National Academies Press: OpenBook

Opportunities for Improving Programs and Services for Children with Disabilities (2018)

Chapter: Appendix C Descriptions of Data Sources

« Previous: Appendix B Glossary
Suggested Citation:"Appendix C Descriptions of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2018. Opportunities for Improving Programs and Services for Children with Disabilities. Washington, DC: The National Academies Press. doi: 10.17226/25028.
×
Page 233
Suggested Citation:"Appendix C Descriptions of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2018. Opportunities for Improving Programs and Services for Children with Disabilities. Washington, DC: The National Academies Press. doi: 10.17226/25028.
×
Page 234
Suggested Citation:"Appendix C Descriptions of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2018. Opportunities for Improving Programs and Services for Children with Disabilities. Washington, DC: The National Academies Press. doi: 10.17226/25028.
×
Page 235
Suggested Citation:"Appendix C Descriptions of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2018. Opportunities for Improving Programs and Services for Children with Disabilities. Washington, DC: The National Academies Press. doi: 10.17226/25028.
×
Page 236
Suggested Citation:"Appendix C Descriptions of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2018. Opportunities for Improving Programs and Services for Children with Disabilities. Washington, DC: The National Academies Press. doi: 10.17226/25028.
×
Page 237
Suggested Citation:"Appendix C Descriptions of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2018. Opportunities for Improving Programs and Services for Children with Disabilities. Washington, DC: The National Academies Press. doi: 10.17226/25028.
×
Page 238
Suggested Citation:"Appendix C Descriptions of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2018. Opportunities for Improving Programs and Services for Children with Disabilities. Washington, DC: The National Academies Press. doi: 10.17226/25028.
×
Page 239
Suggested Citation:"Appendix C Descriptions of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2018. Opportunities for Improving Programs and Services for Children with Disabilities. Washington, DC: The National Academies Press. doi: 10.17226/25028.
×
Page 240

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Appendix C Descriptions of Data Sources This appendix provides background information on the data elicitation methods used to survey the health and functioning of the population of children the committee was asked to address—school-aged children with disabilities. As outlined in Chapter 2, the size and scope of the child population with disabilities depends largely upon two factors: (1) how the information is collected and (2) what criteria were used to define and measure disability in the study. The most common methods used to collect and report data on children with disabilities are detailed here, as these methods can significantly impact whether population statistics are over- or underreported. There is a critical need to obtain accurate population estimates of childhood disability, given that many disabilities experienced by adults first appear during childhood. For instance, nearly three-quarters of all people with mental health problems experience the onset of symptoms before age 25 (Kessler et al., 2007). Where applicable, special attention is given to describing the unique population of children enrolled in the Supplemental Security Income (SSI) benefits program. The SSI program has a legislative mandate to focus on low-income individuals with very severe disabilities meeting a standard of “expected to persist over an extensive period of time or result in death” (NASEM, 2015). The SSI eligibility determination process evaluates whether a child’s physical or mental health conditions are sufficiently severe to markedly impair the ability to function in one or more domains. Because SSI eligibility criteria requires the child to have a severe disability that falls into a qualifying category and be from a low-income family, the percentage of children receiving SSI benefits will constitute a smaller subset of the total number of children with disabilities. That few studies have generated longitudinal data using uniform measures makes the process of quantifying the number of children who have disabilities, as well as charting epidemiological trends over time, quite difficult. Of the existing methods for quantifying childhood disability, the committee focused on population data surveys, and use of administrative or service data, also considering post hoc analyses of subsets of data published by peer-reviewed scientific journals. POPULATION DATA SURVEYS Surveys use a variety of questions to determine whether a child has a disability. Some questions address the limitations associated with activities of daily living or instrumental activities of daily living; some address the consequence of a disability (e.g., the use of an C-1 PREPUBLICATION COPY: UNCORRECTED PROOFS

