Years of Good Life Based on Consumption and Health

Objective: Longevity fails to account for health and functional status during aging. We sought to quantify differences in years of total life, years of healthy life, and years of able life among groups defined by age, sex, and race. Design: Primary analysis of a cohort study. Setting: 18 years of annual evaluations in four U.S. communities. Participants: 5888 men and women aged 65 and older. Measurements: Years of life were calculated as the time from enrollment to death or 18 years. Years of total, healthy, and able life were determined from self-report during annual or semi-annual contacts. Cumulative years were summed across each of the age and sex groups. Results: White women had the best outcomes for all three measures, followed by white men, non-white women, and non-white men. For example, at the mean age of 73, a white female participant could expect 12.9 years of life, 8.9 of healthy life and 9.5 of able life, while a non-white female could expect 12.6, 7.0, and 8.0 years, respectively. A white male could expect 11.2, 8.1, and 8.9 years of life, healthy life, and able life, and a non-white male 10.3, 6.2, and 7.9 years. Regardless of starting age, individuals of the same race and sex groups spent similar amounts (not proportions) of time in an unhealthy or unable state. Conclusion: Gender had a greater effect on longevity than did race, but race had a greater effect on years spent healthy or able. The mean number of years spent in an unable or sick state was surprisingly independent of the lifespan.

BackgroundOverweight older adults are often counseled to lose weight, even though there is little evidence of excess mortality in that age group. Overweight and underweight may be more associated with health status than with mortality, but few clinical trials of any kind have been based on maximizing years of healthy life (YHL), as opposed to years of life (YOL).ObjectiveThis paper examines the relationship of body mass index (BMI) to both YHL and YOL. Results were used to determine whether clinical trials of weight-modification based on improving YHL would be more powerful than studies based on survival.DesignWe used data from a cohort of 4,878 non-smoking men and women aged 65–100 at baseline (mean age 73) and followed 7 years. We estimated mean YHL and YOL in four categories of BMI: underweight, normal, overweight, and obese.ResultsSubjects averaged 6.3 YOL and 4.6 YHL of a possible 7 years. Both measures were higher for women and whites. For men, none of the BMI groups was significantly different from the normal group on either YOL or YHL. For women, the obese had significantly lower YHL (but not YOL) than the normals, and the underweight had significantly lower YOL and YHL. The overweight group was not significantly different from the normal group on either measure.ConclusionsClinical trials of weight loss interventions for obese older women would require fewer participants if YHL rather than YOL was the outcome measure. Interventions for obese men or for the merely overweight are not likely to achieve differences in either YOL or YHL. Evaluations of interventions for the underweight (which would presumably address the causes of their low weight) may be conducted efficiently using either outcome measure.

Objective: To create personalized estimates of future health and ability status for older adults. Method: Data came from the Cardiovascular Health Study (CHS), a large longitudinal study. Outcomes included years of life, years of healthy life (based on self-rated health), years of able life (based on activities of daily living), and years of healthy and able life. We developed regression estimates using the demographic and health characteristics that best predicted the four outcomes. Internal and external validity were assessed. Results: A prediction equation based on 11 variables accounted for about 40% of the variability for each outcome. Internal validity was excellent, and external validity was satisfactory. The resulting CHS Healthy Life Calculator (CHSHLC) is available at http://healthylifecalculator.org. Conclusion: CHSHLC provides a well-documented estimate of future years of healthy and able life for older adults, who may use it in planning for the future.

Little is known about how many years of life and disability-free years seniors can gain through exercise. Using data from the Cardiovascular Health Study, the authors estimated the extra years of life and self-reported healthy life (over 11 years) and years without impairment in activities of daily living (over 6 years) associated with quintiles of physical activity (PA) in older adults from different age groups. They estimated PA from the Minnesota Leisure Time Activities Questionnaire. Multivariable linear regression adjusted for health-related covariates. The relative gains in survival and years of healthy life (YHL) generally were proportionate to the amount of PA, greater among those 75+, and higher in men. Compared with being sedentary, the most active men 75+ had 1.49 more YHL (95% CI: 0.79, 2.19), and the most active women 75+ had 1.06 more YHL (95% CI: 0.44, 1.68). Seniors over age 74 experience the largest relative gains in survival and healthy life from physical activity.

