J Autism Dev Disord DOI 10.1007/s10803-017-3327-6
ORIGINAL PAPER
Parental Action and Referral Patterns in Spatial Clusters of Childhood Autism Spectrum Disorder David Schelly1 · Patricia Jiménez González2 · Pedro J. Solís3
© Springer Science+Business Media, LLC 2017
Abstract Sociodemographic factors have long been associated with disparities in autism spectrum disorder (ASD) diagnosis. Studies that identified spatial clustering of cases have suggested the importance of information about ASD moving through social networks of parents. Yet there is no direct evidence of this mechanism. This study explores the help-seeking behaviors and referral pathways of parents of diagnosed children in Costa Rica, one of two countries in which spatial clusters of cases have been identified. We interviewed the parents of 54 diagnosed children and focused on social network connections that influenced parents’ help seeking and referral pathways that led to assessment. Spatial clusters of cases appear to be a result of seeking private rather than public care, and private clinics are more likely to refer cases to the diagnosing hospital. The referring clinic rather than information spread appears to explain the disparities. Keywords Autism spectrum disorder · Diagnostic disparities · Help-seeking behavior · Social networks · Healthcare · Physician referral
This research is based on Dr. Schelly’s PhD dissertation at the University of Wisconsin, which he submitted in May 2016. * David Schelly [email protected] 1
Present Address: Department of Occupational Therapy, Clarkson University, Box 5883, 8 Clarkson Ave., Potsdam, NY 13699, USA
2
Hospital Nacional de Niños “Dr Sáenz Herrera,” CCSS, Child Developmental and Behavioral Unit, San José, Costa Rica
3
University of Costa Rica, San José, Costa Rica
Introduction Disparities in the diagnosis of autism spectrum disorder (ASD) continue to emerge, and it appears that sociodemographic factors are associated with access to ASD assessment (Dickerson et al. 2017). There is some evidence that either physician referral behaviors or parent help-seeking behaviors lead to racial disparities in diagnosis (Emerson et al. 2016). One popular theory is that social networks may be driving these disparities. Social networks have long been suspected to influence help-seeking behaviors for a range of illnesses and diseases, including how, why, and when individuals seek professional care (e.g., Deri 2005; Pescosolido 1991; Pescosolido et al. 1998). However, precise social network mechanisms related to specific health inequities are difficult to identify, especially because social networks are strongly associated with a range of other personal and structural characteristics (McPherson et al. 2001) that are also associated with health outcomes. To explain the diagnostic disparities of ASD, the presence of a social network mechanism is consistent with the narrative of a seemingly ever-increasing prevalence: sometime in the 1990s, psychologists and physicians accounted for an increased prevalence by pointing to “greater public awareness” (Gernsbacher et al. 2005) and “growing awareness and knowledge among parents” (Wing and Potter 2002). After identifying spatial clusters of diagnosed cases of young children (Mazumdar et al. 2010), sociologists argued that autism awareness may lead parents to recognize symptoms and pursue help (Fountain et al. 2011), and they suggested the presence of what we call a “cul-du-sac effect” on the diagnosis (Liu et al. 2010). The idea is that parents are meeting in and around the neighborhood, and parents of diagnosed children are encouraging parents of symptomatic but undiagnosed
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children to seek medical care. In this view, “knowledge diffusion” leads to the spatial clustering of cases (King and Bearman 2011). Subsequent studies (e.g., Keyes et al. 2012; Liu and Bearman 2015; Mazumdar et al. 2013) and popular accounts, such as Weintraub’s (2011) commentary in Nature, have argued that parents’ exposure to information increases their children’s likelihood of diagnosis; one argument is that populations of parents with relatively low prevalence rates (i.e., Hispanics) simply require information about ASD to reduce or eliminate the disparity (e.g., Colbert et al. 2016). Yet there is no direct evidence that information spreading through social networks is a factor in the helpseeking behaviors of parents. This paper explores the help-seeking behaviors and referral pathways of parents of diagnosed children in one of two countries in which spatial clusters of cases of childhood ASD have been identified. In our study area of Costa Rica, two locales far from the centralized diagnosing clinic were identified as having clusters of low severity cases (parents of severe cases may not require information to pursue professional help) at a time in which there was no evidence of a recruitment bias in the clinic (Schelly et al. 2015). Similar to studies in the U.S., children in higher socioeconomic status (SES) districts had an elevated risk of diagnosis, potentially an indicator of “local resources and the availability of health-related information” (Mazumdar et al. 2013, p. 88). We interviewed parents of diagnosed children, focusing on lay and professional social networks that influenced parents’ decisions to seek professional help for early behavioral abnormalities in their children. These social networks may explain the emergence of diagnostic inequities for a disorder that was only recently adopted by the medical establishment in Costa Rica.
Background Help‑Seeking Behavior Despite a long-held interest in how social networks affect health, sociological studies that directly identify specific mechanisms leading to diagnosis are rare (Smith and Christakis 2008; Berkman et al. 2000).1 Social influence 1 An exception is stochastic actor based models, where studies have considered topics such as the influence of peer networks on adolescent alcohol use (e.g., Mundt al. 2012) and even self-reported behaviors associated with attention deficit and hyperactivity disorder (ADHD) (e.g., Aronson 2016). Importantly, these studies require probability samples and longitudinal data that are difficult to come by; arguably the best existing data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health) study. Data on ASD may be impossible to acquire in the U.S. because of sampling limitations.
