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Relationship between Body Mass Index and Diagnosis of Overweight or Obesity in Veterans Administration Population

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21 April 2023

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23 April 2023

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Abstract
Background: This paper examined the gap between obesity and its diagnosis for cohorts of patients with overweight, obesity, and morbid obesity in the Veterans Administration (VA) population. Using the risk adjustment models, it also identified factors associated with the underdiagnosis of obesity. Methods: Analysis was performed on a VA data set. We identified diagnosed patients and undiag-nosed patients (identified through BMI but not diagnosed by ICD-10 codes). The groups’ de-mographics were compared using nonparametric chi-square tests. We used logistic regression analysis to predict the likelihood of the omission of diagnosis. Results: Of the 2,900,067 veterans with excess weight, 46% were overweight, 46% had obesity, and 8% of them had morbid obesity. The overweight patients were the most underdiagnosed (96%), followed by the obese (75%) and morbidly obese cohorts (69%). Older, male, and White patients were more likely to be undiagnosed as overweight and obese; younger males were more likely to be undiagnosed as morbidly obese. (p<.05) Comorbidities significantly contributed to diagnosis. Conclusion: Underdiagnosis of obesity continues to be a significant problem despite its prevalence. Diagnosing obesity accurately is necessary to provide effective management and treatment.
Keywords: 
Subject: Medicine and Pharmacology  -   Other

1. Introduction

By the year 2000, the human race reached a milestone, when, for the first time in history, the number of adults with excess weight surpassed those who were underweight [1,2]. The average American today is overweight or obese [3]. In the United States (US), the prevalence of obesity has been accelerating every year since the World Health Organization (WHO) declared it a pandemic in 2007, affecting individuals of all ages and all segments of society [2]. The prevalence of obesity in the US has nearly tripled in recent decades, increasing from 13% in 1960-1962 to 36.5% in 2011-2014 [4], thus affecting an estimated 60 million American adults. A 2017 report from the National Health and Nutrition Examination Survey (NHANES) showed that in the US, over 40% of young adults 20 to 39 years of age, 44% of middle-aged adults 40 to 59 years old, and 43% of older (>65 years) adults are obese [5].
Obesity is a particular concern among veterans [3]. People who have served in the US military suffer from obesity in higher numbers and overall have disproportionately poorer health status when compared with that of the older non-veteran US population. This disproportionate effect on veterans may further compound their overall risk for morbidity and mortality [3]. Of the 6 million patients who receive Veterans Administration (VA) health care yearly, 80% fall into the categories of either overweight or obesity [3,6]. Previous studies done in this population have highlighted certain subgroups that are at a higher risk, such as Black women veterans, women veterans with schizophrenia, younger veterans (<65 years), and Native Hawaiian/Other Pacific Islander and American Indian/Alaska Native veterans [3,7]. The prevalence of obesity among veterans with post-traumatic stress disorder (PTSD) is higher than the prevalence of obesity among veterans overall within the VA (47% vs. 41%, respectively) [3,8].
The continued growth of the obesity epidemic is particularly concerning because obesity can have psychological, physical, and social impacts on an individual’s well-being. In addition, obesity is a significant risk factor for chronic diseases such as cardiovascular disease, hypertension, type 2 diabetes mellitus, hyperlipidemia, stroke, certain cancers, obstructive sleep apnea, liver and gallbladder disease, osteoarthritis, and gynecological problems [3,9,10,11,12,13]. Obesity and its comorbidities are a major cause of morbidity and mortality in the US [3,14]. Furthermore, it results in poor long-term health outcomes, which are costly to the affected individual, their families, and, ultimately, to the US healthcare system [3,13].
The accurate identification and diagnosis of obesity are essential for the evaluation and treatment of obesity and its associated comorbidities [15]. The US Preventive Services Task Force (USPSTF) recommends screening all adults for obesity [16]. It also recommends that once a diagnosis of obesity has been established by body mass index (BMI) ≥30 kg/m2, the patient should be offered or referred to an interdisciplinary lifestyle intervention program [16].
Despite these recommendations and formal recognition by the American Medical Association as a disease [17], obesity continues to be underdiagnosed in clinical practice [18]. Furthermore, newly approved medications for chronic weight management have been shown in clinical trials to improve weight loss by 15% or more than 10 pounds in one year. However, since these drugs are available only by prescription, a correct diagnosis of obesity should be made. Without an appropriate prescription and insurance coverage, these drugs cost between $1000 and $1300 per month, placing a heavy economic burden on the patient. Hence, identifying undiagnosed obesity will allow more patients to access these treatments and receive the recommended care, comprising an interdisciplinary lifestyle intervention program.
This paper examines the diagnosis of overweight, obesity, and morbid obesity in VA patients and identifies factors associated with the underdiagnosis of obesity in this population.

