воскресенье, 30 сентября 2012 г.

Medicaid Behavioral Health. - State Health Watch

Mental health and substance abuse services that are mandated by state and federal law are being left out of contracts for managed Medicaid behavioral health programs, says a report by the Center of Health Policy Research at George Washington University.

Regardless of whether state officials think itAEs a covered benefit under managed care, if agreements are ambiguous or silent on these issues, states remain legally bound to provide services because managed care plans can argue oitAEs not in the contract.o

A case in point: Many states are surprised to learn that their managed care programs donAEt necessarily cover services ordered by courts, schools, or other social service agencies but which they are obligated to provide. MCOs might deny coverage because they deem the services to be not medically necessary or primarily educational in nature.

In a much-anticipated survey, GW lawyers reviewed agreements on behavioral health between Medicaid agencies and managed care organizations in 37 states. The survey illustrates the power imbalance between the more sophisticated managed care organizations and the less experienced Medicaid agencies. It also points to a cultural gap between commercial insurers and Medicaid agencies, which serve a much needier population. The many ambiguities in health care such as what is omedically necessaryo also cause problems.

Commercial insurers, for example, generally consider medically necessary care to be treatments needed to restore functioning following an acute illness or injury. Medicaid, by contrast, may ocover preventive, ameliorative, and development enhancing services for children and adults with chronic illnesses and disabilities.o The gap is particularly great in pediatric coverage because of the unique standard of medical necessity under the EPSDT (Early Periodic Screening, Diagnosis and Treatment) program.

E. Clarke Ross, executive director of the American Managed Behavioral Healthcare Association (AMBHA), says his association agrees that there needs to be ogreater specificityo in contracts between Medicaid agencies and managed care organizations. ItAEs been largely a matter of state health policy to cover only a traditional, limited benefit structure under managed care and not to integrate block grants and non-Medicaid funding for more comprehensive coverage, according to Dr. Ross.

There needs to be oclear and conciseo information on owhat is being paid for.o If states osay they want to finance 30 days of hospitalization and 20 days of clinic-based, medically necessary care,o then they shouldnAEt ocriticize the lack of vocational rehabilitation services.o

State officials should sort through Medicaid coverage requirements in order to decide which duties are appropriate for managed care and which should continue to be handled by the state, the report says.

In another report, GW researchers reviewed 50 representative agreements between managed care organizations and providers.

oIt was an unpleasant confirmation of what we feared,o says Eric Goplerud, associate administrator for managed care for the Substance Abuse and Mental Health Services Administration (SAMHSA). oIt confirmed our suspicion that provider contracts are as inequitable.o

In many agreements, providers can be terminated at will; the financial terms of their agreements can be unilaterally modified; and the responsibility for eligibility verifications and determinations for the patient and for recovering payments from multiple payers is left to them.

SAMHSA has developed a manual to educate providers about what the provisions in their contracts mean. Other resources include a technical assistance manual on network formation which has similarities to ounion organizing,o Mr. Goplerud says. The manual shows providers how they may legally and jointly negotiate agreements to have more bargaining clout. SAMHSA also is developing materials to educate providers about financial management and risk assessment.

The best avenue to helping MH/SA agencies may be to focus on the contracts between the Medicaid agency and the managed care organization rather than on provider agreements, Mr. Goplerud says.

The study found that contracts with MH/SA agencies commit to the purchasing of only limited services and not the full range of services available and which the state may expect are being offered.

The report recommends that states review provider agreements. Dr. Ross says he takes exception to the bias toward community-based providers in the study. He notes that other providers in managed care networks offer services such as psych-rehab, self-help peer groups, clubhouses, etc. He does not agree that those comprehensive services should be provided by MS/SA agencies.

суббота, 29 сентября 2012 г.

Measuring behavioral health is a new challenge - Managed Healthcare

Although it has many similarities with physical health, outcome goals vary with each patient

Behavioral healthcare is frequently a contracted service in many health plans. As such, the qualityof-care assessment is even more important. But it poses a significant challenge for quality managers, many of whom come from a medical background. Performance measurement in behavioral health should be approached in the same framework as medical/surgical care. We still need to consider clinical quality, service quality and satisfaction across the continuum of care. A rigorous approach is warranted considering that the practice of psychiatry has undergone even more significant change under the pressures of managed care than some other areas of specialty.

Inpatient on the way out. Care has shifted drastically to the ambulatory setting with few situations and conditions warranting inpatient stays. In addition, when inpatient stays are approved, the length of stay is significantly shorter than it was in previous years. Therefore, quality professionals must design performance measurement programs to encompass traditional domains and keep in mind the considerable shift from inpatient to ambulatory care and its potential impact on outcomes.

Although the physical health of the patient is important, in behavioral health functional outcomes are the key measures by which to determine if the goals of therapy have been achieved. For example, the ability of an adult with bipolar disorder to maintain their 'normal' or desired role of parent and spouse is important. Improved attendance at work or school (or, conversely, decreased use of sick days related to the underlying psychiatric condition) might be an objective measure that is easily captured. But management of the parental or spousal role is more difficult to quantify, making a more qualitative measure necessary.

Adverse outcomes should also be incorporated into performance measurement. Failure of patients to comply with medication and treatment regimens should be tracked. Process and utilization measures should be included in clinical quality measurement. Service quality also is important with this population. Without suggesting a double standard, patients requiring mental healthcare require better access and availability than those seeking other services. The goal is to schedule an appointment as soon as possible.

Outpatient management of medications is a key function in mental healthcare and should be monitored in a performance-measurement program.

Although patient preference for strict confidentiality poses some barriers, the domain of satisfaction, including the patient and his or her family, is of critical importance in providing mental healthcare services. Satisfaction measurement tools for adult patients might differ from those for pediatric and adolescent patients, although the underlying principles and goals remain the same.

The satisfaction of the other 'customers' of mental healthcare servicesprimary care physicians and other referring practitioners-also should be evaluated. Written surveys to PCPs can be helpful, but meetings involving PCPs and mental health professionals to discuss general approaches to treatment and patient management can be quite effective in improving patient care.

Performance measurement in mental healthcare is a critical component of population-based medical care. The extreme changes in the delivery of behavioral healthcare should compel us to carefully measure and monitor the outcomes of clinical care as well as service quality and satisfaction.

Quality-management professionals must collaborate with their mental health colleagues in developing and implementing meaningful performance measurement strategies. Results of these efforts will then guide important quality improvement efforts.

[Author Affiliation]

пятница, 28 сентября 2012 г.

Tracking pediatric asthma: the Massachusetts experience using school health records.(Public Health Tracking / Mini-Monograph) - Environmental Health Perspectives

The Massachusetts Department of Public Health, in collaboration with the U.S. Centers for Disease Control and Prevention Environmental Public Health Tracking Program, initiated a 3-year statewide project for the routine surveillance of asthma in children using school health records as the primary data source. School district nurse leaders received electronic data reporting forms requesting the number of children with asthma by grade and gender for schools serving grades kindergarten (K) through 8. Verification efforts from an earlier community-level study comparing a select number of school health records with primary care provider records demonstrated a high level of agreement (i.e., > 95%). First-year surveillance targeted approximately one-half (n = 958 schools) of all Massachusetts's K-8 schools. About 78% of targeted school districts participated, and 70% of the targeted schools submitted complete asthma data. School nurse-reported asthma prevalence was as high as 30.8% for schools, with a mean of 9.2%. School-based asthma surveillance has been demonstrated to be a reliable and cost-effective method of tracking disease through use of an existing and enhanced reporting structure. Key words: environmental public health tracking, epidemiology, indoor air quality, pediatric asthma, prevalence, school health, surveillance. Environ Health Perspect 112:1424-1427 (2004). doi:10.1289/ehp.7146 available via http://dx.doi.org/[Online 3 August 2004]

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Asthma is one of the most common chronic diseases among children [American Lung Association (ALA) 2003] and has increased in prevalence over the past decades [Centers for Disease Control and Prevention (CDC) 2000]. According to CDC, the prevalence of current asthma among children 5-14 years of age increased from 4 to 7% between 1980 and 1996 (Mannino et al. 1998). More recent National Health Interview Survey (NHIS) findings suggest that prevalence rates may be leveling off, but more data are needed before the trend is clear (Akinbami et al. 2003). Findings from the 2001 Behavioral Risk Factor Surveillance System (BRFSS) show that the prevalence of current asthma for children in Massachusetts younger than 18 years of age is estimated to be 8.8%, whereas the prevalence of lifetime childhood asthma is 12.4% [New England Asthma Regional Council (ARC) 2004]. Asthma health care costs $3.2 billion annually for American children under the age of 18 (ALA 2003).

