PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (1960962)

Clipboard (0)
None

Related Articles

1.  Life expectancy and healthy life expectancy of Japan: the fastest graying society in the world 
BMC Research Notes  2016;9:482.
Background
We appraised time trends of Japanese life expectancy (LE) and healthy life expectancy (HALE) by gender, LE-HALE and (LE-HALE)/LE figures, along with the women–men’s differences.
Methods
Using the Japanese LE and HALE values from 1990 through 2013 by gender in the article by the GBD 2013 DALYs and HALE Collaborators, we examined trends of LE and HALE, and their 5- or 3-year changes. We also probed LE-HALE and (LE-HALE)/LE values, and the women–men’s differences.
Results
LE consistently elongated as reported 76.0, 76.5, 77.6, 78.7, 79.3 and 80.1 years for men from 1990 to 2013; and 82.0, 82.8, 84.3, 85.5, 86.1 and 86.4 years for women, respectively. Both time trends demonstrated a significant linear increase (p for trend < 0.001). LE changes were 0.4, 1.1, 1.1, 0.7 and 0.7 years for men, and 0.9, 1.5, 1.2, 0.6 and 0.3 years for women. The trends were statistically significant (p < 0.001), except for 2010–2013 partly due to 3-year interval. HALE also steadily lengthened as seen 68.1, 68.4, 69.1, 69.9, 70.8 and 71.1 years for men from 1990 through 2013; and 72.2, 72.9, 74.0, 74.8, 75.4 and 75.6 years for women. Both time trends showed almost a linear increase (p < 0.05). HALE changes were 0.4, 0.6, 0.8, 0.9 and 0.3 years for men, and 0.7, 1.0, 0.8, 0.6 and 0.2 years for women, without statistical significant trends. LE-HALE values were 8.0, 8.0, 8.5, 8.8, 8.6 and 8.9 years for men; and 9.7, 9.9, 10.4, 10.7, 10.7 and 10.8 years for women. (LE-HALE)/LE figures were 10.5, 10.5, 10.9, 11.1, 10.8 and 11.2% for men, and 11.9, 12.0, 12.3, 12.5, 12.4 and 12.5% for women. LE women–men’s differences were 5.9, 6.4, 6.8, 6.8, 6.8 and 6.3 years, and the HALE figures were 4.2, 4.5, 4.9, 4.9, 4.6 and 4.5 years.
Conclusions
LE and HALE consistently linearly elongated for both sexes over the study period. Not only LE-HALE but also (LE-HALE)/LE values were still growing for both sexes. Public health measures, nursing-care/services as well as social security schemes are called for to further elevate longevities, HALE in particular, and enhance quality of life and well-being.
doi:10.1186/s13104-016-2281-2
PMCID: PMC5084424  PMID: 27793196
Healthy life expectancy; Life expectancy; Non-communicable disease; Quality of death; Quality of life
2.  Describing the population health burden of depression: health-adjusted life expectancy by depression status in Canada 
Abstract
Introduction:
Few studies have evaluated the impact of depression in terms of losses to both premature mortality and health-related quality of life (HRQOL) on the overall population. Health-adjusted life expectancy (HALE) is a summary measure of population health that combines both morbidity and mortality into a single summary statistic that describes the current health status of a population.
Methods:
We estimated HALE for the Canadian adult population according to depression status. National Population Health Survey (NPHS) participants 20 years and older (n = 12 373) were followed for mortality outcomes from 1994 to 2009, based on depression status. Depression was defined as having likely experienced a major depressive episode in the previous year as measured by the Composite International Diagnostic Interview Short Form. Life expectancy was estimated by building period abridged life tables by sex and depression status using the relative risks of mortality from the NPHS and mortality data from the Canadian Chronic Disease Surveillance System (2007–2009). The Canadian Community Health Survey (2009/10) provided estimates of depression prevalence and Health Utilities Index as a measure of HRQOL. Using the combined mortality, depression prevalence and HRQOL estimates, HALE was estimated for the adult population according to depression status and by sex.
Results:
For the population of women with a recent major depressive episode, HALE at 20 years of age was 42.0 years (95% CI: 40.2–43.8) compared to 57.0 years (95% CI: 56.8–57.2) for women without a recent major depressive episode. For the population of Canadian men, HALE at 20 was 39.0 years (95% CI: 36.5–41.5) for those with a recent major depressive episode compared to 53.8 years (95% CI: 53.6–54.0) for those without. For the 15.0-year difference in HALE between women with and without depression, 12.3 years can be attributed to the HRQOL gap and the remaining 2.7 years to the mortality gap. The 14.8 fewer years of HALE observed for men with depression equated to a 13.0-year HRQOL gap and a 1.8-year mortality gap.
Conclusion:
The population of adult men and women with depression in Canada had substantially lower healthy life expectancy than those without depression. Much of this gap is explained by lower levels of HRQOL, but premature mortality also plays a role.
PMCID: PMC5158123  PMID: 27768557
life expectancy; healthy life expectancy; mortality; health-related quality of life; depression
3.  Comparing life expectancy and health-adjusted life expectancy by body mass index category in adult Canadians: a descriptive study 
Background
While many studies have examined differences between body mass index (BMI) categories in terms of mortality risk and health-related quality of life (HRQL), little is known about the effect of body weight on health expectancy. We examined life expectancy (LE), health-adjusted life expectancy (HALE), and proportion of LE spent in nonoptimal (or poor) health by BMI category for the Canadian adult population (age ≥ 20).
Methods
Respondents to the National Population Health Survey (NPHS) were followed for mortality outcomes from 1994 to 2009. Our study population at baseline (n=12,478) was 20 to 100 years old with an average age of 47. LE was produced by building abridged life tables by sex and BMI category using data from the NPHS and the Canadian Chronic Disease Surveillance System. HALE was estimated using the Health Utilities Index from the Canadian Community Health Survey as a measure of HRQL. The contribution of HRQL to loss of healthy life years for each BMI category was also assessed using two methods: by calculating differences between LE and HALE proportional to LE and by using a decomposition technique to separate out mortality and HRQL contributions to loss of HALE.
Results
At age 20, for both sexes, LE is significantly lower in the underweight and obesity class 2+ categories, but significantly higher in the overweight category when compared to normal weight (obesity class 1 was nonsignificant). HALE at age 20 follows these same associations and is significantly lower for class 1 obesity in women. Proportion of life spent in nonoptimal health and decomposition of HALE demonstrate progressively higher losses of healthy life associated with lowered HRQL for BMI categories in excess of normal weight.
Conclusions
Although being in the overweight category for adults may be associated with a gain in life expectancy as compared to normal weight adults, overweight individuals also experience a higher proportion of these years of life in poorer health. Due to the descriptive nature of this study, further research is needed to explore the causal mechanisms which explain these results, including the important differences we observed between sexes and within obesity subcategories.
doi:10.1186/1478-7954-11-21
PMCID: PMC3842774  PMID: 24252500
Overweight; Obesity; Underweight; Body mass index; Life expectancy; Health expectancy; Mortality; Health-related quality of life
4.  Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Health Expenditure 
PLoS Medicine  2008;5(2):e29.
Background
Obesity is a major cause of morbidity and mortality and is associated with high medical expenditures. It has been suggested that obesity prevention could result in cost savings. The objective of this study was to estimate the annual and lifetime medical costs attributable to obesity, to compare those to similar costs attributable to smoking, and to discuss the implications for prevention.
Methods and Findings
With a simulation model, lifetime health-care costs were estimated for a cohort of obese people aged 20 y at baseline. To assess the impact of obesity, comparisons were made with similar cohorts of smokers and “healthy-living” persons (defined as nonsmokers with a body mass index between 18.5 and 25). Except for relative risk values, all input parameters of the simulation model were based on data from The Netherlands. In sensitivity analyses the effects of epidemiologic parameters and cost definitions were assessed. Until age 56 y, annual health expenditure was highest for obese people. At older ages, smokers incurred higher costs. Because of differences in life expectancy, however, lifetime health expenditure was highest among healthy-living people and lowest for smokers. Obese individuals held an intermediate position. Alternative values of epidemiologic parameters and cost definitions did not alter these conclusions.
Conclusions
Although effective obesity prevention leads to a decrease in costs of obesity-related diseases, this decrease is offset by cost increases due to diseases unrelated to obesity in life-years gained. Obesity prevention may be an important and cost-effective way of improving public health, but it is not a cure for increasing health expenditures.
Using a simulation model, Pieter van Baal and colleagues conclude that obesity prevention leads to a decrease in costs of obesity-related diseases, but this is offset by cost increases due to diseases unrelated to obesity in life-years gained.
Editors' Summary
Background.
Since the mid 1970s, the proportion of people who are obese (people who have an unhealthy amount of body fat) has increased sharply in many countries. One-third of all US adults, for example, are now classified as obese, and recent forecasts suggest that by 2025 half of US adults will be obese. A person is overweight if their body mass index (BMI, calculated by dividing their weight in kilograms by their height in meters squared) is between 25 and 30, and obese if BMI is greater than 30. Compared to people with a healthy weight (a BMI between 18.5 and 25), overweight and obese individuals have an increased risk of developing many diseases, such as diabetes, coronary heart disease and stroke, and tend to die younger. People become unhealthily fat by consuming food and drink that contains more energy than they need for their daily activities. In these circumstances, the body converts the excess energy into fat for use at a later date. Obesity can be prevented, therefore, by having a healthy diet and exercising regularly.
Why Was This Study Done?
Because obesity causes so much illness and premature death, many governments have public-health policies that aim to prevent obesity. Clearly, the improvement in health associated with the prevention of obesity is a worthwhile goal in itself but the prevention of obesity might also reduce national spending on medical care. It would do this, the argument goes, by reducing the amount of money spent on treating the diseases for which obesity is a risk factor. However, some experts have suggested that these short-term savings might be offset by spending on treating the diseases that would occur during the extra lifespan experienced by non-obese individuals. In this study, therefore, the researchers have used a computer model to calculate yearly and lifetime medical costs associated with obesity in The Netherlands.
What Did the Researchers Do and Find?
The researchers used their model to estimate the number of surviving individuals and the occurrence of various diseases for three hypothetical groups of men and women, examining data from the age of 20 until the time when the model predicted that everyone had died. The “obese” group consisted of never-smoking people with a BMI of more than 30; the “healthy-living” group consisted of never-smoking people with a healthy weight; the “smoking” group consisted of lifetime smokers with a healthy weight. Data from the Netherlands on the costs of illness were fed into the model to calculate the yearly and lifetime health-care costs of all three groups. The model predicted that until the age of 56, yearly health costs were highest for obese people and lowest for healthy-living people. At older ages, the highest yearly costs were incurred by the smoking group. However, because of differences in life expectancy (life expectancy at age 20 was 5 years less for the obese group, and 8 years less for the smoking group, compared to the healthy-living group), total lifetime health spending was greatest for the healthy-living people, lowest for the smokers, and intermediate for the obese people.
What Do These Findings Mean?
As with all mathematical models such as this, the accuracy of these findings depend on how well the model reflects real life and the data fed into it. In this case, the model does not take into account varying degrees of obesity, which are likely to affect lifetime health-care costs, nor indirect costs of obesity such as reduced productivity. Nevertheless, these findings suggest that although effective obesity prevention reduces the costs of obesity-related diseases, this reduction is offset by the increased costs of diseases unrelated to obesity that occur during the extra years of life gained by slimming down.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/doi:10.1371/journal.pmed.0050029.
The MedlinePlus encyclopedia has a page on obesity (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of obesity (in English and Spanish)
The UK National Health Service's health Web site (NHS Direct) provides information about obesity
The International Obesity Taskforce provides information about preventing obesity
The UK Foods Standards Agency, the United States Department of Agriculture, and Shaping America's Health all provide useful advice about healthy eating
The Netherlands National Institute for Public Health and the Environment (RIVM) Web site provides more information on the cost of illness and illness prevention in the Netherlands (in English and Dutch)
doi:10.1371/journal.pmed.0050029
PMCID: PMC2225430  PMID: 18254654
5.  Impact of diabetes mellitus on life expectancy and health-adjusted life expectancy in Canada 
The objectives of this study were to estimate life expectancy (LE) and health-adjusted life expectancy (HALE) for Canadians with and without diabetes and to evaluate the impact of diabetes on population health using administrative and survey data.
Mortality data from the Canadian Chronic Disease Surveillance System (2004 to 2006) and Health Utilities Index data from the Canadian Community Health Survey (2000 to 2005) were used. Life table analysis was applied to calculate LE, HALE, and their confidence intervals using the Chiang and the adapted Sullivan methods.
LE and HALE were significantly lower among people with diabetes than for people without the disease. LE and HALE for females without diabetes were 85.0 and 73.3 years, respectively (males: 80.2 and 70.9 years). Diabetes was associated with a loss of LE and HALE of 6.0 years and 5.8 years, respectively, for females, and 5.0 years and 5.3 years, respectively, for males, living with diabetes at 55 years of age. The overall gains in LE and HALE after the hypothetical elimination of prevalent diagnosed diabetes cases in the population were 1.4 years and 1.2 years, respectively, for females, and 1.3 years for both LE and HALE for males.
