Do populations continuously grow?
Not necessarily. The growth of a population depends on a number of issues. Obviously, the average age of the individuals of that population is important. But other factors, such as the local economy, also play a role.
Major changes in the human population first began during the 1700s in Europe and North America. First death rates fell, followed somewhat later by birth rates.
Death Rates Fall
Several advances in science and technology led to lower death rates in 18th century Europe and North America:
- New scientific knowledge of the causes of disease led to improved water supplies, sewers, and personal hygiene.
- Better farming techniques and machines increased the food supply.
- The Industrial Revolution of the 1800s led to new sources of energy, such as coal and electricity. This increased the efficiency of the new agricultural machines. It also led to train transport, which improved the distribution of food.
For all these reasons, death rates fell, especially in children. This allowed many more children to survive to adulthood, so birth rates increased. As the gap between birth and death rates widened, the human population grew faster.
Birth Rates Fall
It wasn’t long before birth rates started to fall as well in Europe and North America. People started having fewer children because large families were no longer beneficial for several reasons.
- As child death rates fell and machines did more work, farming families no longer needed to have as many children to work in the fields.
- Laws were passed that required children to go to school. Therefore, they could no longer work and contribute to their own support. They became a drain on the family’s income.
Eventually, birth rates fell to match death rates. As a result, population growth slowed to nearly zero.
Stages of the Demographic Transition
These changes in population that occurred in Europe and North America have been called the demographic transition. The transition can be summarized in the following four stages, which are illustrated in Figure below:
- Stage 1—High birth and death rates lead to slow population growth.
- Stage 2—The death rate falls but the birth rate remains high, leading to faster population growth.
- Stage 3—The birth rate starts to fall, so population growth starts to slow.
- Stage 4—The birth rate reaches the same low level as the death rate, so population growth slows to zero.
Stages of the Demographic Transition. In the demographic transition, the death rate falls first. After a lag, the birth rate also falls. How do these changes affect the rate of population growth over time?
- Major changes in the human population first began during the 1700s. This occurred in Europe and North America.
- First, death rates fell while birth rates remained high. This led to rapid population growth.
- Later, birth rates also fell. As a result, population growth slowed.
- How did science and technology affect the human population?
- List two important scientific changes that affected the human growth rate.
- Outline the four stages of the demographic transition as it occurred in Europe and North America.
Effect of medroxyprogesterone on depressive symptoms in depressed and non-depressed perimenopausal and postmenopausal women following discontinuation of transdermal estradiol therapy
Concern about adverse effects of progestins on mood has influenced the use of medroxyprogesterone (MPA) and other progestins. In this brief report, we examined whether administration of MPA leads to depressive symptoms in two groups of peri- and postmenopausal women randomly assigned to treatment with estrogen: one currently experiencing clinical depression and another without depression.
Open-label MPA 10-mg/day was administered for 14 days for endometrial protection after completion of double-blinded treatment with 17-β-estradiol 0.1-mg/day for 8 weeks in 40-year-old peri/postmenopausal women enrolled in two separate randomized placebo-controlled trials for treatment of cognitive problems (“non-depressed group”) or clinical depression (pressed group”). Non-parametric tests were used to compare changes in depressive symptoms on the Beck Depression Inventory (BDI) within each group and between groups during MPA therapy.
Of 24 non-depressed (median BDI at baseline 5.5, interquartile range [IQR] 2.5, 8.5) and 14 depressed (median BDI at baseline, 17, IQR 15, 21) women treated with MPA, BDI scores did not change during MPA treatment in either group (median change 0, IQR 𢄢, 0.5, and median 0, IQR 𢄠.5, 1.5, p=0.28 and p=0.50, respectively). Changes in BDI scores during treatment with MPA did not differ between groups (p=0.25).
Among women receiving MPA for two weeks following discontinuation of estradiol, depressive symptoms did not emerge on MPA. These findings were consistent for both depressed and non-depressed women, suggesting that, even among women who are currently experiencing depression, brief treatment with MPA is unlikely to disrupt mood.
Coronavirus disease 2019 (COVID-19) can infect patients in any age group including those with no comorbid conditions. Understanding the demographic, clinical, and laboratory characteristics of these patients is important toward developing successful treatment strategies.
Approach and Results:
In a retrospective study design, consecutive patients without baseline comorbidities hospitalized with confirmed COVID-19 were included. Patients were subdivided into ≤55 and >55 years of age. Predictors of in-hospital mortality or mechanical ventilation were analyzed in this patient population, as well as subgroups. Stable parameters in overall and subgroup models were used to construct a cluster model for phenotyping of patients. Of 1207 COVID-19–positive patients, 157 met the study criteria (80≤55 and 77>55 years of age). Most reliable predictors of outcomes overall and in subgroups were age, initial and follow-up d -dimer, and LDH (lactate dehydrogenase) levels. Their predictive cutoff values were used to construct a cluster model that produced 3 main clusters. Cluster 1 was a low-risk cluster and was characterized by younger patients who had low thrombotic and inflammatory features. Cluster 2 was intermediate risk that also consisted of younger population that had moderate level of thrombosis, higher inflammatory cells, and inflammatory markers. Cluster 3 was a high-risk cluster that had the most aggressive thrombotic and inflammatory feature.
In healthy patient population, COVID-19 remains significantly associated with morbidity and mortality. While age remains the most important predictor of in-hospital outcomes, thromboinflammatory interactions are also associated with worse clinical outcomes regardless of age in healthy patients.
The Future of Disability in America (2007)
As described in Chapter 1, demographic trends&mdashnotably, the aging of the American population&mdashpromise to increase substantially the numbers of people at risk for disability. Whether such trends will translate in the future into increasing numbers of people with limits on their activities and participation in community life is less clear. Avoiding such increases will depend in part on the nation&rsquos will to promote equalization of opportunity for all Americans, irrespective of age or ability.
The good news is that for many people the chances of experiencing activity limitations or participation restrictions can be reduced through a variety of means. These include making effective assistive technologies and accessible general-use technologies more widely available (see Chapter 7) and promoting broader acceptance and stronger enforcement of policies to remove environmental barriers to access and participation in areas such as health care, employment, transportation, and telecommunications (see Chapter 6 and Appendixes D, E, F, and G). In addition, public health and clinical interventions can help prevent the onset of illness or injury and associated physical or mental impairments, as well as minimize the devel-
opment of secondary health conditions and limit the effects of atypical or premature aging among young adults with disabilities (see Chapter 5).
To provide insight into the future of disability, this chapter reviews recent trends in the amount, type, and health-related causes of disability&mdashprimarily in the form of activity limitations&mdashfor people in early, middle, and late life. It considers projections of future levels of disability. The analysis here should be read in the context of the review in Chapter 2 of the inadequacies in the nation&rsquos current disability surveillance system. As in 1991, when the Institute of Medicine (IOM) report Disability in America was published, data sources that can be used to guide the future of disability in America, particularly efforts to identify and remove environmental barriers to participation for people with disabilities, are inadequate.
