Spatial variation in risk for physician diagnosed environmental sensitivity

Spatial and Spatio-temporal Epidemiology 23 (2017) 35–45

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Original Research

Spatial variation in risk for physician diagnosed environmental sensitivity Daniel Rainham a,∗, Patrick Brown b, Tara Sampalli c a

Healthy Populations Institute, Dalhousie University, 1318 Robie Street, Halifax, Nova Scotia B3H 3E2, Canada Department of Statistical Sciences, University of Toronto, 100 St. George St., Toronto, Ontario M5S 3G3, Canada c Primary Health Care, Nova Scotia Health Authority, 6960 Mumford Road, Suite 265, Halifax, Nova Scotia B3L 4P1, Canada b

a r t i c l e

i n f o

Article history: Received 21 February 2017 Revised 20 July 2017 Accepted 29 July 2017 Available online 14 August 2017 Keywords: Multiple chemical sensitivity (MCS) Spatial variation Environmental illness Socioeconomic deprivation

a b s t r a c t Multiple chemical sensitivity (MCS) is a chronic condition characterized by recurring and severe symptoms triggered by exposures to low levels of toxicants or anthropogenic pollution. This study investigated the spatial structure of MCS incidence and estimated the contribution of socio-economic deprivation to variations in rates of MCS at the community level in Nova Scotia, Canada. Patient data were used to calculate cumulative incidence rate ratios for treated multiple chemical sensitivities. Poisson regression with a spatially autoregressive random effect was employed to assess spatial variation in MCS. Odds of MSC incidence are greatest among patients from the highest socioeconomic category using the most socioeconomically deprived category as reference (OR: 1.94, CI [2.5–95%] 1.45–2.56). No discernable spatial pattern for MCS risk was evident after controlling for known risk factors. This is the first study to investigate spatial variations in cumulative incidence of multiple chemical sensitivities. The socioeconomic status of the community in which patients live has a significant influence on the geographic patterns of MCS. Future research will include a smartphone application to assess positional and temporal information on environmental exposures to further explain MCS prevalence. © 2017 Elsevier Ltd. All rights reserved.

1. Introduction Multiple chemical sensitivity (MCS) is a multisystem disorder characterized by symptoms in various body systems. Controversy exists as to the etiology and possible pathogenesis, on whether it is physical or psychological and a disease or an illness. These conditions are characterized by complaints originating from several organ systems not easily identified via standard physical examina-

∗

Corresponding author. E-mail addresses: [email protected] (D. Rainham), [email protected] (P. Brown), [email protected] (T. Sampalli). http://dx.doi.org/10.1016/j.sste.2017.07.002 1877-5845/© 2017 Elsevier Ltd. All rights reserved.

tion or medical testing procedures (Richardson and Engel, 2004). Common symptoms associated include: fatigue, pain, memory difficulty, irritation of the respiratory tract, depression, headache, joint pain, muscle pain, fatigue, nausea and dizziness (Davidoff and Keyl, 1996; Miller and Mitzel, 1995). Symptoms typically overlap among diagnoses and patients typically share several key symptoms although the severity, duration and a nature of symptoms may vary significantly (Park and Knudson, 2007). MCS is attracting increasing attention, largely due to the notion that its etiology arises from exposure to relatively low levels of harmful chemical agents that are now ubiquitous in the environment. At present, over 88 million organic and inorganic substances (excluding biosequences) are indexed by the American Chemical Society’s Chemical

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Fig. 1. Symptoms associated with MCS. Ten most common are: fatigue, difficulty concentrating, persistent tiredness, headaches, poor memory, irritability, difficulty articulating thoughts, sneezing or a congested, runny nose, stuffy sinuses and itchy eyes.

Abstracts Service (CAS) Registry (American Chemical Society, 2014). The sheer number of substances presents a challenge to managing human exposure and many chemicals have yet to be inventoried or regulated. Biomonitoring studies and reviews reveal the presence of several harmful (e.g., carcinogenic and mutagenic) chemicals in adults (Haines and Murray, 2012), as well as in umbilical cord blood in newborns (Cooke, 2014). Despite advances in monitoring there is much less certainty about how low-level exposures to chemical agents result in the development of sensitivity, and about why there is a lack of specificity between specific chemical triggers and symptoms among MCS patients. MCS is commonly referred to as an environmental illness due to its association with the physical and chemical characteristics of our surroundings. However, epidemiological investigations of MCS are relatively rare, possibly a function of challenges associated with diagnosis. For example, patients self-identified as overly sensitive to chemical exposures have reported difficulties with memory, irritation of the respiratory tract, depression, headache, joint and muscle pain, nausea and dizziness (Shinohara et al., 2004). Studies of patients from a clinic specializing in the assessment of MCS found that symptoms varied depending on whether they were reported over the course of their condition, or immediately following an acute event such as an exposure to a chemical (Fox, 2001). While many MCS symptoms (Fig. 1) are common regardless of report timing, chronic symptoms tend to be neurocognitive in origin

