Obesity prevalence rates in Native Americans and Alaska Natives, a geographically and culturally diverse population, have reached alarming rates. In comparison to other populations, such as non-Hispanic whites and Asians, Native Americans/Alaska Natives are more likely to be obese (BMI ≥ 30 kg/m2)( Reference Pleis and Lethbridge-Cejku 1 , Reference Steele, Cardinez and Richardson 2 ). Obesity contributes to morbidity and mortality within a population( 3 ). With Native Americans/Alaska Natives displaying a disproportionate burden for chronic diseases such as CVD, cancer and diabetes( Reference Pleis and Lethbridge-Cejku 1 ), the high prevalence rates of obesity will affect the health status of these unique populations.
The high prevalence of obesity found within the Native American/Alaska Native population today may be related to the transition away from a traditional food system (TFS). A TFS includes all food within a particular culture available from local, natural resources that is culturally accepted and provides all of the essential nutrients necessary for optimal health( Reference Kuhnlein and Receveur 4 ). A TFS incorporates socio-cultural meanings, acquisition and processing techniques, use, composition and the nutritional consequences of consumption( Reference Kuhnlein, Receveur and Chan 5 ). Many of the diets of TFS were dependent on geographic location and season, such as a dominance of meat in the Arctic Circle and a large proportion of carbohydrates from corn in the Southwest USA( Reference West 6 ). A transition away from traditional foods occurs for various reasons including restricted traditional food resource use and harvesting areas, decreases in species density, concern about exposure to contaminants and the availability of market foods( Reference Kuhnlein, Receveur and Chan 5 , Reference Kuhnlein 7 , Reference Sharma, Yacavone and Cao 8 ).
The transition away from TFS is disconcerting given the evidence that TFS have health-promoting benefits( Reference Fujita, Braun and Hughes 9 – Reference Kuhnlein, Receveur and Soueida 12 ). For example, the Mediterranean diet and Asian diets have attracted considerable attention as healthier alternatives to the Western diet( Reference Trichopoulou, Costacou and Bamia 13 – Reference Shimazu, Kuriyama and Hozawa 16 ). With the presence of unique cultural and geographic eating patterns, indigenous populations may benefit from promoting their respective TFS. Such a change might improve health, reduce risk for disease, and positively influence cultural and traditional factors important to these populations.
In the present study we sought to determine the dietary patterns present within a unique group of Native Americans from the Pacific Northwest participating in the Communities Advancing the Studies of Tribal Nations Across the Lifespan (CoASTAL) cohort. The CoASTAL cohort represents a novel population and is of particular interest because of the high rates of obesity( Reference Fialkowski, McCrory and Roberts 17 ). Our primary hypothesis was that traditional foods of the Pacific Northwest Tribal Nations (PNwT), such as shellfish, salmon, venison and berries, would have significant variance in consumption in comparison to other food groups. Our secondary hypothesis was that higher consumption of the traditional food pattern derived from the CoASTAL cohort would be associated with lower BMI and greater adherence to selected Dietary Reference Intakes (DRI)( 18 ). Our final hypothesis was that limiting the sample to those considered to report energy intake plausibly within the CoASTAL cohort would further elucidate the presence of a traditional dietary pattern and its association with lower BMI and current dietary recommendations.
Materials and methods
Study design and participant recruitment
The CoASTAL cohort originated from an official invitation of one of the Tribal Nations of the Pacific Northwest Coast of Washington State. The investigators and members of three neighbouring Tribal Nations worked towards establishing trust, creating communication channels and resolving study design issues prior to initiating the study. Enrolment for the 5-year prospective study began in June 2005.
The sample for the present cross-sectional analysis was selected from the 520 non-pregnant adults (18+ years) participating in the CoASTAL cohort. Dietary patterns were estimated for participants who completed up to four dietary records and had weight and height information collected during the first year (418/520; 80 %). At the enrolment visit, participants provided information about educational attainment, occupation and specific healthful behaviours (e.g. smoking). The Institutional Review Boards from the University of Maryland and Purdue University approved the study protocol. Details of the study rationale and methods have been published elsewhere( Reference Fialkowski, McCrory and Roberts 19 ), but are summarized briefly here.