Current statistics, discussed further below, indicate that the number of people with disability (broadly defined as impairments, activity limitations, or participation restrictions) now exceeds 40 million&mdashand that number could be more than 50 million. Data on trends in disability during early, middle, and late life present a mixed picture of the changes that have taken place during the last two decades and more. Among children, evidence points to increases in some health conditions&mdashincluding asthma, prematurity, autism, and obesity&mdashthat contribute to disability. These increases have been accompanied by increases in certain activity limitations that are not entirely explained by increased health and educational screening of children. The percentage of adults under the age of 65 who had activity limitations, including work limitations, grew during the 1990s, although this increase appears to have leveled off recently. In contrast, among older adults, declines in the prevalence of personal care and domestic activity limitations have been reported, although not all groups appear to have benefited equally, and the reasons for these declines remain unclear.
As described in Chapter 2, data on participation restrictions, in particular, remain relatively limited. Thus a full portrait of trends in disability is not possible. Moreover, although the equalization of opportunities for people with disabilities is an increasing focus of researchers, they cannot yet track the broad range of environmental factors that contribute to activity limitations and participation restrictions. This chapter focuses on trends during the past two decades in a relatively narrow set of activity limitations, the health conditions that contribute to those limitations, and, where relevant, possible explanations for these trends.
CURRENT ESTIMATES OF DISABILITY AND RELATED CONDITIONS
As discussed in Chapter 2, the omission of key groups from national population surveys has important implications for the development of basic
Selected Recent Chartbooks and Other Profiles of Statistical Data on Disability
Centers for Disease Control and Prevention
Disability and Health State Chartbook 2006: Profiles of Health for Adults with Disabilities
Americans with Disabilities, 2002 (Steinmetz, 2006)
Disability and American Families, 2000 (Wang, 2005)
Health Resources and Services Administration, Maternal and Child Health Bureau.
National Survey of Children with Special Health Care Needs Chartbook, 2001 (HRSA, 2004)
National Institute on Disability and Rehabilitation Research
Chartbook on Mental Health and Disability in the United States (Jans et al., 2004)
Cornell Center on Disability Demographics and Statistics
2004 Disability Status Reports: United States Summary (Houtenville, 2005)
Disability Statistics Center, University of California at San Francisco
Improved Employment Opportunities for People with Disabilities (Kaye, 2003)
Population Reference Bureau
Disability in America (Freedman et al., 2004b)
estimates of the population with disabilities. Such estimates must be pieced together from various sources, and the figures vary depending on the choice of survey and definition.
Box 3-1 lists several chartbooks and other profiles of disability data in the United States. The U.S. Census Bureau and most other agencies supply public use data sets so that researchers and others can obtain data more recent than those available in such profiles, and a few agency resources also allow some online analysis of the data. 1
One challenge in using information from different surveys is that different surveys rely upon different conceptual notions of disability, which in
As indicated in the source citations for most of the figures in this chapter, the committee contracted with H. Stephen Kaye of the University of California at San Francisco to supply information from the public use data sets for the National Health Interview Survey.
turn lead to different population estimates. For example, Stein and Silver (2002) found estimates of the rates of disability among children to be in the range of 14 to 17 percent, depending on whether children were identified through chronic conditions, special health care needs, or reports of disability. Moreover, even seemingly minor differences in the phrasing of questions or response options or in the ways of summarizing the data may yield different or even inconsistent pictures of a particular aspect of disability. For example, for older adults, some surveys ask whether they &ldquohave difficulty&rdquo with an activity, whereas others ask if they &ldquoneed help&rdquo and others ask if they &ldquoget help or use special equipment&rdquo to perform the activity. Such differences can lead to different estimates of disability levels and trends. Hence, the use of these measures outside of a coherent conceptual framework and the lack of sufficient attention by users to the implications of differences in measures contribute to inconsistency and confusion.
Recognizing these caveats, the committee reviewed recent disability statistics and concluded that the total number of people in the United States with disabilities (defined to include individuals with impairments in body structure or function, activity limitations, or participation restrictions) currently exceeds 40 million. Depending on the survey from which statistics are drawn, the figure could exceed 50 million. No single data source yields estimates for all age groups living in the community and in institutional settings, so the estimate of the population with disabilities must be drawn from several different surveys.
The committee began with data from the U.S. Census Bureau&rsquos 2004 American Community Survey. As summarized in Table 3-1, an estimated 38 million people (4.1 million people between the ages of 5 and 20, 20.2 million people between the ages of 21 and 64, and 13.5 million people ages 65 and older) who live in the community report a disability. 2 This estimate does not include people living in nursing homes and other institutional settings or children under age 5. The Medicare Current Beneficiary Survey estimates that approximately 2.2 million Medicare beneficiaries live in long-term care facilities and that about 350,000 of this population are adults under age 65 (CMS, 2005a). In addition, other surveys suggest that approximately 16,000 children with intellectual or developmental disabilities are living in out-of-home residential settings, about half which have four or more residents (Prouty et al., 2005). Data from the 2004 National Health Interview Survey indicate that perhaps 700,000 children under age 5 have a limitation in one or more activities because of a chronic condition, which
In the American Community Survey, &ldquodisability&rdquo is defined as a long-lasting sensory, physical, mental, or emotional condition that can make it difficult for a person to walk, climb stairs, dress, bathe, learn, remember, go outside the home alone, or work at a job or business. The term also covers vision or hearing impairments.
TABLE 3-1 Disability Rates by Sex and Age (Excluding Ages 0 to 4), Civilian Population (Excluding Residents of Nursing Homes, Dormitories, and Other Group Housing), 2004
By Type of Disability (number, in millions) a
a One person may have more than one type of disability, so the overall figure may be smaller than the sum of the types. NA = not asked NR = not reported.
b The U.S. Census Bureau refers to &ldquophysical&rdquo rather than &ldquophysical activity&rdquo disabilities or limitations.
c The U.S. Census Bureau uses the term &ldquomental&rdquo rather than &ldquocognitive&rdquo to refer to difficulties remembering, learning, or concentrating.
SOURCE: U.S. Census Bureau (2005a, Tables S1801and B18002 to B18008).
brings the total of children ages 0 to 17 with disabilities to approximately 4.8 million. With these additional groups added to the American Community Survey estimate, the total estimate of people with disabilities exceeds 40 million.
Surveys that use a broader conception of disability than that adopted by this committee yield higher estimates. For example, based on the 2002 Survey of Income and Program Participation, which includes numerous questions to identify individuals of all ages with disabilities living in the community, the U.S. Census Bureau estimated that 51 million people have disabilities, including 32 million who have a severe disability (Steinmetz, 2006). 3 The Behavioral Risk Factor Surveillance System, which includes two broad items to identify the adult community-based population with disabilities, also places the figure for all states close to 50 million (CDC, 2006a). 4 Based on its review, the committee concluded that the number of people in the United States with disabilities exceeds 40 million and may exceed 50 million. Although these figures are not directly comparable to the 35 million estimate from the 1991 IOM report (because the survey questions differ), the number of people with disabilities has almost certainly increased since 1988, when most of the data used in that report were collected.
Table 3-2 lists the most common health conditions reported by respondents in the National Health Interview Survey as &ldquocausing&rdquo or contributing to limitations among people of different ages residing in the community. The survey questions reflect a largely medical model of disability. The committee could identify no national data on the extent to which features of the physical and social environments contribute to disability (see also Chapter 6).