Predicting Future Years of Healthy Life for Older Adults

To determine whether elderly people with different patterns of magnetic resonance imaging (MRI) findings have different long-term outcomes. Longitudinal cohort study. Cardiovascular Health Study. Individuals aged 65 and older were recruited (N = 5,888); 3,660 of these underwent MRI, and 3,230 without a stroke before MRI were included in these analyses. Cluster analysis of brain MRI findings was previously used to define five clusters: normal, atrophy, simple infarct, leukoaraiosis, and complex infarct. Participants were subsequently classified as healthy if they rated their health as excellent, very good, or good and as able if they did not report any limitations in activities of daily living (ADLs). Mean years of life (YoL), years of healthy life (YHL), and years of able life (YAL) were calculated over 16 years after the MRI and compared between clusters using unadjusted and adjusted regression analyses. Mean age of participants was 75.0. With 16 years of follow-up, mean YoL was 11.3; YHL, 8.0; and YAL, 8.4. Outcomes differed significantly between clusters. With or without adjustments, outcomes were all significantly better in the normal than complex infarct cluster. The three remaining clusters had intermediate results, significantly different from the normal and complex infarct clusters but not usually from one another. Over 16 years of follow-up, participants in the complex infarct cluster (n = 368) spent the largest percentage of their 8.4 years alive being sick (38%) and not able (38%). Findings on MRI scans in elderly adults are associated not only with long-term survival, but also with long-term self-rated health and limitation in ADLs. The combination of infarcts and leukoaraiosis carried the worst prognosis, presumably reflecting small vessel disease.

Longevity means living a long life, nowadays often considered a life span over 85 to 100 years. More and more people reach this limit in modern welfare societies, and citizens aged 90 years and over are said to be the fastest increasing group of people. This is a reality, but what are the background factors for this development? Many scholars think that it is mostly due to societal factors like improved hygiene, proper diet and safer environment. These are important but have mainly established the sine qua non for reaching old age through living past dangerous childhood and earlier adult life and becoming old. In modern societies, reaching longevity is jeopardized more by chronic non-communicable diseases which have replaced infectious diseases as primary causes of morbidity and mortality. By the way, according to the latest Global Health Estimates by the World Health Organization, during the first half of 2020, non-communicable diseases killed approximately 25 times more people than the ongoing COVID-19 pandemia (https://www.ecdc.europa.eu/en/covid-19-pandemic and https://www.who.int/healthinfo/global_burden_disease/estimates/en/). The most common non-communicable diseases are cardiovascular diseases, chronic obstructive pulmonary diseases, cancer and degenerative diseases. Many risk factors for them have been identified. The important contribution of cholesterol, smoking and hypertension at the population level was demonstrated, for example, in Finland where in the late 1960s and early 1970s, cardiac mortality amongst men was the highest in the world. The 80% decline in coronary mortality during decades thereafter mainly reflects a great reduction of the cardiovascular disease risk factor levels – most importantly cholesterol, smoking and hypertension – due to health promotion interventions [1]. Numerous randomized drug trials have, on the other hand, shown that also drug treatment of cardiovascular risk factors, especially hypercholesterolaemia and hypertension, can decrease cardiovascular and total mortality and thereby postpone death. But drug trials typically recruit 60- to 70-year-old participants, and trials routinely last only two to five years. The success story of decreasing risk factors amongst Finnish men has involved deaths before retirement age. Do these data prove that risk factor control can also increase longevity, i.e. living up to 90 years and over? In this issue of the Journal, van Oort et al use a clever strategy, Mendelian randomization, to find out whether traditional as well as some newer risk factors are causally related to longevity [2]. They compared genetic traits which are related to longevity, defined as living at or above the 90th survival percentile, and traits which are related to various modifiable risk factors. These factors included those listed in the Life’s Simple 7 initiative of the American Heart Association (glucose, blood pressure, cholesterol, body mass index, smoking, physical activity and diet) as well as education and sleep. Maybe not unexpectedly, but reassuringly, most of the Life’s Simple 7 factors were causally related to longevity, too. The result is important and nicely completes existing evidence. The result also supports possibilities to realize James Fries’ 40-year-old idea of ‘compression of morbidity’ [3] emphasizing prevention. Continuous efforts should be placed on ‘dull’ traditional risk factors like increased low-density lipoprotein (LDL) cholesterol level, high blood pressure, diabetes and preventing initiation of smoking. In contrast, the results of van Oort et al do not suggest, maybe surprisingly, that physical activity or fasting glucose level as such would be important for longevity. Also, the relationships between sleep duration and consumption of alcohol or coffee and longevity were not significant. Because the cardiovascular risk factors were genetically driven in the analyses of van Oort et al, a next question arises: do the results apply to drug-induced control of risk factors? Totality of evidence, including vast number of randomized controlled trials with their post-trial follow-up studies, suggests that this is the case, but treatment should be started early enough [4] and adherence should be secured. Drugs do not work if you do not take them. In Jonathan Swift’s 1726 novel Gulliver’s Travels, the novel’s hero visits the nation of Luggnagg, where individuals belonging to the Struldbrugg family live forever. Unfortunately, they did not enjoy eternal youth. Struldbruggs lived like mortals up to 80 years, where after they started to degenerate and the living was full of ailments related to ageing like loss of eyesight and hair, and loss of mortal friends around them. Living long is not nice if quality of life is bad. If control of cardiovascular risk factors extends life, does it also secure that extra years are worth living? Answering this question requires a life-course setting, because comparing quality of life to contemporary risk factor levels in old age does not necessarily reflect past risk factor status. There is some research in this area relating midlife risk factors to health-related quality of life (HRQoL) in old age. In the Helsinki Businessmen Study cohort (men born 1919-1934) with follow-up through 2018, lower cholesterol, lower blood pressure, non-smoking status and less weight gain in midlife [5-8] were associated with lower follow-up mortality, but also with better HRQoL (assessed with RAND-36/SF-36 instrument) amongst old age survivors. [Correction added on 5th January 2021 after first online publication: higher has been changed to lower in the preceding sentence.] Those men who had generally low cardiovascular disease risk status at midlife – based on smoking, serum lipids, blood pressure, body mass index and glucose tolerance – had significantly higher probability of reaching 90 years of age than men with higher cardiovascular disease risk. Importantly, men in the Helsinki Businessmen Study with better risk factor status in midlife also had clearly better HRQoL when assessed at 82 years (unpublished observations). According to the Bible, ‘The days of our years are threescore years and ten (=70 years); and if by reason of strength they be fourscore years (=80 years), yet is their strength labour and sorrow; for it is soon cut off…’(Psalm 90:10). This well accords with the thoughts of biogerontologists: the warranty period of homo sapiens is 65 years, where after on the average 20 years can be attained, mainly depending on life-course factors. Whilst age 85 years is an upper limit to life expectancy at the population level, ca. 40% of the original birth cohort nevertheless can reach 90 years, 5-6% 100 years, few 100-115 years, and only a handful of individuals over that [9]. The study by van Oort suggests that control of traditional cardiovascular risk factors is important to reach the reasonable goal of 90 years and also supports the usual paradigm that ageing is 75% due to environmental and 25% to genetic factors – up to a certain point of age. Overall, it seems feasible that health span – healthy years of life – extension and successful ageing can be promoted with better and long-term cardiovascular risk factor control. However, for reaching 100 years and over the role of genetic factors affecting longevity strengthens. For most of the population, extending life span and especially health span over 90 years requires new methods to control the biological ageing processes, currently investigated in the realms of Geroscience, the Longevity Dividend, and the Global Roadmap for Healthy Longevity [9, 10]. I have had various cooperation (educational, research, consultative) with companies marketing cardiovascular medications.

To estimate the effects of smoking on quality of life over time, using the Years of Healthy Life (YHL) construct. The Health and Retirement Study (HRS) survey (N=12,652) of persons 50 to 60 years old and the Asset and Health Dynamics Among the Oldest Old (AHEAD) survey (N=8,124) of persons > or =70 years old, plus spouses regardless of age, followed from 1992/1993 to 2000. Years of healthy life from baseline to death were estimated. Regression models were developed with smoking as the main explanatory variable and with both YHL and years of life remaining as the outcome variables. Smoking was strongly and consistently related to YHL lost. In HRS, individuals who had quit smoking at least 15 years prior to baseline had a similar number of YHL left as never smokers. Efforts to encourage smoking cessation should emphasize the impact of these factors on quality of life.

