Open Journal Systems

ORIGINAL CONTRIBUTIONS

Received: March 19^th 2015

Accepted: November 4^th 2015

Chronic low back pain and associated risk factors, in patients with social security medical attention: A case-control study

Jaime Jesús Durán-Nah,^a Carlos René Benítez-Rodríguez,^b Emilio de Jesús Miam-Viana^b

^aHospital General Regional 12

^bHospital General 1

Instituto Mexicano del Seguro Social, Mérida, Yucatán, México

Communication with: Jaime Jesús Durán-Nah

Telephone: (999) 925 8910

Email: jdurannah@gmail.com

Background: Chronic low back pain (CLBP) is frequently seen in the orthopedic outpatient consultation. The aim of this paper is to identify risk factors associated with CLBP in patients cared for during the year 2012, at a General Hospital belonging to Instituto Mexicano del Seguro Social, in Yucatán, Mexico.

Methods: Data of 95 patients with CLBP (cases) was compared with data of 190 patients without CLBP (controls) using a binary logistic model (BLM), from which odd ratios (OR) and 95 % confidence intervals (95 % CI) were obtained.

Results: School level, body mass index (BMI) as a continuous variable, story of heavy weight lifting, some types of comorbidities and dyslipidemia, were identified as statistically significant in the bivariate analysis (p ≤ 0.05 each). In a second step, secondary school level (OR 0.25, 95 % CI: 0.08-0.81), dyslipidemia (OR 0.26, 95 % CI: 0.12-0.56), heavy weights lifting (OR 0.22, 95 % CI: 0.12-0.42), and BMI (OR 1.22, 95 % CI: 1.12-1.32) were all identified by the BLM as statistically significant.

Conclusions: In this sample, secondary school level, dislipidemia and heavy weights lifting reduced the risk of CLBP, while the BMI increased the risk.

Keywords: Low back pain; Back pain; Body mass index; Dyslipidemias

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Lumbago or low back pain is one of the most frequent diseases in the field of orthopedics, and its relevance as a syndromic process is also because it is the second leading cause of work disability, as it can impact negatively on the patient’s quality of life during its clinical course.^1,2 Mooney³ classifies it into nine categories based on the duration of clinical signs and its anatomical distribution, the last three (from category IC to category IIIC) of which are included in the conceptual and operational definition of chronic low back pain (CLBP).  

Its prevalence is between 18 and 45% of the population;⁴ variability depends on demographic,⁵ occupational, and clinical factors.^2,6,7 Anderson,⁸ citing data from other authors, reports that its incidence is 68.7 per 1000 White people, and 38.7 per 1000 Black Americans. In the study from Violante et al.,⁶ the frequency is 19% among health workers, while in the Mexican insured population attended at social security hospitals, it varies between 5 and 13%,2,⁹ but can be up to 41% when counting the complaint of low back pain reported by workers as low back pain.¹⁰

Several factors have been associated with CLBP,^8,11-17 age being one of them, because there has been a progressive increase in non-linear frequency as individuals age. Anderson⁸ reports that its impact on Americans is 80.5 per 1000 adults aged 18 to 44 years, 90.1 per 1000 adults aged 45 to 64 years, and 93.6 per 1000 adults over 84 years. Gender prevalence also varies as shown by the figure from Freburger et al.,¹² in which 4.8 to 12% are women and 2.9 to 8% are men, while in Anderson⁸ 70.3 out of 1000 are women and 57.4 out of 1000 are men.