(e.g., cognitive, neuromuscular and affective symptoms), whereas acute symptoms are associated more with physiological responses (e.g. affecting respiratory or vascular systems). Approximately 85% of all patients report the following ten symptoms: fatigue, difficulty concentrating, persistent tiredness, headaches, poor memory, irritability, difficulty articulating thoughts, sneezing or a congested/ runny nose, stuffy sinuses and itchy eyes. Given the challenges associated with diagnosis, as well as some controversy about whether MCS is a legitimate medical condition, several definitions of MCS have been proposed. The first case definition identified five core criteria for diagnosis of MCS including: reproducible symptoms, a chronic condition, symptoms arising from low levels of exposure, symptom resolution when incitants are removed, and symptoms triggered by a number of chemically unrelated substances (Nethercott et al., 1993). Shortly thereafter the concept that symptoms be present in multiple organ systems was added as an additional criterion for diagnosis (Bartha, 1999). For the purposes of discussing prevalence, the following definition of MCS is useful: a condition where individuals react adversely when exposed to chemicals found in low concentrations in the environment. Since pathogenesis of MCS remains uncertain, current diagnoses are based on a pattern of symptoms (Orriols et al., 2009). Despite issues of diagnostic overlap, association of MCS with several co-morbid chronic conditions and acceptance of MCS as a legitimate “disease” as opposed to a purely psychological condition, there is very little debate regard-

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Table 1 Estimates of MCS prevalence rates. Source

Prevalence rate (%)

Method

Caress and Steinemann (2003)

2.5a 11.2b

McGlone et al. (2002)

2.6

Black et al. (20 0 0)

3.4

Kreutzer et al. (1999)

12.6

Meggs et al. (1996) Reid et al. (2002)

33.0 13.0c 14.0d 28.0e

Telephone survey (“medically diagnosed with MCS” vs. “unusual hypersensitivity to common chemical products”) Nova Scotia Health Survey; self-reporting of receipt of diagnosis Population-based sample; surveyed using a cross-sectional telephone interview Randomly sampled survey (“Hypersensitivity to common chemical substances”) Telephone survey (“Do chemicals make you sick?”) Cross sectional postal survey (“Sensitivity to at least one everyday chemical”)

a b c d e

Sample population Residents of the continental United States (n = 1054) Residents of Nova Scotia (n = 3227) Iowa military personnel (n = 3695) Residents of Atlanta, Georgia (n = 1582) Residents of rural Eastern North Carolina (n = 1027) Three cohorts of United Kingdom military personnel: Bosnia veterans (n = 2050), non-deployed Gulf War Era personnel (n = 2614), Gulf veterans (n = 3531)

Medically diagnosed. Hypersensitive. Bosnia. Non-deployed. Gulf region.

ing the impact of MCS on health care resources. Studies of MCS report prevalence rates of between 2.6% and 33%, values generated from population survey responses indicating physician diagnosis of MCS, environmental illness, or adverse reaction post-exposure to chemical substances (Table 1). The discrepancy in prevalence may be due to lack of standardization in self-report questionnaires, as well as the use of medically-diagnosed MCS interchangeably with self-reporting of sensitivity to one or more chemicals. In a Canadian survey of medically unexplained physical symptoms (MUPS) that would include diagnosis of MCS, 5% of the population over the age of 12 (approximately 1.2 million people) report a confirmed MUPS diagnosis, and rates are twice as high for women and higher still for people with lower socioeconomic status (Park and Knudson, 2007). The phrase “medically unexplained physical symptoms” is not used in the diagnosis of MCS and related outcomes in Nova Scotia. Challenges with diagnosis and similarity of symptoms to other similarly less well-defined chronic conditions has resulted in conditions such as MCS being recognized as common disorders in primary care and occupying a disproportionate use of physician time (Richardson and Engel, 2004). Given the ubiquity of the potential for exposure, the challenges associated with diagnosis, as well as the burden on precious health care resources, a more thorough understanding of the etiology of MCS is a priority and will assist in the development of effective interventions for MCS and associated conditions. There have been few is any studies addressing the spatial distribution of MCS, or assessments of the risk factors associated with MCS. It is unlikely there will be a “cure” for these conditions and thus investigation of how MCS varies within the population and the potential for exposure to incitants that cause MCS are priorities for investigation. The aim of this paper is to develop inference about the spatial distribution of multiple chemical sensitivities, and related chronic conditions, in Nova Scotia, Canada, with the purpose of identifying areas of elevated incidence.

The analysis is an ecological, cross-sectional study of data representing patients who have been medically diagnosed with one or more conditions with multiple chemical sensitivity as a primary condition, and who were referred to a specialist clinic for subsequent evaluation and treatment. To our knowledge the spatial distribution of MCS incidence has not been explored. 2. Materials and methods Patients in Nova Scotia with diagnosed chemical sensitivities (cases) are referred to a government-funded clinical care facility, the Integrated Chronic Care Service (ICCS), for treatment of environmental sensitivities and complex chronic conditions (formerly known as the Nova Scotia Environmental Health Centre). The ICCS is part of Primary Health Care (PHC) and Chronic Disease Management in the provincial health authority in Nova Scotia ICCS offers integrated care for individuals who have challenging and complex diagnoses, multimorbidities and medically unexplained conditions. The clinic receives local, national, and international referrals. PHC in Nova Scotia is a complex system with urban, suburban, and rural service locations, with team-based and individual practices, and various payment plans and support services in the community. As part of this portfolio, ICCS has developed an evidence and experience-based model of care delivery guided by the Chronic Care Model (CCM) (Barr et al., 2003) and the World Health Organization’s International Classification of Functioning, Disability and Health (ICF) (World Health Organization, 2002). Most patients included in this study exhibited the presence of three more diagnoses occurring alongside of diagnosis of MCS. Given the lack of consensus on the diagnosis, controversies around the etiology, or disease versus illness, the ICCS care team has developed and adopted a novel functional health model to address the self-selected functional needs of affected individuals in the absence of standardized clinical care pathways or clinical guidelines (Sampalli et al., 2016). At the crux of the