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mechanisms, which include processes by which individuals variably recognize symptoms and decide to seek help (e.g., the cul-du-sac effect), are difficult to identify empirically. While interest in the topic has waned (see Pescosolido et al. 2007), medical help-seeking research in the 1960s and 1970s focused on social networks. Freidson (1960) speculated that future patients, after experiencing symptoms, first self-diagnose and explore home remedies; if the symptoms persist, individuals consult with “friends, neighbors, relatives, and fellow workers” for advice, but only in the “daily intercourse, initiated first by inquiries about health and only afterward about the weather” (1960, p. 376). These casual advice-seeking interactions in lay referral networks were thought to lead to the professional system of physicians and healthcare professionals, where would-be patients move up a “hierarchy of consultants” before they penetrate the professional system (Freidson 1961, pp. 104–105). In various settings, research in the subsequent two decades suggested that strong lay network ties (especially kinship ties) could actually lead to help-seeking delays (e.g., McKinlay 1973; Horwitz 1977; Birkel and Reppucci 1983), presumably because these contacts provide individuals with information and advice that lowers their need for professional guidance. This suggests that in some cases, lay social networks inhibit help-seeking, producing a negative effect on diagnosis. More recent studies have discussed disparities in helpseeking behaviors more broadly. In the U.S., help-seeking behaviors tend to vary in similar ways as health disparities in general, with low SES families (Pavuluri et al. 1996), Blacks (Hillemeier et al. 2007), and Latinos (Gerdes et al. 2014) being the least likely to seek help or receive diagnoses for their children. Drawing on the early social networks and help-seeking literature, one explanation is that these groups have stronger familial ties and rely heavily on family and friends for medical advice, thus inhibiting their professional help-seeking. An alternative is that lack of knowledge of specific conditions inhibits help-seeking behaviors (e.g., Hillemeier et al. 2007). For ASD in particular, Zuckerman et al. (2014a) argue that knowledge gaps could explain diagnostic disparities between Latinos (and other minorities) and non-Hispanic whites in the U.S., specifically because parents with less information about ASD may not seek professional help when their children present with symptoms of ASD (see also Iland et al. 2012; Magaña et al. 2013). The existence of a knowledge gap is essential to a cul-dusac hypothesis in ASD. The idea is that the number of lay consultations alone should increase the likelihood of diagnosis, explaining some of the well known disparities illustrated by Liu et al. (2010). More specifically, do parents of symptomatic but undiagnosed children discuss their children’s language delays with their friends and family? And if so, does the arrival and diffusion of ASD information in these
J Autism Dev Disord
lay social networks lead to local epidemics of diagnosis? Alternatively, do these lay consultations actually slow the diagnosis by inhibiting professional help-seeking? Study Site The present research focuses on the help-seeking behaviors and referral pathways of parents of children in Costa Rica who are later diagnosed with ASD. The presence of a cul-du-sac effect on ASD diagnosis in Costa Rica is highly plausible: an information campaign in the mid-2000s targeted parents, teachers, and clinicians to obtain referrals for a genetic study of ASD (see McInnes et al. 2005), and in a 4-year period 5 years after recruitment had concluded, low severity cases appeared in spatial clusters (Schelly et al. 2015). During this time, there was universal healthcare for children, and all patients would require a diagnosis through the public system to receive referrals to therapies and special education schools; there was also only one diagnosing public clinic, thus removing clinical variation in diagnostic procedures as a possible confounding variable. Finally, ASD in Costa Rica continues to be underdiagnosed,2 suggesting that many parents of symptomatic but undiagnosed children are vulnerable to any source of influence.3 Several cultural factors may be relevant to parental helpseeking in Costa Rica. First, familism and collectivism among Latinos has been well-documented (Sabogal et al. 1987), suggesting that tight family structures may determine how information spreads or who is consulted for health concerns. Household sizes are often large, and grandparents and extended family commonly live in the same household as parents and children. Second, fatalism associated with religiosity is often cited, albeit with limited empirical evidence (Ramos-Sánchez and Atkinson 2009), as inhibiting helpseeking behaviors, in part because individuals may believe there is little the medical community can do for their problems. Third, the concept of personalism, including values of self-respect and respect for others, can be related to a strong respect for hierarchy and authority structures (Bermúdez et al. 2010); as a result, parents may be less assertive during medical encounters, potentially deferring to medical advice that may not take parental concerns seriously. Finally, developmental problems may be stigmatized as
2 The number of ASD cases in Costa Rica is quite low—we estimate between 500 and 1000 cases. There is no evidence that true prevalence rates of ASD vary drastically across populations (Baxter et al. 2015), so if we extrapolate from population studies (e.g., Kim et al. 2011), Costa Rica is severely underdiagnosed. 3 Based on interviews and ethnographic observations, parents tend not to search for or be influenced by information about ASD on the internet. Importantly, such an influence would not likely lead to spatial clusters.
signs of family dysfunction in the Latino community, possibly inhibiting help-seeking for problem behaviors (Zuckerman et al. 2014b). In the U.S., delays in ASD diagnosis among Latinos may be related to parents being relatively less proactive seeking additional professional help after initial efforts are unsuccessful (Magaña et al. 2013), possibly resulting in diminished access to developmental specialists (Zuckerman et al. 2013). There may be another cultural aspect to these delays as well: Pachter and Dworkin (1997) found that Latino parents expected social developmental milestones to occur at later ages than those of other cultural groups, in part because social deficits commonly associated with ASD may be less evident among families with more direct parenting styles common among Latinos (Blacher et al. 2014). These factors may all inhibit help-seeking behaviors outside the family. When parents do decide to seek professional help, access to healthcare services in Costa Rica is relatively equitable even across income categories, and over 70% of Costa Ricans are satisfied with the quality of healthcare where they live (Savedoff 2009). Healthcare utilization and coverage in Costa Rica are excellent even by international standards (Unger et al. 2008), especially for primary and preventive care. Healthcare services are available either through the dominant public healthcare sector or the newly expanding private sector (see Clark 2011; Cercone et al. 2010). The public sector is managed by the Social Security Institute, widely known as the “Caja.” All workers are constitutionally mandated to contribute to the Caja for public insurance, and additional funding for non-workers is provided by general tax revenue. Thus, the Caja provides near-universal coverage to the population (and to all children), and there are no copayments for services. The Caja manages a chain of primary care clinics, known as EBAIS clinics (Basic Teams for Integrated Healthcare), that provide the first level of care, with each clinic managed by a physician and serving approximately 1000 households. There are over 1000 EBAIS facilities nationwide, providing most of the population with equitable access to primary care (Rosero-Bixby 2004). The Caja’s secondary level of care includes 10 large clinics and 20 regional hospitals, and the third level includes three general and five specialized hospitals in San Jose. For children, the specialized hospital and diagnosing clinic for ASD is the Hospital Nacional de Niños (HNN). One of the primary complaints about the public system is the long waiting times,4 so some patients 4
The public healthcare in Chile in the early 1980s (before increased privatization under Pinochet) was comparable to Costa Rica’s system in several ways, including equitable access to primary care clinics that were burdened with long wait times. Interestingly, among patients in a clinic with a mean wait time of 4.2 h, Scarpaci (1988) found that wait times were unrelated to how patients regarded the
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or secondary care clinic, where those physicians provide a formal referral to the HNN (see Fig. 1). The help-seeking behaviors of parents of children who receive a diagnosis of ASD may include the private and public sectors before visiting the HNN. The question is how parents initiate entry into those sectors and then negotiate their way through the referral system. What role do social networks and the diffusion of health information play in this process?