Dietary assessment
Field coordinators, who were registered tribal members, participated in day-long training sessions with study dietitians initially and annually. Training included distribution of the dietary records, evaluating completeness of food entries, probing, portion size estimation, food preparation methods and accuracy of data recording. These field coordinators were then able to train the participants in record-keeping techniques using various measuring aids. Participants were provided a tool kit of measuring devices (e.g. measuring cups and spoons) and recording materials. Dietary records were completed every 4 months as two 1 d dietary records and one set of 2 d of dietary records for a total of four dietary records over 1 year. Respondents’ recording days were assigned based on the day of their first visit and at least one day included a weekend day. Data coding and entry were performed by staff trained in the use of the Nutrition Data System for Research (NDS-R) Database version 4·07 (© Regents of the University of Minnesota). Food group servings from the dietary records were calculated as the mean of the number of days reported. At least 2 d were reported by 362 individuals (362/418; 87 %) and the mean number of days recorded was 3.
Food groupings
We used reduced rank regression (RRR) to consolidate the 166 NDS-R food groupings from the dietary record data into forty-two groups according to macronutrient composition, culinary usage, cultural specificity and prior classifications found in the literature( Reference Newby, Muller and Hallfrisch 20 – Reference Hu, Rimm and Smith-Warner 24 ). Unit designation for the food groupings was servings/d. Some foods (e.g. eggs) comprised their own group. Multiple combinations of food groupings were tested including classifying all of the traditional foods into one food group. The end result did not differ between these combinations and therefore the food groupings ultimately used are described here. Table 1 shows the final food groupings.
Table 1 The forty-two food groups derived from the dietary records used in reduced rank regression analysis
Anthropometric measures
Participants were measured for height and weight by the trained field coordinators. Prior to measures, participants were instructed to remove heavy outer clothing to a single layer of clothing, remove shoes and empty pockets. Height was measured to the nearest inch (2·54 cm) using a portable stadiometer (Shorr Infant/Child/Adult Portable Height-Length Measuring Board, Olney, MD, USA). Weight was measured on a calibrated electronic scale and recorded to the nearest pound (0·454 kg; SECA Digital Floor Scale, Hanover, MD, USA). BMI was calculated using the formula [weight (kg)]/[height (m)]2. Obesity was defined as BMI ⩾ 30 kg/m2( 25 ).
Plausibility determination
Individuals with plausible reported energy intake (rEI) were classified using previously developed and described methods( Reference McCrory, Hajduk and Roberts 26 , Reference Huang, Roberts and Howarth 27 ). Briefly, DRI equations were used to calculate predicted energy requirements( 28 ). rEI was evaluated as plausible or implausible after applying the 1·4 sd cut-off method to the population sample( Reference Huang, Roberts and Howarth 27 ). Individuals within 1·4 sd were considered to have plausible rEI, those with rEI above or below 1·4 sd were considered to implausibly report energy intake. There were no significant differences in characteristics between those considered to plausibly and implausibly report energy intake.
Statistical analysis
The statistical method RRR, otherwise known as the maximum redundancy analysis, using the PLS procedure in Statistical Analysis Software (SAS), was used to derive dietary pattern scores. The use of this method to derive dietary patterns has been described in detail elsewhere( Reference Hoffmann, Schulze and Schienkiewitz 29 ). In brief, RRR allows for the calculation of dietary pattern scores similarly to those extracted by factor analysis. However, where factor analysis determines dietary pattern scores by maximizing the explained variation of a set of predictor variables (e.g. food groups), RRR derives dietary pattern scores of predictor variables by accounting for as much of the variation in response variables (e.g. nutrients related to weight) as possible( Reference Hoffmann, Schulze and Schienkiewitz 29 , Reference Nettleton, Steffen and Schulze 30 ). The RRR approach has been reported to be preferred to factor analysis for determining dietary patterns that are predictive of risk for chronic disease( Reference Hoffmann, Boeing and Boffetta 31 ) and therefore was selected as the method used to relate BMI to dietary patterns derived from the CoASTAL cohort.
In the present study, the nutrient densities of total fat, total carbohydrates and fibre (g total fat/4184 kJ (1000 kcal), g carbohydrates/4184 kJ (1000 kcal) and g fibre/4184 kJ (1000 kcal)) were chosen as the response variables because these variables have consistently been found to be associated with weight status (e.g. BMI)( Reference Schulz, Nothlings and Hoffmann 32 – Reference Sherwood, Jeffery and French 39 ). Intake data from the food groups (e.g. red meat, fruit, eggs, fish, pasta, etc.) determined by the dietary records served as predictors. These food groups (i.e. predictor variables) are summarized into distinct dietary patterns that capture the variation in the nutrient densities of total fat, total carbohydrates and fibre (i.e. response variables). In RRR, the number of extracted dietary patterns cannot be higher than the number of selected response variables (i.e. total fat, total carbohydrates and fibre); therefore, three dietary patterns were obtained for both the total group and the plausible rEI group( Reference Schulz, Nothlings and Hoffmann 32 ).