The results of the study confirm that diabetes is an important disease burden in Canada impacting the female and male populations differently. The methods can be used to calculate LE and HALE for other chronic conditions, providing useful information for public health researchers and policymakers.
doi:10.1186/1478-7954-10-7
PMCID: PMC3787852  PMID: 22531113
Life expectancy; Health-adjusted life expectancy; Diabetes mellitus; Health utilities index; Summary measure of population health
6.  Health-Adjusted Life Expectancy (HALE) in Korea: 2005–2011 
Journal of Korean Medical Science  2016;31(Suppl 2):S139-S145.
Health-Adjusted Life Expectancy (HALE) is a summary measurement that estimates the average number of years that a person at a given age can expect to live an equivalent of full health. HALE has not been previously reported at national or regional levels in Korea. This study aimed to measure HALE from 2005 to 2011 in Korea at both the national and regional levels as part of the Korean National Burden Study of 2012. To measure life expectancy (LE) and HALE, we used the life table method and an approach proposed by Sullivan. We used three main data sets to estimate HALE: probability of death, prevalence of disease, and disability weights. Overall, LE and HALE have increased from 2005 to 2011. For example, in 2011, LE and HALE at birth in males were 77.6 and 65.8 years, respectively, and 84.4 and 68.9 in females. It might be assumed that the overall health status of Korean population has been increasing. However, we confirmed that a gap between LE and HALE still exists. Additionally, we found out that there was a significant difference between LE and HALE among various sub-regions. This study is the first to measure HALE using our own database, including disability weight that reflected Korean preferences. Finally, the Korean government and society should make an effort to reduce the gap between LE and HALE and to reduce regional differences.
Graphical Abstract
doi:10.3346/jkms.2016.31.S2.S139
PMCID: PMC5081295  PMID: 27775251
Life Expectancy; Health-Adjusted Life Expectancy; Health-Related Quality of Life; Republic of Korea
7.  Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition 
Lancet (London, England)  2015;386(10009):2145-2191.
Summary
Background
The Global Burden of Disease Study 2013 (GBD 2013) aims to bring together all available epidemiological data using a coherent measurement framework, standardised estimation methods, and transparent data sources to enable comparisons of health loss over time and across causes, age–sex groups, and countries. The GBD can be used to generate summary measures such as disability-adjusted life-years (DALYs) and healthy life expectancy (HALE) that make possible comparative assessments of broad epidemiological patterns across countries and time. These summary measures can also be used to quantify the component of variation in epidemiology that is related to sociodemographic development.
Methods
We used the published GBD 2013 data for age-specific mortality, years of life lost due to premature mortality (YLLs), and years lived with disability (YLDs) to calculate DALYs and HALE for 1990, 1995, 2000, 2005, 2010, and 2013 for 188 countries. We calculated HALE using the Sullivan method; 95% uncertainty intervals (UIs) represent uncertainty in age-specific death rates and YLDs per person for each country, age, sex, and year. We estimated DALYs for 306 causes for each country as the sum of YLLs and YLDs; 95% UIs represent uncertainty in YLL and YLD rates. We quantified patterns of the epidemiological transition with a composite indicator of sociodemographic status, which we constructed from income per person, average years of schooling after age 15 years, and the total fertility rate and mean age of the population. We applied hierarchical regression to DALY rates by cause across countries to decompose variance related to the sociodemographic status variable, country, and time.
Findings
Worldwide, from 1990 to 2013, life expectancy at birth rose by 6·2 years (95% UI 5·6–6·6), from 65·3 years (65·0–65·6) in 1990 to 71·5 years (71·0–71·9) in 2013, HALE at birth rose by 5·4 years (4·9–5·8), from 56·9 years (54·5–59·1) to 62·3 years (59·7–64·8), total DALYs fell by 3·6% (0·3–7·4), and age-standardised DALY rates per 100 000 people fell by 26·7% (24·6–29·1). For communicable, maternal, neonatal, and nutritional disorders, global DALY numbers, crude rates, and age-standardised rates have all declined between 1990 and 2013, whereas for non–communicable diseases, global DALYs have been increasing, DALY rates have remained nearly constant, and age-standardised DALY rates declined during the same period. From 2005 to 2013, the number of DALYs increased for most specific non-communicable diseases, including cardiovascular diseases and neoplasms, in addition to dengue, food-borne trematodes, and leishmaniasis; DALYs decreased for nearly all other causes. By 2013, the five leading causes of DALYs were ischaemic heart disease, lower respiratory infections, cerebrovascular disease, low back and neck pain, and road injuries. Sociodemographic status explained more than 50% of the variance between countries and over time for diarrhoea, lower respiratory infections, and other common infectious diseases; maternal disorders; neonatal disorders; nutritional deficiencies; other communicable, maternal, neonatal, and nutritional diseases; musculoskeletal disorders; and other non-communicable diseases. However, sociodemographic status explained less than 10% of the variance in DALY rates for cardiovascular diseases; chronic respiratory diseases; cirrhosis; diabetes, urogenital, blood, and endocrine diseases; unintentional injuries; and self-harm and interpersonal violence. Predictably, increased sociodemographic status was associated with a shift in burden from YLLs to YLDs, driven by declines in YLLs and increases in YLDs from musculoskeletal disorders, neurological disorders, and mental and substance use disorders. In most country-specific estimates, the increase in life expectancy was greater than that in HALE. Leading causes of DALYs are highly variable across countries.
Interpretation
Global health is improving. Population growth and ageing have driven up numbers of DALYs, but crude rates have remained relatively constant, showing that progress in health does not mean fewer demands on health systems. The notion of an epidemiological transition—in which increasing sociodemographic status brings structured change in disease burden—is useful, but there is tremendous variation in burden of disease that is not associated with sociodemographic status. This further underscores the need for country-specific assessments of DALYs and HALE to appropriately inform health policy decisions and attendant actions.
doi:10.1016/S0140-6736(15)61340-X
PMCID: PMC4673910  PMID: 26321261
8.  Adjusting for dependent comorbidity in the calculation of healthy life expectancy 
Background
Healthy life expectancy – sometimes called health-adjusted life expectancy (HALE) – is a form of health expectancy indicator that extends measures of life expectancy to account for the distribution of health states in the population. The World Health Organization has estimated healthy life expectancy for 192 WHO Member States using information from health interview surveys and from the Global Burden of Disease Study. The latter estimates loss of health by cause, age and sex for populations. Summation of prevalent years lived with disability (PYLD) across all causes would result in overestimation of the severity of the population average health state because of comorbidity between conditions. Earlier HALE calculations made adjustments for independent comorbidity in adding PYLD across causes. This paper presents a method for adjusting for dependent comorbidity using available empirical data.
Methods
Data from five large national health surveys were analysed by age and sex to estimate "dependent comorbidity" factors for pairs of conditions. These factors were defined as the ratio of the prevalence of people with both conditions to the product of the two total prevalences for each of the conditions. The resulting dependent comorbidity factors were used for all Member States to adjust for dependent comorbidity in summation of PYLD across all causes and in the calculation of HALE. A sensitivity analysis was also carried out for order effects in the proposed calculation method.
Results
There was surprising consistency in the dependent comorbidity factors across the five surveys. The improved estimation of dependent comorbidity resulted in reductions in total PYLD per capita ranging from a few per cent in younger adult ages to around 8% in the oldest age group (80 years and over) in developed countries and up to 15% in the oldest age group in the least developed countries. The effect of the dependent comorbidity adjustment on estimated healthy life expectancies is small for some regions (high income countries, Eastern Europe, Western Pacific) and ranges from an increase of 0.5 to 1.5 years for countries in Latin America, South East Asia and Sub-Saharan Africa.
Conclusion
The available evidence suggests that dependent comorbidity is important, and that adjustment for it makes a significant difference to resulting HALE estimates for some regions of the world. Given the data limitations, we recommend a normative adjustment based on the available evidence, and applied consistently across all countries.
doi:10.1186/1478-7954-4-4
PMCID: PMC1484491  PMID: 16620383
9.  Association between Class III Obesity (BMI of 40–59 kg/m2) and Mortality: A Pooled Analysis of 20 Prospective Studies 
PLoS Medicine  2014;11(7):e1001673.
In a pooled analysis of 20 prospective studies, Cari Kitahara and colleagues find that class III obesity (BMI of 40–59) is associated with excess rates of total mortality, particularly due to heart disease, cancer, and diabetes.
Please see later in the article for the Editors' Summary
Background
The prevalence of class III obesity (body mass index [BMI]≥40 kg/m2) has increased dramatically in several countries and currently affects 6% of adults in the US, with uncertain impact on the risks of illness and death. Using data from a large pooled study, we evaluated the risk of death, overall and due to a wide range of causes, and years of life expectancy lost associated with class III obesity.
Methods and Findings
In a pooled analysis of 20 prospective studies from the United States, Sweden, and Australia, we estimated sex- and age-adjusted total and cause-specific mortality rates (deaths per 100,000 persons per year) and multivariable-adjusted hazard ratios for adults, aged 19–83 y at baseline, classified as obese class III (BMI 40.0–59.9 kg/m2) compared with those classified as normal weight (BMI 18.5–24.9 kg/m2). Participants reporting ever smoking cigarettes or a history of chronic disease (heart disease, cancer, stroke, or emphysema) on baseline questionnaires were excluded. Among 9,564 class III obesity participants, mortality rates were 856.0 in men and 663.0 in women during the study period (1976–2009). Among 304,011 normal-weight participants, rates were 346.7 and 280.5 in men and women, respectively. Deaths from heart disease contributed largely to the excess rates in the class III obesity group (rate differences = 238.9 and 132.8 in men and women, respectively), followed by deaths from cancer (rate differences = 36.7 and 62.3 in men and women, respectively) and diabetes (rate differences = 51.2 and 29.2 in men and women, respectively). Within the class III obesity range, multivariable-adjusted hazard ratios for total deaths and deaths due to heart disease, cancer, diabetes, nephritis/nephrotic syndrome/nephrosis, chronic lower respiratory disease, and influenza/pneumonia increased with increasing BMI. Compared with normal-weight BMI, a BMI of 40–44.9, 45–49.9, 50–54.9, and 55–59.9 kg/m2 was associated with an estimated 6.5 (95% CI: 5.7–7.3), 8.9 (95% CI: 7.4–10.4), 9.8 (95% CI: 7.4–12.2), and 13.7 (95% CI: 10.5–16.9) y of life lost. A limitation was that BMI was mainly ascertained by self-report.
Conclusions
Class III obesity is associated with substantially elevated rates of total mortality, with most of the excess deaths due to heart disease, cancer, and diabetes, and major reductions in life expectancy compared with normal weight.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The number of obese people (individuals with an excessive amount of body fat) is increasing rapidly in many countries. Worldwide, according to the Global Burden of Disease Study 2013, more than a third of all adults are now overweight or obese. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30 kg/m2 (a 183-cm [6-ft] tall man who weighs more than 100 kg [221 lbs] is obese). Compared to people with a healthy weight (a BMI between 18.5 and 24.9 kg/m2), overweight and obese individuals (who have a BMI between 25.0 and 29.9 kg/m2 and a BMI of 30 kg/m2 or more, respectively) have an increased risk of developing diabetes, heart disease, stroke, and some cancers, and tend to die younger. Because people become unhealthily fat by consuming food and drink that contains more energy (kilocalories) than they need for their daily activities, obesity can be prevented or treated by eating less food and by increasing physical activity.
Why Was This Study Done?
Class III obesity (extreme, or morbid, obesity), which is defined as a BMI of more than 40 kg/m2, is emerging as a major public health problem in several high-income countries. In the US, for example, 6% of adults are now morbidly obese. Because extreme obesity used to be relatively uncommon, little is known about the burden of disease, including total and cause-specific mortality (death) rates, among individuals with class III obesity. Before we can prevent and treat class III obesity effectively, we need a better understanding of the health risks associated with this condition. In this pooled analysis of prospective cohort studies, the researchers evaluate the risk of total and cause-specific death and the years of life lost associated with class III obesity. A pooled analysis analyzes the data from several studies as if the data came from one large study; prospective cohort studies record the characteristics of a group of participants at baseline and follow them to see which individuals develop a specific condition.
What Did the Researchers Do and Find?
The researchers included 20 prospective (mainly US) cohort studies from the National Cancer Institute Cohort Consortium (a partnership that studies cancer by undertaking large-scale collaborations) in their pooled analysis. After excluding individuals who had ever smoked and people with a history of chronic disease, the analysis included 9,564 adults who were classified as class III obese based on self-reported height and weight at baseline and 304,011 normal-weight adults. Among the participants with class III obesity, mortality rates (deaths per 100,000 persons per year) during the 30-year study period were 856.0 and 663.0 in men and women, respectively, whereas the mortality rates among normal-weight men and women were 346.7 and 280.5, respectively. Heart disease was the major contributor to the excess death rate among individuals with class III obesity, followed by cancer and diabetes. Statistical analyses of the pooled data indicate that the risk of all-cause death and death due to heart disease, cancer, diabetes, and several other diseases increased with increasing BMI. Finally, compared with having a normal weight, having a BMI between 40 and 59 kg/m2 resulted in an estimated loss of 6.5 to 13.7 years of life.
What Do These Findings Mean?
These findings indicate that class III obesity is associated with a substantially increased rate of death. Notably, this death rate increase is similar to the increase associated with smoking among normal-weight people. The findings also suggest that heart disease, cancer, and diabetes are responsible for most of the excess deaths among people with class III obesity and that having class III obesity results in major reductions in life expectancy. Importantly, the number of years of life lost continues to increase for BMI values above 50 kg/m2, and beyond this point, the loss of life expectancy exceeds that associated with smoking among normal-weight people. The accuracy of these findings is limited by the use of self-reported height and weight measurements to calculate BMI and by the use of BMI as the sole measure of obesity. Moreover, these findings may not be generalizable to all populations. Nevertheless, these findings highlight the need to develop more effective interventions to combat the growing public health problem of class III obesity.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001673.