Obesity is another variable associated with CLBP as documented by Torres et al.¹³ in the Mexican population, where the risk is 1.5 times higher if men or women are obese, a finding also documented by Fransen et al.¹⁴ among 854 applicants for financial compensation due to CLBP, a group in which obesity increases the risk 1.84 times. It has also been associated with occupational or recreational physical activity,^15-17 as for the latter, Auvinen et al.¹⁶ have found a direct relationship between variables, a finding not documented by Mexican authors,¹³ who on the contrary, report that sedentary lifestyle is associated with the chronic syndrome. Kaila-Kangas et al.¹⁵ state that the occupational and psychosocial factors are presented in conjunction associated with CLBP, while Korean authors¹⁷ identify schooling, specifically middle or low education, as the factor that increases the risk up to 2.4 times, while physical exercise three to six times a week reduces it up to 67%; they also dismiss age, obesity, smoking, or stress level.

Due to the variability found in the data, it is important to then identify the factors affecting the population treated by local social security; therefore this research was proposed with the main objective of identifying risk factors associated with CLBP in patients seen during 2012 in the orthopedics department of a General Hospital of the Instituto Mexicano del Seguro Social (IMSS), in the city of Merida, Yucatan, Mexico. 

Methods

A prevalent case and control design was used, identifying participants from the patient population served for various causes during 2012 in the orthopedics department of Hospital Regional General 1 Lic. Ignacio Garcia Tellez, IMSS. Both cases and controls were included consecutively, non-probabilistically, and only when they met the selection criteria, and the corresponding data were taken from clinical records or by direct interview when attending specialty consultation.

Cases were patients whose diagnoses of CLBP were done by an orthopedic doctor from the study hospital, regardless of the criteria used to diagnose, although researchers also sought to sustain the identification of the criteria in the patient referred to by Freburger et al.,¹² who define it as low back pain that has limited daily physical activities during the three months prior to the interview, or when the patient has had more than 24 episodes of pain that limited their physical activity for at least one day (or more) during the year before the interview- which are more practical to implement than those reported by Mooney.³ Controls were patients who came for the first time or later visits to Orthopedics for reasons other than low back pain, and to discard them they were asked whether they had low back pain or if they were going to the doctor for the first time for back pain.  

The variables analyzed were age, gender, education, comorbidity, body mass index (BMI), tobacco use, history of dyslipidemia, history of handling heavy objects, occupational pressure, job dissatisfaction, and history of practicing a sport.

For schooling, the reference variable (unrelated to CLBP) was taken as middle-high or high school level based on the observation from Kwon et al.,¹⁷ who indicate that individuals with high educational levels are unlikely to have CLBP due to the types of activity they do; this was analyzed as ordinal categorical data. Comorbidity was defined as any pathological process of at least six months duration reported by the patient's medical history, counting: type 2 diabetes mellitus (DM2), hypertension (HT), chronic lung disease, immunological diseases, cirrhosis, etc., analyzed as nominal categorical data (present versus absent), regardless of whether the patient had one or more than one. 

BMI was defined as the ratio of weight in kilograms by height in m². This was considered normal when it was 19 to 24.9 kg/m², as overweight when it was 25 to 29.9, and as obesity when was ≥ 30, also analyzed as a continuous quantitative variable. Tobacco use was defined as their previous or current use, regardless of the amount and duration of use, as Shiri et al.¹⁸ analyzed the variable, and it was categorized as nominal categorical data: "smoking" or "non-smoking”. Dyslipidemia was defined as either having or having had total serum cholesterol or its subtypes, serum triglycerides, or all of the above together, above or below (high-density lipoprotein) normal reference values. Given its relevance to this investigation, it was decided not to include it as part of comorbid conditions, analyzing it as an independent categorical variable (present versus absent).

The history of handling heavy objects is defined as being exposed to the occupational or non- occupational handling of heavy materials that cause the patient physical effort to move, lift, or relocate them, regardless of the frequency. This was analyzed as categorical nominal data (yes versus no). Occupational pressure or stress was defined as the subjective feeling of exertion or emotional stress self-perceived by the patient before or during the duties or obligations imposed by work, a person or group of persons, which is manifested in tachycardia, palpitations, or feelings of anxiety. No instrument was used for its identification, only direct question and answer (Do you have or feel stress when performing your job duties?), after explaining the meaning of the concept. It was analyzed as categorical nominal data (yes versus no).