For children, the primary health conditions contributing to a limitation are cognitive, emotional, or developmental problems, although speech prob-
For adults, questions in the Survey of Income and Program Participation ask about mobility-related assistive technology use activity limitations learning disabilities the presence of a mental or emotional condition, or both mental retardation developmental disabilities Alzheimer&rsquos disease and conditions limiting employment or work around the house. For children, the questions involve specified conditions (autism, cerebral palsy, mental retardation, developmental disabilities) activity limitations (seeing, hearing, speaking, walking, running, taking part in sports) developmental delays difficulty walking, running, or playing or difficulty moving the arms or legs (Steinmetz, 2006). Not all the questions in the survey involve disability as defined in this report.
Widely cited estimates from the 2000 decennial census also put the estimate of the civilian, noninstitutional population with disabilities near 50 million. However, U.S. Census Bureau analysts attributed this estimate to a formatting problem with the census questionnaire that may have incorrectly increased positive responses to questions about disabilities with going-outside-the-home and work limitations (Stern and Brault, 2005). Subsequent estimates from the American Community Survey of the U.S. Census Bureau put the figure closer to 40 million.
TABLE 3-2 Leading Chronic Health Conditions Reported as Causing Limitations of Activities, by Age, Civilian, Noninstitutional Population, 2002 and 2003
Number of People with Activity Limitations Caused by Selected Chronic Health Conditions per 1,000 Population
Asthma or breathing problem
Mental retardation or other development problem
Other mental, emotional, or behavioral problem
Attention deficit or hyperactivity disorder
Fractures or joint injury
Heart or other circulatory
Arthritis or other musculoskeletal
Heart or other circulatory
Arthritis or other musculoskeletal
NOTE: The table shows the numbers per 1,000 population. The respondents could mention more than one condition.
SOURCE: NCHS (2005a, spreadsheet data for Figures 18, 19, and 20, based on the 2002 and 2003 National Health Interview Surveys).
lems figure prominently for children under the age of 12 and asthma contributes to activity limitations among children in all age groups. For the 0.7 percent of children who had limitations so severe that they could not attend school, Msall and colleagues (2003), using data from the 1994&ndash1995 disability supplement to the National Health Interview Survey, found that the most common reported reasons for nonattendance were life-threatening or
other physical disorders, neurodevelopmental disorders, learning-behavior disorders, and asthma.
Among adults under age 65, musculoskeletal problems (including arthritis) and heart problems become increasingly important as people grow older. Mental illness is the second leading chronic condition mentioned as a cause of activity limitation for individuals ages 18 to 44 and is the fifth most frequently mentioned cause for individuals ages 55 to 64. For some people in this age group who are aging with disabilities, their primary health condition (e.g., cerebral palsy or spinal cord injury) is a risk factor for the development of secondary health conditions that have the potential to contribute to additional impairments, activity limitations, or participation restrictions. In general, survey questions have limited ability to distinguish such secondary disabling conditions from primary disabling conditions. (See Chapter 5 for further discussion.) For people ages 65 and over, musculoskeletal and heart problems continue to be leading contributors to limitations. Among people age 75 and over, senility (the term used by the National Center for Health Statistics but now more commonly referred to as dementia) is a major contributor to limitations.
MONITORING TRENDS IN DISABILITY
Monitoring trends in disability is important for several reasons. First, trend data provide a barometer of the nation&rsquos achievements in terms of disability prevention. Second, when such data include measures of social, medical, and environmental risk factors, they can point policy makers to effective strategies for future interventions that will prevent or limit disabilities. Third, by including individuals of all ages, trend data can provide important insights into the future and serve as a basis for making assumptions that can be incorporated into projections.
Studies that track and attempt to explain changes over time in the population require a high degree of consistency in survey design. Changes in the wording of questions, the type and coverage of the sample frame, the use of proxy respondents, and the frequency and timing of interviews, among other factors, can all influence the conclusions drawn from such studies (Freedman et al., 2002). Unfortunately, despite the growing number of data sources and the repetition of certain major surveys, the available data&mdashon the whole&mdashpermit few direct comparisons with data from the 1980s and earlier. Notably, although several surveys that focused on older adults have allowed analysis of late-life trends since the 1980s, discontinuities in the surveys make comparisons of present survey data with data from previous surveys difficult for those in early and middle life. This difficulty will be particularly evident in discussions of data from the National Health Interview Survey before and after the major revisions made to that survey in
1997 (see Chapter 2 for further discussion of the revisions). The 1991 IOM report used data from the 1988 National Health Interview Survey.
With these limitations noted, the next three sections of this chapter review trends in disability in early, middle, and late life. These divisions of the human life span are necessarily artificial to some degree. They are based on a mix of social conventions, statistical convenience, and public policy considerations but they reflect important distinctions. Thus age 18, when people become legal adults in nearly all states, is used as the endpoint for early life (childhood and adolescence), even though important physical and psychological development continues past that age and well into the third decade of life (see the discussion of the transition into adult life in Chapter 4). Midlife, ages 18 to 64, encompasses a particularly broad period of life, almost five decades. As discussed in Chapter 5, many people in this group have conditions that in years past commonly led to early death, and many are experiencing premature or atypical aging that neither they nor their physicians have anticipated. The late-life period is defined as beginning at age 65, although some studies focus on an older group (age 70 and older or age 85 and older) that is at a considerably higher risk of disability than younger groups within the older population.
TRENDS IN EARLY LIFE
In a background paper prepared for an IOM workshop held in August 2005, Stein observed that &ldquoover a 40-year period, the proportion of children reported to have major limitations in their activities related to play and school has gone from less than 2 percent to close to 7 percent&rdquo (Stein, 2006, p. 146). The reasons for this trend are complex and varied. In part, the trend reflects changes in the epidemiology of childhood illness and functioning. For example, data show disturbing increases in recent years in the number of children who are reported to have potentially disabling chronic conditions, such as asthma and autism, as well as increases in the prevalence of preterm births (Stein, 2006). In addition, longer-term trends likely reflect increases in the recognition and treatment of learning-related disabilities and other conditions. The next section reviews these trends in more detail, and the subsequent section describes two public health successes: declines in the rates of spina bifida and lead poisoning.
Examining disability trends among children presents special challenges. Especially in the first few years of life, children&rsquos developmental changes make it difficult or inappropriate to identify certain kinds of behaviors as impairments or activity limitations. Furthermore, &ldquothe functioning of
children is always a moving target, as children mature at different rates, live in different cultures with different expectations of independence and self-sufficiency, and grow up in environments that vary markedly in the demands that they place on the performance of activities by children&rdquo (Stein, 2006, p. 145). Estimates of disability also vary depending on whether children are identified as having disabilities because of chronic conditions, special health care needs, or reports of activity limitations (Stein and Silver, 2002). 5 In addition, as it is true for all age groups, revisions in national surveys complicate comparisons of disability trends for children. Despite these challenges and complexities, the data suggest increases in the proportion of children with activity limitations and conditions that put them at risk of disabilities.