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish)
The World Health Organization provides information on obesity (in several languages); Malri's story describes the health risks faced by an obese child
The UK National Health Service Choices website provides information about obesity, including a personal story about losing weight
The Global Burden of Disease Study website provides the latest details about global obesity trends
The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-Control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)
MedlinePlus provides links to other sources of information on obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001673
PMCID: PMC4087039  PMID: 25003901
10.  Disability weights for comorbidity and their influence on Health-adjusted Life Expectancy 
Background
Comorbidity complicates estimations of health-adjusted life expectancy (HALE) using disease prevalences and disability weights from Burden of Disease studies. Usually, the exact amount of comorbidity is unknown and no disability weights are defined for comorbidity.
Methods
Using data of the Dutch national burden of disease study, the effects of different methods to adjust for comorbidity on HALE calculations are estimated. The default multiplicative adjustment method to define disability weights for comorbidity is compared to HALE estimates without adjustment for comorbidity and to HALE estimates in which the amount of disability in patients with multiple diseases is solely determined by the disease that leads to most disability (the maximum adjustment method). To estimate the amount of comorbidity, independence between diseases is assumed.
Results
Compared to the multiplicative adjustment method, the maximum adjustment method lowers HALE estimates by 1.2 years for males and 1.9 years for females. Compared to no adjustment, a multiplicative adjustment lowers HALE estimates by 1.0 years for males and 1.4 years for females.
Conclusion
The differences in HALE caused by the different adjustment methods demonstrate that adjusting for comorbidity in HALE calculations is an important topic that needs more attention. More empirical research is needed to develop a more general theory as to how comorbidity influences disability.
doi:10.1186/1478-7954-4-1
PMCID: PMC1523368  PMID: 16606448
11.  Self-rated health as a tool for estimating health-adjusted life expectancy among patients newly diagnosed with localized prostate cancer: A preliminary study 
Purpose
Localized prostate cancer (LPC) patients are faced with numerous treatment options, including observation or watchful waiting. The choice of treatment largely depends on their baseline health-adjusted life expectancy (HALE). By consensus, physicians recommend treatment if the patient’s HALE is ten or more years. However, the estimation of HALE is difficult. Although subjective by nature, self-rated health (SRH) is a robust predictor of mortality. We studied the usefulness of SRH in estimating HALE in patients who are considering treatment for LPC.
Methods
A total of 144 LPC patients from a large urology private practice in Norfolk, Virginia, were surveyed before they had chosen a treatment option.
Results
HALE determined by SRH correlated well with objective health measures, and was higher than age-based life expectancy by an average of 2 years. The observed difference in life expectancy due to SRH adjustment was higher among patients with a better socioeconomic and health profile.
Conclusions
SRH is an easy-to-use indicator of HALE in LPC patients. A table for HALE estimation by age and SRH is provided for men aged 70-80 years. Additional research with larger samples and prospective study designs are needed before the SRH method can be used in primary care and urology settings.
doi:10.1007/s11136-010-9805-3
PMCID: PMC3066264  PMID: 21132389
Localized Prostate Cancer; Life Expectancy; Self-rated Health; Urology
12.  Using linked data to calculate summary measures of population health: Health-adjusted life expectancy of people with Diabetes Mellitus 
Objectives
To estimate the health-adjusted life expectancy (HALE) from diabetes mellitus (DM) using a population health survey linked to a population-based DM registry.
Methods
The 1996/97 Ontario Health Survey (N = 35,517) was linked to the Ontario Diabetes Database (N = 487,576). The Health Utilities Index (HUI3) was used to estimate health-related quality of life. HALE was estimated using an adapted Sullivan method.
Results
Life expectancy at birth of people with DM was 64.7 and 70.7 years for men and women – 12.8 and 12.2 years less than for men and women without DM. The HUI3 was lower for physician-diagnosed DM compared to self-reported DM (0.799 versus 0.872). HALE at birth was 58.3 and 62.8 years for men and women – 11.9 and 10.7 years less than that of men and women without DM.
Conclusions
The linked data approach demonstrates that DM is an important cause of disease burden. This approach reduces assumptions when estimating the prevalence and severity of disability from DM compared to methods that rely on self-reported disease status or indirect assessment of disability severity.
doi:10.1186/1478-7954-2-4
PMCID: PMC406424  PMID: 15038828
13.  The Promise of Prevention: The Effects of Four Preventable Risk Factors on National Life Expectancy and Life Expectancy Disparities by Race and County in the United States 
PLoS Medicine  2010;7(3):e1000248.
Majid Ezzati and colleagues examine the contribution of a set of risk factors (smoking, high blood pressure, elevated blood glucose, and adiposity) to socioeconomic disparities in life expectancy in the US population.
Background
There has been substantial research on psychosocial and health care determinants of health disparities in the United States (US) but less on the role of modifiable risk factors. We estimated the effects of smoking, high blood pressure, elevated blood glucose, and adiposity on national life expectancy and on disparities in life expectancy and disease-specific mortality among eight subgroups of the US population (the “Eight Americas”) defined on the basis of race and the location and socioeconomic characteristics of county of residence, in 2005.
Methods and Findings
We combined data from the National Health and Nutrition Examination Survey and the Behavioral Risk Factor Surveillance System to estimate unbiased risk factor levels for the Eight Americas. We used data from the National Center for Health Statistics to estimate age–sex–disease-specific number of deaths in 2005. We used systematic reviews and meta-analyses of epidemiologic studies to obtain risk factor effect sizes for disease-specific mortality. We used epidemiologic methods for multiple risk factors to estimate the effects of current exposure to these risk factors on death rates, and life table methods to estimate effects on life expectancy. Asians had the lowest mean body mass index, fasting plasma glucose, and smoking; whites had the lowest systolic blood pressure (SBP). SBP was highest in blacks, especially in the rural South—5–7 mmHg higher than whites. The other three risk factors were highest in Western Native Americans, Southern low-income rural blacks, and/or low-income whites in Appalachia and the Mississippi Valley. Nationally, these four risk factors reduced life expectancy at birth in 2005 by an estimated 4.9 y in men and 4.1 y in women. Life expectancy effects were smallest in Asians (M, 4.1 y; F, 3.6 y) and largest in Southern rural blacks (M, 6.7 y; F, 5.7 y). Standard deviation of life expectancies in the Eight Americas would decline by 0.50 y (18%) in men and 0.45 y (21%) in women if these risks had been reduced to optimal levels. Disparities in the probabilities of dying from cardiovascular diseases and diabetes at different ages would decline by 69%–80%; the corresponding reduction for probabilities of dying from cancers would be 29%–50%. Individually, smoking and high blood pressure had the largest effect on life expectancy disparities.
Conclusions
Disparities in smoking, blood pressure, blood glucose, and adiposity explain a significant proportion of disparities in mortality from cardiovascular diseases and cancers, and some of the life expectancy disparities in the US.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Life expectancy (a measure of longevity and premature death) and overall health have increased steadily in the United States over recent years. New drugs, new medical technologies, and better disease prevention have all helped Americans to lead longer, healthier lives. However, even now, some Americans live much longer and much healthier lives than others. Health disparities—differences in how often certain diseases occur and cause death in groups of people classified according to their ethnicity, geographical location, sex, or age—are extremely large and persistent in the US. On average, black men and women in the US live 6.3 and 4.5 years less, respectively, than their white counterparts; the gap between life expectancy in the US counties with the lowest and highest life expectancies is 18.4 years for men and 14.3 years for women. Disparities in deaths (mortality) from chronic diseases such as cardiovascular diseases (for example, heart attacks and stroke), cancers, and diabetes are known to be the main determinants of these life expectancy disparities.
Why Was This Study Done?
Preventable risk factors such as smoking, high blood pressure, excessive body fat (adiposity), and high blood sugar are responsible for many thousands of deaths from chronic diseases. Exposure to these risk factors varies widely by race, state of residence, and socioeconomic status. However, the effects of these observed disparities in exposure to modifiable risk factors on US life expectancy disparities have only been examined in selected groups of people and it is not known how multiple modifiable risk factors affect US health disparities. A better knowledge about how disparities in risk factor exposure contribute to health disparities is needed to ensure that prevention programs not only improve the average health status but also reduce health disparities. In this study, the researchers estimate the effects of smoking, high blood pressure, high blood sugar, and adiposity on US life expectancy and on disparities in life expectancy and disease-specific deaths among the “Eight Americas,” population groups defined by race and by the location and socioeconomic characteristics of their county of residence.
What Did the Researchers Do and Find?
The researchers extracted data on exposure to these risk factors from US national health surveys, information on deaths from different diseases in 2005 from the US National Center for Health Statistics, and estimates of how much each risk factor increases the risk of death from each disease from published studies. They then used modeling methods to estimate the effects of risk factor exposure on death rates and life expectancy. The Asian subgroup had the lowest adiposity, blood sugar, and smoking rates, they report, and the three white subgroups had the lowest blood pressure. Blood pressure was highest in the three black subgroups, whereas the other three risk factors were highest in Western Native Americans, Southern rural blacks, and whites living in Appalachia and the Mississippi Valley. The effects on life expectancy of these factors were smallest in Asians and largest in Southern rural blacks but, overall, these risk factors reduced the life expectancy for men and women born in 2005 by 4.9 and 4.1 years, respectively. Other calculations indicate that if these four risk factors were reduced to optimal levels, disparities among the subgroups in deaths from cardiovascular diseases and diabetes and from cancers would be reduced by up to 80% and 50%, respectively.
What Do These Findings Mean?
These findings suggest that disparities in smoking, blood pressure, blood sugar, and adiposity among US racial and geographical subgroups explain a substantial proportion of the disparities in deaths from cardiovascular diseases, diabetes, and cancers among these subgroups. The disparities in risk factor exposure also explain some of the disparities in life expectancy. The remaining disparities in deaths and life expectancy could be the result of preventable risk factors not included in this study—one of its limitations is that it does not consider the effect of dietary fat, alcohol use, and dietary salt, which are major contributors to different diseases. Thus, suggest the researchers, reduced exposure to preventable risk factors through the implementation of relevant policies and programs should reduce life expectancy and mortality disparities in the US and yield health benefits at a national scale.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000248.
The US Centers for Disease Control and Prevention, the US Office of Minority Health, and the US National Center on Minority Health and Health Disparities all provide information on health disparities in the US
MedlinePlus provides links to information on health disparities and on healthy living (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of healthy living
The American Heart Association and the American Cancer Society provide information on modifiable risk factors for patients and caregivers
Healthy People 2010 is a national framework designed to improve the health of people living in the US
The US National Health and Nutrition Examination Survey (NHANES) and the Behavioral Risk Factor Surveillance System (BRFSS) collect information on risk factor exposures in the US
doi:10.1371/journal.pmed.1000248
PMCID: PMC2843596  PMID: 20351772
14.  Adult Mortality Attributable to Preventable Risk Factors for Non-Communicable Diseases and Injuries in Japan: A Comparative Risk Assessment 
PLoS Medicine  2012;9(1):e1001160.
Using a combination of published data and modeling, Nayu Ikeda and colleagues identify tobacco smoking and high blood pressure as major risk factors for death from noncommunicable diseases among adults in Japan.
Background
The population of Japan has achieved the longest life expectancy in the world. To further improve population health, consistent and comparative evidence on mortality attributable to preventable risk factors is necessary for setting priorities for health policies and programs. Although several past studies have quantified the impact of individual risk factors in Japan, to our knowledge no study has assessed and compared the effects of multiple modifiable risk factors for non-communicable diseases and injuries using a standard framework. We estimated the effects of 16 risk factors on cause-specific deaths and life expectancy in Japan.
Methods and Findings
We obtained data on risk factor exposures from the National Health and Nutrition Survey and epidemiological studies, data on the number of cause-specific deaths from vital records adjusted for ill-defined codes, and data on relative risks from epidemiological studies and meta-analyses. We applied a comparative risk assessment framework to estimate effects of excess risks on deaths and life expectancy at age 40 y. In 2007, tobacco smoking and high blood pressure accounted for 129,000 deaths (95% CI: 115,000–154,000) and 104,000 deaths (95% CI: 86,000–119,000), respectively, followed by physical inactivity (52,000 deaths, 95% CI: 47,000–58,000), high blood glucose (34,000 deaths, 95% CI: 26,000–43,000), high dietary salt intake (34,000 deaths, 95% CI: 27,000–39,000), and alcohol use (31,000 deaths, 95% CI: 28,000–35,000). In recent decades, cancer mortality attributable to tobacco smoking has increased in the elderly, while stroke mortality attributable to high blood pressure has declined. Life expectancy at age 40 y in 2007 would have been extended by 1.4 y for both sexes (men, 95% CI: 1.3–1.6; women, 95% CI: 1.2–1.7) if exposures to multiple cardiovascular risk factors had been reduced to their optimal levels as determined by a theoretical-minimum-risk exposure distribution.