2. Methods

The VA data set used for the analysis included data from 25 million enrollees as of December 2022 and contained inpatient and outpatient files, lab information, survival, and vital statistics (e.g., height, weight, and blood pressure) collected from 152 VA hospitals, 133 VA Community Living Centers, and 958 outpatient clinics. BMI was calculated as weight (kg)/[height (m)]2 using the vital statistics from the data set [19].
Three groups of patients were defined using the BMI: overweight (≥25 kg/m2, <30 kg/m2), obese (≥30 kg/m2 to <40 kg/m2), and morbidly obese (≥40 kg/m2). Among these patients, diagnosed patients were classified by ICD-10 codes as follows:
  • Overweight identified by E66.3, Z68.26, Z68.27, Z68.28, Z68.29, Z68.53
  • Obesity identified by E66.9, E66.09, E66.1, E66.8, Z68.3, Z68.54
  • Morbid obesity identified by E66.01, E66.2, Z68.4, Z68.54
We then identified two cohorts: Diagnosed Patients and Undiagnosed Patients (patient groups identified through BMI but not diagnosed by ICD-10 codes). The two groups were compared based on demographics (sex, age, race, and comorbidities, ) using nonparametric chi-square tests. We used logistic regression analysis to predict the likelihood of the omission of diagnosis of all obese patients. Age, gender, race, and comorbidities such as coronary artery disease, hypertension, hyperlipidemia, diabetes, sleep apnea, osteoarthritis, hyperuricemia, and gallbladder disease were used as explanatory variables, and each variable’s impact on the odds ratio (OR) of the omission of the diagnosis was calculated. An alpha level of .05 was used as the threshold level of significance. All statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC).