The earliest data from the National Health Interview Survey (Newacheck et al., 1984, 1986) reported activity limitations for only 1.8 percent of children under age 18 in 1960. The rate group increased to 3.8 percent for 1979 to 1981. For the period from 1984 to 1996, reports of the rates of activity limitations for this group increased from 5.1 to 6.1 percent (Table 3-3).
More recent data, based on new National Health Interview Survey questions that asked about the receipt of special education services, the need for assistance with personal care, and limitations in walking and cognition, suggest that increases in the numbers of children with activity limitations also occurred between 1997 and 2004 (Table 3-4). In this more recent period, the increase in activity limitations was particularly affected by the increasing receipt of special education services, especially for boys. Boys are almost twice as likely as girls to be reported to be receiving such services. In addition, pooled data from the 2000 to 2002 National Health Interview Survey show that boys have higher rates of mental retardation, learning disabilities (including attention deficit or hyperactivity disorder), asthma, and vision or hearing problems (Xiang et al., 2005). Although special education services may be aimed, in particular, at children with conditions that primarily affect cognition (e.g., mental retardation), they also serve children with medical conditions that may secondarily affect the ability to learn.
As defined by the U.S. Maternal and Child Health Bureau, children with special health care needs are &ldquothose who have or are at increased risk for a chronic physical, developmental, behavioral, or emotional condition and who also require health and related services of a type or amount beyond that required by children generally&rdquo (McPherson et al., 1998, p. 137). This broad definition was developed to help in implementing amendments to the Social Security Act that provided for the development of community-based services for children with special health care needs and their families.
TABLE 3-3 Percentage of Children (Under Age 18) with Activity Limitations, by Type of Limitation and Age, 1984 to 1996
Limited in activity (ages 0&ndash17)
Limited in activity (ages 0&ndash4)
Limited in activity (ages 5&ndash17)
NOTE: Only limitations in activity caused by chronic conditions or impairments are included. Data for children residing in group settings are not included. Respondents are classified as having no activity limitation if they report a limitation due to a condition that is not known to be chronic. An activity limitation is defined as follows: children are classified in terms of the major activity usually associated with their particular age group. The major activities for the age groups are (1) ordinary play for children under 5 years of age and (2) attending school for those 5 to 17 years of age. A child is classified as having an activity limitation if he or she is (1) unable to perform the major activity, (2) able to perform the major activity but limited in the kind or amount of this activity, and (3) not limited in the major activity but limited in the kinds or amounts of other activities. ADL = activities of daily living.
SOURCE: H. Stephen Kaye, Disability Statistics Center, University of California at San Francisco, unpublished tabulations from the National Health Interview Survey, as requested by the committee.
(For the activity limitations and major activity limitations reported before 1997, boys also showed a higher prevalence of disability.) 6
Potential Explanations of Trends
As suggested above, the tripling of activity limitations among children over four decades likely reflects a confluence of forces. In part, there have been real changes in the epidemiology of illnesses and related disabilities among children. In addition, the trend may be capturing in part the increasing awareness by parents, health professionals, and other agencies
Other surveys besides the National Health Interview Survey suggest significant increases in the rates of certain potentially disabling chronic conditions among children and youth, especially in recent years. For example, the National Longitudinal Survey of Labor Market Experience, Youth Cohort, provides information on the health-related conditions of children born to women who were ages 14 to 21 in 1979. These data show an increase in the prevalence of chronic conditions from 11 percent in 1994 to 24 percent in 2000 among children who were ages 8 to14 in those years (unpublished tabulations from James Perrin, committee member).
TABLE 3-4 Percentage of Children (Under Age 18) with Activity Limitations, by Type of Limitation, Age Group, and Gender, 1997 to 2004
NOTE: Only limitations in activity caused by chronic conditions or impairments are included. The respondents are reclassified as having no activity limitation if they report a limitation due to a condition that is not known to be chronic. A child is considered to have an activity limitation if the parent responded positively to at least one of the following questions: (1) &ldquoDoes [child&rsquos name] receive Special Education Services?&rdquo (2) &ldquoBecause of a physical, mental, or emotional problem, does [child&rsquos name] need the help of other persons with personal care needs, such as eating, bathing, dressing, or getting around inside the home?&rdquo (3) &ldquoBecause of a health problem does [child&rsquos name] have difficulty walking without using any special equipment?&rdquo (4) &ldquoIs [child&rsquos name] limited in any way because of difficulty remembering or because of periods of confusion?&rdquo (5) &ldquoIs [child&rsquos name] limited in any activities because of physical, mental, or emotional problems?&rdquo
SOURCE: H. Stephen Kaye, Disability Statistics Center, University of California at San Francisco, unpublished tabulations from the National Health Interview Survey, as requested by the committee.
(especially schools) of conditions that merit attention and intervention, particularly through educational programs. 7
Increased awareness may affect the estimation of disability rates for other age groups besides children see, for example, the work of Waidmann and Liu (2000) for the influence of changes in Social Security Disability Insurance policies and criteria on self-reports of disability by adults.
Shifts in Conditions Potentially Contributing to Disability
Over the last few decades, the rise in the rates of potentially disabling childhood conditions&mdashin particular, asthma&mdashalong with increases in the rates of preterm births, deserves special consideration in the analysis of activity limitation trends in children. During the early 1970s, when the rates of severe limitations grew from 2.7 to 3.7 percent, Newacheck and colleagues (1984, 1986) found increasing rates of several health conditions, especially mental health conditions, asthma, orthopedic conditions, and hearing loss. Unfortunately, changes in the questions as part of the redesign of the National Health Interview Survey in 1997 make comparisons over the entire time period inappropriate. 8 Instead, the committee focused its review on the role of changes in the prevalence of a few potentially disabling health conditions.
Approximately 12 percent of children have at some time been diagnosed with asthma&mdashthe single most prevalent condition associated with childhood disability&mdashand 8 percent currently have asthma (Federal Interagency Forum on Child and Family Statistics, 2005). In 2000, an analysis by the Centers for Disease Control and Prevention (CDC) reported that the prevalence of asthma among children increased by approximately 5 percent per year during the period from 1980 to 1995 but dropped by 17 percent in 1996 (CDC, 2000a). For the period from 1969&ndash1970 to 1994&ndash1995, Newacheck and Halfon (2000) documented the contribution of asthma to the rise in the overall prevalence of disabilities among children. They reported that the prevalence of disability related to asthma (based on data from the National Health Interview Survey) increased 232 percent over this time span, whereas the prevalence of disability related to all other chronic childhood conditions increased by 113 percent over the same period.
Changes in the questions about asthma in the National Health Interview Survey in 1997 preclude a comparison of recent data with earlier data. The prevalence of asthma did not, however, change much between 1997 and 2004. During this more recent time period, the percentage of children with asthma or breathing problems that caused limitations declined from 0.9 percent in 1997 to 0.6 percent in 2004 (unpublished tabulations of the National Health Interview Survey prepared by this committee).
In addition, because of further changes in questions in the National Health Interview Survey after 1997, of the six leading conditions (speech problems asthma or breathing problems mental retardation or other development problems other mental, emotional, or behavioral problems attention deficit or hyperactivity disorder and learning disabilities), only the survey questions related to speech problems and asthma are comparable over the 1997 to 2004 time period. For example, before 2000, parents were asked only if they had been told that their child had attention deficit disorder after that they were also asked about attention deficit or hyperactivity disorder (Pastor and Reuben, 2005).