Conclusions
Tobacco smoking and high blood pressure are the two major risk factors for adult mortality from non-communicable diseases and injuries in Japan. There is a large potential population health gain if multiple risk factors are jointly controlled.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, a small number of modifiable risk factors are responsible for many premature or preventable deaths. For example, having high blood pressure (hypertension) increases a person's risk of developing life-threatening heart problems and stroke (cardiovascular disease). Similarly, having a high blood sugar level increases the risk of developing diabetes, a chronic (long-term) disease that can lead to cardiovascular problems and kidney failure, and half of all long-term tobacco smokers in Western populations will die prematurely from diseases related to smoking, such as lung cancer. Importantly, the five major risk factors for death globally—high blood pressure, tobacco use, high blood sugar, physical inactivity, and overweight and obesity—are all modifiable. That is, lifestyle changes and dietary changes such as exercising more, reducing salt intake, and increasing fruit and vegetable intake can reduce an individual's exposure to these risk factors and one's chances of premature death. Moreover, public health programs designed to reduce a population's exposure to modifiable risk factors should reduce preventable deaths in that population.
Why Was This Study Done?
In 2000, the Japanese government initiated Health Japan 21, a ten-year national health promotion campaign designed to prevent premature death from non-communicable (noninfectious) diseases and injuries. This campaign set 59 goals to monitor and improve risk factor management in the Japanese population, which has one of the longest life expectancies at birth in the world (the life expectancy of a person born in Japan in 2009 was 83.1 years). Because the campaign's final evaluation revealed deterioration or no improvement on some of these goals, the Japanese government recently released new guidelines that stress the importance of simultaneously controlling multiple risk factors for chronic diseases. However, although several studies have quantified the impacts on life expectancy and cause-specific death of individual modifiable risk factors in Japan, the effects of multiple risk factors have not been assessed. In this study, the researchers use a “comparative risk assessment” framework to estimate the effects of 16 risk factors on cause-specific deaths and life expectancy in Japan. Comparative risk assessment estimates the number of deaths that would be prevented if current distributions of risk factor exposures were changed to hypothetical optimal distributions.
What Did the Researchers Do and Find?
The researchers obtained data on exposure to the selected risk factors from the 2007 Japanese National Health and Nutrition Survey and from epidemiological studies, and information on the number of deaths in 2007 from different diseases from official records. They used published studies to estimate how much each factor increases the risk of death from each disease and then used a mathematical formula to estimate the effects of the risk factors on the number of deaths in Japan and on life expectancy at age 40. In 2007, tobacco smoking and high blood pressure accounted for 129,000 and 104,000 deaths, respectively, in Japan. Physical inactivity accounted for 52,000 deaths, high blood glucose and high dietary salt intake accounted for 34,000 deaths each, and alcohol use for 31,000 deaths. Life expectancy at age 40 in 2007 would have been extended by 1.4 years for both sexes, the researchers estimate, if exposure to multiple cardiovascular risk factors had been reduced to calculated optimal distributions, or by 0.7 years if these risk factors had been reduced to the distributions defined by national guidelines and goals.
What Do These Findings Mean?
These findings identify tobacco smoking and high blood pressure as the major risk factors for death from non-communicable diseases among adults in Japan, a result consistent with previous findings from the US. They also indicate that simultaneous control of multiple risk factors has great potential for producing health gains among the Japanese population. Although the researchers focused on estimating the effect of these risk factors on mortality and did not include illness and disability in this study, these findings nevertheless identify two areas of public health policy that need to be strengthened to improve health, reduce death rates, and increase life expectancy among the Japanese population. First, they highlight the need to reduce tobacco smoking, particularly among men. Second and most importantly, these findings emphasize the need to improve ongoing programs designed to help people manage multiple cardiovascular risk factors, including high blood pressure.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001160.
The US Centers for Disease Control and Prevention provides information on all aspects of healthy living
The World Health Report 2002—Reducing Risks, Promoting Healthy Life provides a global analysis of how healthy life expectancy could be increased
The American Heart Association and the American Cancer Society provide information on many important risk factors for noncommunicable diseases and include some personal stories about keeping healthy
Details about Health Japan 21 are provided by the Japanese Ministry of Health, Labour and Welfare. Further details about this campaign are available from the World Health Organization
MedlinePlus provides links to further resources on healthy living and on healthy aging (in English and Spanish)
doi:10.1371/journal.pmed.1001160
PMCID: PMC3265534  PMID: 22291576
15.  Bariatric Surgery 
Executive Summary
Objective
To conduct an evidence-based analysis of the effectiveness and cost-effectiveness of bariatric surgery.
Background
Obesity is defined as a body mass index (BMI) of at last 30 kg/m2.1 Morbid obesity is defined as a BMI of at least 40 kg/m2 or at least 35 kg/m2 with comorbid conditions. Comorbid conditions associated with obesity include diabetes, hypertension, dyslipidemias, obstructive sleep apnea, weight-related arthropathies, and stress urinary incontinence. It is also associated with depression, and cancers of the breast, uterus, prostate, and colon, and is an independent risk factor for cardiovascular disease.
Obesity is also associated with higher all-cause mortality at any age, even after adjusting for potential confounding factors like smoking. A person with a BMI of 30 kg/m2 has about a 50% higher risk of dying than does someone with a healthy BMI. The risk more than doubles at a BMI of 35 kg/m2. An expert estimated that about 160,000 people are morbidly obese in Ontario. In the United States, the prevalence of morbid obesity is 4.7% (1999–2000).
In Ontario, the 2004 Chief Medical Officer of Health Report said that in 2003, almost one-half of Ontario adults were overweight (BMI 25–29.9 kg/m2) or obese (BMI ≥ 30 kg/m2). About 57% of Ontario men and 42% of Ontario women were overweight or obese. The proportion of the population that was overweight or obese increased gradually from 44% in 1990 to 49% in 2000, and it appears to have stabilized at 49% in 2003. The report also noted that the tendency to be overweight and obese increases with age up to 64 years. BMI should be used cautiously for people aged 65 years and older, because the “normal” range may begin at slightly above 18.5 kg/m2 and extend into the “overweight” range.
The Chief Medical Officer of Health cautioned that these data may underestimate the true extent of the problem, because they were based on self reports, and people tend to over-report their height and under-report their weight. The actual number of Ontario adults who are overweight or obese may be higher.
Diet, exercise, and behavioural therapy are used to help people lose weight. The goals of behavioural therapy are to identify, monitor, and alter behaviour that does not help weight loss. Techniques include self-monitoring of eating habits and physical activity, stress management, stimulus control, problem solving, cognitive restructuring, contingency management, and identifying and using social support. Relapse, when people resume old, unhealthy behaviour and then regain the weight, can be problematic.
Drugs (including gastrointestinal lipase inhibitors, serotonin norepinephrine reuptake inhibitors, and appetite suppressants) may be used if behavioural interventions fail. However, estimates of efficacy may be confounded by high rates of noncompliance, in part owing to the side effects of the drugs. In addition, the drugs have not been approved for indefinite use, despite the chronic nature of obesity.
The Technology
Morbidly obese people may be eligible for bariatric surgery. Bariatric surgery for morbid obesity is considered an intervention of last resort for patients who have attempted first-line forms of medical management, such as diet, increased physical activity, behavioural modification, and drugs.
There are various bariatric surgical procedures and several different variations for each of these procedures. The surgical interventions can be divided into 2 general types: malabsorptive (bypassing parts of the gastrointestinal tract to limit the absorption of food), and restrictive (decreasing the size of the stomach so that the patient is satiated with less food). All of these may be performed as either open surgery or laparoscopically. An example of a malabsorptive technique is Roux-en-Y gastric bypass (RYGB). Examples of restrictive techniques are vertical banded gastroplasty (VBG) and adjustable gastric banding (AGB).
The Ontario Health Insurance Plan (OHIP) Schedule of Benefits for Physician Services includes fee code “S120 gastric bypass or partition, for morbid obesity” as an insured service. The term gastric bypass is a general term that encompasses a variety of surgical methods, all of which involve reconfiguring the digestive system. The term gastric bypass does not include AGB. The number of gastric bypass procedures funded and done in Ontario, and funded as actual out-of-country approvals,2 is shown below.
Number of Gastric Bypass Procedures by Fiscal Year: Ontario and Actual Out-of-Country (OOC) Approvals
Data from Provider Services, MOHLTC
Courtesy of Provider Services, Ministry of Health and Long Term Care
Review Strategy
The Medical Advisory Secretariat reviewed the literature to assess the effectiveness, safety, and cost-effectiveness of bariatric surgery to treat morbid obesity. It used its standard search strategy to retrieve international health technology assessments and English-language journal articles from selected databases. The interventions of interest were bariatric surgery and, for the controls, either optimal conventional management or another type of bariatric procedure. The outcomes of interest were improvement in comorbid conditions (e.g., diabetes, hypertension); short- and long-term weight loss; quality of life; adverse effects; and economic analysis data. The databases yielded 15 international health technology assessments or systematic reviews on bariatric surgery.
Subsequently, the Medical Advisory Secretariat searched MEDLINE and EMBASE from April 2004 to December 2004, after the search cut-off date of April, 2004, for the most recent systematic reviews on bariatric surgery. Ten studies met the inclusion criteria. One of those 10 was the Swedish Obese Subjects study, which started as a registry and intervention study, and then published findings on people who had been enrolled for at least 2 years or at least 10 years. In addition to the literature review of economic analysis data, the Medical Advisory Secretariat also did an Ontario-based economic analysis.
Summary of Findings
Bariatric surgery generally is effective for sustained weight loss of about 16% for people with BMIs of at least 40 kg/m2 or at least 35 kg/m2 with comorbid conditions (including diabetes, high lipid levels, and hypertension). It also is effective at resolving the associated comorbid conditions. This conclusion is largely based on level 3a evidence from the prospectively designed Swedish Obese Subjects study, which recently published 10-year outcomes for patients who had bariatric surgery compared with patients who received nonsurgical treatment. (1)
Regarding specific procedures, there is evidence that malabsorptive techniques are better than other banding techniques for weight loss and resolution of comorbid illnesses. However, there are no published prospective, long-term, direct comparisons of these techniques available.
Surgery for morbid obesity is considered an intervention of last resort for patients who have attempted first-line forms of medical management, such as diet, increased physical activity, behavioural modification, and drugs. In the absence of direct comparisons of active nonsurgical intervention via caloric restriction with bariatric techniques, the following observations are made:
A recent systematic review examining the efficacy of major commercial and organized self-help weight loss programs in the United States concluded that the evidence to support the use of such programs was suboptimal, except for one trial on Weight Watchers. Furthermore, the programs were associated with high costs, attrition rates, and probability of regaining at least 50% of the lost weight in 1 to 2 years. (2)
A recent randomized controlled trial reported 1-year outcomes comparing weight loss and metabolic changes in severely obese patients assigned to either a low-carbohydrate diet or a conventional weight loss diet. At 1 year, weight loss was similar for patients in each group (mean, 2–5 kg). There was a favourable effect on triglyceride levels and glycemic control in the low-carbohydrate diet group. (3)
A decision-analysis model showed bariatric surgery results in increased life expectancy in morbidly obese patients when compared to diet and exercise. (4)
A cost-effectiveness model showed bariatric surgery is cost-effective relative to nonsurgical management. (5)
Extrapolating from 2003 data from the United States, Ontario would likely need to do 3,500 bariatric surgeries per year. It currently does 508 per year, including out-of-country surgeries.
PMCID: PMC3382415  PMID: 23074460
16.  Comparing population health in the United States and Canada 
Background
The objective of the paper is to compare population health in the United States (US) and Canada. Although the two countries are very similar in many ways, there are potentially important differences in the levels of social and economic inequality and the organization and financing of and access to health care in the two countries.
Methods
Data are from the Joint Canada/United States Survey of Health 2002/03. The Health Utilities Index Mark 3 (HUI3) was used to measure overall health-related quality of life (HRQL). Mean HUI3 scores were compared, adjusting for major determinants of health, including body mass index, smoking, education, gender, race, and income. In addition, estimates of life expectancy were compared. Finally, mean HUI3 scores by age and gender and Canadian and US life tables were used to estimate health-adjusted life expectancy (HALE).
Results
Life expectancy in Canada is higher than in the US. For those < 40 years, there were no differences in HRQL between the US and Canada. For the 40+ group, HRQL appears to be higher in Canada. The results comparing the white-only population in both countries were very similar. For a 19-year-old, HALE was 52.0 years in Canada and 49.3 in the US.
Conclusions
The population of Canada appears to be substantially healthier than the US population with respect to life expectancy, HRQL, and HALE. Factors that account for the difference may include access to health care over the full life span (universal health insurance) and lower levels of social and economic inequality, especially among the elderly.
doi:10.1186/1478-7954-8-8
PMCID: PMC2873793  PMID: 20429875
17.  Health-Adjusted Life Expectancy among Canadian Adults with and without Hypertension 
Hypertension can lead to cardiovascular diseases and other chronic conditions. While the impact of hypertension on premature death and life expectancy has been published, the impact on health-adjusted life expectancy has not, and constitutes the research objective of this study. Health-adjusted life expectancy (HALE) is the number of expected years of life equivalent to years lived in full health. Data were obtained from the Canadian Chronic Disease Surveillance System (mortality data 2004–2006) and the Canadian Community Health Survey (Health Utilities Index data 2000–2005) for people with and without hypertension. Life table analysis was applied to calculate life expectancy and health-adjusted life expectancy and their confidence intervals. Our results show that for Canadians 20 years of age, without hypertension, life expectancy is 65.4 years and 61.0 years, for females and males, respectively. HALE is 55.0 years and 52.8 years for the two sexes at age 20; and 24.7 years and 22.9 years at age 55. For Canadians with hypertension, HALE is only 48.9 years and 47.1 years for the two sexes at age 20; and 22.7 years and 20.2 years at age 55. Hypertension is associated with a significant loss in health-adjusted life expectancy compared to life expectancy.
doi:10.4061/2011/612968
PMCID: PMC3123912  PMID: 21738858
18.  Changes in Intake of Fruits and Vegetables and Weight Change in United States Men and Women Followed for Up to 24 Years: Analysis from Three Prospective Cohort Studies 
PLoS Medicine  2015;12(9):e1001878.