The WHO NCD (Non-Communicable Disease Alliance) Warns South Asian Countries including Bangladesh about the ‘aggressive’ spread of the diseases such as heart problems and diabetes affecting people mostly in younger age. “They should be earning money for the family but they are impacted by diabetes and cardiovascular diseases.” Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels. Cardiovascular disease includes coronary artery diseases (CAD) such as angina and myocardial infarction (commonly known as a heart attack). Other CVDs include stroke, heart failure, hypertensive heart disease, rheumatic heart disease, cardiomyopathy, heart arrhythmia, congenital heart disease, valvular heart disease, carditis, aortic aneurysms, peripheral artery disease, thromboembolic disease, and venous thrombosis. CVDs are the number 1 cause of death globally: more people die annually from CVDs than from any other cause. An estimated 17.7 million people died from CVDs in 2015, representing 31% of all global deaths. Of these deaths, an estimated 7.4 million were due to coronary heart disease and 6.7 million were due to stroke. Over three-quarters of CVD deaths take place in low- and middle-income countries. Out of the 17 million premature deaths (under the age of 70) due to no communicable diseases in 2015, 82% are in low- and middle-income countries, and 37% are caused by CVDs. Most cardiovascular diseases can be prevented by addressing behavioral risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidemia or already established disease) need early detection and management using counselling and medicines, as appropriate. (WHO Media Canter, May 2017). According to the latest WHO data published in May 2014, Coronary Heart Disease Deaths in Bangladesh reached 50,708 or 6.96% of total deaths. The age adjusted Death Rate is 53.53 per 100,000 of population ranks Bangladesh #150 in the world. Cardiovascular Diseases in Bangladesh Statistics on Overall Impact and Specific Effect on Demographic groups. Annual mortality rate (per 100,000 people) = 213.0; annual years of healthy life lost (per 100,000 people 4634.0; change in annual years of healthy life lost (since 1990). The percent change in annual years of healthy life lost per 100,000 people between 1990 and 2013. Years of healthy life lost, also called Disability-Adjusted Life Years (DALYs), is the sum of years of life lost to premature death and years lived with disability (adjusted for the severity of a condition). This accounts for population change and does not standardize on age differences. Cardiovascular Diseases in Bangladesh 100.8%. Range across all Global Disease Burden - Minute - 100%, Average 94.34%, Max 1.62 MILLION, percentage of years of healthy life lost attributed to risk factors 87.9%, These risk factors contributed to, and were thought to be responsible for, an estimated 89.2% of the total deaths caused by cardiovascular diseases in Bangladesh during 2013.

Introduction. Currently, many agro-industrial countries are experiencing a rapid intensification of agricultural and livestock production, which can critically affect the health of workers employed in agriculture, one of the five leading sectors of the economy with an increased risk of occupational diseases. Materials and methods. In order to assess the lost years of healthy life due to occupational diseases among agricultural workers of the Republic of Bashkortostan, 1,199 cases of chronic occupational morbidity of workers over 57 years (1960-2017) were analyzed; the number of years of life not lived or lived insufficiently due to this disease was calculated (the DALY index). Results. During the analyzed period, 41 occupational diseases was diagnosed as a result of exposure to four groups of the main harmful production factors of the working environment and the labour process: physical (43.6%), physical overload (38.9%), harmful chemicals (14.0%) and biological factors (3.5%). It was found that agricultural workers lost 2,256.1 years of healthy life due to occupational diseases, 73.2% accounted for dorsopathies, occupational diseases from vibration exposure and soft tissue diseases. The frequency of detection of occupational diseases and their clinical characteristics are reflected in the total number of years of healthy life lost. More than 75% of a total load of occupational diseases was caused by physical factors together with physical overload. A five-year delay in forming occupational diseases reduces the total lost years of healthy life by 17.0% and a ten-year delay by 33.5%. Limitations. One thousand one hundred ninety-nine cases were assessed over 57 years of observation when studying occupational morbidity, which is a sufficient reference sample. Conclusion. Quantitative analysis of the DALY index due to occupational disease allows to assess the likely medical and social damage by the number of years of healthy life lost for patients and justify priority measures aimed at reducing this damage.

Determine the loss of years of healthy life due to road incidents of motorcyclists in the city of Medellin from 2012 to 2015. Descriptive study with data on health care of injured motorcyclists and deaths adjusted with the Preston and Coale method, and OPS proportional distribution for the period 2012-2015. The years of life lost due to premature death (YLLs), years lived with disability (YLDs), and the disability-adjusted life years (DALYs) were calculated according to the new methodology designed for that purpose. The loss of years of healthy life due to road incidents of motorcyclists in the four-year period was 80,046 DALYs (823.8 per 100,000 inhabitants), with a higher proportion in men (81.3% and a ratio of 5 to 1 compared to women); the YLDs was 66.6% with marked differences in favor of men. There was nearly a 38% difference in the ages of 15 to 19 as well as a 19% difference from 30 to 49, compared to women. Premature death (YLLs) contributed to 33.4% of DALYs, with significant presentation in the above-mentioned age groups. The greatest loss of years of healthy life due to road incidents of motorcyclists in Medellin was due to non-fatal injuries and was concentrated in young men. If the trend of motorcycle road incidents continues, both local and national road safety plans will fail to accomplish the expected results, especially among motorcycle users.