The reasons for the reported increase in asthma prevalence are unclear (Redd 2002). The increase may be a result of greater exposure to allergens and pollutants (Teague and Bayer 2001; Walker et al. 2003), improved identification of the disease (Barraclough et al. 2002), or the influence of other risk factors such as obesity (Gilliland et al. 2003) or infection (Camara et al. 2004). It is clear that asthma affects families through increased medical visits, school absenteeism, and lost work (Mannino et al. 2002). Statistics from national surveys also show disparities in asthma statistics among those affected by the disease (ARC 2004; Bloom et al. 2003; CDC 2004). For example, findings from the NHIS indicate that children 5-14 years of age have higher asthma prevalence than do other age groups and that, generally, African-American children experience more hospitalizations and mortality from asthma than children classified as white or other ethnicity. The NHIS also describes disparities by geographic region, with the northeastern United States experiencing more hospitalizations from asthma than other regions (Bloom et al. 2003). Data from the BRFSS show an inverse relationship between lifetime childhood asthma and household income in New England (ARC 2004). These observations suggest that environmental factors may be important.

The magnitude of prevalence and cost of asthma is a priority concern among public health organizations across the country. Promoting respiratory health and reducing morbidity and mortality from asthma are goals of the U.S. Department of Health and Human Services (U.S. DHHS) Healthy People 2010 (U.S. DHHS 2000). Environmental factors, such as indoor air quality (IAQ), and social factors, such as access to health care, are thought to explain some of the health disparities noted. However, our understanding of the strength of these relationships and our ability to identify opportunities to reduce morbidity and mortality are limited by the lack of systematically collected asthma data at the community level.

Available asthma prevalence information for Massachusetts has been generally limited to prevalence figures for the entire state or selected urban populations estimated through the BRFSS, a random telephone survey implemented by state health departments in conjunction with CDC. National figures have been available through the NHIS, which annually collects health and behavioral information through personal interviews. Historically, community-level data have been limited to communities with specialized surveillance programs or where research studies have been implemented.

In 2002 CDC established the national Environmental Public Health Tracking (EPHT) program. This program, building upon the recommendations of CDC work groups, the Pew Environmental Health Commission (Pew Foundation 2000), and other public health investigations (Lanphear and Gergen 2003), aims to develop a national network for the systematic collection, evaluation, and dissemination of health outcome and environmental hazard data. In response to the CDC program announcement, the Massachusetts Department of Public Health (MDPH) developed a proposal to track pediatric asthma through school health records based on previous work carried out in the Merrimack Valley of Massachusetts. Preliminary findings of this work suggested that school health records were a reliable data source for community-level asthma tracking, or surveillance, in children. This article describes the results of the first year of the Massachusetts pediatric asthma surveillance project and discusses project goals for years 2 and 3.

Materials and Methods

Surveillance design. The objective of the MDPH pediatric asthma surveillance project is to determine the prevalence by school building of pediatric asthma among children enrolled in grades kindergarten (K) through 8. The surveillance system is designed to use the existing infrastructure of the school health system. Massachusetts school health records document demographic and emergency information, immunization history, past medical history, medication administration at school, and results of school physical exams. School nurses also keep medication administration plans for students receiving medications at school. Therefore, the information contained in the school health record is used as the data source for all health and demographic information. The school nurse or school health contact person for each school was asked to complete a pediatric asthma surveillance form reporting the number of children with asthma by gender and by grade. Only aggregate data were requested.

Target population. In year 1 of the MDPH pediatric asthma surveillance project, all schools participating in the MDPH Essential School Health Service (ESHS) program were requested to provide information on the number of children with asthma in grades K-8 during the 2002-2003 academic year. The ESHS is a program designed to build school health capacity in Massachusetts public and private schools. ESHS districts are required to have a full-time master's-prepared district nurse leader coordinating the health activities of that district's schools. All Massachusetts communities were eligible to apply for ESHS grants. The target population included 958 public schools in 173 cities and towns (111 school districts) serving more than 395,000 children, or approximately 57% of Massachusetts's K-8 students.

Surveillance definition of asthma. School nurses were asked to provide information contained in school health records on the number of K-8 students attending the school 'who have asthma of any type or severity' for the 2002-2003 school year. MDPH also requested the number of records documenting diagnosis of asthma made by a health care provider.

Data collection. During January 2003, the MDPH mailed introductory letters regarding the asthma surveillance project to school superintendents, principals, and nurse leaders in eligible school districts. Project staff also made presentations at professional school nurse meetings to address questions or concerns. Additionally, an advisory committee was formed consisting of district nurse leaders from across the state. During the initial stages of the project, advisory committee members reviewed the surveillance form to ensure its ease of use. In March 2003, district nurse leaders in each target community were asked to distribute the two-page surveillance form asking for aggregate numbers of children with asthma by grade, gender, and school building (MDPH, unpublished protocol). Table 1 shows the information requested. When possible, surveillance forms were distributed to nurse leaders via E-mail to facilitate electronic data submission. If E-mail was not available, forms were sent via fax or the U.S. Postal Service. Follow-up telephone calls were placed to nurses who did not respond by April 2003. School enrollment data were collected from the Massachusetts Department of Education or from a school's administrative staff. Schools that did not return complete surveillance data, or for which student enrollment data could not be obtained by June 2003, were considered nonresponders.

Data analysis. Data analysis was performed with SAS (version 8.02; SAS Institute Inc., Cary, NC) and Microsoft Access (Microsoft Office 2000 SR-1 Professional; Microsoft Corp., Redwood, WA). The prevalence of asthma with 95% confidence intervals (95% CIs) was calculated for each participating school and school district and by grade level.

Results

Participation. MDPH received complete information from a total of 760 schools. Of these schools, 668 were targeted ESHS schools, translating to a 70% participation rate. The remaining 92 schools were private schools (n = 52), charter schools (n = 9), and public schools not included in the ESHS (n = 31). At the district level, MDPH received data from at least 1 school in 87 of the 111 targeted ESHS districts (78%). Participation ranged from 6 to 100% within school districts.

Reported asthma prevalence. The reported prevalence of asthma among the 311,610 students enrolled in the 760 participating schools was 9.2% (95% CI, 9.1-9.3%). Sixty percent of students reported to have asthma were male. Reported prevalence by individual schools ranged from 0 to 30.8%, with a median school asthma prevalence of 8.9%. Reported asthma prevalence by school district ranged from 2.7 to 16.2%, with a median district asthma prevalence of 8.8%. Figure 1 presents the frequency distribution of district-wide reported asthma prevalence figures. Reported asthma prevalence by grade ranged from 7.7 to 10.3% (Table 2).

[FIGURE 1 OMITTED]

Other variables. Analyses were conducted to determine the percentage of students with documentation of a health care provider diagnosis of asthma and/or asthma medication order. Results showed that half of all nurses reported that 90-100% of their students with asthma had documentation in the health record of a provider diagnosis of asthma and/or asthma medication orders. Approximately 25% of nurses indicated that 75-85% of student health records contained a diagnosis, and the remaining 25% of nurses reported that less than 75% of the student health records had this documentation.