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treatment is the acknowledgement of treating chronic conditions (widely referenced model is the Chronic Care Model) and significant limitations in functional health (guided by the WHO functional health model). Working closely with primary care providers, patients, families, other providers involved in care, employers and community services, the multidisciplinary care team at ICCS with an overall focus of functional health management supports the following: medical care coordination of multimorbidities, dietary management, pain and fatigue management, environmental control and management of associated symptoms, return to work and rehabilitation supports, and management of psychosocial stressors, symptoms and distress associated with chronic disease management. Data were abstracted from the facility patient database for the period from 1998 to 2011 and included diagnoses for multiple chemical sensitivity and related chronic conditions including fibromyalgia, chronic fatigue syndrome and environmentally-induced allergies. At the time of the study, no single ICD-9 code existed for MCS. In the absence of a code, all patients underwent a comprehensive assessment leading to a diagnosis. Bartha’s (1999) consensus definition was used in the diagnosis of patients. Multiple chemical sensitivity is diagnosed in a patient when the following six criteria are met: (1) the symptoms are reproducible with repeated chemical exposure, (2) the condition is chronic, (3) low levels of exposure result in manifestations of the syndrome, (4) the symptoms improve or resolve when the incitants are removed, (5) responses occurred to multiple, chemically unrelated substances, and (6) symptoms involve multiple organ systems. Diagnosis of MCS is made for those that meet all six criteria and the diagnosis can exist alongside of other diagnoses such as fibromyalgia, chronic fatigue syndrome, asthma, allergy, migraine, irritable bowel syndrome, depression, panic attacks or interstitial cystitis. Implicit in this consensus definition is the recognition that there is wide variability in the clinical presentation and in the degree of disability among patients. Fibromyalgia diagnosis was made using the Canadian Clinical Working Case definition which states the condition is categorized by a history of widespread pain and pain on palpation at 11 or more of the defined tender point sites (Wolfe, 1994). Patients with this condition also experience significant fatigue and cognitive problems. A patient received a diagnosis of chronic fatigue syndrome if they had severe chronic fatigue of 6 months or longer duration with other known medical conditions excluded by clinical diagnosis and they had concurrently four or more of the following symptoms: substantial impairment in shortterm memory or concentration; sore throat; tender lymph nodes; muscle pain; multi-joint pain without swelling or redness; headaches of a new type, pattern or severity; unrefreshing sleep; and post-exertional malaise lasting more than 24 h (Fukuda et al., 1994). The final dataset included 1091 records with full civic address, age and sex. A cumulative incidence rate was calculated as the ratio of new observed cases to the population of Nova Scotia derived from the sum of all dissemination areas as recorded in the 2006 census. This rate was used to compute expected incidence count for each

dissemination areas, the smallest census region for which population (denominator) data are available. Each case was geocoded to a dissemination area and observed counts for each area computed. Census data and electronic boundary files were downloaded through Statistics Canada’s Data Liberation Initiative and the Equinox interface for the 2006 census year. Ethical approval for the analysis was obtained from the Nova Scotia Health Authority Research Ethics Board. Variation of MCS as well as with many chronic diseases in the population can be partially explained by socioeconomic status. Evidence from several studies of the distribution of multiple morbidity in populations from industrialized countries has shown that disease onset can be accelerated up to 10–15 years in locations with the greatest socioeconomic deprivation (when compared to the most affluent locations) (Barnett et al., 2012). Individual-level patient data do not include income, education and employment status or other variables linked to socioeconomic status. Instead, an area-level measure of socioeconomic deprivation was calculated using the 2006 census data for all dissemination areas as a surrogate for socioeconomic status (Terashima et al., 2014). Thus, any residual variation in incidence of MCS may be a result of environmental and/or genetic risk factors. Socioeconomic deprivation was calculated for each dissemination area using six variables from the 2006 Canadian census for the population age fifteen years and older, including: average household income, proportion of population without a high school diploma, rate of unemployment, proportion of single parent families, proportion of population windowed, separated or divorced, and the proportion of individuals living alone (Pampalon et al., 2012). Variables were indirectly standardized to the 2006 Nova Scotia population and normalized. Principal components analysis was employed to derive a combined material and social deprivation score for each dissemination area that were subsequently ranked and then grouped into weighted population quintiles. Area-level socioeconomic deprivation scores have shown to be effective in accounting for social and material factors associated with health outcomes when individual-level data are unavailable (Jones et al., 2009; Terashima et al., 2013). The Besag, York and Mollié (BYM) model is a popular and convenient model for count data referenced to discrete spatial regions (Besag et al., 1991; Waller and Carlin, 2010), and was used to analyze the MCS data considered here. The case count Yi for each dissemination are i is modeled as Poisson distributed, having a mean equal to the product of area’s expected count Ei computed as described above and an unknown relative risk λi . Each λi is log-linear combination of: an intercept parameter μ; a vector of explanatory variables Xi and associated regression coefficients β ; and a spatially varying random effect Ui . The model is written as:

Yi ∼ Poisson(Ei λi ) log(λi ) = μ + Xi β + Ui [U1 U2 , · · · UN ] ∼ BYM(σ 2 , τ 2 ) Random effects U1 to UN have a spatially dependent joint distribution where adjoining regions having a direct

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Fig. 2. Spatial distribution of MCS cases and distance from clinical care facility.

influence upon one another. The sum of the two BYM variance parameters σ ² + τ ² govern the ‘importance’ of the effect (how close to or far from zero each Ui is likely to be), with their ratio σ ²/ τ ² determining the smoothness or the degree to which each Ui is influenced by its neighbors. More specifically, each Ui is the sum of an independent or unstructured random term and a spatially structured (first order Gaussian Markov random field) component with variance parameters σ ² and τ ² respectively. There are two principle reasons for the use of the BYM model. First, modeling Poisson-distributed counts Yi , rather than Standardized Incidence Ratios Yi / Ei , accommodates the phenomenon that data from large regions having high expected counts Ei are more informative than counts of Yi = 0 or Yi = 1 from regions with small Ei values. Second, including the spatial random effect in the model, in contrast to a standard Poisson regression, accounts for less information being contained in a set of correlated observations made in close proximity to one another than in a similar number of independent observations separated by large distances. Failure to address this second issue would result in artificially narrow confidence intervals for the β coefficients and an underestimation of the type I error rate. The model was fit using Bayesian inference and Integrated Nested Laplace Approximations (Rue et al., 2009), as this approach is straightforward to carry out and has been demonstrated to reproduce the results of more labour- and computationally-intensive Markov Chain Monte Carlo algorithms. Bayesian inference requires the specification of prior distributions, though the fixed effects parameters μ and β are well identified even with the uninformative priors (improper and N(0,10 0 0) respectively) used here. The variance parameters σ ² and τ ² are given identical priors having 95% intervals between 0.025 and 1.0 which, considering log-relative risks of Ui = −2 or Ui = 2 give relative

risks exp(Ui ) of 0.135 or 7.4 respectively, has a fairly unrestrictive upper limit. The software used for analysis is R version 3.1.1 (http: //www.r-project.org) in combination with the disease mapping package version 1.1.0 and the INLA software (http: //www.r-inla.org) (Brown, 2015). 3. Results A total of 1091 cases of multiple chemical sensitivity were identified resulting in a cumulative incidence rate of 9.7 cases per 10 0,0 0 0; two cases were removed due to missing data on age. The median age of sample was 56 years (range of 11–93 years) and predominately female (86%). Incidence data included patients with a diagnosis of MCS only, as well as patients with MCS and additional chronic conditions such as chronic fatigue syndrome, fibromyalgia and allergy. Cases are referred to a single, specialized clinic, an Integrated Chronic Care Service facility (formerly the Nova Scotia Environmental Health Centre), located approximately 20 km (road network distance) from the most densely-populated portion of Halifax. Fig. 2 shows the spatial distribution of cases across the province, and the histogram of distance from the clinical care facility reveals the density of cases to be inversely associated with facility distance. Cases were distributed evenly across urban (34%), suburban (35%) and rural (31%) communities. Table 2 shows the percentage of Nova Scotia communities with cases of MCS distributed across socioeconomic deprivation and ethnicity quintiles. The distribution of socioeconomic deprivation is skewed so that approximately 60% of communities with cases of MCS are more socioeconomically deprived than would be found on average across all Nova Scotian communities. Scores of ethnic concentra-

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Table 2 Percentage of communities by deprivation and ethnic concentration scores.

Deprivation Ethnic concentration

1 (least)

2

3

4

5 (most)