BackgroundChoosing cost-effective strategies for improving the health of the public is difficult because the relative effects of different types of interventions are not well understood. The benefits of one-shot interventions may be different from the benefits of interventions that permanently change the probability of getting sick, recovering, or dying. Here, we compare the benefits of such types of public health interventions.MethodsWe used multi-state life table methods to estimate the impact of five types of interventions on mortality, morbidity (years of life in fair or poor health), and years of healthy life (years in excellent, very good, or good health).ResultsA one-shot intervention that makes all the sick persons healthy at baseline would increase life expectancy by 3 months and increase years of healthy life by 6 months, in a cohort beginning at age 65. An equivalent amount of improvement can be obtained from an intervention that either decreases the probability of getting sick each year by 12%, increases the probability of a sick person recovering by 16%, decreases the probability that a sick person dies by 15%, or decreases the probability that a healthy person dies by 14%. Interventions aimed at keeping persons healthy increased longevity and years of healthy life, while decreasing morbidity and medical expenditures. Interventions focused on preventing mortality had a greater effect on longevity, but had higher future morbidity and medical expenditures. Results differed for older and younger cohorts and depended on the value to society of an additional year of sick life.ConclusionInterventions that promote health and prevent disease performed well, but other types of intervention were sometimes better. The value to society of interventions that increase longevity but also increase morbidity needs further research. More comprehensive screening and treatment of new Medicare enrollees might improve their health and longevity without increasing future medical expenditures.

ObjectiveDetermine the loss of years of healthy life due to road incidents of motorcyclists in the city of Medellin from 2012 to 2015.MethodsDescriptive study with data on health care of injured motorcyclists and deaths adjusted with the Preston and Coale method, and OPS proportional distribution for the period 2012–2015. The years of life lost due to premature death (YLLs), years lived with disability (YLDs), and the disability-adjusted life years (DALYs) were calculated according to the new methodology designed for that purpose.ResultsThe loss of years of healthy life due to road incidents of motorcyclists in the four-year period was 80,046 DALYs (823.8 per 100,000 inhabitants), with a higher proportion in men (81.3% and a ratio of 5 to 1 compared to women); the YLDs was 66.6% with marked differences in favor of men. There was nearly a 38% difference in the ages of 15 to 19 as well as a 19% difference from 30 to 49, compared to women. Premature death (YLLs) contributed to 33.4% of DALYs, with significant presentation in the above-mentioned age groups.ConclusionsThe greatest loss of years of healthy life due to road incidents of motorcyclists in Medellin was due to non-fatal injuries and was concentrated in young men. If the trend of motorcycle road incidents continues, both local and national road safety plans will fail to accomplish the expected results, especially among motorcycle users.

The importance of quality of life issues in health care practice and research is steadily growing. This growing interest fits into the definition of health as proposed by the World Health Organization (WHO) in 1948 (1). The WHO defines health as 'a state of complete physical, mental and social well-being and not merely the absence of disease and infirmity'. The attention to health-related quality of life is reflected in the increase in the use of quality-of-life evaluation as a technique of clinical research since 1973, when only five articles listed 'quality of life' as a reference key word in the Medline data base; during the subsequent five-year periods there were 195, 273, 490, and 1252 such articles (2). Also in the field of allergy it has been recognized that allergic disease comprise more than the classical signs and symptoms being part of physical disorders such as allergic rhinitis, asthma and the atopic eczema/dermatitis syndrome (AEDS) (3). In the last decades an increasing effort has been made to understand the socioeconomic burden of atopic disease in terms of effects on health-related quality of life (HRQL) and healthcare costs. It has been acknowledged in several consensus reports that rhinitis and asthma are associated with impairments in the patients' functioning in day-to-day life at home, at work and at school 4-8). With the introduction of questionnaires designed to measure asthma- 9-11) and rhinitis-associated impairments of quality of life (12) it is clear that patients may be bothered by sleep disorders, emotional problems, impairment in activities and social functioning. Also, in general terms, patients with asthma (13) and allergic rhinitis (14) are impaired in their physical and mental functioning, including vitality and the perception of general health. From daily medical practice it can be easily understood that AEDS has a major impact on HRQL. In a way, the use of questionnaires focused on skin disease 15-17) formally confirms this association. Quality of life, QOL, has divergent meanings for different people. Also, HRQL may be considered as ill-defined. More agreement has been reached about the four domains of QOL which are considered to be important: 1) physical status and functional abilities; 2) psychological status and well-being; 3) social functioning; 4) economic and/or vocational status and factors ( 18 ). As the true quality of life value cannot be measured directly, researchers and clinicians have to resort to series of questions (items) to measure this construct indirectly. Combinations of items yield scores referring to