C-2 IMPROVING PROGRAMS AND SERVICES FOR CHILDREN WITH DISABILITIES assistive device); and some identify disability based on the presence of a diagnosis considered to be disabling (e.g., intellectual disability, autism spectrum disorder) (Reichman et al., 2008). Still others use a similar strategy analyzing medical insurance claims with diagnosis and treatment codes indicating a disability (Kuhlthau et al., 2002). Surveys are the most common elicitation method for gathering population data, or data regarding a group of people who share a specified characteristic, typically through surveying a representative sample of the population. Several national surveys on childhood disability and related services are described below. National Health Interview Survey (NHIS) The NHIS, conducted by the Centers for Disease Control and Prevention’s (CDC’s) National Center for Health Statistics (NCHS), is a cross-sectional household interview survey that collects data annually to monitor the health of the U.S. population. The NHIS has been fielded yearly by NCHS since 1957 (NHIS, 2016). The NHIS is the only continuing nationwide survey designed to collect information on disability status (Houtrow et al., 2014). For children, the core in-person interview is conducted with a parent or adult who is knowledgeable about the child’s health and health care (Stein and Silver, 2003). Unfortunately, NHIS data often aggregate all children (<18 years) and do not focus specifically on school-aged children. The survey has a complex multistage probability design that creates a nationally representative sample of the U.S. population. The sample consists of the civilian noninstitutionalized population residing in the United States, and the sample size is approximately 35,000 households with 87,500 persons (CDC, 2012). The survey’s Core questions include four major components: Household, Family, Sample Adult, and Sample Child. Major health topics addressed include physical and mental health status; chronic conditions, including asthma and diabetes; access to and use of health care services; health insurance coverage and type of coverage; health-related behaviors; measures of functioning and activity limitations; immunizations; and injuries and poisonings (CDC, 2014a). The passage of the Patient Protection and Affordable Care Act (ACA) in 2010 created new data requirements to facilitate monitoring the law’s impact on the health care system, which led to several enhancements and edits to the NHIS (CDC, 2017). These changes were first implemented in the 2011 survey and were slightly modified for the 2012 survey. Post hoc analyses of NHIS data have been performed to understand prevalence of childhood disability, as well as external factors that exacerbate or mitigate the severity of a disability. Longitudinal data from the NHIS indicate that in 1960, parents reported 1.8 percent of all children under age 18 having a health condition severe enough to interfere with the usual activities of childhood on a regular basis (IOM, 2007). By 2010, this percentage had risen to almost 8 percent—an increase of more than 400 percent during the half century from 1960 to 2010 (Halfon et al., 2012; Newacheck et al., 2004). Eliciting population data from parent responses to the survey’s evolving questions regarding functional limitations appears to inflate the number of children who experience impairment that constitutes a disability. Houtrow and colleagues (2012, 2014) performed secondary analyses on data from the NHIS reports from 2001–2002, 2004–2005, 2007–2008, and 2011–2012 to understand the prevalence, rate of change, severity, and sociodemographic disparities of parent-reported childhood disability. Their post hoc analyses showed marked increases between 2001 and 2011 (more than 60 percent) in parent-reported cases of disability associated with speech and intellectual impairment (both increasing by 63 percent) and other mental, emotional, or behavioral problems (increasing by nearly 65 percent) (Houtrow et al., PREPUBLICATION COPY: UNCORRECTED PROOFS