11 35

13 28

16 22

23 12

37 3

tion are also skewed in that only 15% of all communities with cases of MCS would have a proportion of the population who are recent immigrants or who self-identify as a visible minority greater than the average of all Nova Scotian communities combined. Table 3 shows the estimates and credible intervals for the model parameters, with estimates being posterior means and intervals arising from the quantiles of the posterior distributions. Shown are odds ratios for each deprivation category relative to the most deprived category (level 5), and the odds ratio for the inter-quartile range (75th percentile versus 25th percentile) of ethnicity. The odds of being diagnosed with MCS increase with socioeconomic status. Deprivation categories 1–4 clearly have higher incidence of MCS than category 5, with odds ratios ranging from 1.5 to 2.0. Although there is a clear gradient with odds ratios increasing as deprivation declines from 4 to 1, the difference between levels 1 and 4 is modest in comparison with the difference between each of these levels and level 5. The ethnicity variable is not statistically significant with an odds ratio of 1, corresponding to no effect, and well within the 95% credible interval. The two standard deviation parameters have estimates and credible intervals above 0.2, indicating a not insignificant amount of residual spatial variation considering the random effects are on the log scale. Fig. 3 shows the fitted risk E(λ( · ) > 2|Y)for MCS at the dissemination area level after adjustment for age and sex, and includes the effect of socioeconomic deprivation. Sensitivity of the estimates to geographic scales was not assessed since the BYM model does not allow for changes in spatial boundaries. The next smallest geographical boundary in the Canadian census hierarchy, or census tracts, is only available for municipalities with a population of 10 0,0 0 0 or more, and would limit the analysis to the Halifax (most urban) area. Spatial distribution of MCS risk is unknown in Nova Scotia and was estimated from the geocoded case locations using the smallest possible area of aggregation. Variation in the

accuracy of risk estimates is due in part to population size and case distribution and there is significant unexplained spatial variation (σ , spatial) due to factors not included in the model. Spatial clustering of rate ratios, or in this case an assessment of consistently high or low risks with little spatial variance, was evaluated using the local Moran’s I statistic of spatial association. In addition to finding areas of high and low rates, the statistic was used to determine statistically significant spatial outliers, or high rates surrounded by low rates and vice versa. Fig. 4 shows areas of clustering of high rates in the most urban (Halifax) population and low rates in the Sydney area, the province’s second most populated area. However, rates also varied considerably across dissemination areas in urban Halifax, in the southwest shore area of Lunenburg, and in some locations in the Annapolis Valley (Canning and Greenwood), and indicate are indicative of a statistically significant spatial heterogeneity. While the identification of high or low risk clusters is useful for hypothesis generation, the fitted model has a much greater non-spatial variance (than spatial variance). Thus, there are factors not included in the analysis causing extra variation in risk that are not necessarily spatially clustered. Zones of above-average fitted risk as shown in Fig. 5, defined as posterior probability that the risk of multiple chemical sensitivity is >1.2. There is no detectable pattern of significantly elevated risk of MCS in the province. Although there are more cases and a cluster of significant risk of MCS in the urban area around the clinic of diagnosis, the severity of risk or probability of excess risk (particularly for high probabilities) does not seem to be influenced by clinic location. Therefore, there is a low likelihood of reporting bias associated with the location of MSC diagnosis in Nova Scotia. 4. Discussion The primary aim of this work was to ascertain the spatial distribution of multiple chemical sensitivity incidence. To our knowledge this study represents a first attempt to explore geographic variation in incidence of multiple chemical sensitivities. Research to date has focused mainly on ascertaining MCS prevalence through written and telephone-based surveys and results based on selfreported diagnosis. The current study uses data from a government-funded complex chronic disease service. Patients are referred to the service either from their own primary care provider or specialists involved in their care. The

Table 3 Parameter estimates (posterior means) and 95% credible intervals from the spatial model for multiple chemical sensitivity incidence.

Deprivation (relative to most deprived)

Ethnicity Residual variation

Dep1 (least deprived) Dep2 Dep3 Dep4 (Missing) Inter-quartile range σ , spatial τ , independent

Mean

2.5%

97.5%

1.94 1.86 1.86 1.57 0.06 1.02 0.39 0.84

1.45 1.42 1.44 1.24 0.00 0.71 0.23 0.74

2.56 2.40 2.36 1.95 0.23 1.42 0.63 0.94

D. Rainham et al. / Spatial and Spatio-temporal Epidemiology 23 (2017) 35–45

Fig. 3. Age and sex-adjusted rates of multiple chemical sensitivity∗ after controlling for socioeconomic deprivation.

dearth of studies on MCS incidence in the literature makes it difficult to situate the results in the context of similar research. This work is tremendously important for several reasons. First, there is strong evidence that the environment is becoming increasingly more polluted, particularly with chemicals and toxins that are not easily detected, which may have deleterious impacts to health at very low concentrations. It is entirely reasonable and logical to conclude that some individuals in the population may be more susceptible to environmental exposures, particularly if they have experienced a prior exposure event with one or more harmful chemicals (e.g. see work on the TILT hypothesis) (Miller, 2001). Second, for many patients with MCS or similarly-related outcomes diagnosis may be generated without clear cause. Determination of the geographic distribution of MCS provides an opportunity to develop hypotheses about possible etiologic agents, particularly those with environmental origin. Third, this study employed state-of-the-art modeling to assess the spatial variation in the relative rate of MCS, where each dissemination areas relative rate was dependent on the rate in adjacent areas. And finally, in order to plan and deliver rel-

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∗

Dissemination area level.

evant care for its citizens, this type of research and data is extremely important from a health system perspective to generate a better understanding of the health needs and disparities in care by dissemination area. PHC and the provincial health authority has in fact included geographic mapping and generation of community profiles an important aspect of the health systems planning work in the more recent merger into a single health authority (Capital Health, 2015). Dissemination area geography is the smallest geographic unit available for obtaining census-derived data, including age, sex, and variables to estimate socioeconomic status and ethnicity. Thus the model was able to incorporate spatial dependency while also adjusting for age, sex categories and other factors potentially associated with MCS for each area (variable area incidence calculations) using Bayesian model fitting. The results presented here are from a relatively large dataset of patient records over several years with physician-determined diagnosis of multiple chemical sensitivity. Such datasets present a rare opportunity to study the incidence dynamics of MCS at a relatively fine spatial resolution. Previous studies of MCS have either focused on symptomology or population prevalence and have not ex-