Factor loadings, which reflect the correlation of individual food groups within each of the derived dietary patterns, were obtained from the RRR. To focus on food groups that significantly contributed to the dietary pattern, we only considered those food groups with an absolute factor loading >0·2( Reference Hoffmann, Schulze and Schienkiewitz 29 , Reference Schulz, Nothlings and Hoffmann 32 , Reference Heidemann, Hoffmann and Spranger 40 – Reference Heroux, Janssen and Lam 44 ). The food groups above the cut-off were used to label the dietary patterns. For each participant, a dietary pattern score was calculated by summing the product of the contributing food group intakes and scoring coefficients. Those food groupings with an absolute factor loading <0·2 did not contribute to the dietary pattern score. The scores for each dietary pattern were then converted into quartiles for use in further analysis. Thus, for each dietary pattern quartile 4 would be composed of those who conform most (e.g. consume the most) to that particular pattern, while quartile 1 would be the lowest conformers (e.g. consume the least).
In order to assess the relationship between BMI and quartile of dietary pattern intake from the dietary records, multiple linear regression models were used. BMI classification does not differ by gender so men and women were analysed both together and separately. These findings were confirmed with binary logistic regression models using obesity as the dependent variable. For evaluating attainment of nutrient recommendations, the Institute of Medicine specifies using the information from 24 h dietary recalls, observation or dietary records( 18 ). Therefore, binary logistic regression models were used to evaluate how the dietary patterns derived from the dietary records related to the DRI for total fat, saturated fat and dietary fibre. All models were adjusted for age (ages were calculated from date of birth and date of first visit), education, employment and smoking status. Interaction terms were examined but none were significant. For those patterns found to be significantly associated with BMI, the general linear model was used to determine the mean BMI of participants within each quartile after adjustment for age, education, employment and smoking. All RRR analyses were performed using the SAS statistical software package version 9·1 (SAS Institute, Cary, NC, USA). All other analyses were completed using the SPSS statistical software package version 16·0 (SPSS Inc., Chicago, IL, USA). Results were considered significant at P < 0·05, using two-sided tests.
Results
Men and women included in the present analysis were similar in age and BMI (Table 2). A majority of the individuals in the sample were between the ages of 31 and 50 years and had attended at least some college. Foods with a factor loading >|0·2|, which indicates the level of correlation to the derived dietary patterns, are shown in Table 3. A traditional food pattern did not emerge in either the total group or the group with plausible rEI. A dietary pattern that loaded positively high in only fruit and sweet drinks explained most of the variation between the response variables and predictors in the total sample. The dietary pattern that explained the most variation for the plausible sample was a vegetarian and grains pattern. Legumes, tomato, pasta, sweetened drinks and unsweetened cereals had high positive loadings on this pattern.
Table 2 Characteristics of adults participating in the CoASTAL cohort with complete weight, height and diet information
CoASTAL, Communities Advancing the Studies of Tribal Nations Across the Lifespan; rEI, reported energy intake.
†Percentages may not add up to 100 due to rounding.
Table 3 Factor loading matrix and percentage of variance explained for adults (18+ years) participating in the CoASTAL cohort who completed dietary records†
CoASTAL, Communities Advancing the Studies of Tribal Nations Across the Lifespan; rEI, reported energy intake.
†Factor loadings <|0·20| are not shown.
Only those dietary patterns that were significantly associated with BMI and/or obesity are shown in Tables 4 and 5, as well as the adjusted mean BMI for each dietary pattern quartile. When examining the total group, significant associations were noted only when evaluating by gender. In men only, moderate consumption of the vegetables, fruit and whole grains pattern was significantly associated with a lower BMI and a lower risk for being obese (see Table 4). For the plausible reporters of energy intake (Table 5), the highest quartile of healthy pattern consumers was associated with a significantly higher BMI than the lowest consumers. When plausible reporters were evaluated by gender, only women demonstrated a significant association between body size and the healthy pattern. The highest quartile of healthy pattern consumers had a BMI significantly higher than the lowest quartile of consumers (see Table 5). Furthermore, the sweet drinks pattern was associated significantly with body weight in women (Table 5), with moderately high consumption significantly associated with a lower BMI.