Why the rate of asthma has increased and whether the rates have truly leveled off remain unclear. A recent review of evidence to explain the increase in the prevalence and severity of asthma in the United States and a number of other countries since the 1960s suggested that &ldquothe combination of the control of infectious diseases, prolonged indoor exposure, and a sedentary lifestyle &hellip is the key to the asthma epidemic and, in particular, the key to the rise in severity&rdquo (Platts-Mills, 2005, p. 1026).
Another condition potentially contributing to disability is preterm birth. For a variety of reasons, including the increased rates of survival of high-risk infants and the growth in the number of multiple births associated with certain fertility treatments, the numbers of infants born prematurely and with low birth weights have increased (IOM, 2006a). For example, the rate of premature births (as a percentage of all births) increased from 10.6 percent in 1990 to 12.5 percent in 2004. Prematurity (birth before 37 weeks of completed gestation) and low birth weight (birth weight less than 2,500 grams) are risk factors for a number of short-term and long-term neurodevelopmental and other health problems and disabilities related to cerebral palsy, mental retardation, and sensory impairments, as well as more subtle disorders, such as attention deficit or hyperactivity disorder.
The reported prevalence of autism and related disorders has also grown significantly (Newschaffer et al., 2005 CDC, 2007). These conditions are characterized by impairments in social interactions and communication patterns and restricted, stereotyped, repetitive sets of activities and interests (WHO, 2006). CDC recently reported data from 14 sites showing an average rate of autism of 6.5 per 1,000 children aged 8 years in 2002 or approximately one child in 150 (CDC, 2007), a figure that is considerably higher than those suggested in earlier studies and reviews (see, e.g., Gillberg and Wing  and Fombonne ). The range reported in the CDC study was 3.3 to 10.6 per 1,000 children. An IOM study that examined the relationship between autism and childhood vaccines concluded that the published literature was generally uninformative in helping assess trends in the condition (IOM, 2004c). The growth in the reported rates of autism undoubtedly reflects a variety of factors, including the broader range of conditions currently encompassed in the spectrum of autism disorders (e.g., Asperger&rsquos syndrome, other childhood disintegrative disorder, and pervasive developmental disorder, unspecified) and the addition of a substantial number of young people on the milder edge of the autism spectrum (APA, 2000 Shattuck, 2006). A substantial growth in the levels of awareness of autism and related disorders by both parents and clinicians has also led to the better identification of children who have autism spectrum disorders. In years past, these children might have been given another diagnosis (e.g., unspecified mental retardation) (Gurney et al., 2003 Barbaresi et al., 2005
Newschaffer, 2006). Some of the growth in autism rates likely reflects a true growth in the prevalence of autism.
Although the connection of obesity to disability warrants more investigation, another IOM committee has described the increasing prevalence of childhood obesity as a &ldquostartling setback&rdquo for child health (IOM, 2005a, p. 21). Obesity is a risk factor for a number of serious health conditions, such as diabetes, that are, in turn, risk factors for disabilities. Figure 3-1 shows trend data for children 6 to 11 and 12 to 19 years of age. Based in part on concerns about the stigmatization of children and in part on concerns about the reliability of the body mass index (BMI) as a measure of fatness for children, CDC has used the term &ldquooverweight&rdquo for children who would count as overweight or obese on the basis of BMI criteria (Wechsler, 2004). The earlier IOM committee, however, concluded that the term &ldquoobesity&rdquo
FIGURE 3-1 Trends in overweight (obesity) for children ages 6 to 11 and adolescents ages 12 to 19, civilian noninstitional population, 1963 to 2002. Overweight (the term used by CDC) is defined as a BMI value at or above the 95th percentile cutoff points in the sex- and gender-specific BMI growth charts developed by CDC in 2000, based on BMI data from earlier surveys (see IOM [2005a]). Data exclude those for pregnant women starting with the period from 1971 to 1974. Pregnancy status was not available for the periods from 1963 to 1965 and 1966 to 1970. Data for 1963 to 1965 are for children 6 to 11 years of age data for 1966 to 1970 are for adolescents 12 to 17 years of age, not 12 to 19 years.
SOURCE: CDC National Health and Nutrition Examination Surveys (CDC, 2005b).
was appropriate for children 2 years of age and older who have a BMI at or above the 95th percentile for their age and sex groups (IOM, 2005a).
Explanations for the increases in the rates of childhood obesity, which occurred primarily in the 1980s and 1990s, were cited in a 2001 report from the U.S. Surgeon General (U.S. Public Health Service, 2001). Factors include inactive lifestyles (e.g., watching television and playing computer and video games) and unhealthy eating patterns exacerbated by media exposure.
The Role of Increased Identification
Although the data of Newacheck and colleagues (1984, 1986) did not allow an estimate of how much of the growth in the activity limitations among children in the 1970s might reflect an increased awareness and identification of these limitations, their data raised the possibility that these factors might have played a role. For example, after noting that the marginal increase in activity limitations in the late 1970s (from 3.7 to 3.8 percent) was primarily accounted for by an increase in educationally related impairments, the authors proposed that the increase might have been associated with the implementation of the Education for All Handicapped Children Act of 1975 (which was renamed Individuals with Disabilities Education Act in 1990) and associated efforts to identify and provide mainstream education to children with disabilities (see additional discussion in Chapter 4).
In addition, other policies that have promoted screening of children for health and learning problems may have contributed to increases in the measured prevalence of disabilities. For example, the Social Security Amendments of 1967 created the Early and Periodic Screening, Diagnostic, and Treatment (EPSDT) benefit for children eligible for Medicaid. The objective was the early identification and management of problems that can impede children&rsquos normal development. (Screening for the EPSDT benefit includes a comprehensive health and developmental history and a physical examination.) The implementation of the EPDST benefit has been far from universal, but millions of children in low-income families have been evaluated for disabling or potentially disabling conditions and were identified to have conditions that might otherwise have gone undiagnosed (Herz et al., 1998 GAO, 2001a).
Changes in eligibility criteria for the Supplemental Security Income (SSI) program in the early 1990s could also have influenced the rates of diagnosis or identification of functional limitations in children, especially among children with more severe disabilities (Ettner et al., 2000 Perrin et al., 1999). One change (resulting from the 1990 U.S. Supreme Court decision in Sullivan v. Zebley [493 U.S. 521]) required that disability de-
terminations for children include individual functional assessments, and the implementing regulations allowed the enrollment of children who had multiple health problems that, taken together (if not singly), were disabling. 9 Also in 1990, the Social Security Administration expanded from 4 to 11 the number of qualifying mental impairments for children, one of which was attention deficit or hyperactivity disorder. Moreover, both the U.S. Congress (in 1989) and the U.S. Supreme Court provided for increased efforts to inform families of the SSI program and the eligibility criteria for the program, with an emphasis on active outreach to hard-to-find populations. Between 1989 and 1996, the number of children receiving SSI benefits grew from approximately 265,000 to 955,000 (or from 5.8 to 14.4 percent of all SSI beneficiaries) (SSA, 2006a). A study by Perrin and colleagues (1999) suggests, however, that the SSI changes had some impact but that the impact on the rates of reported potentially disabling conditions was fairly minimal. That study examined Medicaid claims for the period from 1989 to 1992 and found similar increases in the rates of asthma and mental health conditions among children insured by Medicaid, whether they received SSI benefits or not. A tightening of eligibility criteria in 1996 cut the number of children&rsquos receiving SSI benefits to 847,000 by 2000, after which the number grew again to almost 960,000 at the end of 2003 and to more than 1,035,000 by the end of 2005 (Schmidt, 2004 SSA, 2006a).