Methods Data
Fig. 1 Referral pathways for children who are eventually diagnosed with ASD at the HNN in Costa Rica
utilize emergency rooms (Unger et al. 2008) or the private sector to avoid waiting (Rosero-Bixby 2004). With the signing of the Dominican Republic-Central American Free Trade Agreement (CAFTA-DR) in 2009, the market for private healthcare has slowly expanded (Clark 2011). The Superintendency of Insurance (Superintendencia General de Seguros, or SUGESE) has been established to oversee all insurance plans, and because SUGESE has been slow to approve private insurers, there are only a small number of private providers. Importantly, private healthcare utilization is somewhat more common in the highest income brackets (Clark 2011). Costa Rica’s private facilities, which are concentrated in the San Jose area and partially cater to medical tourism, only provide primary and secondary care (Muiser and Rafael 2012); for advanced specialty care, private clinicians must refer patients back to the public sector, especially if a formal diagnosis is needed to receive public services (e.g., language therapy or special education). Thus, the pathway from the private sector to ASD diagnosis involves seeing a private physician who is permitted to refer to the HNN, such as clinicians who also work within the public system. Nonreferring clinicians are known to write an informal referral note to be taken by parents back to the EBAIS clinic
Footnote 4 (continued) quality of care; proximity to the clinic and the quality of physicians were the important factors.
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The present study interviewed parents of children diagnosed with ASD, many of whom lived within the previously identified spatial clusters, about factors that influenced their helpseeking behaviors and referral pathways. Semi-structured interviews were conducted with 54 families of children who were recently diagnosed at the HNN. Medical file reviews were used to corroborate referral information and reported parental concerns at intake. The study was approved by the medical ethics board at the University of Wisconsin and the bioethics committee at the HNN. We approached parents who attended an appointment in 2013, which included newly diagnosed patients and follow-up appointments.5 After their appointments, the parents, typically only the mother, were briefly told about the study, and all parents expressed interest in participating. After obtaining written informed consent, interviews were conducted in Spanish in a private room at the HNN. In addition to demographic questions, parents were asked about their children’s behaviors, their knowledge of ASD throughout their children’s lives, and their knowledge of other individuals with ASD. They were also asked openended questions that explored whether, beginning at symptom recognition and leading up to the diagnosis, they had been influenced to pursue medical advice for their children’s behaviors, and whether, after receiving information about ASD at the informing interview, they had influenced other parents to pursue medical advice. Both lay networks (family, friends, neighbors, and work contacts) and professional networks (EBAIS clinics, secondary care clinics, private clinics, and daycares/schools) were considered.
5
The clinical characteristics and geographic distribution of our sample were similar to those of the country-wide population of children who were assessed for ASD from 2010 to 2013. Only the percentage of low severity cases was high compared to the 2010–2013 population (40.0 vs. 20.3%), which if anything will accentuate any cul-dusac effect.
J Autism Dev Disord
For information that may have influenced the respondents, we asked parents to detail the process of symptom recognition, including what led them to notice something different in their child, who they talked to during the process, and whether anyone suggested that they receive medical advice. They were asked to create a timeline of their visits to clinicians in which they discussed or were asked about the symptoms, and they were asked to expound upon the referral information that was available in the medical files to detail the lay and professional referral steps leading to diagnosis. Subsequently, parents were asked to detail how they may have influenced other parents to seek medical care for their children, focusing specifically on children the parents deemed to possibly have ASD. If applicable, they were asked to detail how often and in what circumstances they spoke to the families. To determine whether any information spread actually influenced other families, names were provided for later cross-checking in the medical records. Analyses This paper uses interview data from the 54 families with diagnosed children to explore parental action and referral patterns. The data come from a larger mixed methods project that included complete medical file reviews of 4 years of children diagnosed with autism, interview data with clinicians at the HNN and surrounding area, and ethnographic data from the HNN and from a small city far from the hospital where there was recently a disproportionately high number of cases identified. The present paper focuses specifically on interview data from the 54 cases, many of whom lived in a previously identified spatial cluster of cases. Two interviewers were present for each interview and took notes independently, compared and combined notes immediately afterwards, and made coding decisions based on what data could be meaningfully quantified. When possible, interview data were compared with data from medical files, including referral information and complaints at initial intake at the HNN. Stata 13.1 (StataCorp, College Station, TX, USA) was used for all statistical analyses. The following analyses are reported: (1) multiple regression models are used to predict age at symptom recognition and time between symptom recognition and the first visit in the developmental unit at the HNN; (2) influential lay and professional network connections leading to diagnosis are presented in a network diagram; (3) referral pathways leading to diagnosis are presented to illustrate private vs. public healthcare pathways; and (4) comparisons, using two-sample t-tests and tests of proportions, are used to compare family characteristics of mild vs. moderate/severe cases of ASD. First, the regression models provide an initial exploratory step for tracking down the diagnostic disparity producing mechanisms. Two sets of models predict the age at symptom