Table 4 The relationship of dietary patterns as quartiles and BMI or obesity among men (18+ years) participating in the CoASTAL cohort†‡
CoASTAL, Communities Advancing the Studies of Tribal Nations Across the Lifespan; Ref., referent category.
a,b,c,dMean values within a row with unlike superscript letters were significantly different (P < 0·05).
**P < 0·01, ***P < 0·001.
†No significant relationship was apparent in the total sample or women; therefore data are not shown.
‡All models adjusted for age, education, employment and smoking.
§Quartile 1 corresponds to the lowest dietary pattern intake.
∥Quartile 4 corresponds to the highest dietary pattern intake.
¶β Coefficient represents the mean difference from quartile 1.
††Obesity defined as BMI ⩾ 30 kg/m2.
Table 5 The relationship of dietary patterns as quartiles and BMI or obesity among non-pregnant adults (18+ years) participating in the CoASTAL cohort who plausibly reported their energy intake†‡
CoASTAL, Communities Advancing the Studies of Tribal Nations Across the Lifespan; Ref., referent category.
a,b,cMean values within a row with unlike superscript letters were significantly different (P < 0·05).
*P < 0·05, **P < 0·01.
†No significant relationship was apparent in men; therefore data are not shown.
‡All models adjusted for age, education, employment and smoking.
§Quartile 1 corresponds to the lowest dietary pattern intake.
∥Quartile 4 corresponds to the highest dietary pattern intake.
¶β Coefficient represents the mean difference from quartile 1.
††Obesity defined as BMI ⩾ 30 kg/m2.
The likelihood of meeting the Acceptable Macronutrient Distribution Range (AMDR) for percentage of energy consumed from total fat and saturated fat, as well as the Adequate Intake (AI) for dietary fibre, was evaluated for the dietary patterns (see Table 6). Adjusted models only are shown. The likelihood of meeting the AMDR for total fat and saturated fat was significantly higher among the highest consumers of the fruit and sweet drinks pattern. The highest consumers of the vegetables, fruit and whole grains pattern were about six times more likely to meet the AI for dietary fibre. The highest consumers of the high fat and sugar pattern were almost 70 % less likely to meet the AMDR for saturated fat. When limiting the sample only to those with plausible rEI, the third and fourth quartiles of the vegetarian and grains pattern were much more likely to meet the AMDR for total fat and saturated fat. The highest consumers of the sweet drinks pattern were less likely to meet the AMDR for saturated fat and the AI for dietary fibre.
Table 6 Adjusted odds ratios of dietary patterns derived from dietary records meeting the Dietary Reference Intakes among non-pregnant adults (18+ years) participating in the CoASTAL cohort†
CoASTAL, Communities Advancing the Studies of Tribal Nations Across the Lifespan; rEI, reported energy intake; AMDR, Acceptable Macronutrient Distribution Range; AI, Adequate Intake.
*P < 0·05, **P < 0·01, ***P < 0·001.
†All models adjusted for age, education, employment and smoking.
‡Quartile 1 = referent category.
§20–35 % of energy intake.
∥<10 % of energy intake.
¶21–38 g/d.
Discussion
Among the present sample of PNwT adults, a traditional food pattern predominant in foods such as shellfish, fish, game, berries and tea did not emerge using dietary records. Traditional foods were modelled in two different configurations and did not load positively high in any of the extracted dietary patterns examined. This would suggest that the variance was not great enough for traditional foods to emerge as an influential pattern. RRR seeks to capture the variation in intake with regard to certain response variables( Reference Hoffmann, Schulze and Schienkiewitz 29 ). In the present study, the nutrient densities of total fat, carbohydrates and dietary fibre were used as the response variables to maximize the explained variation among the dietary patterns( Reference Schulz, Nothlings and Hoffmann 32 – Reference Sherwood, Jeffery and French 39 ). Although not detected by RRR, we know that in this CoASTAL cohort population, traditional foods are being consumed at some level( Reference Fialkowski, McCrory and Roberts 19 ). Previously, we reported that over 50 % of participants who completed a dietary record were identified as a seafood consumer in comparison to 98 % of those completing the FFQ( Reference Fialkowski, McCrory and Roberts 19 ). However, their consumption of seafood, which would be considered a traditional food, did not describe the variance in intake based on the selected response variables. To capture the contributions of traditional foods to the health and nutrient intakes of this population, methods other than dietary patterns may need to be used( Reference Kuhnlein, Receveur and Soueida 12 , Reference Tooze, Midthune and Dodd 45 ). For example, the propensity method( Reference Tooze, Midthune and Dodd 45 ) takes advantage of the information from an FFQ as well as dietary records simultaneously.