Declines in Selected Health Conditions That Contribute to Disability in Childhood
For a number of health conditions that contribute to disability, such as spina bifida and exposure to lead paint, the trends have been encouraging (Stein, 2006). These two conditions illustrate the success of public health interventions aimed at women of childbearing age and their children.
Decreases in the rates of spina bifida reflect the successes of a major public health strategy, specifically, the implementation of campaigns to promote folic acid supplementation for women of childbearing age. During the period from 1991 to 2003, the incidence of spina bifida dropped from 24.9 to 18.9 per 100,000 live births (Mathews, 2006). All of the decrease came after the U.S. Food and Drug Administration authorized the enrichment of cereals with folic acid in 1996 and then made it mandatory in 1998. The decrease in the incidence of spina bifida was larger and the economic benefit was greater than had been projected before adoption of the policy (Grosse et al., 2005). To reduce further the rates of spina bifida and other
The Zebley decision was based on a notion of equity. Until that decision, the Social Security Administration provided functional assessments for adults who might not otherwise qualify for SSI benefits on the basis of their clinical symptoms alone.
9.5.1: Demographic Transition - Biology
The burden of diabetes is a global problem, wherein the significant growth of diabetes in Colombia reflects a complex pathophysiology and epidemiology found in many other South American nations.
The aim of this study was to analyze epidemiologic data from Colombia and the South American region in general to identify certain disease drivers and target them for intervention to curb the increasing prevalence of diabetes.
A detailed search was conducted using MEDLINE, SciELO, HINARI, LILACS, IMBIOMED, and Latindex databases, in addition to clinical practice guidelines, books, manuals, and other files containing relevant and verified information on diabetes in Colombia.
According to the International Diabetes Federation and the World Health Organization, the prevalence of diabetes in Colombia is 7.1% and 8.5%, respectively. In contrast, a national survey in Colombia shows a prevalence ranging from 1.84% to 11.2%, depending on how the diagnosis is made, the criteria used, and the age range studied. The prevalence exclusively in rural areas ranges from 1.4% to 7.9% and in urban areas from 1% to 46%. The estimated mean overall (direct and indirect) cost attributed to type 2 diabetes is 5.7 billion Colombian pesos (US $2.7 million). Diabetes is the fifth leading cause of death in Colombia with a rate of 15 deaths per 100,000 individuals.
Based on a clustering of factors, 4 relevant disease drivers emerge that may account for the epidemiology of diabetes in Colombia: demographic transition, nutritional transition, forced displacement/internal migration and urban development, and promotion of physical activity.
Poverty and Gender in Affluent Nations
3.3 Government Policy
All individuals and families receive income from two primary sources—the market and the state—in addition to any private transfers they may receive. Government policy has a significant effect on women's economic well-being because it can either obviate or intensify the inequalities that result from the operation of the market economy. Among single individuals living without children, government policy has little impact on women's poverty because men and women are largely treated the same. Although there may be some differences between elderly men and women in how pensions are allocated in various countries, differences in government policies primarily affect the economic status of single mothers and their children.
Welfare states vary dramatically in the extent to which they ‘socialize’ the costs of children. Feminist scholars have noted that many members of society benefit from children being brought up well (Folbre 1994a , 1994b ), and yet market mechanisms fail to equitably apportion costs for this ‘public good’ (Christopher et al. 2001 ). In countries where more of the costs of childrearing are borne by governments instead of parents, women's poverty rates tend to be lower. In general, single mothers tend to benefit if a greater proportion of welfare assistance is universal rather than means-tested, because they are not faced with high marginal tax rates on their earnings that result from decreased welfare benefits. The overall level of income assistance provided to single mothers varies notably across industrialized nations mean transfers for single mothers as a percentage of median equivalent income range from 22 percent in the USA to 71 percent in The Netherlands (unpublished tabulations of Luxembourg Income Study data by Timothy Smeeding and Lee Rainwater).
Numerous scholars have developed typologies that classify welfare states along particular dimensions. Probably the best known is by Esping-Anderson ( 1990 ) which classifies welfare states into three categories according to decommodification (the ability of individuals to subsist apart from labor and capital markets) and stratification (differences between classes).
Social democratic states, typified by the Scandinavian countries, promote gender equality and provide the most generous support to single-parent families. These nations emphasize full employment, and work and welfare are intricately intertwined, with some part-time public jobs provided for mothers. Many benefits and allowances are universal, supplemented by specific programs targeted to needy families. Women's employment is encouraged by the provision of high-quality childcare services and extensive parental leave. Also, the government guarantees that support is provided to children who do not live with both parents. Because of these policies, mothers are able to combine work and parental responsibilities and to ‘package’ income from both market and government sources.
Conservative-corporatist nations also have generous transfer systems, but they are more traditional with respect to family values and expectations (largely due to the significant influence of the Church). Benefits are generally targeted toward families (rather than individuals), and inequalities across families and households may be sustained. An important component of welfare policy is social insurance, which is linked to labor force participation and may differ by occupation. Austria, France, Germany, and Italy are representative conservative-corporatist states.
In the liberal welfare states (notably the Anglo countries), assistance is primarily means-tested with modest universal transfers, primarily paid to the elderly or disabled. Emphasis is on the market and individuals' labor force activity as the primary means for resource allocation. Benefit levels are typically meager when compared to average wage levels, and recipients are often stigmatized by nonrecipients. Supports for working mothers, including public childcare, are limited, with little or no paid parental leave. Parents who live away from their children are expected to pay child support, although the government does not provide any assistance for children of noncustodial parents who fail to pay. Because welfare benefits are income-tested, women have a difficult time combining work and welfare in order to escape the ‘poverty trap.’
These variations in welfare state policy lead to dramatic differences in the economic status of women and children. Table 4 presents poverty rates for children living in single-mother families both before and after government taxes and transfers are included in income (while childcare and other non-cash benefits are not included). The table shows that, with several exceptions, the percentage decline in the poverty rates roughly clusters according to the three categories of nations in Esping-Anderson's typology. Government tax and transfer policies reduce the poverty rates in each of the four social democratic nations shown in the table (Denmark, Finland, Norway, and Sweden) by 68 percent or more. In the corporatist nations, poverty reductions fall in the middle of the spectrum with a 60 percent decline in France and a 56 percent decline in Italy. Germany is an important exception, with its government policies reducing poverty for children in single-mother families by fully 90 percent. The lowest reductions in poverty rates are noted in the liberal states, such as 26 percent for Canada, 23 percent for Australia, and 15 percent for the USA. The UK represents another exception because its policies reduce poverty by 76 percent—a much greater decline than in the other liberal states.