Background
Current dietary guidelines recommend eating a variety of fruits and vegetables. However, based on nutrient composition, some particular fruits and vegetables may be more or less beneficial for maintaining or achieving a healthy weight. We hypothesized that greater consumption of fruits and vegetables with a higher fiber content or lower glycemic load would be more strongly associated with a healthy weight.
Methods and Findings
We examined the association between change in intake of specific fruits and vegetables and change in weight in three large, prospective cohorts of 133,468 United States men and women. From 1986 to 2010, these associations were examined within multiple 4-y time intervals, adjusting for simultaneous changes in other lifestyle factors, including other aspects of diet, smoking status, and physical activity. Results were combined using a random effects meta-analysis. Increased intake of fruits was inversely associated with 4-y weight change: total fruits -0.53 lb per daily serving (95% CI -0.61, -0.44), berries -1.11 lb (95% CI -1.45, -0.78), and apples/pears -1.24 lb (95% CI -1.62, -0.86). Increased intake of several vegetables was also inversely associated with weight change: total vegetables -0.25 lb per daily serving (95% CI -0.35, -0.14), tofu/soy -2.47 lb (95% CI, -3.09 to -1.85 lb) and cauliflower -1.37 lb (95% CI -2.27, -0.47). On the other hand, increased intake of starchy vegetables, including corn, peas, and potatoes, was associated with weight gain. Vegetables having both higher fiber and lower glycemic load were more strongly inversely associated with weight change compared with lower-fiber, higher-glycemic-load vegetables (p < 0.0001). Despite the measurement of key confounders in our analyses, the potential for residual confounding cannot be ruled out, and although our food frequency questionnaire specified portion size, the assessment of diet using any method will have measurement error.
Conclusions
Increased consumption of fruits and non-starchy vegetables is inversely associated with weight change, with important differences by type suggesting that other characteristics of these foods influence the magnitude of their association with weight change.
Using longitudinal data from health practitioners, Bertoia and colleagues explore associations between specific food choices and weight change.
Editors' Summary
Background
Obesity—having an unhealthy amount of body fat—is increasing worldwide. In the United States, for example, more than a third of adults are obese and another third are overweight. Obesity is defined as having a body mass index (BMI; an indicator of body fat calculated by dividing a person’s weight in kilograms by their height in meters squared) of more than 30 kg/m2; overweight individuals have a BMI of 25.0–29.9 kg/m2. Compared to people with a healthy weight, overweight and obese individuals have an increased risk of developing diabetes and cardiovascular diseases (conditions that affect the heart and/or the blood vessels), and tend to die younger. People gain too much fat by consuming food and drink that contains more energy (calories) than they need for their daily activities. So, people can avoid becoming obese or reduce their BMI by eating a healthy diet that contains fewer calories and by exercising more.
Why Was This Study Done?
The 2010 Dietary Guidelines for Americans recommend that adults and children should eat a variety of fruits and vegetables to help them achieve and maintain a healthy weight. But are all fruits and vegetables equally good at controlling weight? Fruits and vegetables differ in their dietary fiber content and their glycemic load. High fiber foods increase satiety (feeling full after eating), which can reduce total energy intake. Foods with a low glycemic load produce smaller and fewer blood sugar spikes after they are consumed, which may reduce hunger later on. In this study, the researchers investigate whether consumption of fruits and vegetable with a higher fiber content or lower glycemic load is more strongly associated with a healthy weight than consumption of fruits and vegetables with a lower fiber content or higher glycemic load by analyzing data on weight and diet changes among US men and women enrolled in three large prospective cohort studies set up to examine risk factors for major chronic diseases.
What Did the Researchers Do and Find?
The researchers examined associations between changes in the intake of specific fruits and vegetables recorded in dietary questionnaires completed every 4 y and self-reported weight changes in 133,468 US men and women followed for up to 24 y. After adjusting for self-reported changes in other lifestyle factors likely to affect weight, such as smoking status and physical activity, an increased intake of fruits and of several vegetables was inversely associated with 4-y weight change. Thus, an increase in total fruit intake was associated with a change in weight over a 4-y interval of -0.53 lb (a weight loss of 0.24 kg) for each extra daily serving, and an increase in total vegetable intake was associated with a weight change of -0.25 lb (-0.11 kg) for each extra daily serving. However, increased intake of starchy vegetables such as corn, peas, and potatoes was associated with weight gain. Notably, higher-fiber, lower-glycemic load vegetables (for example, broccoli and Brussels sprouts) were more strongly inversely associated with weight change than lower-fiber, higher-glycemic load vegetables (for example, carrots and cabbage).
What Do These Findings Mean?
These findings suggest that increased consumption of fruits and non-starchy vegetables is inversely associated with weight change and that different fruits and vegetables have different effects on weight. The benefits of increased consumption were greater for fruits than for vegetables and strongest for berries, apples/pears, tofu/soy, cauliflower, and cruciferous and green leafy vegetables. Increased satiety with fewer calories could be partly responsible for the beneficial effects of increasing fruit and vegetable intake. These findings may not be generalizable—nearly all the participants were well-educated white adults. Moreover, the use of dietary questionnaires and self-reported weight measurement may have introduced measurement errors into this study and, although the researchers accounted for some key lifestyle factors that are likely to affect weight, individuals who increased their fruit and vegetable intake and lost weight may have shared other unknown characteristics that were actually responsible for their weight loss. Overall, however, these findings provide new food-specific guidance for the prevention of obesity, a primary risk factor for many life-shortening health conditions.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001878. The World Health Organization provides information on obesity (in several languages)The Global Burden of Disease websitey provides the latest details about global obesity trends; the International Obesity Taskforce also provides information about the global obesity epidemicThe United Kingdom National Health Service Choices website provides information about obesity, cardiovascular disease, and diabetes (including some personal stories), and about healthy eatingThe American Heart Association provides information on cardiovascular disease and diabetes and on keeping healthy, including nutritional informationThe US Centers for Disease Control and Prevention has information on all aspects of overweight and obesityChooseMyPlate.gov is a resource provided by the US Department of Agriculture that provides individuals and healthcare professionals with user-friendly nutritional information; the 2010 Dietary Guidelines for Americans are availableMedlinePlus provides links to other sources of information on obesity, heart disease, vascular disease, and diabetes (in English and Spanish)More information about the three cohort studies that provided data for this analysis (Nurses' Health Study and Nurses' Health Study II and Health Professionals Follow-up Study) is available
doi:10.1371/journal.pmed.1001878
PMCID: PMC4578962  PMID: 26394033
19.  Polysomnography in Patients With Obstructive Sleep Apnea 
Executive Summary
Objective
The objective of this health technology policy assessment was to evaluate the clinical utility and cost-effectiveness of sleep studies in Ontario.
Clinical Need: Target Population and Condition
Sleep disorders are common and obstructive sleep apnea (OSA) is the predominant type. Obstructive sleep apnea is the repetitive complete obstruction (apnea) or partial obstruction (hypopnea) of the collapsible part of the upper airway during sleep. The syndrome is associated with excessive daytime sleepiness or chronic fatigue. Several studies have shown that OSA is associated with hypertension, stroke, and other cardiovascular disorders; many researchers believe that these cardiovascular disorders are consequences of OSA. This has generated increasing interest in recent years in sleep studies.
The Technology Being Reviewed
There is no ‘gold standard’ for the diagnosis of OSA, which makes it difficult to calibrate any test for diagnosis. Traditionally, polysomnography (PSG) in an attended setting (sleep laboratory) has been used as a reference standard for the diagnosis of OSA. Polysomnography measures several sleep variables, one of which is the apnea-hypopnea index (AHI) or respiratory disturbance index (RDI). The AHI is defined as the sum of apneas and hypopneas per hour of sleep; apnea is defined as the absence of airflow for ≥ 10 seconds; and hypopnea is defined as reduction in respiratory effort with ≥ 4% oxygen desaturation. The RDI is defined as the sum of apneas, hypopneas, and abnormal respiratory events per hour of sleep. Often the two terms are used interchangeably. The AHI has been widely used to diagnose OSA, although with different cut-off levels, the basis for which are often unclear or arbitrarily determined. Generally, an AHI of more than five events per hour of sleep is considered abnormal and the patient is considered to have a sleep disorder. An abnormal AHI accompanied by excessive daytime sleepiness is the hallmark for OSA diagnosis. For patients diagnosed with OSA, continuous positive airway pressure (CPAP) therapy is the treatment of choice. Polysomnography may also used for titrating CPAP to individual needs.
In January 2005, the College of Physicians and Surgeons of Ontario published the second edition of Independent Health Facilities: Clinical Practice Parameters and Facility Standards: Sleep Medicine, commonly known as “The Sleep Book.” The Sleep Book states that OSA is the most common primary respiratory sleep disorder and a full overnight sleep study is considered the current standard test for individuals in whom OSA is suspected (based on clinical signs and symptoms), particularly if CPAP or surgical therapy is being considered.
Polysomnography in a sleep laboratory is time-consuming and expensive. With the evolution of technology, portable devices have emerged that measure more or less the same sleep variables in sleep laboratories as in the home. Newer CPAP devices also have auto-titration features and can record sleep variables including AHI. These devices, if equally accurate, may reduce the dependency on sleep laboratories for the diagnosis of OSA and the titration of CPAP, and thus may be more cost-effective.
Difficulties arise, however, when trying to assess and compare the diagnostic efficacy of in-home PSG versus in-lab. The AHI measured from portable devices in-home is the sum of apneas and hypopneas per hour of time in bed, rather than of sleep, and the absolute diagnostic efficacy of in-lab PSG is unknown. To compare in-home PSG with in-lab PSG, several researchers have used correlation coefficients or sensitivity and specificity, while others have used Bland-Altman plots or receiver operating characteristics (ROC) curves. All these approaches, however, have potential pitfalls. Correlation coefficients do not measure agreement; sensitivity and specificity are not helpful when the true disease status is unknown; and Bland-Altman plots measure agreement (but are helpful when the range of clinical equivalence is known). Lastly, receiver operating characteristics curves are generated using logistic regression with the true disease status as the dependent variable and test values as the independent variable. Thus, each value of the test is used as a cut-point to measure sensitivity and specificity, which are then plotted on an x-y plane. The cut-point that maximizes both sensitivity and specificity is chosen as the cut-off level to discriminate between disease and no-disease states. In the absence of a gold standard to determine the true disease status, ROC curves are of minimal value.
At the request of the Ontario Health Technology Advisory Committee (OHTAC), MAS has thus reviewed the literature on PSG published over the last two years to examine new developments.
Methods
Review Strategy
There is a large body of literature on sleep studies and several reviews have been conducted. Two large cohort studies, the Sleep Heart Health Study and the Wisconsin Sleep Cohort Study, are the main sources of evidence on sleep literature.
To examine new developments on PSG published in the past two years, MEDLINE, EMBASE, MEDLINE In-Process & Other Non-Indexed Citations, the Cochrane Database of Systematic Reviews and Cochrane CENTRAL, INAHTA, and websites of other health technology assessment agencies were searched. Any study that reported results of in-home or in-lab PSG was included. All articles that reported findings from the Sleep Heart Health Study and the Wisconsin Sleep Cohort Study were also reviewed.
Diffusion of Sleep Laboratories
To estimate the diffusion of sleep laboratories, a list of sleep laboratories licensed under the Independent Health Facility Act was obtained. The annual number of sleep studies per 100,000 individuals in Ontario from 2000 to 2004 was also estimated using administrative databases.
Summary of Findings
Literature Review
A total of 315 articles were identified that were published in the past two years; 227 were excluded after reviewing titles and abstracts. A total of 59 articles were identified that reported findings of the Sleep Heart Health Study and the Wisconsin Sleep Cohort Study.
Prevalence
Based on cross-sectional data from the Wisconsin Sleep Cohort Study of 602 men and women aged 30 to 60 years, it is estimated that the prevalence of sleep-disordered breathing is 9% in women and 24% in men, on the basis of more than five AHI events per hour of sleep. Among the women with sleep disorder breathing, 22.6% had daytime sleepiness and among the men, 15.5% had daytime sleepiness. Based on this, the prevalence of OSA in the middle-aged adult population is estimated to be 2% in women and 4% in men.
Snoring is present in 94% of OSA patients, but not all snorers have OSA. Women report daytime sleepiness less often compared with their male counterparts (of similar age, body mass index [BMI], and AHI). Prevalence of OSA tends to be higher in older age groups compared with younger age groups.
Diagnostic Value of Polysomnography
It is believed that PSG in the sleep laboratory is more accurate than in-home PSG. In the absence of a gold standard, however, claims of accuracy cannot be substantiated. In general, there is poor correlation between PSG variables and clinical variables. A variety of cut-off points of AHI (> 5, > 10, and > 15) are arbitrarily used to diagnose and categorize severity of OSA, though the clinical importance of these cut-off points has not been determined.