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Fig. 4. Clusters of fitted risk of multiple chemical sensitivity∗ . HH = cluster of high risk; HL = area of high risk surrounded by low risk; LH = area of low risk surrounded by high risk; LL = cluster of low risk. ∗ Dissemination area level.

plored how risk of MCS varies with socioeconomic status or ethnicity (Black et al., 20 0 0; Kreutzer et al., 1999; McGlone et al., 2002; Meggs et al., 1996; Reid et al., 2002). A provincial map of smoothed, age and sex standardized dissemination area-level incidence risk of MCS did not reveal any clear rate gradients across the province. Visual misinterpretation of increased risk of MCS incidence in urban areas of Nova Scotia is due to the varying size of dissemination area geography associated with urban or rural geometries. There is a clear indication that the risk of MCS increased with socioeconomic status. After adjusting for age and sex characteristics, areas with high socioeconomic status (SES) were associated with a greater relative rate of MCS, almost two-fold, than low SES areas. SES was represented by a calculation of social and material deprivation that includes income, education, employment status, social isolation. Deprivation measures have proven to be extremely powerful as an explanation for population-level variation among many chronic diseases and were thus included to control for socioeconomic influences of MCS (Terashima et al., 2014). Although based on self-reported prevalence data and educational attainment, studies have shown MCS to be more common in

individuals with a higher level of education (Caress and Steinemann, 2003; Cullen et al., 1992). However, the result may also be an indicator of access to care for complex chronic conditions where more highly educated individuals, or those with a higher level of socioeconomic status, are more likely to seek treatment or be referred by a physician for specialist care. MCS incidence was not associated with ethnicity. This result is expected given the relative ethnic homogeneity of the provincial population. Although there is no observable spatial patterning of MCS across the province there are dissemination areas with significantly greater risk of incidence. Hypothetically, these patterns could be created by conditions where risk of exposure to environmental contamination is greater, possibly from a known local source, e.g. air emissions, or from natural characteristics of the environment leading to increased chemical exposure, such as high areas with high concentrations of arsenic in the water supply. Alternatively, areas with greater risk of MCS incidence may capture the home address of individuals more susceptible to the effects of chemical exposures but where there is no known source of environmental contamination. Within the urban area of Halifax (the largest city in Nova Scotia) there are

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Fig. 5. Posterior probability of risk of multiple chemical sensitivity∗ being above average or greater than twice the expected risk. level.

a few areas with higher rates associated with known increased environmental pollution, specifically areas adjacent to a large industrial park, an oil and gas refinery and a container ship terminal. However, there are also areas with higher rates that are not adjacent to known pollution sources as well. A more detailed exposure assessment study is warranted to determine the type and concentrations of pollutants associated with areas of greater MCS incidence. While visualization of increased risk of MCS incidence provides opportunities to generate hypotheses associated with exposure, the diagnosis of MCS is not predicated on the notion that a patient experienced exposure to extraordinary concentrations of pollutants. Many MCS patients will claim, for example, that sensitivity developed over a long time period and may have arisen from repeated exposure to many chemicals or toxicants found in typical households, e.g. cleaning products, air deodorizers and fresheners, personal care products and many others. There is usually no characteristic pattern of exposure and exposure histories can be mired by limitations in the patient’s ability to recall details and onset of symptoms. In addition to information derived from patient recall it would seem prudent to develop more rigorous, sophisticated and objective approaches to exposure assessment with an aim to more accurately characterize individual exposures to environmental contaminants.

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∗

Dissemination area

Although this is the first study to examine populationlevel spatial variation in multiple chemical sensitivity the results should be interpreted in the context of inherent methodological limitations. First, patients included in the analytic dataset were diagnosed with multiple chemical sensitivities but may also have had up to three additional diagnoses including fibromyalgia and chronic fatigue. Factors associated with these other outcomes may not have been included in the analysis, and these outcomes may have their own independent spatial signature. Moreover, smoking rates were not available although smoking rates in the province are low and much less so in higher SES patient groups who are avoiding environmental exposures. Second, patient addressing information was collected at the time of diagnosis which may introduce confounding from unknown geographic-related exposures. As chemical sensitivities tend to develop over a period of time the exposures and geographic patterns of MCS may not be consistent with the geocoded locations and geographic factors examined. For example, patients may have been exposed to a chemical excitant at a previous residential location and then moved to avoid further exposure before seeking medical attention. Third, referral and reporting biases may contribute to the formation of hot spots even though there was no indication of association with the clinic. Certainly the influence of patient advocacy groups, particularly among higher SES patient groups, as well as the knowl-

