The worst life expectancies are found in Africa with its lower economic development and very high rate of mortality from HIV/AIDS infection. The top ten populations in terms of descending life expectancy (83.52 to 80.59 years) are Andorra, Macao, Japan, Singapore, San Marino, Hong Kong, Sweden, Australia, Switzerland, and France. (Rankings shift from year to year but most of these states stay in the top ten or close to it.)

The low life expectancy of developing populations does not mean that elderly people are absent from developing populations. Since poor countries tend to have much higher infant death rates, this situation lowers the average life expectancy. In addition, early deaths of children and young adults due to malnutrition, trauma, and infections reduce the average life expectancy. However, if early death is avoided, adults often reach advanced ages (Walker, 1974; Samaras, 2007).

A common misconception in comparing today's life expectancy to that of earlier times, such as the 1900s, is the belief that individuals can expect to live 25–30 years longer. However, if we look at the life expectancy of elderly people, the picture is quite different; e.g., 60-year-old white males lived 6.6 years longer in 2004 vs 1900, and 80-year-olds lived 3 years longer.

Active life expectancy, the average number of years a person can expect to live without chronic disability, is a better measure of the health status of populations in economically developed countries than total life expectancy because the latter has reached levels where most early deaths have been prevented and large survival increases are now likely only at later ages. It is important to assess whether such life expectancy increases are associated with improved quality of life, the capability for independent living, and continued economic activity. Active life expectancy calculations are illustrated with recent disability data from the United States.

Worldwide Variations in Life Expectancy

Life expectancy is the median age at death for a particular population group. For example, if a group of people have a life expectancy at birth of 70 years, half died before 70 and half survived beyond this age. Note that life expectancies are also determined for each age group, usually in 10-year increments.

Life expectancies are available for over 200 populations ranging from China to the tiny sovereign states of Andorra and San Marino. As shown in Table 1, life expectancy at birth for males and females combined range from 33.22 years for Swaziland to 83.51 years for Andorra (Wikipedia, 2006).

Consistent with findings that a good environment and SES promote longevity, most of the longest living populations are found in highly developed populations with superior sanitation, education, and health care. The worst life expectancies are found in Africa with its lower economic development and very high rate of mortality from HIV/AIDS infection. The top 10 populations in terms of descending life expectancy (83.51–80.42 years) are Andorra, Macau, San Marino, Singapore, Hong Kong, Japan, Sweden, Switzerland, Australia, and Guernsey (UK). (Depending on the year, the ranking varies somewhat, but has essentially remained unchanged over the last 15 years).

The low life expectancy of developing versus developed populations does not mean that elderly people are absent from developing populations. Since poor countries tend to have much higher infant death rates, this situation lowers the average life expectancy. In addition, early deaths of children and young adults due to malnutrition and infections reduce the average life expectancy. However, if early death is avoided, adults often reach advanced ages (Walker, 1974; Samaras, 2007, 2013).

A common misconception in comparing today's life expectancy to that of earlier times, such as the 1900s, is the belief that individuals can expect to live 25–30 years longer. However, if we look at the life expectancy of elderly people, the picture is quite different; e.g., 60-year-old white males could expect to live 6.6 years longer in 2004 versus 1900, and 80-year-olds could live 3 years longer. This is a small increase in view of the exceptional health and medical developments over the last century. Our workweek has also declined from about 60 to 40 h per week with increased worker safety and benefits.

Life expectancy at birth increased rapidly in industrialized countries from 1950 to 1970 mainly as a result of the decline in mortality from infectious diseases brought about by the introduction of antibiotics. The increase in life expectancy continued after 1970 in market economy countries. This was mainly attributable to the decline in mortality from cardiovascular diseases among middle-aged and elderly people. In the former socialist countries the increase almost ended. Female life expectancy is higher than male life expectancy in all countries, but the size of the difference varies. The sex differential increased in practically all countries from the 1950s to the late 1970s, but then started to diminish in Northern and Western Europe, North America, and Oceania, where the average difference was approximately six years in the 1990s. The lower life expectancy of men is due partly to biological and partly to behavioral factors. Persons in lower socioeconomic positions die on average at a younger age than those in higher positions. This difference is smaller among women than men. The socioeconomic mortality gap seems to have increased in many countries since the 1970s, due to the more rapid decline in mortality from cardiovascular diseases among persons in nonmanual than manual occupations.