APPENDIX C C-3 2014). In contrast, a 2014 Child Trends study reviewing NHIS children aged 5–17 found that the proportion of children and adolescents reported as having at least one limitation remained constant from 1998 to 2013 (fluctuating between 17 and 20 percent). National Comorbidity Assessment-Adolescent Supplement (NCS-A) The NCS-A differs from the NHIS in that its methodology involved face-to-face interviews with adolescents aged 13–18 (N = 10,123). Secondary analyses of the in-person NCS-A data have also been undertaken to assess the prevalence of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) mental disorders in adolescents using a modified version of the World Health Organization’s Composite International Diagnostic Interview (Merikangas et al., 2010). Merikangas and colleagues (2010) analyzed the most commonly reported symptoms, finding that anxiety disorders were the most prevalent among this subpopulation (31.9 percent). This study also used NCS-A survey data to quantify how many adolescents experienced severe impairment or distress as a result of their mental health condition. Results indicated that the prevalence of severe DSM-IV mental disorders among adolescents was stable at approximately 22 percent. National Longitudinal Survey of Adolescent Health (ADD-Health) ADD-Health began in 1994–1995 with a sample of 7th- through 12th-grade schools. Interviews were attempted with the more than 100,000 students attending these schools, with three follow-up personal interviews being conducted with a random one-fifth of these students. Health-related behaviors have been relatively well measured in each survey wave through questionnaire responses. Absent from current efforts to measure children’s behavioral influences is consideration of their attitudes, beliefs, expectations, and cultural factors that shape decisions to seek health care or engage in health promotion or illness prevention activities. For example, “local” instruments have been developed by researchers exploring in a cross-sectional and prospective fashion the relative roles of parents’ and peers’ perceptions and risk involvement on risk and protective behaviors among adolescents (Cottrell al., 2003; Stanton et al., 2000). Substantial evidence indicates that these factors exert major influences on youths’ health behaviors and subsequent health, whether related to their health behavior choices, tobacco/alcohol/substance use, diet, or exercise or to their compliance with health care interventions. National Survey of Children’s Health (NSCH) Another survey that relies on parental reporting of symptoms is the NSCH, through which the CDC gathers information on the prevalence of, among other conditions, mental health disorders in children. The 2011 NSCH suggested that one out of seven U.S. children between the ages of two and eight had been diagnosed with a mental health disorder (CDC, n.d.). The NSCH also asks demographic questions about the children, allowing researchers to search for correlations between personal characteristics and the prevalence of specific disabilities, such as the relationship between family income (as a percentage of the federal poverty level) and various outcomes for children rated by their parents as being in “fair or poor overall health.” PREPUBLICATION COPY: UNCORRECTED PROOFS

C-4 IMPROVING PROGRAMS AND SERVICES FOR CHILDREN WITH DISABILITIES National Survey of Children with Special Health Care Needs (NS-CSHCN) Sponsored by the Maternal and Child Health Bureau of the Health Resources and Services Administration, the NS-CSHCN is a national survey that was conducted using the State and Local Area Integrated Telephone Survey (SLAITS) in years 2001, 2005–2006, and 2009– 2010 (DRC, 2012). The NS-CSHCN provides parent-reported information on the health and functional status of children with special health care needs at the national and state levels; the survey sample consists of the noninstitutionalized population of children in the United States with special health care needs aged 0–17 (DRC, 2012). The survey included 38,866 interviews in 2001 and 40,840 interviews in 2005–2006 (CDC, 2014b). The 2009–2010 survey consisted of 40,242 detailed interviews (CDC, 2014b). Topics covered in the NS-CSHCN include the child’s health and functional status (expanded to include current conditions and functional limitations beginning in 2005–2006), the child’s health insurance status, the adequacy of coverage, and the impact of the child’s health on the family. The survey also covers access to health care, including types of health care services required by a child, any unmet health care needs, care coordination, and the family-centeredness of the child’s health care (DRC, 2012). National Longitudinal Transition Survey-2 (NLTS2) In addition to surveys of children’s health, national research studies have used population data to assess outcomes for students who receive special education services. The NLTS2 surveyed a nationally representative sample of students who were 13 to 16 years old at the time of the first surveyed in 2000 and were receiving special education services. Students were followed until 2010 to document their educational, vocational, social, and personal experiences as they transitioned from adolescence to early adulthood. The NLTS2 involved five cycles of data collection and employed a variety of data gathering methods, including phone surveys with the child and family, in-person interviews at school, and interviews with teachers and school administrators. The study population was restricted to students who received special education services, not all children with all forms of disability, which reduces the generalizability of the findings to a prespecified subset of the population of children and youth with disabilities. USE OF ADMINISTRATIVE OR SERVICE DATA Individuals with Disabilities Education Act (IDEA) Data Since 1975, the U.S. Department of Education has collected data on early intervention and special education services provided to children with disabilities as required by IDEA. The U.S. Department of Education maintains and provides public access to state-supplied administrative records on children and young adults with disabilities until the age of 21 (ED, 1995). The act requires each state that receives assistance to report annually the number and percentage of children with disabilities who are receiving educational services, by race, ethnicity, limited English proficiency status, gender, and disability category (Wexler et al., 2015). IDEA data include annual counts of services provided to children aged 0–2 (Part C) and 3–21 (Part B). Part B data are collected from 60 reporting entities and provide information on the number of children with disabilities aged 3–21 who received special education and related services under IDEA between October 1 and December 1 each year (based on the state- PREPUBLICATION COPY: UNCORRECTED PROOFS