Table 4 . Poverty rates for children in single-mother families a before and after government programs b
Source: Rainwater and Smeeding 1995 .
|Country (year)||Pretransfer poverty rate||Post-transfer, post-tax poverty rate||Percent decline|
|United Kingdom (1986)||76.2||18.7||75.5|
|United States (1991)||69.9||59.5||14.9|
Some feminist scholars have criticized Esping-Anderson's framework because it emphasizes workers' dependence on employers while ignoring women's dependence on men. Orloff ( 1993 ) argues, for example, that welfare regimes should be judged by the extent to which they allow women to establish independent households. In contrast, other feminist scholars have raised questions about women's dependence on the welfare state (Gordon 1990 , 1994 ) and gender biases in the welfare state (Nelson 1990 ).
Cannabis is a highly used substance among youth, but has the potential to negatively impact the developing brain. Recreational cannabis use was legalized in Canada in October 2018. This exploratory study examines cannabis use profiles of high-risk youth before and after the national legalization of recreational cannabis use.
This cross-sectional cohort study examines the cannabis use profiles of two cohorts of youth, one recruited prior to legalization (N = 101) and one recruited after legalization (N = 168).
This study found few changes in cannabis use patterns after legalization. The rate of high-frequency cannabis use, polysubstance use, social circles of use, and mental health and substance use challenges showed no change, and the study found no associations with age. Exceptions were that youth were more likely to report purchasing cannabis from a legal source after legalization. Concealment of cannabis use from legal authorities declined after legalization among youth over the age of majority (19+ years), but not among younger youth (<19 years).
Minimal changes have occurred in the cannabis use patterns of service-seeking youth in the short term following legalization. This holds true both for youth who have reached the age of majority and those who have not. Nevertheless, this population has overlapping substantial mental health and substance use challenges. Integrated services should address cannabis use and other concurrent challenges and be sensitive to the postlegalization social climate.
Investigation and Results
During March 27&ndash28, three symptomatic University of Texas students had positive test results for SARS-CoV-2. All three had traveled to Cabo San Lucas, Mexico, during March 14&ndash19 and became symptomatic after returning (March 22&ndash25). On March 28, the UTHA COVID-19 Center, a multidisciplinary team established in early March to conduct testing, contact tracing, and monitoring for the University of Texas community with authority delegated from Austin Public Health, initiated an investigation. Additional travelers were identified through contact tracing interviews and review of flight manifests gathered with assistance from Austin Public Health. Travelers on chartered or private flights were traced by UTHA and any potential commercial flight exposures were escalated through Austin Public Health to the Texas Department of State Health Services. Travelers and contacts of any travelers with a positive SARS-CoV-2 test result were classified into one of three categories: Cabo San Lucas travelers (i.e., persons who traveled to Cabo San Lucas), household contacts (i.e., persons who did not travel to Cabo San Lucas, but who lived with a Cabo San Lucas traveler who had a positive test result), or community contacts (i.e., persons who did not travel to Cabo San Lucas, but who had close contact in a community setting to a Cabo San Lucas traveler who had a positive test result). A case was defined as a positive SARS-CoV-2 reverse transcription&ndashpolymerase chain reaction (RT-PCR) test result in any traveler to Cabo San Lucas during March 14&ndash19 or any of the travelers&rsquo household or community contacts identified during March 19&ndashApril 2.
With oversight from a university epidemiologist and infectious diseases physician, UTHA trained medical students, public health students, and clinical and research staff members to trace contacts. UTHA contact tracers communicated with travelers and contacts by telephone, first texting an initial message about the potential exposure and then attempting to call each traveler and contact up to three times. Through interviews with travelers and contacts, the date and method of return travel (i.e., commercial or charter flight and flight number for those who traveled to Cabo San Lucas), date of last exposure to a patient with known COVID-19, presence of symptoms, symptom onset date, and current address were collected and recorded. For those travelers and contacts without symptoms, the date of testing was used as a proxy for symptom onset date to estimate an infectious period. During the telephone call, contact tracers advised asymptomatic travelers and contacts to self-quarantine and self-monitor for symptoms for 14 days from the last potential exposure date. Symptomatic travelers and contacts were offered a SARS-CoV-2 test and asked to self-isolate until either a negative test result was obtained or, following CDC recommendations at the time, until 7 days after symptom onset, including 3 days with no fever and no worsening of symptoms. Following CDC guidance at the time,* persons were considered symptomatic if they had a documented temperature of &ge100.0°F (37.8°C) or reported subjective fever, acute cough, shortness of breath, sore throat, chills, muscle aches, runny nose, headache, nausea, vomiting, diarrhea, or loss of sense of smell or taste. In addition, travelers and contacts were offered the opportunity to enroll in a home-monitoring program developed by UTHA in partnership with Sentinel Healthcare. &dagger During the contact tracing interview, data were recorded and stored in a secure, online drive.
If testing was recommended, UTHA nurses used a person-under-investigation (PUI) form to collect information on symptom status, any underlying medical conditions, and smoking status § before scheduling a test. Nasopharyngeal swab specimens were collected at UTHA&rsquos drive-through testing site. A private reference laboratory in Austin, Texas, conducted RT-PCR testing on collected samples using a cobas SARS-CoV-2 qualitative assay (Roche Molecular Systems, Inc.), which was given emergency use authorization by the Food and Drug Administration. ¶ For those who were not residing in Austin but were recommended for testing, Austin Public Health passed on their information to the appropriate public health jurisdiction. Once a traveler or contact had a positive test result, further identification of contacts was conducted. Because of the limited number of tests available at the time, travelers and contacts were only tested once.
By March 30, nine of the first 19 travelers and contacts tested had a positive test result. Because approximately one half of persons identified and tested had a positive test result 2 days into the investigation, testing criteria were broadened to include any traveler to Cabo San Lucas, regardless of symptom status, but only symptomatic contacts continued to qualify for testing. Based on the SARS-CoV-2 incubation period of 14 days from date of exposure (1), the presumptive incubation period that began on March 19 when travelers returned from Cabo San Lucas ended on April 2. Therefore, after April 2, testing was only performed for exposed, symptomatic travelers and contacts. The investigation ended on April 5 when the last symptomatic contacts received negative test results.
Descriptive statistics and bivariate analyses were performed using Stata (version 16 StataCorp). Unadjusted logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), which were used to evaluate differences in symptoms and smoking status between persons who did and did not have positive SARS-CoV-2 test results. Because seven contacts and travelers had testing for SARS-CoV-2 performed at other sites and PUI forms were incomplete for 26, data on symptoms and underlying medical conditions are missing for 33 (14%) persons.
Among 298 persons identified during the investigation, 289 (97%) were interviewed. Contact tracing interviews revealed that Cabo San Lucas travelers used a variety of commercial, charter, and private flights to return to the United States. Although the index patient whose illness started the investigation was not symptomatic until after arriving home (March 22), other travelers experienced symptoms during March 15&ndash19 while in Cabo San Lucas ( Figure). Further, many Cabo San Lucas travelers reported prolonged exposure and reexposure to multiple other travelers because they shared hotel rooms in Mexico and apartments or other shared living spaces upon return to Austin.