recognition and the referral time, or the time between symptom recognition and the first visit to the HNN developmental unit, for the 54 patients. The models include explanatory variables for SES (education and household income), patient characteristics (severe symptoms, comorbidities, and typical ASD symptoms), household characteristics (number in household, whether there is an older sibling, whether the father is in the household, and whether the mother works), knowledge characteristics (knowledge of autism before the diagnosis, whether individuals with autism were known before the diagnosis, and maternal and paternal age), and district characteristics (distance to the HNN, district population density, and district poverty level). Models for referral time also include a dichotomous variable for having utilized private care. If the mechanisms involve parental information, then symptom recognition should be predicted by parental knowledge of ASD and having known other children with ASD before the diagnosis, and these variables may also be associated with a shorter referral time. If the mechanism primarily involves referral practices, then patient characteristics should predict the referral time, and features of the household and parental knowledge should not predict the referral time. Second, for the two-mode network diagram of lay and professional network connections, the software Visone (Brandes and Wagner 2004) is used to represent the influential help-seeking and referral steps that were determined from the interview and medical file data. Figure 2 shows the stepwise process of how the steps were established for four hypothetical patients. First, each patient and any family (F), friends (fr), neighbors (N), or work contacts (W) (lay connections) that were meaningful for the parents’ help-seeking are represented as circles. “Meaningful” includes individuals whose advice was sought or individuals who provided advice regarding the associated health concerns for the child, regardless of its consequence. Figure 2 shows that the parent of patient 0 spoke with two family members, a friend, and a neighbor about her child’s condition. The parent of patient 1 (patient 0’s neighbor) spoke with a friend, the parent of patient 0, and a second neighbor. The parents of patients 2 and 3 each spoke with a family member. In the second step, all professionals at EBAIS clinics (E), secondary care clinics (SC), private clinics (Pvt), or daycares/schools (d/s) that played a meaningful role in the referral steps, including any informal consultation or advice, are represented as squares.6 Connections are established between all meaningful nodes for each child, and if a referral was made (formal or informal), the connections continue between the meaningful
6
Names of professionals were sometimes forgotten by parents, so the nodes represent the relevant organizations or clinics rather than the specific individuals.
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Fig. 2 Hypothetical influential lay (circular) and professional (square) network connections for children (in red) eventually diagnosed with ASD. (Color figure online)
node and the referred node, eventually showing the complete referral pathways for each patient. Figure 2 shows that patients 0 and 1 visited EBAIS and secondary care clinics (it is not yet apparent in step two that they were the same clinics), and the parent of patient 1 does so following the parent of patient 0’s suggestion. Patients 2 and 3 each visited different EBAIS clinics but were eventually seen by the same private clinician who referred onwards to the HNN. The third step is to link the connections for each patient where appropriate, so patients 1 and 0 are connected to each other, and patients 2 and 3 are connected to the private clinician; the diagnosing clinic is dropped because all patients in the sample are connected to it, obscuring otherwise central nodes that may have influenced many patients. Finally, step four shows the entire network with “stress minimization” (Ortmann et al. 2016), which reorders the network in the most efficient layout, thereby showing the most popular nodes in central locations; thus, any nodes with more connections will be more central, and nodes with few connections will be peripheral. When patients or lay connections are directly connected, such as between the first and second
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diagnosis (patients 0 and 1), the layout provides evidence of a lay network mechanism. When professional connections are central, such as between the third and fourth diagnosis (patients 2 and 3), the layout provides evidence of a referral mechanism. Third, public and private referral pathways are mapped out for all 54 patients, including their pathways via lay network connections that were mentioned in the interviews. Family SES is compared for groups of families pursuing pathways through daycare/school, private clinicians, and the public pathway via EBAIS. If a referral mechanism leads to spatial clusters, the most likely explanation exists in differences between the public and private sectors. Fourth, among the 54 cases, 22 were classified as mild because they attended regular school. This cutoff was used because the intention was to determine why difficult-todetect cases are geographically distributed in clusters, and fitting in with typically developing children is a good proxy for these cases. For continuous variables, we use two-sample t-tests, and for dichotomous variables, we use tests of proportions to compare these mild cases with the remaining
J Autism Dev Disord Table 1 Five models predicting age (months) at first symptom recognition among 54 children later diagnosed with ASD, Costa Rica (SEs in parentheses) Variable
Model 1: SES
Model 2: Patient char.
Model 3: household char.
Model 4a: knowledge
Model 5: district char.