3. Results

A total of 2,900,067 veterans had excess weight problems: 46% of these veterans were overweight, 46% had obesity, and 8% of them had morbid obesity. The overweight population was the most underdiagnosed by ICD-10 codes. Of 1,333,473 patients who were overweight, only 57,675 (4%) were diagnosed by ICD-10 codes as overweight at VA facilities. Of 1,343,968 obese patients, only 329,338 patients (25%) were diagnosed as obese. Of the total 222,626 patients with morbid obesity, only 63,380 patients (31%) were diagnosed as having morbid obesity (Table 1).
For the overweight groups, relative to diagnosed patients, undiagnosed patients were more likely to be older (63.99 years vs. 61.11 years, p<.001), male (93.53% vs. 88.92%, p<.001), and White (71.36% vs. 69.22%, p<.001). Additionally, undiagnosed patients were more likely to have hypertension (40.87% vs. 43.15%, p<.001), hyperlipidemia (29.10% vs. 34.61%, p<.001), diabetes (20.72% vs. 23.97%, p<.001), sleep apnea (6.92% vs. 10.74%, p<.001), osteoarthritis (11.46% vs. 12.80%, p<.001), and hyperuricemia (0.84% vs. 1.22%, p<.001). There were no statistical differences in coronary artery disease (7.13% vs. 6.92%, p=.0568) and gallbladder disease (0.06% vs. 0.08%, p=.1041) (Table 2).
Results for the obese groups were similar to the overweight groups except for comorbidities. For obese groups, relative to diagnosed patients, undiagnosed patients were more likely to be older (61.63 years vs. 59.45 years, p<.001), male (92.69% vs. 89.64%, p<.001), White (70.86% vs. 68.80%, p<.001), and more likely to have coronary artery disease (7.06% vs. 7.52%, p<.0001), hypertension (45.91% vs. 50.33%, p<.001), hyperlipidemia (30.95% vs. 36.26%, p<.001), diabetes (28.83% vs. 33.71%, p<.001), sleep apnea (14.02% vs. 21.97%, p<.001), osteoarthritis (13.57% vs. 16.00%, p<.001), hyperuricemia (1.02% vs. 1.49%, p<.001). There were no statistical differences in gallbladder disease (0.06% vs. 0.08%, p=.0008).
For morbidly obese patients, unlike the obese and overweight group, relative to the diagnosed group, undiagnosed patients were slightly younger (59.00 years vs. 59.22 years, p<.0001) and were more likely to be male (88.68% vs. 90.32%, p<.001) and White (68.73% vs. 70.78%, p<.001). Having a comorbidity increased the likelihood of diagnosis. These patients were more likely to have coronary artery disease (6.85% vs. 9.19%, p<.0001), hypertension (52.70% vs. 61.85%, p<.001), hyperlipidemia (32.31% vs. 39.01%, p<0.001), diabetes (39.55% vs. 49.03%, p<.001), sleep apnea (28.56% vs. 42.21%, p<.001), osteoarthritis (16.62% vs. 22.57%, p<.001), and hyperuricemia (1.25 % vs. 1.78 %, p<.001). There were no statistical differences in gallbladder disease (0.06% vs. 0.09%, p=.0095).
Results of logistic regression analyses indicate that the patient characteristics assessed were predictive of an obesity diagnosis (including overweight, obesity, or morbid obesity). As the ORs indicated, overweight, obese, and morbidly obese patients were more likely to be undiagnosed if they were older (OR=1.34, 1.44, and 1.16, respectively) and male (OR=1.64, 1.38, and 0.92, respectively. Other than white and black, patients were more likely to be undiagnosed for the obese and morbidly obese population (OR= 0.97 [95% confidence interval (CI), 0.95-0.99]; OR= 0.94 [95% CI, 0.88-0.99], respectively) (Table 3).
Any comorbidity increased the likelihood of an obesity diagnosis, including coronary artery disease and gallbladder disease. Therefore, comorbidity consistently increased the likelihood of diagnosis across the groups after controlling for age, gender, and race. As a result, almost all ORs are under one and significant. For the overweight and obese population, patients were more likely to be diagnosed if they had hypertension (OR=0.96 [95% CI, 0.94-0.98]; OR= 0.92 [95% CI, 0.91-0.92], respectively). However, for the morbid obesity population, patients were more likely to be diagnosed if they had hyperlipidemia (OR=0.94 [95% CI, 0.92-0.96]).