Among the 231 (80%) persons tested, 183 (79%) were Cabo San Lucas travelers, and 48 (21%) were contacts of travelers with diagnosed COVID-19, including 13 (6%) household contacts and 35 (15%) community contacts ( Table 1). Among all persons tested, 110 (55%) were male, and the median age was 22 years (range = 19&ndash62 years) 179 (89%) were non-Hispanic white. The prevalence of underlying medical conditions was low (15 8%), but nearly a quarter (45 24%) were current smokers. Overall, 64 (28%) persons had a positive test result, including 60 (33%) of 183 Cabo San Lucas travelers, one (8%) of 13 household contacts, and three (9%) of 35 community contacts. Persons for whom testing was performed reported a median of four contacts (range = 0&ndash15) from the 2 days preceding symptom onset (or date of testing, if asymptomatic) through their date of self-isolation. No persons were hospitalized, and none died.
Among the 64 persons with positive SARS-CoV-2 RT-PCR test results, 14 (22%) were asymptomatic and 50 (78%) were symptomatic at the time of testing ( Table 2). Among those who had a positive test result, the most commonly reported symptoms were cough (21 38%), sore throat (18 32%), headache (14 25%), and loss of sense of smell or taste (15 25%) only six (11%) reported fever. Among persons with negative test results, 84 (50.3%) reported symptoms the most commonly reported symptoms were cough (58 41%), sore throat (46 32%), headache (29 20%), and loss of sense of smell or taste (22 14%) 13 (9%) reported fever. The odds of having a positive test result were significantly higher among those who were symptomatic than among those who were asymptomatic (OR = 3.5 95% CI = 1.8&ndash7.4). There were no significant differences in the types of symptoms reported among persons with positive and negative test results, nor were there any significant differences in smoking status among persons with positive and negative test results.
Meso-level links: The intergenerational transmission of parents’ divorce/children's cohabitation
Studies in many countries have found that parental divorce is a strong predictor of children's divorce (e.g. Amato 1996 McLanahan and Sandefur 1994 Wagner and Weiß 2006 Dronkers and Härkönen 2008 Wolfinger 2005 ) and children's cohabitation (e.g., Axinn and Thornton 1992 , 1996 Amato 1996 Bumpass, Sweet, and Cherlin 1991 Thornton 1991 Berrington and Diamond 2000 Liefbroer and Elzinga 2012 Wolfinger 2005 ). Parents’ marital dissolution can change children's attitudes and decisions about relationships through a process of social learning (Cui and Fincham 2010 Smock et al. 2013 ) and socialization (Axinn and Thornton 1996 ). The experience of parental divorce may lead children to be more accepting of alternatives to life-long marriage, reduce the perceived rewards of marriage, and make children more reluctant to enter committed relationships (McRae 1993 Amato 1996 Axinn and Thornton 1992 , 1996 Cui and Fincham 2010 Dronkers and Härkönen 2008 ). Research in the US has also shown that parents’ experience of cohabitation, especially after divorce, is positively associated with adult children's own cohabitation, since they would have observed their parents choose this arrangement (Sassler et al. 2009 Smock et al. 2013 ).
Explanations and mechanisms from qualitative research
The intergenerational transmission of parents’ divorce/children's cohabitation emerged repeatedly in the focus groups. Individuals whose parents divorced stated that they were unlikely to marry and would choose cohabitation instead. Both the previous sociological literature on the intergenerational transmission of family behaviors and the focus group discussions point to cohabitation as a way to cope with parental marital breakdown and the ensuing skepticism about marriage. Focus group participants were aware that parental separation often leads individuals to reject the institution of marriage, or at the very least to cohabit first to see whether their relationship will last. The qualitative research shows how attitudes, and indeed strong emotions, are an important mechanism in understanding the divorce/cohabitation link.
Analyses with quantitative data
To examine the divorce/cohabitation link at the meso level, we use the Harmonized Histories to ascertain whether people whose parents separated or divorced are more likely to enter cohabitation (rather than direct marriage) for their first partnership compared to people whose parents remained married in childhood. This analysis allows us to directly investigate the causal link based on temporal ordering: by definition, parental divorce when children are young occurs before the latter make decisions about their first union. Figure 2 shows the proportion of ever-partnered women aged 20–49 in 2005 who started their first union with cohabitation rather than marriage by whether their parents lived together when the women were aged 15. This measure was obtained from a survey question that is relatively consistent across countries.5 5 In the UK, the question referred to age 16. The question was not asked in the Netherlands.
Percent of ever-partnered women aged 20–49 in 2005 who started their first union with cohabitation (compared to direct marriage), by parents’ union status at women's age 15
NOTES: Solid bars indicate significant differences (non-overlapping confidence intervals) between parents' union at age 15 for those whose first union type is cohabitation. Diagonal bars indicate no significant difference. Weights applied if available. Years of analysis may differ depending on survey.
SOURCE: Harmonized Histories.
In all countries except Sweden, the proportion of women who began their first union with cohabitation was higher for those whose parents separated than for those whose parents did not. The difference between the two groups is significant in all countries except France and Sweden. In most countries, about 10 percent more women started their unions with cohabitation among those whose parents separated compared to those whose parents did not separate. The figure also indicates that direct marriage (marriage without prior cohabitation) has remained more common among those whose parents stay married. In Sweden, France, and Norway, however, fewer than 25 percent of couples directly married, indicating that cohabitation is now the normative way of entering a co-residential partnership.6 6 Note that these results do not control for potential covariates. Our data do not provide further detail about the parents’ partnerships, including the experience of cohabitation or the formation of step-families.
Because we look at women aged 20–49 in 2005, these analyses reflect cohorts who were aged 15 in 1971–2000. In some countries, divorce legislation and the increase in divorce would have occurred earlier and would not be reflected here. In addition, selectivity into cohabitation may have declined over time as cohabitation became more normative. As a check, we repeated the analyses for each ten-year age group and found the same relationship hence, the relationship is not due to the increase in both cohabitation and experience of parental separation across cohorts. Ideally we would have liked to repeat our analyses for the same age group in earlier years when divorce had just started to emerge in some countries however, given the small sample sizes in most countries, this was not possible. Nonetheless, these results are consistent overall with the idea that the intergenerational transmission of parents’ divorce/children's cohabitation is common across countries, and that intergenerational transmission can be considered a potential causal pathway helping to explain the link between divorce and cohabitation.
Jia Song and Ming Zeng collected data, performed data analysis, and prepared manuscript Hai Wang did cell culture and immunohistochemistry experiment Hong-Yan Hou, Zi-Yong Sun, and Cui-Lian Guo did flow cytometry San-Peng Xu and Guo-Ping Wang did immunohistochemistry and read the histology Chuan Qin, Yi-Ke Deng, Zhi-Chao Wang, Jin Ma, Bo Liao, Zhi-Hui Du, and Qi-Miao Feng participated in data collection Li Pan did statistical analysis Yang Liu, Jun-Gang Xie, and Zheng Liu designed the study, interpreted data, and prepared manuscript.
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