Responses to questions eliciting other sources of information used by nurses to identify children with asthma showed that almost 90% listed parent or student communications as an alternative source of knowledge of a student's asthma status (41 and 48%, respectively). Direct observation of an asthma attack was rarely a source of information (< 0.5%).

Discussion

Comparison with other data sources. The MDPH was successful in obtaining asthma surveillance data from 70% of targeted schools serving more than 311,000 students through its school-based pediatric asthma surveillance system. While the reported prevalence of pediatric asthma observed during the first year of the MDPH pediatric asthma surveillance project was 9.2%, it is important to note that prevalence ranged as high as 16.2% by district and nearly 31% by individual school. The statewide prevalence estimate is somewhat higher but nonetheless similar to the 8.8% prevalence of current childhood asthma in Massachusetts reported by the ARC based on BRFSS data collected in 2001 (ARC 2004). A Connecticut school-based surveillance effort by Environment and Human Health, Inc., similar to the one implemented in Massachusetts, reported a 9.7% asthma prevalence among students in grades K-5 (Storey et al. 2003). In comparison, K-5 prevalence estimate in Massachusetts was 8.8%.

Practical considerations. A number of issues are important in assessing the utility of school health records as a pediatric asthma surveillance tool. These include the resource impacts on the individual school, the completeness of the data, the utility of the data to decisions makers, the ability to link health data with environmental databases, and compatibility with other state and national asthma surveillance programs. As a part of its CDC-funded EPHT program, the MDPH has begun addressing these issues.

Through close collaboration with school nurses and school nurse leaders, the MDPH has been able to develop a surveillance system that is responsive to concerns regarding impacts on schools. These concerns included requesting information once per year and at a time that is in less competition with other school nurse work demands, simplifying the data collection form, keeping school administrators informed, and sharing results in a timely fashion.

During the next 2 years, the MDPH will be evaluating the reliability and quality of the surveillance data collected. However, preliminary work carried out as part of the Merrimack Valley project suggested that data reliability and quality are excellent. In that project 184 schools serving grades K-8 located in 21 communities with 64,000 students participated. As in the current surveillance project, nurses were asked to provide data from school health records on the number of children with asthma. MDPH staff worked with school nurses and area physicians to confirm the diagnostic information contained in the school record and to validate the information collected to determine if asthma had been identified in children but not reported in the school record. The findings confirmed that the diagnostic information was accurate in 98% of the records evaluated and suggested that children with physician-diagnosed asthma were usually identified in the school health record as having asthma.

Although there was notable variation in reported asthma prevalence between schools and school districts, caution is needed when comparing the prevalence estimates between specific schools or districts during the surveillance project's first year. Some school district prevalence estimates were based on reporting by a small percentage of the district's schools and therefore may not be representative of that district's actual asthma prevalence. Differences in school health systems between schools may further complicate the issue of comparability of asthma prevalence estimates. Such differences arise because there is not presently a requirement for systematic and standardized collection of asthma information in Massachusetts schools. Opportunities exist to improve the collection of asthma information through enhancements of the school-required medical history form and through encouraging the use of asthma action plans for all students with asthma. These improvements would facilitate more systematic and standardized data collection and aid in managing a student's asthma.

It is also important to note that a higher prevalence of asthma within one school or district does not necessarily indicate the presence of environmental problems within that district's schools. Pediatric respiratory symptoms have been associated with a number of factors including exposures in the outdoor environment (Boezen et al. 1999; Delfino et al. 2002; Tolbert et al. 2000), exposures in the home environment (Rosenstreich et al. 1997; Smith et al. 2000; Sturm et al. 2004), genetic factors (El-Sharif et al. 2003; Lee et al. 2003), and lifestyle factors (Aligne et al. 2000; Heinrich et al. 2002). The MDPH pediatric asthma surveillance project is a surveillance system, and information about risk factors is not available. The collected information can be used to target intervention activities and to generate hypotheses about possible etiology. For example, IAQ is being assessed in approximately 100 schools as part of the MDPH's overall EPHT program. The assessments are conducted following a standardized protocol (MDPH, unpublished protocol) and include the measurement of total volatile organic compounds, particulate matter with an aerodynamic diameter < 2.5 [micro]m, carbon monoxide, carbon dioxide, and evaluation of indicators of moisture and mold. IAQ assessment data for individual schools will be linked with asthma data to evaluate whether IAQ may be associated with asthma prevalence in students. School asthma data can also be linked with ambient air quality data by geocoding school addresses and connecting to existing ambient air quality data.

Local public health officers and other stakeholders often express interest in community-level prevalence estimates, but little information is available (Boss et al. 2001; Lanphear and Gergen 2003; White et al. 2002). This interest is based on the desire to identify and address the impacts of local environmental factors, as well as to delineate the need for health intervention programs. In a surveillance system that relies on aggregate data from school health records, prevalence estimates are generated by school and by school district. Therefore, the ability to generate community-specific prevalence is somewhat limited. Although it usually is possible to estimate town/city prevalence based on school data, some school districts are regional and draw students from multiple communities. Nevertheless, even school district-level prevalence estimates offer a more comprehensive view of pediatric asthma prevalence on the local level than do other surveillance data currently available. Sources such as hospitalization, emergency department, and Medicaid data look only at select segments of the population. These data sources can provide important insights into certain high-risk populations but exclude most individuals with asthma (Boss et al. 2001).

Another factor that may warrant consideration relates to the definition of asthma, which may not conform to the definitions used in the NHIS and BRFSS surveys and recommended by the Council of State and Territorial Epidemiologists (CSTE 1998). These definitions estimate asthma prevalence based upon responses to questions such as '[Has this child] ever been diagnosed with asthma?', 'Does this child still have asthma?' (CDC 2001), and 'During the past 12 months has [child's name] had an episode of asthma or an asthma attack?' (Bloom et al. 2003). It is unclear at this time which of the above definitions compares best with school nurse-reported asthma. The MDPH will be evaluating this issue over the next 2 years of the surveillance project.

Finally, the lack of electronic reporting to the MDPH may inhibit the utility of school-based surveillance. Many school nurses do not have direct access to a computer and/or the Internet, which presently limits electronic reporting of asthma data. In addition to the reporting methods employed in year 1 (fax, postal mail, and E-mail), other options are being explored that include web-based reporting and using electronic data collection forms on computer disks. To facilitate the transfer of information to CDC and other public health officials, the MDPH will use the National Electronic Disease Surveillance System (NEDSS). NEDSS is a standards-based electronic information system architecture that states can use to gather and disseminate information from a variety of sources.

Whether school-based asthma surveillance would be as successful in other states is an important question to resolve in order to meet the long-term goal of developing a national environmental public health tracking program. A Healthy People 2010 objective is to increase the proportion of U.S. schools with a nurse-to-student ratio of at least 1:750 (U.S. DHHS 2000). At present, however, not every school (including those in Massachusetts) has a nurse, or a nurse may be responsible for more than one school. Implementation of computerized school health records may help to overcome this limitation.

Additionally, the MDPH is working with the ARC to determine the feasibility of a coordinated asthma surveillance program for New England. Differences in laws governing school health, the definition of asthma, and the school health infrastructure in the region are among the issues being discussed.

This public health surveillance effort provides community-level asthma surveillance data for the first time in Massachusetts. It represents an important first step in the establishment of a statewide asthma surveillance system and in identifying the components and methodologic issues for a nationwide tracking system for pediatric asthma. During years 2 and 3 of the pediatric asthma surveillance project, the MDPH is expanding its target population to include all public, private, and charter schools serving any of grades K-8 in each of the state's 372 school districts. Preliminary analysis suggested that on the local level, asthma prevalence might not follow the socioeconomic patterns typically referenced as determinants of asthma patterns and trends. For that reason, it may be important to consider potential contributions of environmental factors in the indoor and ambient environments. As the project is extended statewide, MDPH will conduct statistical analyses to help characterize school populations in relation to reported asthma prevalence. Additionally, the MDPH plans to evaluate pediatric asthma prevalence in relation to school IAQ. The MDPH pediatric asthma surveillance project may prove a valuable tool for tracking asthma prevalence, planning intervention activities, and improving our understanding of pediatric asthma by providing both community-level and statewide asthma prevalence data for the first time in Massachusetts.