4. Discussion

It is estimated that less than 30% of adults with obesity receive this diagnosis during their primary care physician visits [18]. The present study confirmed that underdiagnoses of obesity is clear for both genders and across all age groups and races within the VA population. Our data showed that 69% of morbidly obese patients were undiagnosed, 75% of obese patients were underdiagnosed, and almost all of the overweight patients (96%) went undiagnosed. Our findings are consistent with those of Betancourt et al. [3], who examined the obesity and morbidity risk in the US veteran population and found that 69.7% of the population had obesity/overweight status, similar to the 69% of undiagnosed morbidly obese patients in our study. This highlights that patients’ overweight or obese status according to BMI does not necessarily translate to clinical practice and administrative data, therefore increasing the likelihood of not receiving proper treatment. However, comparisons must be interpreted with the understanding that VA data reflect a treatment-seeking population, who may be older and sicker than the general population [7].
Several reasons have been attributed to the underdiagnosis of obesity, including the perception by healthcare providers that obesity is not a disease, low expectations for patient success, lack of time or knowledge to provide appropriate advice regarding nutrition, societal stigma, concerns with denials of payment for services, and limited therapeutic tools to treat patients with obesity [20]. A recent survey of people with obesity (n = 3,008) and healthcare providers (HCPs) (n = 606) discovered that although obesity is perceived as a disease by the majority of people with obesity (65%) and HCPs (80%), providers and patients struggle with talking about weight for various reasons [15,21]. Patients report that they do not seek help from their HCP because they believe it is their personal responsibility (44%); they already know what to do (37%); and/or they do not have financial means to support a weight loss effort (23%) [15,21]. The primary reasons HCPs do not initiate discussions about weight loss are time constraints (52%) and having more important issues to discuss with the patient (45%) [15,21]. Other HCPs cite that the patient was not motivated to lose weight (27%) or was not interested in weight loss (26%), and/or expressed concern about the patient’s emotional state or psychological issues (22%) [15,21]. Furthermore, recognition and coding for obesity in civilian healthcare settings are poor [15]. Previous studies have shown that among patients who met the objective criteria for obesity, few were diagnosed with an obesity code [15,22,23] or clinical visits lacked a complete height and weight record to facilitate calculating BMI [15]. Additionally, another study showed that patients with known comorbidities (e.g., type 2 diabetes, hypertension, sleep apnea,) were not diagnosed with obesity [15,21]. Additionally, some studies indicated that older patients and men were significantly less likely to have an obesity diagnosis, and the presence of an obesity diagnosis was the strongest predictor of creating a plan to address obesity [15,24].
Our data add to the literature indicating that the underdiagnosis of obesity could be attributable to the low documentation of overweight and obesity due to under-coding and the general perception that these conditions are not identified as a significant problem in primary care [15,22]. Similarly, Mattar et al. [22] examined the prevalence of obesity documentation among patients with corresponding BMI as contained in patients’ electronic medical records and found that 5.6% of patient records listed obesity as a problem.
Furthermore, a reason why morbid obesity is more likely to be diagnosed could be the visual undeniability of the problem’s severity [22]. Additionally, these results indicate that providers may often wait until the health condition becomes severe enough to warrant an ICD-10 diagnosis.
There was a clear demographic divide among our overweight, obese, and morbidly obese patient population as to whether they received a diagnosis. Older, male, and White patients were more likely to have undiagnosed overweight and obesity, while younger male patients were more likely to have undiagnosed morbid obesity. This finding is consistent with a recent study of nearly 5 million VA primary care patients (347,112 females; 4,567,096 males), in which 37% were overweight and 41% were obese [7,9]. In that study, obesity was common among male veterans 18 to 44 years of age [7]. Our data support these statistics, showing that patients with obesity and morbid obesity were more likely to be male (295,498 males and 61,763 males, respectively). However, in contrast with that study, our statistics found that older patients (>65 years) were undiagnosed with obesity or and morbid obesity (147,232 males and 27,934 males, respectively). Again, this discrepancy could be due to VA data reflecting a treatment-seeking population that may be older and sicker than the general population [7].
The discrepancies involving gender and age could be explained by weight-related beliefs and behaviors. The younger male veterans may have the perception that men are heavier due to muscle mass, although with a BMI of 30 or higher, the correlation with obesity is generally strong [15]. In light of the negative connotation of being labeled “obese,” this finding might imply a negative effect on a population who is susceptible to depression [3,9]. Regardless, obesity should be identified and addressed early to prevent morbidity and mortality from associated medical conditions.
Our study highlights that comorbidities can signify a diagnosis of obesity or morbid obesity. Hypertension, hyperlipidemia, diabetes, and osteoarthritis were significantly associated with a diagnosis of overweight, obesity, and morbid obesity. Furthermore, a multivariate analysis study [22] showed that in addition to age and gender, morbid obesity and the cumulative number of comorbidities were significantly associated with obesity documentation. Nonetheless, this highlights the concern that delaying the proper diagnosis of obesity until a comorbidity is diagnosed might delay prevention, health education, and early weight management and treatment.
In an effort to stem the tide of the obesity epidemic, the VA healthcare system offers various weight-management programs [3,9]. Some VA initiatives aimed at providing the tools for veterans to better manage their weight include education on proper nutrition and the benefit of regular exercise, the use of technology such as daily apps, medications, and, when warranted, bariatric surgery [3]. However, these interventions are not being properly utilized due to the underdiagnosis of obesity in the veteran population.

5. Limitations

This study has several limitations related to the use of administrative data sets, which may be subject to inaccurate coding of patient clinical diagnoses and procedures, with clinical information limited to conditions and treatments defined by ICD-10-CM codes. Furthermore, a consensus is still lacking among experts regarding how to define and measure obesity properly. While BMI is the accepted standard, it has been shown to suffer from various limitations. First, BMI is an indirect measure of body fat and has been shown to have high specificity but low sensitivity to identify adiposity [25]. In addition, BMI measurements do not factor in age-related changes in body composition such as increased body fat and decreased muscle mass [26].
We have studied the VA population. Although the data set is nationally representative, it is predominantly male and contains a vulnerable population. Replication of this study in other data sets that are more representative in terms of gender and income distribution would be useful.