physical, mental and social domains. An HRQL instrument must meet several criteria. It should address each component (symptom, condition) that is important to the patient. Attributes of an instrument are described in Table 1. It will be clear that the construction of quality of life questionnaires is a complex task, drawing from the fields of clinimetrics, psychometrics and clinical decision-making (2). Differences in approach, for instance item selection using factor analysis vs the impact method which select items that are most frequently perceived as important by patients -- yields different questionnaires (19). In general two types of instruments, generic and specific, have been used in allergy research. Generic questionnaires measure physical, psychological and social domains in all health conditions irrespective of the underlying disease. A frequently used generic instrument is the Medical Outcomes Survey Short Form 36 (SF-36) (20). The SF-36 was developed as part of the Medical Outcomes Study and analyzes health status using 36 questions to measure nine different health dimensions. It has been used to characterize patients with asthma. Bousquet (13) compared the FEV1 and a clinical score of asthma severity for 252 asthmatic patients. There was a significant positive correlation between all nine quality of life domains of the SF-36 and the clinical score of Aas. Eight of the nine domains also correlated with the FEV1. Also in perennial rhinitis there was a significant impairment in eight of nine QOL dimensions in patients compared with healthy subjects (14). Furthermore, the SF-36 is used to evaluate the effects of a nonsedating antihistamine on quality of life. In this study all of the nine quality of life dimensions improved significantly after one and six weeks of cetirizine treatment compared with placebo (21). Other generic instruments that have been used in allergy research are the Sickness Impact Profile (SIP) (22) and the Nottingham Health Profile (NHP) (23). The 136 items in 12 categories of the SIP describe activities of everyday living. This instrument has been used to evaluate the effect of salmeterol on asthma (24). Salmeterol led to significant improvements over salbutamol on virtually all clinical outcomes. Although all four quality of life instruments used in this study showed the same trend in favor of salmeterol, only the disease-specific Asthma Quality of Life Questionnaire (AQLQ) and the Rating Scale utilities showed significantly greater improvement on salmeterol than on salbutamol. In severe AEDS it was shown, using the SIP, that cyclosporin improves quality of life significantly (25). In particular, the SIP has been used for comparison with disease-specific instruments (24, 26-28). The NHP, the only generic instrument derived entirely from lay people, has been used to validate a disease-specific instrument for patients with dermatitis and psoriasis (29). In asthma the NHP was not able to capture clinical improvement by treatment with pulmonary steroids (30). The latter observations underline the disadvantage that the generic instruments miss depth and therefore may not be responsive enough to detect changes in general health states in spite of important changes in disease-related problems (26). The advantage of generic instruments, however, is that the burden of illness across different disorders and patient populations can be compared. In a comparison between asthma and epilepsy the major finding was that children with epilepsy had a relatively more compromised quality of life in the psychological, social, and school domains (31. In contrast, children with asthma had a more compromised quality of life in the physical domain. These findings suggested that attention simply to seizure control in the clinical setting will not address the full range of quality of life problems in children with epilepsy. Specific instruments have been designed by asking patients what kind of problems they experience from their disease. Both the frequency and the importance of impairments are measured by means of the questionnaires. These instruments have the advantage that they describe the disease-associated problems of the patients. As stated above, they seem to be more responsive to changes in HRQL than do the generic instruments. Several instruments for patients with asthma have been developed. The Asthma Quality of Life Questionnaire of Juniper is focused on symptoms, emotions, exposure to environmental stimuli, and activity limitation (32). Modifications of this questionnaire have been published recently (33, 34). When using HRQL outcome in clinical trials, the question arises whether a change in HRQL is of clinical importance. For the AQLQ, which uses a seven-point scale, the minimal important difference of quality of life score per item is considered to be very close to 0.5 (35). A change of 1.0 in the score represents a moderate change and a change in score of greater than 2.0 represents a large change in HRQL. The minimal important difference as described by Juniper is based upon patient opinions. Measures such as the standardized response mean or the effect size can be used to standardize changes. These measures are based solely upon the distribution of the observed data, in particular upon the variance (36). Recently, it has been shown that both the SF-36 and AQLQ were able to characterize a group of patients with moderate asthma very well, whereas the AQLQ domains were found to have the best discriminative properties (37. The Asthma Quality of Life Questionnaire of Marks captures breathlessness, physical restrictions, mood disturbance and concerns for health (38). St. George's Respiratory Questionnaire (11) is designed for patients with asthma and chronic obstructive pulmonary disorder COPD. It can be applied in both reversible and fixed airway obstruction. In contrast to other questionnaires, the Living with Asthma Questionnaire (10) does not include impairments experienced as a direct consequence of asthma symptomatology. Other