Recently, a study of the use of a therapeutic trial of CPAP to diagnose OSA was reported. The authors studied habitual snorers with daytime sleepiness in the absence of other medical or psychiatric disorders. Using PSG as the reference standard, the authors calculated the sensitivity of this test to be 80% and its specificity to be 97%. Further, they concluded that PSG could be avoided in 46% of this population.
Obstructive Sleep Apnea and Obesity
Obstructive sleep apnea is strongly associated with obesity. Obese individuals (BMI >30 kg/m2) are at higher risk for OSA compared with non-obese individuals and up to 75% of OSA patients are obese. It is hypothesized that obese individuals have large deposits of fat in the neck that cause the upper airway to collapse in the supine position during sleep. The observations reported from several studies support the hypothesis that AHIs (or RDIs) are significantly reduced with weight loss in obese individuals.
Obstructive Sleep Apnea and Cardiovascular Diseases
Associations have been shown between OSA and comorbidities such as diabetes mellitus and hypertension, which are known risk factors for myocardial infarction and stroke. Patients with more severe forms of OSA (based on AHI) report poorer quality of life and increased health care utilization compared with patients with milder forms of OSA. From animal models, it is hypothesized that sleep fragmentation results in glucose intolerance and hypertension. There is, however, no evidence from prospective studies in humans to establish a causal link between OSA and hypertension or diabetes mellitus. It is also not clear that the associations between OSA and other diseases are independent of obesity; in most of these studies, patients with higher values of AHI had higher values of BMI compared with patients with lower AHI values.
A recent meta-analysis of bariatric surgery has shown that weight loss in obese individuals (mean BMI = 46.8 kg/m2; range = 32.30–68.80) significantly improved their health profile. Diabetes was resolved in 76.8% of patients, hypertension was resolved in 61.7% of patients, hyperlipidemia improved in 70% of patients, and OSA resolved in 85.7% of patients. This suggests that obesity leads to OSA, diabetes, and hypertension, rather than OSA independently causing diabetes and hypertension.
Health Technology Assessments, Guidelines, and Recommendations
In April 2005, the Centers for Medicare and Medicaid Services (CMS) in the United States published its decision and review regarding in-home and in-lab sleep studies for the diagnosis and treatment of OSA with CPAP. In order to cover CPAP, CMS requires that a diagnosis of OSA be established using PSG in a sleep laboratory. After reviewing the literature, CMS concluded that the evidence was not adequate to determine that unattended portable sleep study was reasonable and necessary in the diagnosis of OSA.
In May 2005, the Canadian Coordinating Office of Health Technology Assessment (CCOHTA) published a review of guidelines for referral of patients to sleep laboratories. The review included 37 guidelines and associated reviews that covered 18 applications of sleep laboratory studies. The CCOHTA reported that the level of evidence for many applications was of limited quality, that some cited studies were not relevant to the recommendations made, that many recommendations reflect consensus positions only, and that there was a need for more good quality studies of many sleep laboratory applications.
Diffusion
As of the time of writing, there are 97 licensed sleep laboratories in Ontario. In 2000, the number of sleep studies performed in Ontario was 376/100,000 people. There was a steady rise in sleep studies in the following years such that in 2004, 769 sleep studies per 100,000 people were performed, for a total of 96,134 sleep studies. Based on prevalence estimates of the Wisconsin Sleep Cohort Study, it was estimated that 927,105 people aged 30 to 60 years have sleep-disordered breathing. Thus, there may be a 10-fold rise in the rate of sleep tests in the next few years.
Economic Analysis
In 2004, approximately 96,000 sleep studies were conducted in Ontario at a total cost of ~$47 million (Cdn). Since obesity is associated with sleep disordered breathing, MAS compared the costs of sleep studies to the cost of bariatric surgery. The cost of bariatric surgery is $17,350 per patient. In 2004, Ontario spent $4.7 million per year for 270 patients to undergo bariatric surgery in the province, and $8.2 million for 225 patients to seek out-of-country treatment. Using a Markov model, it was concluded that shifting costs from sleep studies to bariatric surgery would benefit more patients with OSA and may also prevent health consequences related to diabetes, hypertension, and hyperlipidemia. It is estimated that the annual cost of treating comorbid conditions in morbidly obese patients often exceeds $10,000 per patient. Thus, the downstream cost savings could be substantial.
Considerations for Policy Development
Weight loss is associated with a decrease in OSA severity. Treating and preventing obesity would also substantially reduce the economic burden associated with diabetes, hypertension, hyperlipidemia, and OSA. Promotion of healthy weights may be achieved by a multisectorial approach as recommended by the Chief Medical Officer of Health for Ontario. Bariatric surgery has the potential to help morbidly obese individuals (BMI > 35 kg/m2 with an accompanying comorbid condition, or BMI > 40 kg/m2) lose weight. In January 2005, MAS completed an assessment of bariatric surgery, based on which OHTAC recommended an improvement in access to these surgeries for morbidly obese patients in Ontario.
Habitual snorers with excessive daytime sleepiness have a high pretest probability of having OSA. These patients could be offered a therapeutic trial of CPAP to diagnose OSA, rather than a PSG. A majority of these patients are also obese and may benefit from weight loss. Individualized weight loss programs should, therefore, be offered and patients who are morbidly obese should be offered bariatric surgery.
That said, and in view of the still evolving understanding of the causes, consequences and optimal treatment of OSA, further research is warranted to identify which patients should be screened for OSA.
PMCID: PMC3379160  PMID: 23074483
20.  Long-Term Risk of Incident Type 2 Diabetes and Measures of Overall and Regional Obesity: The EPIC-InterAct Case-Cohort Study 
PLoS Medicine  2012;9(6):e1001230.
A collaborative re-analysis of data from the InterAct case-control study conducted by Claudia Langenberg and colleagues has established that waist circumference is associated with risk of type 2 diabetes, independently of body mass index.
Background
Waist circumference (WC) is a simple and reliable measure of fat distribution that may add to the prediction of type 2 diabetes (T2D), but previous studies have been too small to reliably quantify the relative and absolute risk of future diabetes by WC at different levels of body mass index (BMI).
Methods and Findings
The prospective InterAct case-cohort study was conducted in 26 centres in eight European countries and consists of 12,403 incident T2D cases and a stratified subcohort of 16,154 individuals from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. We used Prentice-weighted Cox regression and random effects meta-analysis methods to estimate hazard ratios for T2D. Kaplan-Meier estimates of the cumulative incidence of T2D were calculated. BMI and WC were each independently associated with T2D, with WC being a stronger risk factor in women than in men. Risk increased across groups defined by BMI and WC; compared to low normal weight individuals (BMI 18.5–22.4 kg/m2) with a low WC (<94/80 cm in men/women), the hazard ratio of T2D was 22.0 (95% confidence interval 14.3; 33.8) in men and 31.8 (25.2; 40.2) in women with grade 2 obesity (BMI≥35 kg/m2) and a high WC (>102/88 cm). Among the large group of overweight individuals, WC measurement was highly informative and facilitated the identification of a subgroup of overweight people with high WC whose 10-y T2D cumulative incidence (men, 70 per 1,000 person-years; women, 44 per 1,000 person-years) was comparable to that of the obese group (50–103 per 1,000 person-years in men and 28–74 per 1,000 person-years in women).
Conclusions
WC is independently and strongly associated with T2D, particularly in women, and should be more widely measured for risk stratification. If targeted measurement is necessary for reasons of resource scarcity, measuring WC in overweight individuals may be an effective strategy, since it identifies a high-risk subgroup of individuals who could benefit from individualised preventive action.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 350 million people have diabetes, and this number is increasing rapidly. Diabetes is characterized by dangerous levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone that the pancreas releases after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest form of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. The long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes.
Why Was This Study Done?
A high body mass index (BMI, a measure of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) is a strong predictor of type 2 diabetes. Although the risk of diabetes is greatest in obese people (who have a BMI of greater than 30 kg/m2), many of the people who develop diabetes are overweight—they have a BMI of 25–30 kg/m2. Healthy eating and exercise reduce the incidence of diabetes in high-risk individuals, but it is difficult and expensive to provide all overweight and obese people with individual lifestyle advice. Ideally, a way is needed to distinguish between people with high and low risk of developing diabetes at different levels of BMI. Waist circumference is a measure of fat distribution that has the potential to quantify diabetes risk among people with different BMIs because it estimates the amount of fat around the abdominal organs, which also predicts diabetes development. In this case-cohort study, the researchers use data from the InterAct study (which is investigating how genetics and lifestyle interact to affect diabetes risk) to estimate the long-term risk of type 2 diabetes associated with BMI and waist circumference. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease and in a randomly chosen subcohort.
What Did the Researchers Do and Find?
The researchers estimated the association of BMI and waist circumference with type 2 diabetes from baseline measurements of the weight, height, and waist circumference of 12,403 people who subsequently developed type 2 diabetes and a subcohort of 16,154 participants enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC). Both risk factors were independently associated with type 2 diabetes risk, but waist circumference was a stronger risk factor in women than in men. Obese men (BMI greater than 35 kg/m2) with a high waist circumference (greater than 102 cm) were 22 times more likely to develop diabetes than men with a low normal weight (BMI 18.5–22.4 kg/m2) and a low waist circumference (less than 94 cm); obese women with a waist circumference of more than 88 cm were 31.8 times more likely to develop type 2 diabetes than women with a low normal weight and waist circumference (less than 80 cm). Importantly, among overweight people, waist circumference measurements identified a subgroup of overweight people (those with a high waist circumference) whose 10-year cumulative incidence of type 2 diabetes was similar to that of obese people.
What Do These Findings Mean?
These findings indicate that, among people of European descent, waist circumference is independently and strongly associated with type 2 diabetes, particularly among women. Additional studies are needed to confirm this association in other ethnic groups. Targeted measurement of waist circumference in overweight individuals (who now account for a third of the US and UK adult population) could be an effective strategy for the prevention of diabetes because it would allow the identification of a high-risk subgroup of people who might benefit from individualized lifestyle advice.
Additional Information
Please access these web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001230.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health care professionals, and the general public, including detailed information on diabetes prevention (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (including some information in Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes, about the prevention of type 2 diabetes, and about obesity; it also includes peoples stories about diabetes and about obesity
The charity Diabetes UK also provides detailed information for patients and carers, including information on healthy lifestyles for people with diabetes, and has a further selection of stories from people with diabetes; the charity Healthtalkonline has interviews with people about their experiences of diabetes
More information on the InterAct study is available
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention and about obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001230
PMCID: PMC3367997  PMID: 22679397
21.  How Has the Age-Related Process of Overweight or Obesity Development Changed over Time? Co-ordinated Analyses of Individual Participant Data from Five United Kingdom Birth Cohorts 
PLoS Medicine  2015;12(5):e1001828.
Background
There is a paucity of information on secular trends in the age-related process by which people develop overweight or obesity. Utilizing longitudinal data in the United Kingdom birth cohort studies, we investigated shifts over the past nearly 70 years in the distribution of body mass index (BMI) and development of overweight or obesity across childhood and adulthood.
Methods and Findings
The sample comprised 56,632 participants with 273,843 BMI observations in the 1946 Medical Research Council National Survey of Health and Development (NSHD; ages 2–64 years), 1958 National Child Development Study (NCDS; 7–50), 1970 British Cohort Study (BCS; 10–42), 1991 Avon Longitudinal Study of Parents and Children (ALSPAC; 7–18), or 2001 Millennium Cohort Study (MCS; 3–11). Growth references showed a secular trend toward positive skewing of the BMI distribution at younger ages. During childhood, the 50th centiles for all studies lay in the middle of the International Obesity Task Force normal weight range, but during adulthood, the age when a 50th centile first entered the overweight range (i.e., 25–29.9 kg/m2) decreased across NSHD, NCDS, and BCS from 41 to 33 to 30 years in males and 48 to 44 to 41 years in females. Trajectories of overweight or obesity showed that more recently born cohorts developed greater probabilities of overweight or obesity at younger ages. Overweight or obesity became more probable in NCDS than NSHD in early adulthood, but more probable in BCS than NCDS and NSHD in adolescence, for example. By age 10 years, the estimated probabilities of overweight or obesity in cohorts born after the 1980s were 2–3 times greater than those born before the 1980s (e.g., 0.229 [95% CI 0.219–0.240] in MCS males; 0.071 [0.065–0.078] in NSHD males). It was not possible to (1) model separate trajectories for overweight and obesity, because there were few obesity cases at young ages in the earliest-born cohorts, or (2) consider ethnic minority groups. The end date for analyses was August 2014.
Conclusions
Our results demonstrate how younger generations are likely to accumulate greater exposure to overweight or obesity throughout their lives and, thus, increased risk for chronic health conditions such as coronary heart disease and type 2 diabetes mellitus. In the absence of effective intervention, overweight and obesity will have severe public health consequences in decades to come.
In a longitudinal analysis, William Johnson and colleagues examine how individual lifetime BMI trajectories among white citizens of the UK have changed from 1946 to 2014.
Editors' Summary
Background
Overweight and obesity are major threats to global health. The global prevalence of obesity (the proportion of the world's population that is obese) has more than doubled since 1980; 13% of the adult population, or 0.6 billion people, are now classified as obese, while an additional 1.3 billion adults are overweight. Both classifications are determined by body mass index (BMI), which is calculated by dividing a person's weight in kilograms by the square of their height in meters. Obese individuals have a BMI of 30 kg/m2 or more, while overweight individuals have a BMI of 25–30 kg/m2. BMI values above 25 kg/m2 increase the risk of developing non-communicable diseases (NCDs), including cardiovascular diseases, cancers and diabetes. Each year, NCDs kill 38 million people (including 28 million people in low- and middle-income countries and 9 million people under 60 years of age), thereby accounting for more than 75% of the world's annual deaths.