6. Conclusion

Despite obesity's high prevalence in the US veteran population, underdiagnosis continues to be a significant problem. Additionally, our study findings also highlight how obesity is improperly coded and could be a reason for low documentation. Furthermore, we identified predictors of obesity documentation such as age, gender, and comorbidities. Specifically, we demonstrated that obesity is underdiagnosed, but those with morbid obesity were much more likely to be diagnosed than overweight patients. This finding could be associated with the visual undeniability of the problem severity and the fact that morbidly obese patients tend to have more severe associated comorbidities. Nonetheless, obesity is a modifiable risk factor for multiple comorbidities that, when promptly and properly treated, can improve the overall health of the patient, and lower healthcare costs. Therefore, it is crucial to diagnose obesity accurately to provide effective management, improving patients' overall quality of life. Additionally, it is important to address the factors that are associated with the underdiagnosis of obesity. For example, BMI as a core measure of vital signs is not fully harnessed and applied in the delivery of health care. Another factor contributing to the underdiagnosis of obesity could be the low documentation of obesity in the administrative data and electronic health records, as shown in our data.
Further studies are recommended to investigate the underlying causes of lower rates of obesity documentation for certain socio-demographic groups. Moreover, studies looking at physician-specific factors that play a role in determining diagnosed obesity are warranted. Especially with the availability of new treatments, insurance companies require a diagnosis for coverage. Therefore, closing the underdiagnosis gap is important.