Table 1. Information collected by the MDPH pediatricasthma surveillance project, 2002-2003.Variable name           DescriptionSchool address          Street address of the schoolMale                    Number of male K-8 students with asthmaFemale                  Number of female K-8 students with asthmaGrades K-8              Number of students in each grade with                          asthma (9 separate variables, 1 for                          each of grades K-8)Percentage documented   Percentage of students with health care                          provider documentation of asthma                          in health recordsSources                 Source(s) other than health care provider                          documentation that supplied nurses with                          knowledge of student asthma statusTable 2. Reported Asthma Prevalence by Grade, MOPHpediatric asthma surveillance project, 2002-2003.Grade   Prevalence % (n)    95% CIK          8.1 (2,561)      7.8-8.41          7.7 (2,598)      7.4-8.02          8.3 (2,780)      8.0-8.63          9.0 (3,052)      8.7-9.34          9.5 (3,266)      9.2-9.85         10.0 (3,535)      9.7-10.36         10.3 (3,692)     10.0-10.67         10.0 (3,656)      9.6-10.28          9.8 (3,598)      9 5-10.2Total      9.2 (28,738)     9.1-9.3Total number of K-8 students enrolled inparticipating schools is 311,610.

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Robert S. Knorr, Suzanne K. Condon, Frances M. Dwyer, and Danielle F. Hoffman

Massachusetts Department of Public Health, Center for Environmental Health, Boston, Massachusetts, USA

This article is part of the mini-monograph 'National Environmental Public Health Tracking,' which is sponsored by the Centers for Disease Control and Prevention (CDC).

Address correspondence to R.S. Knorr, Massachusetts Department of Public Health, 250 Washington St., 7th Floor, Boston, MA 02108 USA. Telephone: (617) 624-5757. Fax: (617) 624-5777. E-mail: robert.knorr@dph.state.ma.us

The Massachusetts Department of Public Health thanks school nurses, the Pediatric Asthma Surveillance Advisory Committee, and the staff of the Department's Bureau of Family and Community Health who collaborated on this project.

This project is funded through cooperative agreement u50/ccu122451-02 from CDC, National Center for Environmental Health, Environmental Public Health Tracking Program.

This article was supported by an environmental public health tracking cooperative agreement from CDC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.

The authors declare they have no competing financial interests.

четверг, 27 сентября 2012 г.

Parent-identified barriers to pediatric health care: a process-oriented model.(Child Health Services) - Health Services Research

Children's health care services access, utilization, and outcomes in the U.S. are characterized by disparities across vulnerability factors such as socioeconomic status (SES), race/ethnicity, and language (Newacheck, Hughes, and Stoddard 1996; Smedley et al. 2003). While much research has documented the associations between variables such as insurance status, race/ethnicity, education, and English language ability on health care access and quality, less is known about the processes by which these associations arise. Qualitative, patient-centered research methods hold great promise for expanding our knowledge in this area.

Building on Andersen and Aday's behavioral model of health care access (Aday and Andersen 1974; Andersen and Aday 1978), Aday's model of vulnerability (Aday 1993, 1994), and the noncategorical approach (Stein et al. 1993) to pediatric quality-of-care measurement, Seid et al. (2003) have proposed a conceptual model to organize examinations of how health care structures and processes affect health-related quality of life for vulnerable children. Earlier versions of this model have been used to generate a parent-report primary care measure (Seid et al. 2001), examine the effects of language, race/ ethnicity, and access to care on parents' reports of primary care experience (Seid, Stevens, and Varni 2003), and compare child health services access and primary care experiences on both sides of the U.S.-Mexico border (Seid et al. 2003). However useful for describing relationships between vulnerability factors and access to and quality of care, these quantitative studies fell short in elucidating the processes by which these relationships might arise.

For example, Seid, Stevens, and Varni (2003) documented, in a sample drawn from 18 elementary schools in an urban school district, that insurance status, language, and presence of a regular provider of care were significantly related to scores on a parent-report measure of pediatric primary care experiences called the Parent's Perceptions of Primary Care survey or P3C (Seid et al. 2001). A closer look at these data reveals that more than half (56 percent) of those with the lowest P3C scores (Z-scores less than--1.96) were insured, 43.5 percent completed the survey in English, and 39 percent reported having a regular doctor. In other words, despite the significant associations between vulnerability factors (insurance status, language, having a regular source of care) and the quality of primary care reported via the P3C score, a substantial number of children without these vulnerability markers experienced poor primary care. Conversely, a substantial number of children with vulnerability markers experienced better primary care.

Marker Variables and the Processes They Can Mark

This variation implies that insurance status, language, and presence of a regular provider are 'marker' variables--variables that assign an individual to categories that mark other sociobehavioral processes. These other processes can be understood to account for the differences seen in the P3C scores.

The Seid et al. (2003) model, revised accordingly to include the construct 'barriers to care,' posits that disparities in care and outcomes arise, in part, because barriers to care moderate each child's journey through the health care services system. Barriers to care are conceptualized as processes related to, but distinct from, sociodemographic vulnerability characteristics (Schulman et al. 1995; Committee on Pediatric Research 2000). Thus, every individual may experience barriers to care (Aday 1993), but certain vulnerability characteristics (e.g., race/ethnicity) increase the risk, and effects of, barriers to care. (1) Refocusing from vulnerability factors to barriers to care entails a shift in attention to process variables, which provides a way to theoretically link patient experience to quality of care. That is, instead of merely knowing which groups are experiencing what disparities, by focusing on processes through a qualitative lens, we can begin to know why and how.

In order to best understand these processes, it is essential to understand the perspective of the patient, or in this case, the parent. The research to be described here attempts to do just that.

Identifying Barriers to Care: The Importance of Parents' Perceptions

Parents are, increasingly, an important part of the pediatric care team (McMahon, Rimsza, and Bay 1997; Bryan and Burstein 2002; Sobo and Kurtin 2003). They are in a unique position to report on the care their children receive (Crain et al. 1998; Dinkevich, Cunningham, and Crain 1998; Garwick et al. 1998; Homer et al. 1999). And their perceptions and experiences of barriers to care may differ in important ways from those of health care professionals. These differences signify, among other things, the sometimes vast social and cultural gaps that separate parents and health care professionals. Professionals are acculturated to the world of health care, while parents experience this world (initially at least) as foreign and opaque--as a new and different culture (Sobo and Seid 2003). And parents are in a different social position in relation to this world than professionals. Understanding parents' perceptions, and the ways they might differ from those of health care professionals, is the key to developing programs and interventions to minimize barriers and is central to the provision of patient-centered care.

Maximizing Qualitative Methods for Health Services Research

In this article, we describe the parent experience of seeking care based on findings from Spanish and English language focus groups. We used these groups to gather information on process variables that the marker variables from the school study described above might be marking, and to devise a barriers to care questionnaire (BCQ) to measure their intensity for large populations (Seid et al. 2004).

Focus groups have long been utilized for developing questionnaires (Sudman, Bradburn, and Schwarz 1996). They are also useful for describing the potential range of responses that a given group might have to a particular question, from that group's point of view, and for providing a rich, in-depth understanding of the meaning of those responses so that the reasons behind questionnaire findings, such as frequencies, can be better understood (Basch 1987; Bender and Ewbank 1994).