Parental education Income Severe symptoms^ Comorbidities^ ASD symptoms^ No. in household Older sibling^ Father home^ Mother works^ Knowledge^ Knew individuals^ Maternal ageb Paternal age Distance (km) Pop. densityc Poverty Intercept Model F Partial Fd R2 Adj R2
− 0.41 (0.24) 0.49 (0.27)
− 0.41 (0.24) 0.45 (0.27) 0.00 (2.85) − 5.83* (3.17) 4.74* (2.74)
− 0.89** (0.25) 0.79** (0.25) 4.77 (2.85) − 7.65** (2.82) 2.87 (2.49) 0.47 (0.91) − 7.80** (2.64) − 6.89 (3.44) 5.26 (3.66)
− 0.83** (0.23) 0.66** (0.23) 5.34* (2.47) − 10.28** (2.53) 0.84 (2.20) 0.41 (0.79) − 3.37 (2.58) − 1.81 (3.20) 5.76 (3.19) 3.87 (2.36) 1.25 (2.47) − 0.65** (0.23) 0.04 (0.26)
20.13 1.92
19.29 2.12* 2.16 0.181 0.095
30.24 3.92** 5.25** 0.445 0.332
39.65 4.27** 3.30* 0.587 0.450
− 0.73** (0.23) 0.66** (0.22) 5.57* (2.47) − 10.07** (2.46) 1.10 (2.20) 0.42 (0.77) − 3.73 (2.51) − 2.43 (3.14) 5.39 (3.19) 2.48 (2.42) 0.56 (2.55) − 0.55* (0.23) − 0.03 (0.26) − 0.05 (0.05) − 0.84* (0.35) 0.10 (0.12) 39.77 4.12** 2.02 0.647 0.490
0.070 0.034
*p
ORIGINAL PAPER
Parental Action and Referral Patterns in Spatial Clusters of Childhood Autism Spectrum Disorder David Schelly1 · Patricia Jiménez González2 · Pedro J. Solís3
© Springer Science+Business Media, LLC 2017
Abstract Sociodemographic factors have long been associated with disparities in autism spectrum disorder (ASD) diagnosis. Studies that identified spatial clustering of cases have suggested the importance of information about ASD moving through social networks of parents. Yet there is no direct evidence of this mechanism. This study explores the help-seeking behaviors and referral pathways of parents of diagnosed children in Costa Rica, one of two countries in which spatial clusters of cases have been identified. We interviewed the parents of 54 diagnosed children and focused on social network connections that influenced parents’ help seeking and referral pathways that led to assessment. Spatial clusters of cases appear to be a result of seeking private rather than public care, and private clinics are more likely to refer cases to the diagnosing hospital. The referring clinic rather than information spread appears to explain the disparities. Keywords Autism spectrum disorder · Diagnostic disparities · Help-seeking behavior · Social networks · Healthcare · Physician referral
This research is based on Dr. Schelly’s PhD dissertation at the University of Wisconsin, which he submitted in May 2016. * David Schelly [email protected] 1
Present Address: Department of Occupational Therapy, Clarkson University, Box 5883, 8 Clarkson Ave., Potsdam, NY 13699, USA
2
Hospital Nacional de Niños “Dr Sáenz Herrera,” CCSS, Child Developmental and Behavioral Unit, San José, Costa Rica
3
University of Costa Rica, San José, Costa Rica
Introduction Disparities in the diagnosis of autism spectrum disorder (ASD) continue to emerge, and it appears that sociodemographic factors are associated with access to ASD assessment (Dickerson et al. 2017). There is some evidence that either physician referral behaviors or parent help-seeking behaviors lead to racial disparities in diagnosis (Emerson et al. 2016). One popular theory is that social networks may be driving these disparities. Social networks have long been suspected to influence help-seeking behaviors for a range of illnesses and diseases, including how, why, and when individuals seek professional care (e.g., Deri 2005; Pescosolido 1991; Pescosolido et al. 1998). However, precise social network mechanisms related to specific health inequities are difficult to identify, especially because social networks are strongly associated with a range of other personal and structural characteristics (McPherson et al. 2001) that are also associated with health outcomes. To explain the diagnostic disparities of ASD, the presence of a social network mechanism is consistent with the narrative of a seemingly ever-increasing prevalence: sometime in the 1990s, psychologists and physicians accounted for an increased prevalence by pointing to “greater public awareness” (Gernsbacher et al. 2005) and “growing awareness and knowledge among parents” (Wing and Potter 2002). After identifying spatial clusters of diagnosed cases of young children (Mazumdar et al. 2010), sociologists argued that autism awareness may lead parents to recognize symptoms and pursue help (Fountain et al. 2011), and they suggested the presence of what we call a “cul-du-sac effect” on the diagnosis (Liu et al. 2010). The idea is that parents are meeting in and around the neighborhood, and parents of diagnosed children are encouraging parents of symptomatic but undiagnosed
13
Vol.:(0123456789)
children to seek medical care. In this view, “knowledge diffusion” leads to the spatial clustering of cases (King and Bearman 2011). Subsequent studies (e.g., Keyes et al. 2012; Liu and Bearman 2015; Mazumdar et al. 2013) and popular accounts, such as Weintraub’s (2011) commentary in Nature, have argued that parents’ exposure to information increases their children’s likelihood of diagnosis; one argument is that populations of parents with relatively low prevalence rates (i.e., Hispanics) simply require information about ASD to reduce or eliminate the disparity (e.g., Colbert et al. 2016). Yet there is no direct evidence that information spreading through social networks is a factor in the helpseeking behaviors of parents. This paper explores the help-seeking behaviors and referral pathways of parents of diagnosed children in one of two countries in which spatial clusters of cases of childhood ASD have been identified. In our study area of Costa Rica, two locales far from the centralized diagnosing clinic were identified as having clusters of low severity cases (parents of severe cases may not require information to pursue professional help) at a time in which there was no evidence of a recruitment bias in the clinic (Schelly et al. 2015). Similar to studies in the U.S., children in higher socioeconomic status (SES) districts had an elevated risk of diagnosis, potentially an indicator of “local resources and the availability of health-related information” (Mazumdar et al. 2013, p. 88). We interviewed parents of diagnosed children, focusing on lay and professional social networks that influenced parents’ decisions to seek professional help for early behavioral abnormalities in their children. These social networks may explain the emergence of diagnostic inequities for a disorder that was only recently adopted by the medical establishment in Costa Rica.
Background Help‑Seeking Behavior Despite a long-held interest in how social networks affect health, sociological studies that directly identify specific mechanisms leading to diagnosis are rare (Smith and Christakis 2008; Berkman et al. 2000).1 Social influence 1 An exception is stochastic actor based models, where studies have considered topics such as the influence of peer networks on adolescent alcohol use (e.g., Mundt al. 2012) and even self-reported behaviors associated with attention deficit and hyperactivity disorder (ADHD) (e.g., Aronson 2016). Importantly, these studies require probability samples and longitudinal data that are difficult to come by; arguably the best existing data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health) study. Data on ASD may be impossible to acquire in the U.S. because of sampling limitations.