1.2 Development of the pharmaceutical industry and its impact

This industry is gigantic. Many people recognize the big name pharmaceutical companies or their logos, such as Abbott, Amgen, Astra-Zeneca, Bayer, Bristol-Myers Squibb, Eisai, Eli Lilly, GlaxoSmithKline (GSK), Johnson & Johnson, Merck, Novartis, Pfizer, Roche, Sanofi-aventis, Schering-Plough, or Wyeth (Figure 1.3). Some of these companies were either small pharmacy shops on a street corner or nonexistent 100 years ago.

In the early 1600 s in Europe, street corner pharmacies or apothecaries used to make galenicals (medicines based on herbs or vegetable matter), decoctions, or balms using botanicals or herbs, in addition to powder, or pills, some of which contained very toxic compounds such as antimony and mercury salts. Some of them became profitable pharmaceutical industries.

The first pharmacopoeia (a book describing drugs and medicinal preparations) containing a list of useable drugs appeared in 1546 in Nürnberg, Germany. In England, the first nationally recognized pharmacopoeia was the London pharmacopoeia (pharmacopeia Londinensis), which became mandatory for the preparation of medicines in 1618 (Figure 1.4). The US pharmacopeia, with a national formulary containing 217 drugs, was published in 1820. Pharmacopoeias in different countries helped to develop pharmaceutical industries in Europe and the USA.

During the early 1800 s, Germany was the center of the development of the dye industry, and the chemistry of the dye industry helped Germany to be an early leader in the development of the pharmaceutical industry, later continued by well-known companies such as Boehringer Ingelheim, Hoechst, Bayer, Kalle, and Agfa. Swiss companies also emerged, some of which are still thriving. In the early 1800 s, French chemists Pelletier and Caventou developed a method to isolate pure quinine from imported cinchona bark and set up a factory in Paris for quinine extraction in 1826. In the UK there were family-owned pharmacy shops and small firms. Many corporate entities surfaced in the USA around the mid-1800 s. Figure 1.5 illustrates the development of the pharmaceutical industry since 1870.

Most pharmaceutical companies originated in the 1870 s, especially in the UK, the USA, Germany, Switzerland, and France (Figure 1.6).

Many of the giant pharmaceutical companies of today started as small pharmacy shops, which grew into small companies and then expanded further. Figures 1.7–1.11 illustrate the development of some well-known pharmaceutical companies: Pfizer, Merck, GlaxoSmithKline, Roche, and Bayer.

1.2.1 The impact of pharmaceutical industries on human lives

Life expectancy throughout the world increased from an average of 30–40 years in the early twentieth century to an estimated 66.12 years in 2009.7 In the USA, life spans increased from an average of 47 years in 1950 to 78.06 years today, and US life expectancy continues to grow (Figure 1.12).

Figure 1.12. US life expectancy in the USA, 1950–2007

Source: Department of Health and Human Services, CDC, US, and Innovation.org8

Research data suggest there is a correlation between life expectancy and the development of the pharmaceutical industry. Using aggregate time series data, Dr Frank R. Lichtenberg, a researcher at Columbia University, studied the impact of new drug approval by the US Food and Drug Administration (FDA) on US lifespan longevity. The results show that a 40% increase in life expectancy can be attributed to new medicines (Figure 1.13). According to another study, by innovation.org and the Pharmaceutical Research and Manufacturers of America (PhRMA), the new drugs reduce the risk of death (Figure 1.14).

Figure 1.13. Life expectancy in more and less developed regions of the USA, 1950/55–1995/2000

Source: Frank R. Lichtenberg9

Figure 1.14. Total increase in longevity compared with increase in longevity resulting from new medicines, USA, 1988–2000

Source: Frank R. Lichtenberg10

Alan Sheppard of IMS Health made an effective correlation between life expectancy and drug use in an aging Europe after collecting data from the United Nations and an IMS market prognosis (Figure 1.15).

Figure 1.15. Correlation between life expectancy and drug expenditure per capita in Europe, 2005–2010

Source: IMS Heath11

The effect that drugs have in countering disease is exemplified by data on HIV/AIDS cases. In the 1980s there were only one or two drugs available to treat AIDS patients, but the number and availability of newer medicines increased dramatically since 1996, when highly active antiretroviral treatment (HAART) was introduced, which had an immediate effect (Figure 1.16).

Figure 1.16. Mortality rate 1995–2007, showing effect of introducing highly active antiretroviral treatment in 1997 for AIDS patients in the USA

Source: PhRMA12

During the last decade, the US FDA approved more than 300 new medicines in various disease categories, for example cancer and cardiovascular disease, which have increased patients’ survival rates and longevity, and decreased disability.