But much focus group research in health services research (HSR) stops short of this. Overformalized, discussion-limiting moderation processes can yield 'equivalently limited data' (Morgan 1997, p. 40). Moreover, despite the existence of powerful qualitative analysis methods (e.g., Glaser and Strauss 1967; Strauss and Corbin 1998; Quinn 2005), much HSR subjects qualitative data to quantitative analyses. To develop questionnaires, many researchers simply code concepts and then sort them by frequency of mention (although this may not represent a concept's salience to a group). Beyond such classic content analyses, some mine transcripts for quotations to exemplify frequently mentioned concepts. (2)

This article provides a robust example of the type of actionable conceptual model building that can be done with focus group data using qualitative analysis techniques--in this case, simple techniques adapted from anthropological discourse analysis for use in applied HSR. Qualitative analysis provides a powerful tool for achieving a more holistic perspective on issues related to health care experiences and quality than quantitative analysis methods can offer, thus enhancing the applicability and utility of HSR.

METHODS

Subjects and Recruitment

Potential focus group subjects were identified through participation in the previously mentioned school-based study (R01 HS 010317). This study, which examined health care access, primary care, and health-related quality of life, surveyed parents at 18 elementary schools in the San Diego Unified School District. Schools were purposively sampled based on the proportion of target-language speakers (Spanish, Vietnamese, and Tagolog) and heterogeneity of SES as measured by percent of the school student body eligible for Federal free or reduced-price lunch programs. At selected schools, classes were randomly selected within grade. As part of the survey parents indicated their primary language and their willingness to be contacted for further research.

The sampling frame for the present project's focus groups, convened in Summer 2002, included parents who reported their child having a chronic health condition, who spoke English or Spanish, and who had consented to further contact (n = 246). Children with chronic conditions require more health care than normal and so it was assumed that their parents would be a rich and efficient source of data for understanding and developing strategies for overcoming barriers to care (Garwick et al. 1998). (3) The children had a wide variety of chronic conditions; asthma was the most common.

To ensure that parent perspectives were represented regardless of English proficiency, focus group parents were randomly sampled within language (language and other demographic data used to describe the present project's sample were collected as part of the original school-based research). Language groups for the present project were English and Spanish; only about 10 percent of San Diegans cannot speak these languages (Sanchez 2001). Because this part of the project (identifying barriers to care) was generative, no further a priori sampling stratification was done.

The study design called for quota samples of 10 English- and 10 Spanish-speaking adults who were parents (or guardians) of children with chronic health conditions. Given that each parent had extensive experience in health care seeking because of the children's conditions, and given the particular focus group methods that we would use, a sample size of 20 was deemed sufficient both for instrument development and for eliciting the depth of information necessary for descriptive and hypothesis-generating purposes, which do not depend on large numbers for power.

Potential participants were each assigned a random number and contacted in the random number order by telephone, or mail (if no working number), until quotas were reached. The study was described, and potential participants were invited to participate. In the course of achieving our quota of 10 Spanish- and 10 English-speaking participants, we cumulatively attempted to contact 27 English speakers and 32 Spanish speakers, or 59 of the 246 eligible adults. Of these 59 individuals, 20 (34 percent) had moved, leaving no further contact information. Of the 39 actually contacted, six (15 percent) refused.

We scheduled the 36 consenting individuals for focus group participation, knowing that some would not actually attend. Thirteen (33 percent) never showed up, even with repeat appointments. When 20 (51 percent of those ever actually contacted) had participated, recruitment efforts ended.

Focus Group Processes

Focus groups were conducted by a pair of facilitators (a moderator and a recorder) trained in health promotion and education and in focus group methods. Three focus groups were conducted in each language, for a total of six focus groups. On average the groups each included three participants. Small numbers of participants can be useful in experience-oriented research; smaller groups can yield less normative rhetoric and provide time for more in-depth discussion regarding particular experiences than is possible with large numbers of participants (Morgan 1997). (4)

The groups met for 2-hour sessions, which were audiotaped with participant permission. The focus groups were informal and the moderator was nondirective so as to generate as much experience-based narrative data as possible and encourage 'sharing and comparing' (Morgan 1997, p. 21) among the participants (generation of data through the interaction of participants is a key feature of focus groups). After informed consent was procured, open-ended discussions focused on (1) families' experiences in general with the health care system, (2) barriers to access, use, and receipt of quality care, and (3) strategies families have used to overcome these barriers. This 'funnel based' forum (Morgan 1997, p. 41), with its broad beginning and narrower ending, allowed for free discussion while ensuring an acceptable degree of comparability across groups. Families were compensated for focus group participation with $50 in gift certificates.

Data Analysis

A qualitative content analysis protocol designed by the first author was used to analyze the focus group tapes. (5) While not generally applied in HSR, the various techniques drawn on are commonly used in anthropological discourse analysis for identification, interpretation, and ordering of themes; a review of such techniques is found in Ryan and Bernard (2003). The protocol included triangulation through the inclusion of multiple researchers (Patton 1999) to offset possible concerns regarding subjective bias. It is described in full in a Web-based appendix to this article.

The form of analysis used for this project goes beyond simply counting (quantifying) terms or phrases. It aims to characterize the variously connected frames of reference that parents use to order their perceptions and understandings of the concepts (in this case, barriers) represented in those terms or phrases. These can be derived by carefully and systematically attending to the narrative contexts in which particular concepts are mentioned (Glaser and Strauss 1967; Bernard 1995; deMunck and Sobo 1998; Strauss and Corbin 1998; Patton 1999; Quinn 2005).

In brief, study staff listened systematically to all focus group tapes and carefully listed all barriers mentioned, bearing in mind the situation-specific contexts in which they were brought up (i.e., the narratives or story lines that they were part of) as well as negative cases or cases in which barriers were surmounted or mitigated. Rather than sorting listed barriers into a priori categories, researchers met together and named potential categories that emerged from their repeated, iterative reviews of the barriers. Category validity was ensured through a team approach in which all research team members individually considered and then together discussed and arrived at consensus regarding barriers and categories, and their definitions. In keeping with the methodological focus on meaning and experience, much discussion centered on the situation-specific contexts in which the various contested barriers were encountered.

In tandem with the category-development process, a process-based, experientially motivated conceptual model of parents' experiences of barriers to care was generated. The model sought to capture the experience of trying to access health care, and so the categories were arranged bearing in mind the temporal sequence of the clinic visit, the basic spatial parameters of the experience, and the cyclical nature of health care utilization. As categories solidified, the model was refined in an iterative and intersubjective process of reflection on the focus group findings (including negative cases) and in which rival hypotheses regarding the connections were proposed.

FINDINGS

The Sample

The sample is described in Table 1, which compares the sample to those eligible but not contacted, those who refused or did not show, and those who could not be contacted. The four groups were similar in terms of the relationship of the survey respondent to the child ([chi square](15) = 19.5, p = .192) and insurance status ([chi square](3) = 3.6, p = .31) (a proxy for access; see Halfon, Inkelas, and Wood 1995). The four groups differed, however, in terms of race/ethnicity ([chi square](9) = 22.3, p = .008), language spoken ([chi square](9) = 26.2, p = .002), and maternal education ([chi square](6) = 20.3, p = .002), because of the quota sampling scheme, in which Spanish speakers were oversampled. That is, those with whom we attempted contact (who fell into the categories of 'participants,' 'no show or refused,' 'could not contact') were more likely than the 'eligible not contacted' group to be Latino or Spanish speaking. Also, those contacted had a lower overall maternal educational attainment (Asians and whites were more likely, in the sampling pool, to have a college degree or greater; Seid, Stevens, and Varni 2003). (6)

Penetrating, Navigating, and Completing a Journey through the Health Care System

Findings from the BCQ can tell us how many people from what groups encountered which barriers and to what degree. These are important data. But what is the meaning of such quantifications in the everyday lives of respondents? Figure 1 depicts the process-oriented, experientially motivated conceptual model of barriers to care that can inform how analysts might answer this question.