instruments are presented in Table 2. The properties of the most frequently used questionnaire are described in Table 3. Specific instruments have been developed for children and caregivers (Table 2). In addition, questionnaires have been constructed for different age-groups of patients with rhinitis (12, 39-41). A simple practical questionnaire technique for routine clinical use, the Dermatology Life Quality Index (DLQI) has been introduced to characterize patients with skin disorders (15). This instrument has been used to compare patients with psoriasis and dermatitis (42). Also versions for children are available: the Children's Dermatology Life Quality Index (CDLQI) and the Infant's Dermatology Life Quality Index (IDLQI) (16). Other questionnaires are the Skindex (43) the Dermatology-Specific Quality of Life (DSQL) (17) and the patient-generated Dermatology Quality of Life Scales (DQOLS) (44). Recently, a questionnaire has been developed to measure HRQL in patients with allergy to insect stings. Subsequently, this instrument has been used in the evaluation of venom immunotherapy (45). It appeared that venom immunotherapy resulted in a statistically and clinically significant improvement in HRQL. Both in clinical practice and in research physicians and investigators rely on physiological and objective measures, whenever possible. However in asthma an increase in FEV1 or a decrease in PC20 histamine or methacholine may occur without any improvement experienced by the patient. Medical intervention may improve physiologic measures, whereas for instance side-effects of drugs or the cumbersome aspects of subcutaneous immunotherapy may unfavorably influence day-to-day life and compliance with treatment. It has been put forward that the classical outcome variables may only partially characterize the disease of the patient. From that point of view it has been advocated to measure HRQL along with the conventional clinical indices (46). In line with this reasoning is the weak association between classical asthma measures and the outcome of HRQL questionnaires. Comparison between de AQLQ of Marks with asthma symptoms and lung function variables revealed that a change in AQLQ score was weakly correlated with change in symptom score (r = 0.37, 95% CI 0.04–0.64) and change in BHR (r = 0.38, 95% CI 0.06–0.64). The association with change in peak flow variability was weak (r = 0.12, 95% CI 0.26–0.47) (27). Similar observations have been reported by others 47-50). An interesting study shows that the mere presence of respiratory symptoms or a (gradually) reduced lung function is insufficient reason for patients to seek medical help. Subjects are more likely to consult their general practitioner once their quality of everyday life is affected or they experience variability in lung function (51). Also, rhinitis related quality of life appears to be moderately correlated to the more classical outcome variables used in clinical trials, such as daily symptom scores and nasal hyperreactivity (52). Another argument to use quality of life instruments lies in the headstart with respect to the knowledge of their validation, reliability and responsiveness compared to the common symptom scores or visual analogue scores (VAS) scales used at clinical trials. In the field of nasal allergy, validation or standardization of symptom scores has rarely been the subject of research. In asthma, even quite recently introduced measures, such as the number of symptom-free days, merit more attention in terms of standardization and validation (53). Other reasons to assess quality of life are conceivable. Measurement of quality of life can also be useful for screening purposes or for evaluation of therapy. Quality of life may be a determinant of effectiveness or efficacy of treatment. Moreover, its assessment might be relevant to striving for optimal decision-making. As the perception of patients is clearly important in the management of disease and patient compliance (Fig. 1), measurement of this 'dimension' by HRQL questionnaires in clinical trials may be justified. The emphasis on quality of life has sometimes resulted in a routine inclusion of HRQL questionnaires in clinical trials. The inclusion of such an instrument is valuable only if the changes can be interpreted by clinicians and contributes to optimal medical decision-making. In an editorial, criticism has been directed to the routine inclusion of such instruments when the structure of the evaluation and its rationale appears ill-defined (54). A model representing the relationships between clinical aspects of therapy, HRQL and factors influencing HRQL (adapted from Cramer and Spilker (17)). Generally in clinical trials the effect of treatment or intervention on HRQL runs parallel with the effect on conventional medical outcome measures. However, in some studies differences can be found. In a study evaluating the combined effect of steroids and antihistamines no differences were demonstrated between patients treated with antihistamine and steroids vs steroids alone in terms of quality of life, whereas for some patient-rated symptoms the combination turned out to be superior (55). In a large multicenter study comparing budesonide and fluticasone it was found that both drugs were equally effective in suppressing symptoms (56), although budesonide had a better effect on general quality of life (57). This might indicate that patients perceive differences not captured by conventional symptom scores. The reverse situation, i.e. significant effects on classical outcomes (symptom scores, medication use, peak flow or FEV1) without important change in two generic and two specific HRQL measures has been described in a study on the effect of formoterol, a long-acting α2-agonist, in mild to moderate asthmatic patients (58). The latter discrepancies can be explained by a limited performance of HRQL measures in mild asthmatic patients. Alternatively, it is possible that the minor changes in symptom scores and lung function due