Author Contributions

O.B. provided the supervision, conceptualization, methodology, validation, and visualization of the research and participated in the writing process from the original draft preparation to the reviewing and editing of the manuscript. E.B. participated in the investigation of the data, methodology, software, validation, analysis and data curation. G.S. participated in the project management, supervision, and investigation of the literature review and in the writing process from the original draft preparation to the reviewing and editing of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Attrition.
Table 1. Attrition.
Overweight (BMI
≤25 to <30)		 Obesity (BMI
≥30 to < 40)		 Morbid Obesity (BMI >40)
BMI measurement 1,275,798 1,014,330 154,246
ICD-10 codes 57,675 329,638 68,380
Total population 1,333,473 1,343,968 222,626
Table 2. Population Characteristics.
Table 2. Population Characteristics.
n/Mean %/SD n/Mean %/SD P Value n/Mean %/SD n/Mean %/SD P Value N/Mean %/SD n/Mean %/SD P Value
Age (years) 63.99 15.71 61.11 14.54 <.0001 61.63 14.61 59.45 13.64 <.0001 59.00 13.19 59.22 11.90 .0001
18-45 182,585 14.31% 9,163 15.89% <.0001 157,969 15.57% 56,166 17.04% <.0001 26,084 16.91% 9,588 14.02% <.0001
46-54 117,499 9.21% 7,074 12.27% <.0001 124,346 12.26% 49,427 14.99% <.0001 25,027 16.23% 11,562 16.91% .0001
55-64 231,405 18.14% 12,490 21.66% <.0001 204,899 20.20% 76,813 23.30% <.0001 37,871 24.55% 19,296 28.22% <.0001
65+ 744,309 58.34% 28,948 50.19% <.0001 527,116 51.97% 147,232 44.66% <.0001 65,264 42.31% 27,934 40.85% <.0001
Gender
Male 1,193,314 93.53% 51,283 88.92% <.0001 940,225 92.69% 295,498 89.64% <.0001 136,790 88.68% 61,763 90.32% <.0001
Female 82,484 6.47% 6,392 11.08% <.0001 74,105 7.31% 34,140 10.36% <.0001 17,456 11.32% 6,617 9.68% <.0001
Race
White 910,352 71.36% 39,923 69.22% <.0001 718,767 70.86% 226,780 68.80% <.0001 106,017 68.73% 48,398 70.78% <.0001
Black 202,865 15.90% 10,576 18.34% <.0001 173,197 17.08% 65,461 19.86% <.0001 30,538 19.80% 13,296 19.44% .0527
Other 41,167 3.23% 2,110 3.66% <.0001 30,437 3.00% 9,895 3.00% .9749 4,828 3.13% 1,839 2.69% <.0001
Unknown 121,414 9.52% 5,066 8.78% <.0001 91,929 9.06% 27,502 8.34% <.0001 12,863 8.34% 4,847 7.09% <.0001
Comorbidities
Coronary artery disease 90,943 7.13% 3,991 6.92% .0568 71,596 7.06% 24,801 7.52% <.0001 10,568 6.85% 6,286 9.19% <.0001
Hypertension 521,406 40.87% 24,884 43.15% <.0001 465,660 45.91% 165,903 50.33% <.0001 81,292 52.70% 42,291 61.85% <.0001
Hyperlipidemia 371,263 29.10% 19,962 34.61% <.0001 313,911 30.95% 119,540 36.26% <.0001 49,843 32.31% 26,675 39.01% <.0001
Diabetes 264,291 20.72% 13,826 23.97% <.0001 292,407 28.83% 111,105 33.71% <.0001 61,012 39.55% 33,530 49.03% <.0001
Sleep apnea 88,262 6.92% 6,196 10.74% <.0001 142,258 14.02% 72,405 21.97% <.0001 44,050 28.56% 28,862 42.21% <.0001
Osteoarthritis 146,248 11.46% 7,382 12.80% <.0001 137,617 13.57% 52,730 16.00% <.0001 25,637 16.62% 15,433 22.57% <.0001
Hyperuricemia 10,661 0.84% 702 1.22% <.0001 10,306 1.02% 4,901 1.49% <.0001 1,932 1.25% 1,220 1.78% <.0001
Gallbladder disease 757 0.06% 44 0.08% .1041 595 0.06% 249 0.08% .0008 88 0.06% 60 0.09% .0095
Table 3. Logistic Regressions.
Table 3. Logistic Regressions.
Overweight Obesity Morbid Obesity
Odds Ratio Z-Value 95% Confidence Limits Odds Ratio Z-Value 95% Confidence Limits Odds Ratio Z-Value 95% Confidence Limits
Lower Upper Lower Upper Lower Upper
Age (years)
  46-54 0.8925 0.000 0.8641 0.9218 0.9927 0.324 0.9784 1.0072 0.9337 0.000 0.9033 0.9651
  55-64 1.0039 0.794 0.9752 1.0334 1.0951 0.000 1.0805 1.1100 0.9302 0.000 0.9018 0.9596
  65+ 1.3461 0.000 1.3103 1.3830 1.4449 0.000 1.4268 1.4633 1.1632 0.000 1.1282 1.1994
Gender
  Male 1.6614 0.000 1.6149 1.7093 1.3992 0.000 1.3796 1.4190 0.9285 0.000 0.9000 0.9579
Race
  White 1.0613 0.010 1.0144 1.1103 0.9510 0.000 0.9290 0.9736 0.8326 0.000 0.7875 0.8802
  Black 1.0334 0.182 0.9848 1.0844 0.9091 0.000 0.8869 0.9318 0.9131 0.002 0.8614 0.9679
  Unknown 1.1141 0.000 1.0574 1.1738 0.9900 0.459 0.9639 1.0167 0.9411 0.063 0.8828 1.0032
Comorbidities
  Coronary artery disease 1.0879 0.000 1.0513 1.1259 1.0121 0.136 0.9962 1.0282 0.8982 0.000 0.8679 0.9296
  Hypertension 0.9657 0.001 0.9466 0.9852 0.9210 0.000 0.9124 0.9297 0.8739 0.000 0.8551 0.8931
  Hyperlipidemia 0.7737 0.000 0.7586 0.7891 0.8501 0.000 0.8422 0.8580 0.9455 0.000 0.9259 0.9656
  Diabetes 0.8314 0.000 0.8137 0.8495 0.8126 0.000 0.8049 0.8204 0.7829 0.000 0.7667 0.7994
  Sleep apnea 0.6644 0.000 0.6462 0.6830 0.6248 0.000 0.6185 0.6313 0.6175 0.000 0.6055 0.6297
  Osteoarthritis 0.9345 0.000 0.9109 0.9587 0.8958 0.000 0.8857 0.9060 0.7798 0.000 0.7619 0.7982
  Hyperuricemia 0.7655 0.000 0.7086 0.8271 0.7761 0.000 0.7496 0.8035 0.8366 0.000 0.7773 0.9004
  Gallbladder disease 0.8725 0.381 0.6432 1.1837 0.8636 0.055 0.7435 1.0031 0.7658 0.117 0.5488 1.0687
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