[FIGURE 1 OMITTED]

In Figure 1, the barriers categories derived from the focus group transcripts are depicted as fitted against focus group members' expressed understanding of the formal U.S. health care system, which is seen as something that must be penetrated or to which entry must be gained before people can get access to or use system resources. The left-to-right flow of the model reflects the temporal sequence of the clinic visit as parents described it, as well as the basic spatial parameters of the experience. Importantly, it can accommodate the fact that no visit exists in isolation; each visit influences the experience of the next. The major barrier domains that emerge against this ordering are listed in Table 2.

In Figure 1, the formal U.S. clinical health care system occupies the middle rectangle or box. To the left are prerequisites to potential system access: having insurance, documentation (e.g., a social security number or proof of legal residence), money, language skills, and navigational skills, which include knowing the landscape of the system so that one can move through it (Sobo and Seid 2003). (7) And, as will become clear when the various parts of the system are discussed, the system itself was characterized as arbitrary, fragmented, and not child friendly (this is indicated in the horizontal bar superimposed on the rectangular health system box).

More narrowly encountered barriers are listed in the figure in relation to the parts of the clinical care system in which they are most often found. For example and to begin with, once a child's caregiver starts to try to penetrate the system (middle box), he or she has to negotiate access to a care site, for example by phone, as shown in the column just inside the health system box (under 'System Access'). This is often easier said than done: 'It's hard getting through. You have to call when you don't have anything else better to do with your time.' And 'Talking to someone who seems to have some kind of authority is impossible.'

Phone trees are very discouraging ('Push 1 for Mira Mesa, press 2 for da da da da. Is there a person at the end of this or what?!'). When a call from the health system is expected, 'You better be there when they call because if you do call their little extension, you still go through those phone trees.' Sometimes, 'the machine tells you it is full, from messages; it can't receive anymore.'

Questions relative to access go beyond whether one can make contact with the right health care worker by phone. They include (again, as shown in the first column inside the health system box) whether a timely appointment can be secured and whether office hours are compatible with a family's schedule. Having to wait too many days or weeks was definitely a problem; so were scattered appointments for parents of more than one child or a child with multiple providers.

Once a suitable appointment has been secured, a parent has to get him-or herself and the child to the care site (Getting There), which often entails long bus rides with packed lunches. Parents leave extra early, 'just in case the bus stops too many times.' Further, a parent has to balance other responsibilities (Balancing Priorities), such as to other children, or to a job boss, or to get dinner on the table by a certain time. As one parent said, 'The day that I have an appointment to the doctor was the time that I wouldn't pick up my house, wouldn't make meals. I would get home [and my husband would ask], 'So what did you do all day?' 'I was with the doctor!' [laughs].'

A parent who relied on a school lunch program and had multiple children and no after-school care-taking help explained, 'I have to take all of them and then I don't feed them ... I would have to pay to feed them.' Missed school also could be problematic, although it did mean that other children could be in school while the ill child was being taken to be seen.

Once inside the care site, represented by the larger square on the left side of the clinical health system rectangle, there is the front office to get through (Front Office, left side of smaller square). In the front office, parents reported office staff issues, such as encountering uncaring staff ('They answer you with a rude tone or sometimes they don't pay attention to you. They pretend that they haven't heard you') and overt prejudice. This included socioeconomic prejudice ('The more poor you look, the worse you are treated'), lifestyle prejudice (from a reformed drug user: 'They look at who they think I am'), and ethno-racial or linguistic prejudice. Rudeness was also encountered in the clinical staff, as the following story, which also contains elements regarding care quality and safety, demonstrates:

  

They were doing some tests on my girl and I took them [the papers]

to the lab and I always tend to look and see if it is the correct

name and everything and I noticed that it was the lab work of another girl. So then I went to complain with the nurse and she was

not pleased that I had found out this. I told her, 'What if you had given my girl another medicine,' and then it seemed that the nurse got upset and she thought that I didn't speak English and she went to complain with the receptionist.... I then understood everything that they were saying.... It was ugly how they were talking about me.... They.... speak Spanish but [are still] racist.

After dealing with reception and initial intake procedures, participants experienced long waiting times, during which, for example, 'The sick one is screaming and the well one is screaming because the other one is pulling their hair.' Then, they are left to wait again, with the sick child undressed in a cold examining room, which some think exposes ill children to possible further sickness. And, in the words of one participant, 'Then they have the audacity to knock on the door and ask you if you are ready.' Another explains:

    The  appointment is three o'clock; it's already three-thirty you  are    still there on the waiting room ... forty-five minutes before  they    call you to go inside. And then they will let you sit there....  There is an assistant ... and then you have to wait again for the  doctor ... another twenty minutes in there.... The kid is so sick,    he  will fall asleep in there already.... And then you have to pay    extra  on parking ... and they will only look and see your kids for    like  five, seven minutes. 

When finally seen by a doctor (Physician Visit; right side of small square in the clinical health care system box), the parents sometimes encountered differing health beliefs or ignorance about health facts because of the context of health system practice. For example:

    The doctors say that  we should give our children a bath when they    have a fever, but I have  noticed that in a hospital everything is    closed. We have the beliefs  about air currents, and the person is    going to be bathing the child  when there are many air currents in    the home, the air comes in, the  baby will die. 

Parents also dealt with what they saw as inexcusable incompetence:

    I'm  expecting that [the doctor] is going to check [the boy's] ears,  his temperature, I don't know--he doesn't do anything. He  looks in    and asks how he is doing and I say 'The same,'  then he prescribes    another thing.... Or they go to their books and  start reading, to    see what they are going to prescribe, what does he  have--If I    had a book on my side, then I wouldn't need to come  to the doctor. 

Parents commonly reported being practiced on, or being treated by doctors who were not trained in the right specialty, or who fixed the symptom but did not deal with the cause. At this point, if not before, parents sometimes albeit infrequently) packed up their children and left:

  I don't know how many things he was asking her, things that are not  appropriate. That time I got angry. She felt bad. She goes there  like--she had a high fever, she had a headache, she had vomited,    it  was the time when a virus was going around. That time, I got    mad and  I left the clinic.... If one is not happy, comfortable,    what can we  do? I'm not going to start arguing with them, I    wouldn't  win. Simply, if I'm not satisfied, I'm going to leave and  not go back. 

Not all parents were so immediately assertive. Parents sometimes endured intimidation and communication problems (with doctors not listening and not explaining things). In one story, the doctor 'didn't introduce himself ... had a brusque manner ... just prescribed medicine not saying what it was for ... and he was gone.' In another, the doctor asked the parent, ''Can't you get [the child] to stop crying?' He is a child.... If you don't have the bedside manner to deal with kids or [unclear], you shouldn't be in this business!' Another parent explained how hard it is to listen to instructions and participate in decision making: 'The doctor is giving medical attention ... with a toddler or preschooler for me, you are there and you are, 'Uh-huh, uh-huh' [to doctor]; 'Stop doing that' [to child]; 'Uh-huh'; 'Let go.''

Another problem was a lack of consistency in doctors: 'They don't even let me know that they have changed the doctor, when I'm already inside, an unknown doctor comes in.' The lack of consistency was problematic because each physician seemed to start from scratch, and 'They don't read the chart, because if they read the chart, they wouldn't ask you the same questions over and over' or 'prescribe their own prescription' instead of what the previous doctor had prescribed.

The referral system also proved problematic, as was coordinating information between insurer and provider, and among providers. 'They give you one thing and tell the doctors another thing, so I never know' said one participant. Another told of a particular situation in which a referral to an allergist was needed:

    My doctor is actually with [Group X] and she.... goes to  put a    referral in and would like to go through a [Group X] doctor  'cause    there is communication between the two and [the insurer]  will come    back and say, 'No, you have to go see a [doctor in  Group Y].' And    they just won't talk. So when I go to the  primary care--it's like    what happened--I mean, I'm not a  doctor. I'll say, 'Well, basically    they did this.' She  will ask me questions and it's like--you know,    I don't have  the answer. And there is no paperwork. I try to get    copies of things  and [Group Y] is obviously losing their records    on their end. So  it's like, I go in a circle. 