APPENDIX C C-5 designated child count date). The data are collected by disability category, race/ethnicity, gender, and discrete age (Wexler et al., 2015). The Part B data are reported by primary disability category. As a result, these data may be artificially deflated when speech and language impairments are reported as “developmental delay” or “multiple disabilities.” On the other hand, there is potential for inflation in the speech or language impairment category when autism, for example, is reported in this category (Wexler et al., 2015). In 2014, a total of 5.9 million children aged 6–21 were served under IDEA Part B—a figure representing about 8.7 percent of the total number of U.S. children in this age group. This represents a decrease in the proportion of all children, which reached a high of 9.0 percent in 2005 (NCES, 2016). Since then, the proportion of all children served by IDEA Part B has remained relatively consistent. SSI Data The Social Security Administration’s (SSA’s) Program Operations Manual requires that the diagnosis or medical basis for an applicant’s disability most pertinent to the determination of eligibility be recorded. SSA collects information on a primary diagnosis and an optional secondary diagnosis for each determination. According to the Program Operations Manual, “The primary diagnosis for an allowance refers to the basic condition that rendered the individual disabled, or in [the case of] a denial, the one which the evidence shows to have the most significant effect on the individual’s ability to work.” A secondary diagnosis is defined as the “most significant diagnosis following the primary diagnosis in severity” (SSA, 2014). SSA disability examiners are required to record an “impairment code” for every disability determination. SSA’s impairment codes are a list of numeric codes loosely informed by the International Classification of Diseases, 9th Revision (ICD-9), used to classify medical diagnoses that are the basis for disability claims. Each impairment code is linked to a diagnostic category within SSA’s “Listing of Impairments” based in part on the Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised (DSM-III-R) (SSA, 2010). For each allowance that meets or equals a listing, the examiner is required to record the correlated impairment code. For every allowance that functionally equals a Listing, the examiner must record the impairment code that most closely matches the diagnosis in the applicant’s case file and is the basis of his or her disability. For denials, examiners are instructed to record an impairment code for the diagnosis that has the most effect on the claimant’s function, or a code for “none established” if there is no diagnosis. Health Services Data As mentioned in Chapter 2, SSI eligibility automatically guarantees Medicaid eligibility in many states, but not all. Therefore, Medicaid maintains its own population statistics through the Medicaid Analytics eXtract (MAX). The Medicaid Analytics eXtract Study population included all Medicaid-eligible youth aged 3–17 from a geographically diverse subset of states for the years 2001–2010. To qualify as study participants, children had to have been enrolled in Medicaid for 11 of the 12 months in a given study year. The primary data source consisted of Medicaid enrollment, claims, and prescription drug-fill data, as well as mandatory data submitted semiannually through state Medicaid offices. Study participants were assigned to one of three basis-of-eligibility (BoE) groups based on their eligibility records: SSI/Medicaid, foster care, and other (including a large group eligible solely because of household income). A threshold of either one or more inpatient claims or two or more outpatient claims with qualifying International PREPUBLICATION COPY: UNCORRECTED PROOFS