to the intervention are not perceived by patients as relevant. Moreover, patients with a chronic condition may adapt themselves to their disease. The strength of HRQL questionnaires, that is the patient-centred approach, is also one of its weaknesses. Perceptions of quality of life experienced by persons may shift in time. It is easy to understand that a dramatic personal accident or a serious disease will not only cause deterioration in quality of life but will eventually also influence the patient's values and internal standards. For instance, in a study of quality of life after radiotherapy for laryngeal cancer, a temporary deterioration of physical functioning and symptoms was reported, mostly caused by side-effects of treatment. Despite physical deterioration, there was an improvement of emotional functioning and mood after treatment, probably as a result of psychological adaptation and coping processes (59). It is possible also that in less dramatic circumstances, disease and treatments will induce shifts in perception due to changes in the patient's values. Such subjective changes in patients' perception are known as response shift. Socioeconomic status is an additional important independent factor influencing HRQL. In a recent study with asthmatic patients it was shown that socioeconomic status attributes to HRQL. More importantly, in this study it was difficult to separate out the unique effects of socioeconomic status and race/ethnicity (60). Recently, a significant relationship between the mental health of children with asthma and family functioning has been shown (61). These findings suggest that the domains comprising the HRQL of children with asthma are related to both disease and non-disease factors. Psychological functioning influences the burden of a specific disease. A study designed to assess the effects of depressive symptoms on asthma patients' reports of functional status and health-related quality of life revealed that asthma patients with more depressive symptoms reported worse health-related quality of life than asthma patients with similar disease activity, but fewer depressive symptoms (62). Interestingly, these findings were seen not only in generic (SF-36) but also in specific (AQLQ) instruments. This means that a disease-specific instrument may be also influenced by phenomena such as fear and depression. Finally, patients may either intentionally or unconsciously mask their symptoms or trivialize their diseases. They may tend to ignore or discount those problems which they believe are unrelated to their illness. Others may tend to give socially desirable answers. Response shifts and illusory mental health (63) are not easily captured with HRQL instruments, but they will certainly influence the outcome of a clinical trial, when HRQL is chosen as the primary endpoint. In summary, one has to realize that the translation of clinical effects of treatment into perceived and reported changes in quality of life finds a place at the integration level of the patient and this is, in a way, a black box which is not easy to assess (Fig. 1). For these reasons it is strongly recommended to use HRQL outcome measures in parallel with conventional physiological outcome measures. Asthma, allergic rhinitis and AEDS often coexist. The question to what extent concomitant allergic disease affects quality of life has infrequently been addressed. In a recent study the SF-36 questionnaire from 850 subjects recruited in two French centers participating in the European Community Respiratory Health Survey was evaluated. Both asthma and allergic rhinitis were associated with impairment in quality of life. However, 78% of asthmatics also had allergic rhinitis. Subjects with allergic rhinitis but not asthma were more likely to report problems with social activities, difficulties with daily activities as a result of emotional problems, and low mental well-being than subjects with neither asthma nor rhinitis. Patients with both asthma and allergic rhinitis experienced more physical limitations than patients with allergic rhinitis alone, but no difference was found between these two groups for concepts related to social/mental health (64). In another study focusing on asthma, rhinitis and AEDS, comprising 325 subjects allergic to house dust mites, it was found that patients did show impaired quality of life compared to irrespective of the of the atopic Patients with the of asthma did out in terms of physical In addition, asthma symptoms with a visual had a major effect on social functioning, emotional functioning and disorders, in patients with AEDS, appeared to be associated with physical functioning, social functioning, mental health and general health It is not only concomitant atopic disease that has an impact on quality of life. such as and and nasal may patients with rhinitis and asthma. the SF-36 and a quality of life measure it has been shown that HRQL is impaired and that may improve quality of life for patients that is a other specific instruments such as the Index and the have been The impact of on social life in children during the four of life is not easily can be by use of a specific which measures the quality of life is a chronic disease of the respiratory which is frequently associated with respiratory compared the HRQL in patients with nasal with those of patients with perennial rhinitis and healthy It appeared that nasal impaired HRQL more than perennial allergic rhinitis The impairment of HRQL was greater when nasal was associated with asthma In addition, of nasal symptoms, and pulmonary function were after the evaluation in patients with nasal These demonstrated that nasal treatment either with nasal steroids or significantly improved both nasal symptoms and QOL without significant changes in pulmonary may a if the or is in one particular disease. 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Adverse childhood experiences and health-related quality of life among women undergoing hysterectomy for uterine leiomyoma