And just getting a referral is hard enough: 'It's usually me that has to get on their back and basically say 'come on''; 'I had to wait such a long time for him to be approved [for an operation] and the boy used to complain, that his ear hurt a lot and blood would come out of it, out of the ear, and I would take him to the clinic and they would tell me, 'No, until he is approved.'' The problem is not always with the payer, as the following story shows:

    We went to--my daughter, she broke her, we  didn't know it was    broken, she had a sore wrist so somehow we  ended up.... We  

didn't get particularly good service. We knew that something was wrong with it, but he wouldn't authorize.... When I asked him for a splint, there was nothing.

Throughout the health care journey, which often ended with perceived suboptimal care leading to a sense of deprivation and distrust ('marginalization'; see Seid et al. 2004; Kreps 1996), parents endured a sense of the system's fragmentation and arbitrariness. In other words, and as represented in the horizontal bar running across the health system box in Figure 1, rules changed from visit to visit ('change like the wind'), fees were inconsistent ('It all depends on who is there'), records were not sent from the lab to the office, referrals were delayed or not forthcoming, and paperwork went missing.

One child sent for an X-ray to an off-site location arrived there only to find that they were closed for the day. Another, according to her parent, 'kept having to get multiple blood tests and she hated that--she is only four--getting poked too many times and that is because they lost the records.' One caregiver received a prescription for medicine for her asthmatic grandson from a first doctor, only to have the next doctor who saw the boy disagree with the prescription that she had already spent her co-pay money on. She said, 'He goes, 'Oh ! don't want you to take that medicine.' And he dumps it in the trash. So, if you have a co-pay you then go, 'Oh.' So I learned my lesson.'

Access and care site features affect outcomes, but the opposite also can be the case, as shown by the arrow leading back from the outcomes box on the right in Figure 1 to the beginning of the visit chain (left side of figure). A negative feedback loop can be created when parents experience a sense of relative deprivation, distrust the system, and have their children still not cured. This can underwrite poor adherence and low expectations, and undermine the desire to return to the system for follow-up or repeat visits.

Surmounting Barriers

Barriers could sometimes be mitigated or circumnavigated if encountered. This generally involved learning, and then playing by (or figuring out how to positively manipulate) the rules of the system. ('You have to jump through hoops and you just got to play the system to be able to get what you want.')

It was true that the arbitrariness noted above, experienced perhaps because of quickly changing or capriciously enforced rules, or disagreement between various parts of the system, could make understanding the rules problematic; however, in some cases it could be done. For example, one participant shared what she had learned regarding asthma inhalers with her focus group: 'Always make sure you tell them to put at least two or three on a prescription, so you don't just go and get one inhaler for ten or twenty dollars which your co-pay is. You can get like three inhalers under the same prescription, so some doctors, you have to tell them that is what you want. They go, 'Oh, oh I see.''

Managing to get a direct number for a physician office was a strategy some used to get around the centralized appointing system that one common insurance carrier offered. Similarly, to get to an unlisted extension, one might call a known extension, and 'say, 'Oh, I'm sorry, I was trying to get this department.' They'll put you right through. That is how you do it.'

But such 'functional acculturation' to the health system (Sobo and Seid 2003) was only something that could be acknowledged by caregivers in hindsight. In other words, with few exceptions (completing paperwork being the primary one), not knowing how to do something was not listed as a barrier to care because people did not know that they did not know how to do that thing until someone else showed them what they could have been doing all along, or they happened on a strategy that advanced their quest for high-quality pediatric care.

'A lot of times,' one parent said, 'I'll kind of go around the receptionist and talk to them in the back.... They will tell me what other people have done to get what they needed to get.' And parents sometimes had to 'be very, very assertive'; for example, 'even getting a referral, it's usually me that has to get on their back and basically say, 'Come on.'' One parent related a situation requiring her to 'get ugly,' or take loudly assertive action. She said, 'You shouldn't have to do that.' But as another noted, 'We had to fight so hard for our children.'

DISCUSSION

Implications

The conceptual model that we have described represents the experience of the parent trying to access health care for his or her child. It illuminates the findings of the quantitative project that it stems from, enriching our understanding of the fact that, for example, many of those reporting poor primary care experiences belong to groups traditionally thought as having good experiences of primary care. It contributes to the conceptualization and operationalization of the construct of barriers to care described in the introductory section of this article. It furthers the conceptualization of barriers to care as a multidimensional construct, as potentially impacting children's health care at several points in the care process, and as distinct from, yet related to SES and race/ethnicity. Importantly, it addresses, in depth, the processes by which disparities may arise. It thereby supports a more robust, practical, and actionable understanding of the construct of barriers to care.

The model also contributes to the ongoing discussion contrasting the patient experience with patient satisfaction. A distinction has been made between self-reported experiences with health care delivery and ratings of satisfaction with health care delivery (e.g., 'How satisfied were you with your wait time?') (Flocke 1997; Starfield et al. 1998). Satisfaction ratings depend upon an individual's expectations, such that high satisfaction may result when low expectations are met (Dougall et al. 2000). For example, if one expects to wait 4 hours to be seen, then a 3-hour wait time might be rated as very satisfactory. Therefore, satisfaction research yields few suggestions regarding how the health system can be improved (Starfield et al. 1998). Patient experiences, however, can be compared with specific prescriptive criteria (e.g., that wait times be less than 1 hour) (Bindman et al. 1996). As such, criterion deviations can actually index areas for improvement. Parents' experiences of pediatric care, as we have described them here, have many implications then for organizations interested in improving the care experience.

While the stories represented in the model's parts may be very familiar to those working on the front lines of health care, in HSR circles the phrase 'barriers to care' often serves as shorthand for lack of insurance or of English proficiency (but see Friedman 1994; Halfon et al. 1995). Importantly, the experience of low-quality care is not recognized as a barrier to future care because traditional HSR barriers models assume that all care is good care. The model generated through our research questions that assumption.

The model also contributes to the barriers to care debate because it is comprehensive and patient centered. It considers the entire health system. It includes items generated by parents themselves, as opposed to health services researchers, and positions parents as quite capable of being innovative, active care team members, as opposed to passive recipients of care. It shows that lack of health care services background, or low functional biomedical acculturation, is itself a major barrier to maximizing service provision (Sobo and Seid 2003). Further, it considers the outcomes of experiencing the barriers noted and shows how they contribute later on to suboptimal use of the system by parents.

Methodological Keys

The model could not have been created without the adoption of a more holistic orientation toward the data than is typical in HSR. This entailed a focus group process designed to garner extensive experience-based narrative data from each participant, a concentrated qualitative data analysis phase, and the inclusion of data collection staff in the analytical endeavor.

All five of the category-development sessions were conducted within a 10-day time period and all investigators were available to concentrate on the process. Immersion in the analytic endeavor is key to the validity and reliability of the products of the data reduction process used. It is crucial that, for this type of analysis, meetings are closely sequenced, time to reflect on meeting discussions and data in between meetings is provided, and researchers are not distracted by other substantive projects or data during the intensive analytic phase.

Qualitative research is not amenable to outsourcing the various components of a project because of the centrality of holism and interpretation to its epistemological basis. This point is paramount. Traditional ethnographically oriented qualitative researchers demand research designs in which one researcher conducts all study functions. For HSR, at the least, each team member's participation in the analysis must be informed by actual participation in data collection. No shortcuts can be taken.