C-6 IMPROVING PROGRAMS AND SERVICES FOR CHILDREN WITH DISABILITIES Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes (on different dates) was used to establish the presence of the conditions listed in Table C-1. According to the MAX data, from 2001 to 2010, the total number of SSI/Medicaid child enrollees increased by 33 percent, from 361,106 to 478,822, while the total number of child Medicaid enrollees among the 20 states increased by 57 percent, from 5.23 million in 2001 to 8.21 million in 2010. TABLE C-1 Diagnoses by Age Using MAX Data for the SSI/Medicaid Enrollee Subpopulation, 2010 Diagnosis Ages 3–5 (%) Ages 6–11 (%) Ages 12–17 (%) Attention-deficit hyperactivity disorder 6.5 21.9 17.6 (ADHD) Conduct disorder 3.0 4.4 5.3 Emotional disturbances 1.9 5.2 6.8 Oppositional defiant disorder 1.0 4.3 5.8 Depression 0.6 3.8 8.7 Bipolar disorders 0.2 1.9 4.6 Anxiety disorders 0.7 2.0 2.3 Autism spectrum disorders 7.9 7.8 5.2 Intellectual disorders 2.9 6.3 7.6 Speech and language disorders 20.7 15.3 5.8 Hearing disorders 19.8 8.6 4.6 Learning disorders 12.1 7.4 4.4 Cerebral palsy 7.3 6.0 5.0 Asthma 12.8 8.8 5.5 Sample size 72,940 193,479 212,403 SOURCE: Medicaid Analytic Extract (MAX) data for 20 states: Alabama, Alaska, Arkansas, California, Florida, Idaho, Illinois, Indiana, Louisiana, Michigan, Mississippi, Montana, North Carolina, North Dakota, New Hampshire, New Mexico, South Dakota, Vermont, Virginia, Wyoming. REFERENCES CDC (Centers for Disease Control and Prevention). 2012. National Health and Nutrition Examination Survey. Atlanta, GA: CDC. https://www.cdc.gov/nchs/nhanes/index.htm (accessed December 11, 2017). CDC. 2014a. NCHS fact sheet. Atlanta, GA: CDC. CDC. 2014b. State and Local Area Integrated Telephone Survey. https://www.cdc.gov/nchs/slaits/index.htm (accessed December 18, 2017). CDC. 2017. About the National Health Interview Survey. https://www.cdc.gov/nchs/nhis/about_nhis.htm (accessed March 5, 2018). CDC. n.d. Data and statistics: National Survey of Children’s Health (NSCH): Mental, behavioral and developmental health of children aged 2–8. https://www.cdc.gov/childrensmentalhealth/data.html (accessed April 10, 2018). PREPUBLICATION COPY: UNCORRECTED PROOFS