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J Autism Dev Disord
mechanisms, which include processes by which individuals variably recognize symptoms and decide to seek help (e.g., the cul-du-sac effect), are difficult to identify empirically. While interest in the topic has waned (see Pescosolido et al. 2007), medical help-seeking research in the 1960s and 1970s focused on social networks. Freidson (1960) speculated that future patients, after experiencing symptoms, first self-diagnose and explore home remedies; if the symptoms persist, individuals consult with “friends, neighbors, relatives, and fellow workers” for advice, but only in the “daily intercourse, initiated first by inquiries about health and only afterward about the weather” (1960, p. 376). These casual advice-seeking interactions in lay referral networks were thought to lead to the professional system of physicians and healthcare professionals, where would-be patients move up a “hierarchy of consultants” before they penetrate the professional system (Freidson 1961, pp. 104–105). In various settings, research in the subsequent two decades suggested that strong lay network ties (especially kinship ties) could actually lead to help-seeking delays (e.g., McKinlay 1973; Horwitz 1977; Birkel and Reppucci 1983), presumably because these contacts provide individuals with information and advice that lowers their need for professional guidance. This suggests that in some cases, lay social networks inhibit help-seeking, producing a negative effect on diagnosis. More recent studies have discussed disparities in helpseeking behaviors more broadly. In the U.S., help-seeking behaviors tend to vary in similar ways as health disparities in general, with low SES families (Pavuluri et al. 1996), Blacks (Hillemeier et al. 2007), and Latinos (Gerdes et al. 2014) being the least likely to seek help or receive diagnoses for their children. Drawing on the early social networks and help-seeking literature, one explanation is that these groups have stronger familial ties and rely heavily on family and friends for medical advice, thus inhibiting their professional help-seeking. An alternative is that lack of knowledge of specific conditions inhibits help-seeking behaviors (e.g., Hillemeier et al. 2007). For ASD in particular, Zuckerman et al. (2014a) argue that knowledge gaps could explain diagnostic disparities between Latinos (and other minorities) and non-Hispanic whites in the U.S., specifically because parents with less information about ASD may not seek professional help when their children present with symptoms of ASD (see also Iland et al. 2012; Magaña et al. 2013). The existence of a knowledge gap is essential to a cul-dusac hypothesis in ASD. The idea is that the number of lay consultations alone should increase the likelihood of diagnosis, explaining some of the well known disparities illustrated by Liu et al. (2010). More specifically, do parents of symptomatic but undiagnosed children discuss their children’s language delays with their friends and family? And if so, does the arrival and diffusion of ASD information in these
J Autism Dev Disord
lay social networks lead to local epidemics of diagnosis? Alternatively, do these lay consultations actually slow the diagnosis by inhibiting professional help-seeking? Study Site The present research focuses on the help-seeking behaviors and referral pathways of parents of children in Costa Rica who are later diagnosed with ASD. The presence of a cul-du-sac effect on ASD diagnosis in Costa Rica is highly plausible: an information campaign in the mid-2000s targeted parents, teachers, and clinicians to obtain referrals for a genetic study of ASD (see McInnes et al. 2005), and in a 4-year period 5 years after recruitment had concluded, low severity cases appeared in spatial clusters (Schelly et al. 2015). During this time, there was universal healthcare for children, and all patients would require a diagnosis through the public system to receive referrals to therapies and special education schools; there was also only one diagnosing public clinic, thus removing clinical variation in diagnostic procedures as a possible confounding variable. Finally, ASD in Costa Rica continues to be underdiagnosed,2 suggesting that many parents of symptomatic but undiagnosed children are vulnerable to any source of influence.3 Several cultural factors may be relevant to parental helpseeking in Costa Rica. First, familism and collectivism among Latinos has been well-documented (Sabogal et al. 1987), suggesting that tight family structures may determine how information spreads or who is consulted for health concerns. Household sizes are often large, and grandparents and extended family commonly live in the same household as parents and children. Second, fatalism associated with religiosity is often cited, albeit with limited empirical evidence (Ramos-Sánchez and Atkinson 2009), as inhibiting helpseeking behaviors, in part because individuals may believe there is little the medical community can do for their problems. Third, the concept of personalism, including values of self-respect and respect for others, can be related to a strong respect for hierarchy and authority structures (Bermúdez et al. 2010); as a result, parents may be less assertive during medical encounters, potentially deferring to medical advice that may not take parental concerns seriously. Finally, developmental problems may be stigmatized as
2 The number of ASD cases in Costa Rica is quite low—we estimate between 500 and 1000 cases. There is no evidence that true prevalence rates of ASD vary drastically across populations (Baxter et al. 2015), so if we extrapolate from population studies (e.g., Kim et al. 2011), Costa Rica is severely underdiagnosed. 3 Based on interviews and ethnographic observations, parents tend not to search for or be influenced by information about ASD on the internet. Importantly, such an influence would not likely lead to spatial clusters.
signs of family dysfunction in the Latino community, possibly inhibiting help-seeking for problem behaviors (Zuckerman et al. 2014b). In the U.S., delays in ASD diagnosis among Latinos may be related to parents being relatively less proactive seeking additional professional help after initial efforts are unsuccessful (Magaña et al. 2013), possibly resulting in diminished access to developmental specialists (Zuckerman et al. 2013). There may be another cultural aspect to these delays as well: Pachter and Dworkin (1997) found that Latino parents expected social developmental milestones to occur at later ages than those of other cultural groups, in part because social deficits commonly associated with ASD may be less evident among families with more direct parenting styles common among Latinos (Blacher et al. 2014). These factors may all inhibit help-seeking behaviors outside the family. When parents do decide to seek professional help, access to healthcare services in Costa Rica is relatively equitable even across income categories, and over 70% of Costa Ricans are satisfied with the quality of healthcare where they live (Savedoff 2009). Healthcare utilization and coverage in Costa Rica are excellent even by international standards (Unger et al. 2008), especially for primary and preventive care. Healthcare services are available either through the dominant public healthcare sector or the newly expanding private sector (see Clark 2011; Cercone et al. 2010). The public sector is managed by the Social Security Institute, widely known as the “Caja.” All workers are constitutionally mandated to contribute to the Caja for public insurance, and additional funding for non-workers is provided by general tax revenue. Thus, the Caja provides near-universal coverage to the population (and to all children), and there are no copayments for services. The Caja manages a chain of primary care clinics, known as EBAIS clinics (Basic Teams for Integrated Healthcare), that provide the first level of care, with each clinic managed by a physician and serving approximately 1000 households. There are over 1000 EBAIS facilities nationwide, providing most of the population with equitable access to primary care (Rosero-Bixby 2004). The Caja’s secondary level of care includes 10 large clinics and 20 regional hospitals, and the third level includes three general and five specialized hospitals in San Jose. For children, the specialized hospital and diagnosing clinic for ASD is the Hospital Nacional de Niños (HNN). One of the primary complaints about the public system is the long waiting times,4 so some patients 4
The public healthcare in Chile in the early 1980s (before increased privatization under Pinochet) was comparable to Costa Rica’s system in several ways, including equitable access to primary care clinics that were burdened with long wait times. Interestingly, among patients in a clinic with a mean wait time of 4.2 h, Scarpaci (1988) found that wait times were unrelated to how patients regarded the
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or secondary care clinic, where those physicians provide a formal referral to the HNN (see Fig. 1). The help-seeking behaviors of parents of children who receive a diagnosis of ASD may include the private and public sectors before visiting the HNN. The question is how parents initiate entry into those sectors and then negotiate their way through the referral system. What role do social networks and the diffusion of health information play in this process?