Limitations

The model does have limitations. For example, although it accounts for experience with the system and positions each visit as part of a cycle rather than an isolated incident, it does not account for the impact of vulnerability factors such as SES; it therefore cannot show how more vulnerable parents experiences are in any way different from those of less vulnerable (e.g., wealthy, white) individuals. The difference between, for example, poor and affluent parents' experiences may lie in the quantity. (8) of barriers encountered rather than in what the barriers are to begin with. Encountering more barriers may underwrite marginalization, the internalization and personalization of disempowering experiences within the health care system (Kreps 1996). This may be especially so in persons already marginalized by mainstream society because of skin color, language, poverty, gender, or other factors. Marginalization may in turn lead to low adherence and limit interest in pursuing follow-up care, negatively impacting health outcomes. We can also speculate that parents with little bureaucratic experience and little scientific background may feel more marginalized than others by the health care system and therefore may find care barriers particularly daunting.

Secondly, the model cannot represent every parent's actual and specific personal experience. This is normal in studies where findings are aggregated as were ours. Moreover, because the model is a composite, it reflects a general schema inferred from the analysis of all participants' explications and comments. In other words, not all parents had such explicit conceptualizations of the health care system.

A related limitation stems from the fact that we did not use participatory methods in drafting the model. Some of the category labels, such as 'navigational skills,' are our own; our choice to impose them, and our use of clinic-isolating temporal and spatial frameworks to help organize the categories, reflects our aim to generate a model that has broad health care applications, and that is, accordingly, comprehensible to health care workers. (9)

Having said that, our focus on the parents' perspective may need further translation for actual use in health care settings. That is, because of their experience within the system, health care workers may not be able to see barriers from parents' point of view. Despite ample evidence regarding organizational and provider contributions to disparities in care (van-Ryn and Burke 2000; Smedley et al. 2003; Good et al. 2003), health care workers may hold an occupational bias and exhibit defensiveness rather than empathy. For example, two physicians viewing a first draft of the model pointed to patients' missed appointments and other forms of perceived nonadherence in defense of some of the practices that parents found problematic, such as overbooking. The limitations of such bias are part of the reason for the present interest in patient-centered care that this research seeks to address.

CONCLUSION

Because of our qualitative approach, findings from the original quantitative study suggesting that traditional marker variables failed to capture substantial variation in primary care experiences were illuminated. Further, the quantitative BCQ generated as part of the research described here (Seid et al. 2004) is a better tool than it otherwise would be. (10) Its questions are more reflective of parent experience than they would have been using traditional (quantitative) content analysis alone. And in future research using the BCQ the validity of interpretations of BCQ findings can be enhanced through use of the model of parent experience we derived.

Our goal in describing our experientially motivated conceptual model of parent experience is not to shift the competency burden to the shoulders of already vulnerable and disenfranchised health care consumers. Rather, it is to raise awareness in the biomedical world of the essential strangeness of the system, and to create a bridge between two worlds--a bridge that can lead to measurable increases in quality of care.

Our focus on parental report also does not imply that the locus for intervention must be at the individual level. Although barriers are encountered on the individual level, they are generated and maintained by, and organized according to, higher-order social structural arrangements (Singer et al. 1992; Loustaunau and Sobo 1997). Although we cannot, in the context of this research, alter macrolevel social structures, we can identify modifiable barriers that have the potential to affect entire patient populations, not just individual families. Hypotheses generated using the model can be tested in future HSR studies.

For example, one popular intervention aimed at increasing patient centeredness and decreasing health disparities is cultural competence training (Brach and Fraser 2000). But as the findings described here show, health care workers are not the only people who need to become competent in cross-cultural exchange. Policies and programs must ensure that patients and families, too, are provided education and assistance so that they can navigate the health care system, which, as anyone who has known or has been a patient knows, is a culture unto its own. The barriers to care model that we have described can help us to gain insight into (testable) ways to better equip all health care consumers with the cultural competence necessary to navigate the biomedical world (Sobo and Seid 2003).

The information provided in this article will be of value to health care workers, program planners, and policy makers who seek to understand why parents sometimes act in ways that seem, on the surface, nonsensical or counterproductive, and to address parents' needs in a truly patient-centered fashion. Moreover, they will be useful to those who seek to improve parents' experiences of, and thereby change their responses to, the pediatric health care system. Doing so will help to reduce health disparities by increasing each child's likelihood of receiving the highest quality health services available.

ACKNOWLEDGMENTS

The 'Barriers to Care for Chronically Ill Vulnerable Children' project that this paper describes was funded by the Agency for Healthcare Research and Quality (R03 HS 013058; Michael Seid, PI), as was the original study referred to (R01 HS 010317). Gabriela Hussong assisted with the focus groups. Versions of various parts of this article were presented at the 2001, 2002, and 2003 annual meetings of the American Anthropological Association; panel organizers and co-organizers included Lauren Clark, Elisa Gordon, Suzanne Heurtin-Roberts, and Robert Schrauf. Final revisions were completed with the support of Allen L. Gifford, VA San Diego Healthcare System, Health Services Research and Development. The authors have no disclaimers or disclosures to report.

SUPPLEMENTARY MATERIAL

The following supplementary material is available for this article online:

APPENDIX S1. Qualitative Content Analysis Protocol for Developing Process-Oriented Models in Health Services Research: Case Example

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NOTES

(1.) We define vulnerability to poor health outcomes, after Aday (1993), as an individual's risk for poor physical, psychological, and social health. Vulnerability is often represented by social status (age, sex, race), social capital (family, community), and personal capital (SES, language ability) factors that affect the risk of poor health outcomes (Aday 1993). We would add to this vulnerability as measured by degree of functional acculturation to the biomedical system (Sobo and Seid 2003). Vulnerability has been shown to affect access, continuity, and care coordination (Newacheck, Hughes, and Stoddard 1996).

(2.) Another problem precedes analysis: many HSR focus groups are highly formalized affairs in which moderators take a very directive approach and rely on highly structured elicitation activities. Participants generally acquiesce to a directive moderator, keeping silent regarding ideas that do not overtly fit the moderator's data-limiting approach. In addition, formal exercises can occupy much of the focus group's time, eclipsing the time spent in open-ended, interactive discussion among participants.

(3.) Documented disparities are compounded for children with special health care needs (CSHCN), who use substantially more pediatric health care services than their healthy peers (Newacheck et al. 1998) and account for the majority of pediatric health care costs (Ireys et al. 1997). The importance of timely access to high-quality health care is greater for these children (Newacheck et al. 1996). Yet a substantial minority of CSHCN experience significant barriers to care (Newacheck et al. 1998), in particular to specialty care (Fox, Wicks, and Newacheck 1993; Newacheck et al. 1996).

(4.) Group size ranged from two (one group) to five participants. Some researchers may call small focus groups 'group interviews'; however, as focus group expert David L. Morgan (1997) notes, the term 'focus group' should be understood as an umbrella term, designating a ''big tent' that can include many variations' (p. 6). Morgan further points out that the purpose of a research project and field constraints are more important than the six to 10 participant rule of thumb in determining ideal group size. Although little empirical research has been done, Morgan endorses smaller groups when participants are highly involved in the topic at hand, and when researchers desire a clear sense of each participant's experiences (p. 42), as in this particular project (Morgan 1997).

(5.) The protocol was developed when it became clear that a priori (HSR) categories could not accommodate the data.

(6.) We used maternal education as a proxy for SES, as more educated mothers may be able to access care, communicate, and assert their child's needs more effectively (Heck and Parker 2002).

(7.) We do not discuss these prerequisites here because our focus is barriers to quality care for those who already have potential access to the health care system.

(8.) This is something that the quantitative BCQ will measure.

(9.) Participants' frameworks included other health systems, such as in Tijuana (Mexico), unauthorized allopathic systems or underground clinics encountered in the U.S., and complementary or alternative systems of health care. Further, some discussed hospital inpatient as well as outpatient and ambulatory care (the model focuses on the latter).