In the United Kingdom, studies report that roughly one quarter of adults are obese, and a further third or more are overweight. This “obesity epidemic” extends to children; according to the National Child Measurement Programme for England (NCMP), about 9% of 4–5-year-olds and 19% of 10–11-year-olds were obese in 2013. In parallel, the UK has not seen the improvements in child and young adult mortality seen in comparable European states.
Why Was This Study Done?
Cross-sectional surveys in the UK, United States, and elsewhere have documented the obesity epidemic, but longitudinal data—drawn from periodic BMI measurements from individuals over their lifetimes—are needed to clarify the time course, or trajectory, of overweight and obesity. Longitudinal data can answer practical questions important for designing health policy interventions. Is the age at which individuals develop overweight or obesity changing over time? In which individuals are the greatest increases in BMI occurring? The authors leveraged longitudinal data from five birth cohort studies (studies that follow a selected group of individuals born during a short window of time), incepted in 1946, 1958, 1970, 1991, and 2001. These large cohort projects were funded by the UK government for the purpose of providing data for long-term health analyses such as this one; in total, the current study’s included sample comprised 56,632 participants with 273,843 BMI observations from participants aged 2 through 64.
What Did the Researchers Do and Find?
The present study aimed to investigate (1) shifts from the 1940s to the 2000s in the distribution of BMI across age and (2) shifts over the same period in the probability of developing overweight or obesity across age. For each of the five cohorts, subdivided by sex and childhood versus adulthood (thus, a total of 20 datasets), the authors applied statistical models to produce trajectories for each BMI centile (subset that results from dividing the distribution of BMI measurements into 100 groups with equal frequency; here, the 90th centile is the group for which 90% of the relevant population has lower BMI). They then investigated secular trends (long-term, non-periodic variations) at different centiles of the BMI distribution. For example, by comparing the trajectories of the 50th centile for adult males across the five cohorts, the researchers could see how the age at which BMI values reached the obese range varied between eras among this group.
The data revealed that most of the between-cohort, and thus between-era, increases in BMI took place in the highest centiles, indicating that overall gains in BMI mainly comprised very high BMI individuals carrying even more weight. Across the 1946, 1958, and 1970 cohorts, the age at which the 50th centile of adults entered the overweight range decreased from 41 to 33 to 30 years in males and 48 to 44 to 41 years in females. The probabilities of overweight and obesity across adulthood also increased. While children in the 50th BMI centile have remained at normal weight through the decades, the overall childhood probability of developing overweight or obesity has increased 2–3-fold from before to after the 1980s.
What Do These Findings Mean?
These findings describe the changing pattern of age-related progression of overweight and obesity from early childhood in white citizens of the UK. The findings may not be generalizable because other populations have distinct genetic predispositions, environmental exposures, and access to health care. In addition, the accuracy of the findings may be affected by differences between cohorts in how weight and height (and thus BMI) were measured. Nevertheless, these findings—in particular, the increased risk of overweight and obesity at younger ages—suggest that compared to previous generations, current and future generations will accumulate greater overweight or obesity exposure across their lives, likely resulting in increased risk for NCDs. Further research is now needed to determine whether lifestyle factors in the UK have affected the trajectory of BMI and to discover the extent to which these shifting weight trajectories have contributed to morbidity and mortality.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001828. The World Health Organization provides information on obesity and non-communicable diseases around the world (in several languages)The UK National Health Service Choices website also provides detailed information about obesity and a link to a personal story about losing weightThe International Obesity Taskforce provides information about the global obesity epidemicThe US Centers for Disease Control and Prevention provides information on non-communicable diseases around the world and on overweight and obesity and diabetes (including some information in Spanish)The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating planThe Weight-control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)MedlinePlus has links to further information about obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001828
PMCID: PMC4437909  PMID: 25993005
22.  Preventing Weight Gain in Women in Rural Communities: A Cluster Randomised Controlled Trial 
PLoS Medicine  2016;13(1):e1001941.
Background
Obesity is reaching epidemic proportions in both developed and developing countries. Even modest weight gain increases the risk for chronic illness, yet evidence-based interventions to prevent weight gain are rare. This trial will determine if a simple low-intensity intervention can prevent weight gain in women compared to general health information.
Methods and Findings
We conducted a 1-yr pragmatic, cluster randomised controlled trial in 41 Australian towns (clusters) randomised using a computer-generated randomisation list for intervention (n = 21) or control (n = 20). Women aged 18 to 50 yr were recruited from the general population to receive a 1-yr self-management lifestyle intervention (HeLP-her) consisting of one group session, monthly SMS text messages, one phone coaching session, and a program manual, or to a control group receiving one general women’s health education session. From October 2012 to April 2014 we studied 649 women, mean age 39.6 yr (+/− SD 6.7) and BMI of 28.8 kg/m2 (+/− SD 6.9) with the primary outcome weight change between groups at 1 yr. The mean change in the control was +0.44 kg (95% CI −0.09 to 0.97) and in the intervention group −0.48kg (95% CI −0.99 to 0.03) with an unadjusted between group difference of −0.92 kg (95% CI −1.67 to −0.16) or −0.87 kg (95% CI −1.62 to −0.13) adjusted for baseline values and clustering. Secondary outcomes included improved diet quality and greater self-management behaviours. The intervention appeared to be equally efficacious across all age, BMI, income, and education subgroups. Loss to follow-up included 23.8% in the intervention group and 21.8% in the control group and was within the anticipated range. Limitations include lack of sensitive tools to measure the small changes to energy intake and physical activity. Those who gained weight may have been less inclined to return for 1 yr weight measures.
Conclusions
A low intensity lifestyle program can prevent the persistent weight gain observed in women. Key features included community integration, nonprescriptive simple health messages, small changes to behaviour, low participant burden, self-weighing, and delivery including a mix of group, phone, and SMS text reminders. The findings support population strategies to halt the rise in obesity prevalence.
In a pragmatic, cluster-randomised controlled trial, Catherine Lombard and colleagues assess the value of a self-management lifestyle intervention to prevent weight gain among women living in rural Australia.
Editors' Summary
Background
Obesity—having an unhealthy amount of body fat—is a global public health problem. In the US, for example, more than one-third of adults are obese and another third are overweight. Obesity is defined as having a body mass index (BMI; an indicator of body fat calculated by dividing a person’s weight in kilograms by their height in meters squared) of equal to or more than 30 kg/m2; overweight individuals have a BMI of 25.0–29.9 kg/m2. Increased body fat is associated with an increased risk of developing diabetes, cancer, cardiovascular disease and other chronic diseases.. People gain body fat by consuming food and drink that contains more energy (calories) than they need for their daily activities. So excess body fat can be prevented and reversed by eating a diet that contains fewer calories and by being more active.
Why Was This Study Done?
BMI increases with age in most adults although in recent years young adults have been shown to be gaining body fat faster than older adults. However, the adult weight gain per year is generally less than 1 kg and could be prevented by encouraging people to eat just a little less and exercise just a little more. Prevention of weight gain is likely to be easier than reversal of established obesity, but few interventions designed to prevent weight gain have been rigorously tested. In this pragmatic randomized controlled trial, the researchers investigate whether a simple low-intensity intervention can prevent weight gain among 18–50-year-old women living in rural communities in Australia. Rates of obesity are generally higher among women than men and, in affluent countries, rural-dwelling women have higher rates of weight gain and obesity than urban-dwelling women—in Australia, young women living in rural and metropolitan areas gain an average of 700 g and 550 g per year, respectively. A pragmatic cluster randomized controlled trial randomly assigns groups of people (here, women living in different towns) to receive alternative interventions and compares outcomes in the differently treated “clusters” under real-life conditions.
What Did the Researchers Do and Find?
The researchers assigned 41 Australian towns to receive a 1 yr self-management lifestyle intervention (HeLP-her) or to act as controls. The intervention consisted of one group session during which facilitators delivered general health information and five simple health messages (for example, try to eat two servings of fruit and five servings of vegetables a day), a program manual to help participants develop a personalized weight gain prevention strategy, monthly text message to remind participants of key behaviors for weight gain prevention, and a 20-min personal phone coaching session delivered three months into the trial. Participants in the control clusters received a group education session on general women’s health topics at the start of the trial. In total, 649 women with an average baseline BMI of 28.2kg/m2 participated in the trial. After one year, the average weight change was +0.44 kg in the control arm of the trial and −0.48 kg in the intervention arm (a between group difference in weight change of −0.92 kg). The intervention also improved diet quality and self-management behavior and was equally efficacious across all age, BMI, income, and education subgroups.
What Do These Findings Mean?
These findings suggest that a low-intensity lifestyle program can prevent persistent weight gain among women. Specifically, the year-long HeLP-her intervention prevented a weight gain of nearly 1 kg on average among women living in rural Australia. Notably, a recent modeling study estimated that a 1 kg weight loss, if applied across the US population, could avoid 2 million cases of diabetes, 1.5 million cases of cardiovascular disease, and more than 73,000 cases of cancer. Although it is difficult to identify the successful elements of any intervention that targets multiple behaviors, key components of the HeLP-her intervention probably include the use of simple, non-prescriptive health messages, the focus on small behavioral changes, regular self-weighing, and the use of both personal and electronic means to deliver the intervention. Some aspects of this trial (for example, nearly a quarter of the participants did not complete the trial) may affect the accuracy of its findings and a longer follow-up is needed to determine the long-term effects of the intervention. Nevertheless, these findings provide new information on effective weight gain prevention strategies that align with current clinical guidelines and population strategies designed to halt the global rise in obesity.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001941.
The World Health Organization provides information on obesity (in several languages)
The Global Burden of Disease website provides the latest details about global obesity trends; the International Obesity Taskforce also provides information about the global obesity epidemic
The UK National Health Service Choices website provides information about obesity (including some real stories), healthy eating, exercising
The US Centers for Disease Control and Prevention has information on all aspects of overweight and obesity
ChooseMyPlate.gov is a resource provided by the US Department of Agriculture that provides individuals and health care professionals with user-friendly information on nutritional and physical exercise
The US National Institute of Diabetes and Digestive and Kidney Diseases provides information on weight control and healthy living
MedlinePlus provides links to other sources of information on obesity (in English and Spanish)
More information about obesity in Australia, this trial, and the HeLP-her intervention is available
doi:10.1371/journal.pmed.1001941
PMCID: PMC4718637  PMID: 26785406
23.  The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors 
PLoS Medicine  2009;6(4):e1000058.
Majid Ezzati and colleagues examine US data on risk factor exposures and disease-specific mortality and find that smoking and hypertension, which both have effective interventions, are responsible for the largest number of deaths.
Background
Knowledge of the number of deaths caused by risk factors is needed for health policy and priority setting. Our aim was to estimate the mortality effects of the following 12 modifiable dietary, lifestyle, and metabolic risk factors in the United States (US) using consistent and comparable methods: high blood glucose, low-density lipoprotein (LDL) cholesterol, and blood pressure; overweight–obesity; high dietary trans fatty acids and salt; low dietary polyunsaturated fatty acids, omega-3 fatty acids (seafood), and fruits and vegetables; physical inactivity; alcohol use; and tobacco smoking.
Methods and Findings
We used data on risk factor exposures in the US population from nationally representative health surveys and disease-specific mortality statistics from the National Center for Health Statistics. We obtained the etiological effects of risk factors on disease-specific mortality, by age, from systematic reviews and meta-analyses of epidemiological studies that had adjusted (i) for major potential confounders, and (ii) where possible for regression dilution bias. We estimated the number of disease-specific deaths attributable to all non-optimal levels of each risk factor exposure, by age and sex. In 2005, tobacco smoking and high blood pressure were responsible for an estimated 467,000 (95% confidence interval [CI] 436,000–500,000) and 395,000 (372,000–414,000) deaths, accounting for about one in five or six deaths in US adults. Overweight–obesity (216,000; 188,000–237,000) and physical inactivity (191,000; 164,000–222,000) were each responsible for nearly 1 in 10 deaths. High dietary salt (102,000; 97,000–107,000), low dietary omega-3 fatty acids (84,000; 72,000–96,000), and high dietary trans fatty acids (82,000; 63,000–97,000) were the dietary risks with the largest mortality effects. Although 26,000 (23,000–40,000) deaths from ischemic heart disease, ischemic stroke, and diabetes were averted by current alcohol use, they were outweighed by 90,000 (88,000–94,000) deaths from other cardiovascular diseases, cancers, liver cirrhosis, pancreatitis, alcohol use disorders, road traffic and other injuries, and violence.
Conclusions
Smoking and high blood pressure, which both have effective interventions, are responsible for the largest number of deaths in the US. Other dietary, lifestyle, and metabolic risk factors for chronic diseases also cause a substantial number of deaths in the US.