The patterns derived in this population reflected two different types of eating habits. The pattern contributing the most variance to fat, carbohydrates and fibre density was dominated by food items considered high in energy, such as sweetened beverages, similar to results found in other Native populations( Reference Sharma, Yacavone and Cao 8 ). In contrast, the dietary pattern contributing the second highest variance to those nutrient densities was heavily influenced by foods considered healthful such as whole grains and vegetables. The presence of a healthy pattern within this population is consistent with dietary pattern studies done in other populations( Reference Schulz, Nothlings and Hoffmann 32 , Reference Weikert, Hoffmann and Dierkes 43 , Reference Drogan, Hoffmann and Schulz 46 – Reference Newby, Weismayer and Akesson 49 ). However, in contrast to most of the other studies( Reference Newby, Muller and Hallfrisch 20 , Reference Schulz, Nothlings and Hoffmann 32 , Reference Fung, Rimm and Spiegelman 50 , Reference Murtaugh, Herrick and Sweeney 51 ), high intake of the healthy pattern from the present study was associated with a higher BMI. Only one study found a similar association in women( Reference Reedy, Wirfält and Flood 52 ). Women from the NIH-AARP Diet and Health study with a dietary pattern dominated by foods low in energy were associated with poorer health characteristics( Reference Reedy, Wirfält and Flood 52 ). Interestingly, similarly to the NIH-AARP Diet and Health study( Reference Reedy, Wirfält and Flood 52 ), we also found this association to differ by gender. Men tended to be ‘health conscious’ with moderately high consumption of a pattern dominated by foods considered to be healthy, associated with a lower BMI and risk for being obese. However, this relationship did not remain once plausibly reporting energy intake was accounted for. Also consistent with findings in other populations was the presence of an ‘empty calorie’ (e.g. fruit juice and sweet beverage) dietary pattern( Reference Ogden, Carroll and Flegal 53 – Reference Millen, Quatromoni and Copenhafer 55 ). Although a previous study did report this pattern to be associated with a higher BMI( Reference Millen, Quatromoni and Copenhafer 55 ), we did not find this association in the CoASTAL cohort.
The differences noted between the present population and findings in other populations may be methodological. The use of RRR to determine dietary patterns is a relatively new approach to determining dietary patterns in population-based studies( Reference Hoffmann, Schulze and Schienkiewitz 29 ). RRR has not been used within Native American populations and applying this method to the CoASTAL cohort data set may further establish its effectiveness in deriving dietary patterns related to risk factors for chronic disease (e.g. obesity). Previously, dietary patterns have been derived using methods such as principal component analysis( Reference Moeller, Reedy and Millen 54 , Reference Kant 56 ). RRR and principal component analysis are both dimension reduction techniques that result in uncorrelated summary variables (e.g. dietary patterns). However, RRR has become the recommended method to use when evaluating how certain predictors (e.g. food groups) relate to a risk factor for disease (e.g. body weight) because dietary patterns are derived from predictor variables (e.g. food groups) by maximizing the amount of variation in response variables (e.g. body weight). RRR was successfully used to extract dietary patterns that predicted weight change among the cohort of the European Prospective Investigation into Cancer and Nutrition( Reference Schulz, Nothlings and Hoffmann 32 ). To our knowledge, most studies have used data from an FFQ or 24 h dietary recall(s) to derive dietary patterns and limited studies have used dietary records.
The noted differences from previous literature in reported associations between dietary patterns and BMI may be reflected by the cross-sectional nature of the present study. For example, the high consumers of the healthier patterns may be trying to adopt a healthier eating pattern to lose weight or prevent further weight gain( Reference Reedy, Wirfält and Flood 52 ). These individuals may also be adopting healthier foods but not adopting recommended eating portions. Further study will need to occur to determine whether these dietary patterns are consistent and maintain the same relationship with body weight over time.
In comparison to the guidelines set for total fat, saturated fat and dietary fibre, high consumption of some of the extracted dietary patterns can be promoted for increasing the likelihood of meeting these recommendations. For example, higher consumption of (i) the fruit and sweet drink pattern, (ii) the vegetables, fruit and whole grains pattern and (iii) the vegetarian and grains pattern were associated with a significantly higher likelihood for meeting the above recommendations. Other dietary patterns, such as the high fat and sugar pattern, were consistent with expectations. High consumption of the high fat and sugar pattern reduced the likelihood for meeting the AMDR for saturated fat.