APPENDIX C C-7 Cottrell, L., X. Li, C. Harris, D. D’Alessandri, M. Atkins, B. Richardson, and B. Stanton. 2003. Parent and adolescent perceptions of parental monitoring and adolescent risk involvement. Parenting: Science and Practice 3(3):179–195. DRC (Data Resource Center for Child and Adolescent Health). 2012. The National Survey of Children’s Health. http://www.childhealthdata.org/learn/NSCH (accessed January 30, 2017). ED (U.S. Department of Education). 1995. Individuals with Disabilities Education Act amendments of 1995: Reauthorization of the Individuals with Disabilities Education Act (IDEA). Washington, DC: ED. https://eric.ed.gov/?id=ED399739 (accessed December 18, 2017). Halfon, N., A. Houtrow, K. Larson, and P. W. Newacheck. 2012. The changing landscape of disability in childhood. Future of Children 22(1):13–42. Houtrow, A., J. Jones, R. Ghandour, B. Strickland, and P. Newacheck. 2012. Participation of children with special health care needs in school and the community. Academic Pediatrics 12(4):326–334. Houtrow, A. J., K. Larson, L. M. Olson, P. W. Newacheck, and N. Halfon. 2014. Changing trends of childhood disability, 2001–2011. Pediatrics 134(3):530–538. IOM (Institute of Medicine). 2007. The future of disability in America. Washington, DC: The National Academies Press. Kessler, R. C., G. P. Amminger, S. Aguilar‐Gaxiola, J. Alonso, S. Lee, and T. Bedirhan Ustun. 2007. Age of onset of mental disorders: A review of recent literature. Current Opinion in Psychiatry 20(4):359–364. Kuhlthau, K. A., A. C. Beal, T. G. Ferris, and J. M. Perrin. 2002. Comparing a diagnosis list with a survey method to identify children with chronic conditions in an urban health center. Ambulatory Pediatrics 2(1):58–62. Merikangas, K. R., J.-P. He, M. Burstein, S. A. Swanson, S. Avenevoli, L. Cui, C. Benjet, K. Georgiades, and J. Swendsen. 2010. Lifetime prevalence of mental disorders in U.S. adolescents: Results from the National Comorbidity Survey Replication-Adolescent Supplement (NCS-A). Journal of the American Academy of Child & Adolescent Psychiatry 49(10):980–989. NASEM (National Academies of Sciences, Engineering, and Medicine). 2015. Mental disorders and disabilities among low-income children. Washington, DC: The National Academies Press. NCES (National Center for Education Statistics) 2016. Enrollment in public elementary and secondary schools, by level and grade: Selected years, fall 1980 through fall 2026. Digest of Education Statistics. https://nces.ed.gov/programs/digest/current_tables.asp (accessed January 27, 2018). Newacheck, P. W., M. Inkelas, and S. E. Kim. 2004. Health services use and health care expenditures for children with disabilities. Pediatrics 114(1):79–85. NHIS (National Health Interview Survey). 2016. NHIS sample design. https://nhis.ipums.org/nhis/userNotes_sampledesign.shtml (accessed April 10, 2018). Reichman, N. E., H. Corman, and K. Noonan. 2008. Impact of child disability on the family. Maternal and Child Health Journal 12(6):679–683. SSA (Social Security Administration). 2010. 20 CFR parts 404 and 416 revised medical criteria for evaluating mental disorders; proposed rule. Washington, DC: SSA. SSA. 2014. Program Operations Manual System: Medicaid and the Supplemental Security Income program. https://secure.ssa.gov/poms.nsf/lnx/0501715010 (accessed December 18, 2017). Stanton, B. F., X. Li, J. Galbraith, G. Cornick, S. Feigelman, L. Kaljee, and Y. Zhou. 2000. Parental underestimates of adolescent risk behavior: A randomized, controlled trial of a parental monitoring intervention. Journal of Adolescent Health 26(1):18–26. Stein, R. E., and E. J. Silver. 2003. Patterns of medical, educational, and mental health service use in a national sample of U.S. children. Ambulatory Pediatrics: The Official Journal of the Ambulatory Pediatric Association 3(2):87–92. Wexler, L., M. Chandler, J. P. Gone, M. Cwik, L. J. Kirmayer, T. La Fromboise, T. Brockie, V. O’Keefe, J. Walkup, and J. Allen. 2015. Advancing suicide prevention research with rural American Indian and Alaska native populations. American Journal of Public Health 105(5):891–899. PREPUBLICATION COPY: UNCORRECTED PROOFS

Next: Appendix D Legislation to Support Positive Outcomes forChildren with Disabilities »
Opportunities for Improving Programs and Services for Children with Disabilities Get This Book
×
Buy Prepub | $84.00 Buy Paperback | $75.00
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

Although the general public in the United States assumes children to be generally healthy and thriving, a substantial and growing number of children have at least one chronic health condition. Many of these conditions are associated with disabilities and interfere regularly with children’s usual activities, such as play or leisure activities, attending school, and engaging in family or community activities. In their most severe forms, such disorders are serious lifelong threats to children’s social, emotional well-being and quality of life, and anticipated adult outcomes such as for employment or independent living. However, pinpointing the prevalence of disability among children in the U.S. is difficult, as conceptual frameworks and definitions of disability vary among federal programs that provide services to this population and national surveys, the two primary sources for prevalence data.

Opportunities for Improving Programs and Services for Children with Disabilities provides a comprehensive analysis of health outcomes for school-aged children with disabilities. This report reviews and assesses programs, services, and supports available to these children and their families. It also describes overarching program, service, and treatment goals; examines outreach efforts and utilization rates; identifies what outcomes are measured and how they are reported; and describes what is known about the effectiveness of these programs and services.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!