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edge, awareness and adeptness of physicians about MCS may lead to an increase in the number of referrals from a specific provincial region. Small area-level socio-economic factors explained a non-negligible proportion of the spatial patter of MCS in both urban and rural communities. Conversely, a significant proportion of residual spatial dependence remains, suggesting that the influence of environmental factors and variability in both referral and diagnosis must be examined at the local level. Future research on the spatial determinants of MCS will endeavor to include MCS patient populations from other jurisdictions. In North America, for example, there are several clinics with active patient populations (e.g. Dallas, TX and Toronto, ON) from which to expand the current analysis. Modern data collection techniques, such as ecological momentary assessment, would provide an opportunity to explore finer temporal and spatial environmental characteristics associated with acute symptoms of MCS. Our team is currently in the process of developing a smartphone application to assist with identification of exposures and conditions associated with the development of MCS and related symptoms using an electronic diagnostic tool linked with global positioning system sensors. Applications that include both spatial and temporal information on potential exposures and timing of diagnosis will be helpful. Sensitivity to common environmental exposures may also be influenced by genetic variation in combination with socio-economic and related health determinants. Incorporating new knowledge derived from the inclusion of gene-environment interactions, how genetic and environmental factors jointly influence risk of developing health outcomes, may provide insight into the biological pathways most relevant to environmental sensitivities, the specific compounds in the environment that cause disease, or the development of tailored advice to prevent exposure (Hunter, 2005). Epigenetic modifications may also result from exposure to many ubiquitous chemical substances. For example, a case-control study found significant differences for individuals with and without self-reported chemical sensitivity in relation to the distribution of gene variants driving acetylation and hydroxylation processes (Schnakenberg et al., 2007). Although there is still limited evidence gleaned from clinical/laboratory studies of MCS patients, there are indications that repeated chemical exposures affect the human complex detoxification system resulting in chronic oxidative stress and metabolic alterations (De Luca et al., 2011). Repeated exposures to chemicals in food, air and water are postulated to be associated with the development of environmental sensitivities and associated conditions. Prevalence studies reveal a wide range of estimates and few, if any, explore symptom etiology. This study reveals significant variations in risk of MCS incidence are observed over small areas. A relatively small proportion of this risk may be explained by area-level socioeconomic characteristics, even among a population with relatively homogenous healthcare provision. Geostatistical modeling affords an opportunity to investigate spatial variations in risk of MCS incidence, could also be used as a tool for the allocation of health care resources in the management of MCS, and gen-

erates hypotheses about the location and nature of environmental exposures associated with MCS etiology. Spatial variation in MCS incidence remains even after accounting for age, sex and socioeconomic status, suggesting that environmental and/or genetic factors may play a significant role. Future research will investigate environmental and associated determinants of multiple chemical sensitivities. Conflict of interest There is no conflict of interest. Source of financial support None. Data and computing code Data may be requested from the Nova Scotia Health Authority. Demographic data for the calculation of rates may be obtained by request from Statistics Canada. A request for the code used in the calculation of Bayesian risk estimates can be forwarded to Dr. Patrick Brown. References American Chemical Society. American Chemical Society’s Chemical Abstracts Service (CAS) Registry 2014. [https://acswebcontent.acs.org/ annualreport/2014/2014_annual_report.pdf]. Barnett K, Mercer S, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 2012;380:37–43. Barr VJ, Robinson S, Marin-Link B, Underhill L, Dotts A, Ravensdale D, et al. The expanded chronic care model: an integration of concepts and strategies from population health promotion and the chronic care model. Healthcare Q 2003 http://www.longwoods.com/content/16763. (accessed January 29, 2017). Bartha L. Multiple chemical sensitivity: a 1999 consensus. Arch Environ Health 1999;54:147–9. Besag J, York J, Mollie A. Bayesian image restoration with two applications in spatial statistics. Ann Inst Stat Math 1991;43:1–20. Black DW, Doebbeling BN, Voelker MD, Clarke WR, Woolson RF, Barrett DH, et al. Multiple chemical sensitivity syndrome: symptom prevalence and risk factors in a military population. Arch Intern Med 20 0 0;160:1169–76. doi:10.1001/archinte.160.8.1169. Brown P. Model-based geostatistics the easy way. J Stat Softw 2015;63:1–24. Capital Health. Community health profiles 2015, [https://www.cdha. nshealth.ca/system/files/sites/123/documents/community-clinicalservices- health- system- planning- initiative- summary.pdf]. Caress SM, Steinemann AC. A national population study of the prevalence of multiple chemical sensitivity. Arch Environ Health Int J 20 03;58:30 0–5. doi:10.320 0/AEOH.58.6.30 0-305. Cooke GM. Biomonitoring of human fetal exposure to environmental chemicals in early pregnancy. J Toxicol Environ Health Part B 2014;17:205–24. doi:10.1080/10937404.2014.898167. Cullen M, Pace P, Redlich C. The experience of the Yale occupational and environmental medicine clinics with multiple chemical sensitivities, 1986–1991. Toxicol Ind Health 1992;8:15–19. Davidoff AL, Keyl PM. Symptoms and health status in individuals with multiple chemical sensitivities syndrome from four reported sensitizing exposures and a general population comparison group. Arch Environ Health Int J 1996;51:201–13. doi:10.1080/0 0 039896.1996.9936017. De Luca C RD, Pacifico V, Thai JCS, Korkina L. The search for reliable biomarkers of disease in multiple chemical sensitivity and other environmental intolerances. Int J Environ Res Public Health 2011;8:2770– 97. doi:10.3390/ijerph8072770. Fox R. The environment and multiple chemical sensitivity. Dalhousie University; 2001. Fukuda K, Straus S, Hickie I, Sharpe M, Dobbins J, Komaroff A. International chronic fatigue study group. The chronic fatigue syndrome: a comprehensive approach to its definition and study. Ann Intern Med 1994;121:953–9.