Please see later in the article for Editors' Summary
Editors' Summary
Background
A number of modifiable factors are responsible for many premature or preventable deaths. For example, being overweight or obese shortens life expectancy, while half of all long-term tobacco smokers in Western populations will die prematurely from a disease directly related to smoking. Modifiable risk factors fall into three main groups. First, there are lifestyle risk factors. These include tobacco smoking, physical inactivity, and excessive alcohol use (small amounts of alcohol may actually prevent diabetes and some types of heart disease and stroke). Second, there are dietary risk factors such as a high salt intake and a low intake of fruits and vegetables. Finally, there are “metabolic risk factors,” which shorten life expectancy by increasing a person's chances of developing cardiovascular disease (in particular, heart problems and strokes) and diabetes. Metabolic risk factors include having high blood pressure or blood cholesterol and being overweight or obese.
Why Was This Study Done?
It should be possible to reduce preventable deaths by changing modifiable risk factors through introducing public health policies, programs and regulations that reduce exposures to these risk factors. However, it is important to know how many deaths are caused by each risk factor before developing policies and programs that aim to improve a nation's health. Although previous studies have provided some information on the numbers of premature deaths caused by modifiable risk factors, there are two problems with these studies. First, they have not used consistent and comparable methods to estimate the number of deaths attributable to different risk factors. Second, they have rarely considered the effects of dietary and metabolic risk factors. In this new study, the researchers estimate the number of deaths due to 12 different modifiable dietary, lifestyle, and metabolic risk factors for the United States population. They use a method called “comparative risk assessment.” This approach estimates the number of deaths that would be prevented if current distributions of risk factor exposures were changed to hypothetical optimal distributions.
What Did the Researchers Do and Find?
The researchers extracted data on exposures to these 12 selected risk factors from US national health surveys, and they obtained information on deaths from difference diseases for 2005 from the US National Center for Health Statistics. They used previously published studies to estimate how much each risk factor increases the risk of death from each disease. The researchers then used a mathematical formula to estimate the numbers of deaths caused by each risk factor. Of the 2.5 million US deaths in 2005, they estimate that nearly half a million were associated with tobacco smoking and about 400,000 were associated with high blood pressure. These two risk factors therefore each accounted for about 1 in 5 deaths in US adults. Overweight–obesity and physical inactivity were each responsible for nearly 1 in 10 deaths. Among the dietary factors examined, high dietary salt intake had the largest effect, being responsible for 4% of deaths in adults. Finally, while alcohol use prevented 26,000 deaths from ischemic heart disease, ischemic stroke, and diabetes, the researchers estimate that it caused 90,000 deaths from other types of cardiovascular diseases, other medical conditions, and road traffic accidents and violence.
What Do These Findings Mean?
These findings indicate that smoking and high blood pressure are responsible for the largest number of preventable deaths in the US, but that several other modifiable risk factors also cause many deaths. Although the accuracy of some of the estimates obtained in this study will be affected by the quality of the data used, these findings suggest that targeting a handful of risk factors could greatly reduce premature mortality in the US. The findings might also apply to other countries, although the risk factors responsible for most preventable deaths may vary between countries. Importantly, effective individual-level and population-wide interventions are already available to reduce people's exposure to the two risk factors responsible for most preventable deaths in the US. The researchers also suggest that combinations of regulation, pricing, and education have the potential to reduce the exposure of US residents to other risk factors that are likely to shorten their lives.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000058.
The MedlinePlus encyclopedia contains a page on healthy living (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of healthy living
Healthy People 2010 is a national framework designed to improve the health of people living in the US. The Healthy People 2020 Framework is due to be launched in January 2010
The World Health Report 2002Reducing Risks, Promoting Healthy Life provides a global analysis of how healthy life expectancy could be increased
The National Health and Nutrition Examination Survey (NHANES) is “a program of studies designed to assess the health and nutritional status of adults and children in the United States”
The US Centers for Disease Control and Prevention's site Smoking and Tobacco Use offers a large number of informational and data resources on this important risk factor
The American Heart Association and American Cancer Society provide a rich resource for patients and caregivers on many important risk factors including diet, sodium intake, and smoking
doi:10.1371/journal.pmed.1000058
PMCID: PMC2667673  PMID: 19399161
24.  Future healthy life expectancy among older adults in the US: a forecast based on cohort smoking and obesity history 
Background
In the past three decades, the elderly population in the United States experienced increase in life expectancy (LE) and disability-free life expectancy (LEND), but decrease in life expectancy with disability (LED). Smoking and obesity are two major risk factors that had negative impacts on these trends. While smoking prevalence continues to decline in recent decades, obesity prevalence has been growing and is currently at a high level. This study aims to forecast the healthy life expectancy for older adults aged 55 to 85 in the US from 2011 to 2040, in relation to their smoking and obesity history.
Methods
First, population-level mortality data from the Human Mortality Database (HMD) and individual-level disability data from the US National Health Interview Survey (NHIS) were used to estimate the transition rates between different health states from 1982 to 2010, using a multi-state life table (MSLT) model. Second, the estimated transition rates were fitted and projected up to 2040, using a modified Lee-Carter model that incorporates cohort smoking and obesity history from NHIS.
Results
Mortality and morbidity for both sexes will continue to decline in the next decades. Relative to 2010, men are expected to have 3.2 years gain in LEND and 0.8 years loss in LED. For women, there will be 1.8 years gain in LEND and 0.8 years loss in LED. By 2040, men and women are expected to spend respectively 80 % and 75 % of their remaining life expectancy between 55 and 85 disability-free.
Conclusions
Smoking and obesity have independent negative impacts on both the survival and disability of the US older population in the coming decades, and are responsible for the present and future gender disparity in mortality and morbidity. Overall, the US older population is expected to enjoy sustained health improvements and compression of disability, largely due to decline in smoking.
Electronic supplementary material
The online version of this article (doi:10.1186/s12963-016-0092-2) contains supplementary material, which is available to authorized users.
doi:10.1186/s12963-016-0092-2
PMCID: PMC4941025  PMID: 27408607
Healthy life expectancy; Forecast; Mortality; Morbidity; Smoking; Obesity; Multi-state life table; Lee-Carter model
25.  Bariatric Surgery in the United Kingdom: A Cohort Study of Weight Loss and Clinical Outcomes in Routine Clinical Care 
PLoS Medicine  2015;12(12):e1001925.
Background
Bariatric surgery is becoming a more widespread treatment for obesity. Comprehensive evidence of the long-term effects of contemporary surgery on a broad range of clinical outcomes in large populations treated in routine clinical practice is lacking. The objective of this study was to measure the association between bariatric surgery, weight, body mass index, and obesity-related co-morbidities.
Methods and Findings
This was an observational retrospective cohort study using data from the United Kingdom Clinical Practice Research Datalink. All 3,882 patients registered in the database and with bariatric surgery on or before 31 December 2014 were included and matched by propensity score to 3,882 obese patients without surgery. The main outcome measures were change in weight and body mass index over 4 y; incident diagnoses of type 2 diabetes mellitus (T2DM), hypertension, angina, myocardial infarction (MI), stroke, fractures, obstructive sleep apnoea, and cancer; mortality; and resolution of hypertension and T2DM. Weight measures were available for 3,847 patients between 1 and 4 mo, 2,884 patients between 5 and 12 mo, and 2,258 patients between 13 and 48 mo post-procedure. Bariatric surgery patients exhibited rapid weight loss for the first four postoperative months, at a rate of 4.98 kg/mo (95% CI 4.88–5.08). Slower weight loss was sustained to the end of 4 y. Gastric bypass (6.56 kg/mo) and sleeve gastrectomy (6.29 kg/mo) were associated with greater initial weight reduction than gastric banding (2.77 kg/mo). Protective hazard ratios (HRs) were detected for bariatric surgery for incident T2DM, 0.68 (95% CI 0.55–0.83); hypertension, 0.35 (95% CI 0.27–0.45); angina, 0.59 (95% CI 0.40–0.87);MI, 0.28 (95% CI 0.10–0.74); and obstructive sleep apnoea, 0.55 (95% CI 0.40–0.87). Strong associations were found between bariatric surgery and the resolution of T2DM, with a HR of 9.29 (95% CI 6.84–12.62), and between bariatric surgery and the resolution of hypertension, with a HR of 5.64 (95% CI 2.65–11.99). No association was detected between bariatric surgery and fractures, cancer, or stroke. Effect estimates for mortality found no protective association with bariatric surgery overall, with a HR of 0.97 (95% CI 0.66–1.43). The data used were recorded for the management of patients in primary care and may be subject to inaccuracy, which would tend to lead to underestimates of true relative effect sizes.
Conclusions
Bariatric surgery as delivered in the UK healthcare system is associated with dramatic weight loss, sustained at least 4 y after surgery. This weight loss is accompanied by substantial improvements in pre-existing T2DM and hypertension, as well as a reduced risk of incident T2DM, hypertension, angina, MI, and obstructive sleep apnoea. Widening the availability of bariatric surgery could lead to substantial health benefits for many people who are morbidly obese.
In a UK cohort study, Ian Douglas and colleagues investigate weight, BMI, and related health outcomes after bariatric surgery.
Editors' Summary
Background
Obesity—having an unhealthy amount of body fat—is a growing threat to global public health. Worldwide, 13% of adults are obese, and, in the UK and the US, the statistics are even worse. A quarter and a third, respectively, of adults in these countries are obese. Obesity is defined as having a body mass index (BMI; an indicator of body fat calculated by dividing a person’s weight in kilograms by their height in meters squared) of ≥30 kg/m2. Compared to people with a healthy weight (a BMI of 18.5–24.9 kg/m2), overweight and obese people have an increased risk of developing type 2 diabetes, cardiovascular conditions such as hypertension (high blood pressure), myocardial infarction (heart attack), angina, and stroke, and they tend to die younger. People become overweight, and eventually obese, by consuming food and drink that contain more energy (calories) than they need for their daily activities. So, obesity can be prevented and reversed by eating less and exercising more.
Why Was This Study Done?
People with severe obesity (BMI of 40 kg/m2 or more) who have tried but failed to control their weight through lifestyle changes sometimes undergo bariatric surgery (weight loss surgery). In the UK and the US, this approach is also recommended for obese individuals who have an obesity-related illness such as type 2 diabetes with a lower BMI of 35 kg/m2 or more. Techniques such as gastric band surgery, gastric bypass, and sleeve gastrectomy all lead to reduced energy intake, and in randomized controlled trials comparing bariatric surgery and lifestyle interventions, bariatric surgery is associated with greater weight loss. However, the results of clinical trials are not always replicated in routine clinical practice. Here, the researchers investigate whether there is an association between bariatric surgery and weight, BMI, and obesity-related co-morbidities (illnesses) in the UK by undertaking a retrospective cohort study (an observational study that compares recorded clinical outcomes in non-randomized groups of patients who received different treatments).
What Did the Researchers Do and Find?
The researchers used the UK Clinical Practice Research Datalink, which contains anonymized clinical information about patients provided by general practitioners (primary care physicians), to identify 3,882 patients who had had bariatric surgery. They matched each patient (average BMI 44.7 kg/m2), according to the patient’s medications and constellation of risk factors, to an obese individual from the dataset who had not had bariatric surgery. This “propensity matching” technique is used in studies where patients are not allocated at random to receive a treatment, and is meant to control for confounding—the possibility that patients who receive the treatment may be otherwise distinct from patients who do not. According to this analysis, patients who had had bariatric surgery lost weight rapidly during the first four post-operative months (4.98 kg/month); their weight loss was sustained at a slower rate for up to four years. By contrast, there were no weight changes in the patients who did not have surgery. Notably, bariatric surgery was associated with a lower risk of type 2 diabetes onset, hypertension onset, angina onset, myocardial infarction, and obstructive sleep apnea (a sleep disorder) onset, and with the resolution of both type 2 diabetes and hypertension in those who already had these conditions when they underwent surgery. However, over an average of 3.4 years of follow-up, there was no evidence of any difference in the risk of death.
What Do These Findings Mean?
These findings show that bariatric surgery delivered in routine clinical practice in the UK is associated with a substantial initial weight loss that is sustained for at least four years after surgery. They also show that bariatric surgery is associated with improvements in pre-existing type 2 diabetes and hypertension and with a reduced risk of developing several obesity-related co-morbidities. Because the data used in the study were recorded for patient management by primary care physicians, the researchers were unable to use strict diagnostic criteria for some outcomes, which may limit the accuracy of these findings. Nevertheless, these results suggest that widening the availability of bariatric surgery in the UK could provide substantial health benefits for many people who are morbidly obese. Indeed, the researchers calculate that, if the associations seen in this study are causal (an observational study cannot prove that a treatment causes a specific outcome), bariatric surgery could prevent and/or resolve many tens of thousands of cases of hypertension and type 2 diabetes and prevent similar numbers of cases of other obesity-related illnesses among the 1.4 million morbidly obese people living in the UK.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001925.
The World Health Organization provides information on obesity (in several languages)
The Institute for Health Metrics and Evaluation website provides the latest details about global obesity trends; the World Obesity Federation also provides information about the global obesity epidemic
The UK National Health Service Choices website provides information about obesity (including some real stories), bariatric surgery (including some comments from patients), and healthy eating
The US Centers for Disease Control and Prevention has information on all aspects of overweight and obesity
ChooseMyPlate.gov is a resource provided by the US Department of Agriculture that provides individuals and healthcare professionals with user-friendly information on nutrition and physical exercise
The US National Institute of Diabetes and Digestive and Kidney Diseases provides information on bariatric surgery and on weight control and healthy living
MedlinePlus provides links to other sources of information on obesity and bariatric surgery (in English and Spanish)
doi:10.1371/journal.pmed.1001925
PMCID: PMC4687869  PMID: 26694640

Results 1-25 (1960962)