Methods Data
Fig. 1 Referral pathways for children who are eventually diagnosed with ASD at the HNN in Costa Rica
utilize emergency rooms (Unger et al. 2008) or the private sector to avoid waiting (Rosero-Bixby 2004). With the signing of the Dominican Republic-Central American Free Trade Agreement (CAFTA-DR) in 2009, the market for private healthcare has slowly expanded (Clark 2011). The Superintendency of Insurance (Superintendencia General de Seguros, or SUGESE) has been established to oversee all insurance plans, and because SUGESE has been slow to approve private insurers, there are only a small number of private providers. Importantly, private healthcare utilization is somewhat more common in the highest income brackets (Clark 2011). Costa Rica’s private facilities, which are concentrated in the San Jose area and partially cater to medical tourism, only provide primary and secondary care (Muiser and Rafael 2012); for advanced specialty care, private clinicians must refer patients back to the public sector, especially if a formal diagnosis is needed to receive public services (e.g., language therapy or special education). Thus, the pathway from the private sector to ASD diagnosis involves seeing a private physician who is permitted to refer to the HNN, such as clinicians who also work within the public system. Nonreferring clinicians are known to write an informal referral note to be taken by parents back to the EBAIS clinic
Footnote 4 (continued) quality of care; proximity to the clinic and the quality of physicians were the important factors.
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The present study interviewed parents of children diagnosed with ASD, many of whom lived within the previously identified spatial clusters, about factors that influenced their helpseeking behaviors and referral pathways. Semi-structured interviews were conducted with 54 families of children who were recently diagnosed at the HNN. Medical file reviews were used to corroborate referral information and reported parental concerns at intake. The study was approved by the medical ethics board at the University of Wisconsin and the bioethics committee at the HNN. We approached parents who attended an appointment in 2013, which included newly diagnosed patients and follow-up appointments.5 After their appointments, the parents, typically only the mother, were briefly told about the study, and all parents expressed interest in participating. After obtaining written informed consent, interviews were conducted in Spanish in a private room at the HNN. In addition to demographic questions, parents were asked about their children’s behaviors, their knowledge of ASD throughout their children’s lives, and their knowledge of other individuals with ASD. They were also asked openended questions that explored whether, beginning at symptom recognition and leading up to the diagnosis, they had been influenced to pursue medical advice for their children’s behaviors, and whether, after receiving information about ASD at the informing interview, they had influenced other parents to pursue medical advice. Both lay networks (family, friends, neighbors, and work contacts) and professional networks (EBAIS clinics, secondary care clinics, private clinics, and daycares/schools) were considered.
5
The clinical characteristics and geographic distribution of our sample were similar to those of the country-wide population of children who were assessed for ASD from 2010 to 2013. Only the percentage of low severity cases was high compared to the 2010–2013 population (40.0 vs. 20.3%), which if anything will accentuate any cul-dusac effect.
J Autism Dev Disord
For information that may have influenced the respondents, we asked parents to detail the process of symptom recognition, including what led them to notice something different in their child, who they talked to during the process, and whether anyone suggested that they receive medical advice. They were asked to create a timeline of their visits to clinicians in which they discussed or were asked about the symptoms, and they were asked to expound upon the referral information that was available in the medical files to detail the lay and professional referral steps leading to diagnosis. Subsequently, parents were asked to detail how they may have influenced other parents to seek medical care for their children, focusing specifically on children the parents deemed to possibly have ASD. If applicable, they were asked to detail how often and in what circumstances they spoke to the families. To determine whether any information spread actually influenced other families, names were provided for later cross-checking in the medical records. Analyses This paper uses interview data from the 54 families with diagnosed children to explore parental action and referral patterns. The data come from a larger mixed methods project that included complete medical file reviews of 4 years of children diagnosed with autism, interview data with clinicians at the HNN and surrounding area, and ethnographic data from the HNN and from a small city far from the hospital where there was recently a disproportionately high number of cases identified. The present paper focuses specifically on interview data from the 54 cases, many of whom lived in a previously identified spatial cluster of cases. Two interviewers were present for each interview and took notes independently, compared and combined notes immediately afterwards, and made coding decisions based on what data could be meaningfully quantified. When possible, interview data were compared with data from medical files, including referral information and complaints at initial intake at the HNN. Stata 13.1 (StataCorp, College Station, TX, USA) was used for all statistical analyses. The following analyses are reported: (1) multiple regression models are used to predict age at symptom recognition and time between symptom recognition and the first visit in the developmental unit at the HNN; (2) influential lay and professional network connections leading to diagnosis are presented in a network diagram; (3) referral pathways leading to diagnosis are presented to illustrate private vs. public healthcare pathways; and (4) comparisons, using two-sample t-tests and tests of proportions, are used to compare family characteristics of mild vs. moderate/severe cases of ASD. First, the regression models provide an initial exploratory step for tracking down the diagnostic disparity producing mechanisms. Two sets of models predict the age at symptom