D. Rainham et al. / Spatial and Spatio-temporal Epidemiology 23 (2017) 35–45 Haines D, Murray J. Human biomonitoring of environmental chemicals—early results of the 20 07–20 09 Canadian Health Measures Survey for males and females. Environ Health 2012;215:133–7. Hunter DJ. Gene–environment interactions in human diseases. Nat Rev Genet 2005;6:287–98. doi:10.1038/nrg1578. Jones J, Terashima M, Rainham DG. Fast food and deprivation in Nova Scotia. Can J Public Health 20 09;10 0:32–5. Kreutzer R, Neutra RR, Lashuay N. Prevalence of people reporting sensitivities to chemicals in a population based survey. Am J Epidemiol 1999;150:1–12. doi:10.1093/oxfordjournals.aje.a009908. McGlone J, Rowan PJ, Davidson K, McLean DR. Environmental illness prevalence: a population-based study in Nova Scotia. Brit J Health Psychol 2002;7:23–9. doi:10.1348/135910702169330. Meggs WJ, Dunn KA, Dunn KA, Goodman PE, Davidoff AL. Prevalence and nature of allergy and chemical sensitivity in a general population. Arch Environ Health Int J 1996;51:275–82. doi:10.1080/0 0 039896.1996.9936026. Miller CS. The compelling anomaly of chemical intolerance. Ann N Y Acad Sci 2001;933:1–23. doi:10.1111/j.1749-6632.2001.tb05810.x. Miller CS, Mitzel HC. Chemical sensitivity attributed to pesticide exposure versus remodeling. Arch Environ Health Int J 1995;50:119–29. doi:10.1080/0 0 039896.1995.9940889. Nethercott JR, Davidoff LL, Curbow B, Abbey H. Multiple chemical sensitivities syndrome: toward a working case definition. Arch Environ Health Int J 1993;48:19–26. doi:10.1080/0 0 039896.1993.9938389. Orriols R, Costa R, Cuberas G, Jacas C, Castell J, Sunyer J. Brain dysfunction in multiple chemical sensitivity. J Neurol Sci 2009;287:72–8. doi:10.1016/j.jns.20 09.09.0 03. Pampalon R, Hamel D, Gamache P, Philibert M, Raymond G, Simpson A. An area-based material and social deprivation index for public health in Québec and Canada. Can J Public Health 2012;103:S17–22. Park J, Knudson S. Medically unexplained physical symptoms. Health Rep 2007;18:43–7. Reid S, Hotopf M, Hull L, Ismail K, Unwin C, Wessely S. Reported chemical sensitivities in a health survey of United Kingdom military personnel. Occup Environ Med 2002;59:196–8. doi:10.1136/oem.59.3.196.

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Richardson R, Engel C. Evaluation and Management of medically unexplained physical symptoms. Neurologist 2004;10:18–30. Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Ser B 2009;71:319–92. doi:10.1111/j.1467-9868.20 08.0 070 0.x. Sampalli T, Dickson R, Hayden J, Edwards L, Salunkhe A. Meeting the needs of a complex population: a functional health- and patient-centered approach to managing multimorbidity. J Comorbidity 2016;6:76–84. doi:10.15256/joc.2016.6.83. Schnakenberg E, Fabig K-R, Stanulla M, Strobl N, Lustig M, Fabig N, et al. A cross-sectional study of self-reported chemical-related sensitivity is associated with gene variants of drug-metabolizing enzymes. Environ Health 2007;6:6. doi:10.1186/1476-069X-6-6. Shinohara N, Mizukoshi A, Yanagisawa Y. Identification of responsible volatile chemicals that induce hypersensitive reactions to multiple chemical sensitivity patients. J Exp Sci Environ Epidemiol 2004;14:84–91. doi:10.1038/sj.jea.7500303. Terashima M, Rainham DGC, Levy AR. A small-area analysis of inequalities in chronic disease prevalence across urban and non-urban communities in the Province of Nova Scotia, Canada, 2007–2011. BMJ Open 2014;4. doi:10.1136/bmjopen-2013-004459. Terashima M, Rainham DG, Levy AR. Should we enhance the commonly used deprivation index for a regional context? Can J Public Health 2013;104:e311–16. Waller L, Carlin B. Handbook of spatial statistics. Disease mapping. CRC Press; 2010. Wolfe F. Fibromyalgia: on criteria and classification. J Musculoskelet Pain 1994;2:23–39. World Health Organization. Towards a common language for function, disability and health 2002, [http://www.who.int/classifications/icf/ training/icfbeginnersguide.pdf].