The present study is different from other dietary pattern studies in that we accounted for plausible rEI. Dietary assessment methods will likely always have some level of error and adults’ ability to accurately self-report their dietary intake may pose challenges( Reference Mahabir, Baer and Giffen 57 , Reference Champagne, Bray and Kurtz 58 ). In a previous study, when accounting for plausible rEI the results of the CoASTAL cohort's energy intake correlated significantly with objective measures, such as body weight and BMI( Reference Fialkowski, McCrory and Roberts 17 , Reference Fialkowski, McCrory and Roberts 19 ). In the present study, the amount of variation that was explained increased by 12 % when limiting the sample to plausible reporters of energy intake. However, in the present study we found that the dietary patterns extracted from the CoASTAL cohort were robust and not strongly influenced by under-reporting, suggesting that dietary patterns may reduce some of the error associated with dietary assessment. The dietary patterns extracted from the total sample were similar to those patterns extracted in the plausible group. This consistency may validate the presence of these dietary patterns.
In the present study, the extracted dietary patterns are limited by the response variables that were chosen (e.g. total fat, carbohydrates, dietary fibre). These theoretically derived response variables based primarily on non-Hispanic white population groups( Reference Schulz, Nothlings and Hoffmann 32 – Reference Sherwood, Jeffery and French 39 ) could be different from Native American populations. RRR has never been used to assess the diet of a Native American population; therefore, the response variables chosen may not fully explain the variance in intake of the predictor variables (e.g. food groups) with regard to body weight. Also, we did not determine how these dietary patterns associate with current dietary recommendations for other nutrients. Meeting the recommendations for total and saturated fat and dietary fibre was evaluated because of these nutrients commonly being over- or underconsumed, respectively, in other Native populations( Reference Stang, Zephier and Story 59 – Reference Nobmann and Lanier 68 ). The proportion meeting the dietary recommendations for other nutrients will need to be explored. Finally, many of the defined food groups are composed of foods not commonly misreported; therefore, there is less of an opportunity for under-reporting to affect our results( Reference Bingham, Cassidy and Cole 69 ).
Conclusions
We were not able to document a traditional food pattern in the CoASTAL cohort using RRR. This finding may mean that alternative response variables or methods are needed to describe traditional food patterns consumed today. In the present study, dietary patterns that were high in healthier foods such as vegetables or in less healthful foods such as sweetened beverages were consistently derived. These dietary patterns were also found to be significantly associated with the likelihood of meeting or not meeting the dietary recommendations for total fat, saturated fat and dietary fibre. However, with regard to meeting recommendations for body weight, further longitudinal assessment will be needed to confirm these results.
Acknowledgements
Sources of funding: This work was supported by the National Institute of Environmental Health Sciences (NIEHS) grant number 5R01ES012459-05. The project was also partially supported by the National Institute of Health/National Center for Research Resources (NIH/NCRR) grant number RR025761 and the Alfred P. Sloan Foundation. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS, the NIH, the NCRR or the Alfred P. Sloan Foundation. Conflict of interest: None of the authors had a conflict of interest. Authors’ responsibilities: M.K.F., M.A.M., S.M.R., J.K.T., L.M.G. and C.J.B. designed the research; M.K.F., S.M.R., L.M.G. and C.J.B. conducted the research; M.A.M. and J.K.T. provided statistical guidance; M.K.F. analysed the data and wrote the manuscript; L.M.G. and C.J.B. had primary responsibility for the final content. All authors were involved in critical review of the manuscript and approved the final manuscript. Acknowledgements: The authors thank the following for their contributions and participation: Makah, Quinault and Quileute Indian Nation Tribal Councils; Vincent Cooke and Rachel Johnson from the Makah Environmental Health Division; Bill Parkin from the Makah Marina; Mel Moon, Mitch Lesoing, Jay Burns and Cathy Salazar from the Quileute Department of Natural Resources; Joe Schumacker and Dawn Radonski from the Quinault Department of Fisheries; the tribal medical advisory board, Thomas Van Eaton of Makah Health Services, Robert Young of the Quinault Health Center and Brenda Jaime-Nielson and Brad Krall of the Quileute Health Center; and the tribal advisory committee, Theresa Parker, Deanna Buzzell-Gray, June Williams, Melissa Peterson-Renault, Mary Jo Butterfield and Edith Hottowe from the Makah Indian Nation and Alena Lopez, Ervin Obi and Carolyn Gennari from the Quinault Indian Nation.