recognition and the referral time, or the time between symptom recognition and the first visit to the HNN developmental unit, for the 54 patients. The models include explanatory variables for SES (education and household income), patient characteristics (severe symptoms, comorbidities, and typical ASD symptoms), household characteristics (number in household, whether there is an older sibling, whether the father is in the household, and whether the mother works), knowledge characteristics (knowledge of autism before the diagnosis, whether individuals with autism were known before the diagnosis, and maternal and paternal age), and district characteristics (distance to the HNN, district population density, and district poverty level). Models for referral time also include a dichotomous variable for having utilized private care. If the mechanisms involve parental information, then symptom recognition should be predicted by parental knowledge of ASD and having known other children with ASD before the diagnosis, and these variables may also be associated with a shorter referral time. If the mechanism primarily involves referral practices, then patient characteristics should predict the referral time, and features of the household and parental knowledge should not predict the referral time. Second, for the two-mode network diagram of lay and professional network connections, the software Visone (Brandes and Wagner 2004) is used to represent the influential help-seeking and referral steps that were determined from the interview and medical file data. Figure 2 shows the stepwise process of how the steps were established for four hypothetical patients. First, each patient and any family (F), friends (fr), neighbors (N), or work contacts (W) (lay connections) that were meaningful for the parents’ help-seeking are represented as circles. “Meaningful” includes individuals whose advice was sought or individuals who provided advice regarding the associated health concerns for the child, regardless of its consequence. Figure 2 shows that the parent of patient 0 spoke with two family members, a friend, and a neighbor about her child’s condition. The parent of patient 1 (patient 0’s neighbor) spoke with a friend, the parent of patient 0, and a second neighbor. The parents of patients 2 and 3 each spoke with a family member. In the second step, all professionals at EBAIS clinics (E), secondary care clinics (SC), private clinics (Pvt), or daycares/schools (d/s) that played a meaningful role in the referral steps, including any informal consultation or advice, are represented as squares.6 Connections are established between all meaningful nodes for each child, and if a referral was made (formal or informal), the connections continue between the meaningful
6
Names of professionals were sometimes forgotten by parents, so the nodes represent the relevant organizations or clinics rather than the specific individuals.
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Fig. 2 Hypothetical influential lay (circular) and professional (square) network connections for children (in red) eventually diagnosed with ASD. (Color figure online)
node and the referred node, eventually showing the complete referral pathways for each patient. Figure 2 shows that patients 0 and 1 visited EBAIS and secondary care clinics (it is not yet apparent in step two that they were the same clinics), and the parent of patient 1 does so following the parent of patient 0’s suggestion. Patients 2 and 3 each visited different EBAIS clinics but were eventually seen by the same private clinician who referred onwards to the HNN. The third step is to link the connections for each patient where appropriate, so patients 1 and 0 are connected to each other, and patients 2 and 3 are connected to the private clinician; the diagnosing clinic is dropped because all patients in the sample are connected to it, obscuring otherwise central nodes that may have influenced many patients. Finally, step four shows the entire network with “stress minimization” (Ortmann et al. 2016), which reorders the network in the most efficient layout, thereby showing the most popular nodes in central locations; thus, any nodes with more connections will be more central, and nodes with few connections will be peripheral. When patients or lay connections are directly connected, such as between the first and second
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diagnosis (patients 0 and 1), the layout provides evidence of a lay network mechanism. When professional connections are central, such as between the third and fourth diagnosis (patients 2 and 3), the layout provides evidence of a referral mechanism. Third, public and private referral pathways are mapped out for all 54 patients, including their pathways via lay network connections that were mentioned in the interviews. Family SES is compared for groups of families pursuing pathways through daycare/school, private clinicians, and the public pathway via EBAIS. If a referral mechanism leads to spatial clusters, the most likely explanation exists in differences between the public and private sectors. Fourth, among the 54 cases, 22 were classified as mild because they attended regular school. This cutoff was used because the intention was to determine why difficult-todetect cases are geographically distributed in clusters, and fitting in with typically developing children is a good proxy for these cases. For continuous variables, we use two-sample t-tests, and for dichotomous variables, we use tests of proportions to compare these mild cases with the remaining
J Autism Dev Disord Table 1 Five models predicting age (months) at first symptom recognition among 54 children later diagnosed with ASD, Costa Rica (SEs in parentheses) Variable
Model 1: SES
Model 2: Patient char.
Model 3: household char.
Model 4a: knowledge
Model 5: district char.
Parental education Income Severe symptoms^ Comorbidities^ ASD symptoms^ No. in household Older sibling^ Father home^ Mother works^ Knowledge^ Knew individuals^ Maternal ageb Paternal age Distance (km) Pop. densityc Poverty Intercept Model F Partial Fd R2 Adj R2
− 0.41 (0.24) 0.49 (0.27)
− 0.41 (0.24) 0.45 (0.27) 0.00 (2.85) − 5.83* (3.17) 4.74* (2.74)
− 0.89** (0.25) 0.79** (0.25) 4.77 (2.85) − 7.65** (2.82) 2.87 (2.49) 0.47 (0.91) − 7.80** (2.64) − 6.89 (3.44) 5.26 (3.66)
− 0.83** (0.23) 0.66** (0.23) 5.34* (2.47) − 10.28** (2.53) 0.84 (2.20) 0.41 (0.79) − 3.37 (2.58) − 1.81 (3.20) 5.76 (3.19) 3.87 (2.36) 1.25 (2.47) − 0.65** (0.23) 0.04 (0.26)
20.13 1.92
19.29 2.12* 2.16 0.181 0.095
30.24 3.92** 5.25** 0.445 0.332
39.65 4.27** 3.30* 0.587 0.450
− 0.73** (0.23) 0.66** (0.22) 5.57* (2.47) − 10.07** (2.46) 1.10 (2.20) 0.42 (0.77) − 3.73 (2.51) − 2.43 (3.14) 5.39 (3.19) 2.48 (2.42) 0.56 (2.55) − 0.55* (0.23) − 0.03 (0.26) − 0.05 (0.05) − 0.84* (0.35) 0.10 (0.12) 39.77 4.12** 2.02 0.647 0.490
0.070 0.034
*p