Job dissatisfaction was defined as the presence in the patient of unpleasant feelings triggered by an adverse work environment, and it was investigated in the same way as Occupational pressure or stress, by express question, after explaining the concept. It was analyzed as categorical data (yes versus no). The history of practicing a sport was defined as past or current performance of any planned physical activity, in which the patient might have had violent body contact or lift weight, as in bodybuilding. It was analyzed as categorical nominal data (yes versus no). As for the patients who served as cases, the duration of low back pain was defined as the time in months since the syndrome began until the current date or the last visit. 

Patients of either gender ≥ 30 years of age, with or without chronic disease, with current or previous established diagnosis of CLBP, were included as cases. We excluded those who reported having had a self-limited episode of lumbosacral pain, and those whose CLBP was associated with cancer; patients whose data could not be fully documented were eliminated. Inclusion criteria for controls were the same as those used in cases, with the exception that they had no past or present diagnosis or suspicion of CLBP.

Sample size information analysis  

For the internal validity of the study, a sample size was calculated that took into account the 95% confidence level, beta 20% (power 80%), and minimum CLBP prevalence in the sample 13%, which accounted for that of Violante et al.6 (17%), less than that documented by Freburger et al.¹² (4%) in their respective samples, data that helped decide to include 270 patients, who would have to be 90 cases and 180 controls (ratio of two controls per case), a calculation made with the Epi-info 2010 statistical package (CDC, Atlanta, Ga.).

For the information analysis, first the data were compared univariately, using parametric tests for continuous quantitative variables (Student’s t-test for one and for two independent means) and a nonparametric test for qualitative categorical, nominal, or ordinal variables (Chi-squared test). Those with p < 0.10 in this first analysis were included in a binary logistic regression model (LRM, Enter method) to obtain odds ratios (OR) and their respective 95% confidence intervals (95% CI), taking a p-value ≤ 0.05 as statistically significant. SPSS version 14.0 (SPSS, Chicago Ill, USA) was used for this.

Results

The study included 285 patients whose mean age was 52.2 ± 13.6 years (95% CI 51 to 54.2), of which 171 (60%) were women whose average age (52.1 ± 12.8) was not significantly different from the group of 114 men (53.3 ± 14.7) (p = 0.46). Comorbidity of some kind was identified in 42.5%, in order of frequency: HT in 29.1%, DM in 26%, dyslipidemia in 20.4%, chronic lung disease, cirrhosis, or any immune disorder in < 2% each. The duration of low back pain in cases ranged from three to 120 months, with a median of 10 (interquartile ranges 25 to 75% from 5 to 24). Complementary sample data are presented in Table I.

95 cases were identified and matched with 190 controls whose average ages (53.3 ± 13 vs. 52.3 ± 13.9) were not statistically different (p = 0.54), but the average BMI was (30.3 ± 4.2 versus 27.4 ± 3.5 for cases and controls, respectively; p < 0.001). By gender, men were 40% of cases and 40% of controls (OR 1.0, 95% CI 0.60 to 1.65); 29.5% and 22.1% had middle-high education (Chi-squared p = 0.06), and 50.5% and 38.4% had at least one comorbid pathology (OR 1.63, 95% CI 0.99 to 2.69), with DM in 24.2% and 26.8% (OR 0.87, 95% CI 0.49 to 1.53), HT in 33.7% and 26.8% (OR 1.38, 95% CI 0.81 to 2.35), and dyslipidemia in 36.8% and 12.1% (OR 4.23, 95% CI 2.31 to 7.74) of cases and controls, respectively.

BMI was in the category of obesity in 64.2% of cases and 12.6% of controls (Chi-squared p < 0.001), tobacco use in 28.4% and 26% (OR 1.08, 95% CI 0.62 to 1.87), a history of handling heavy objects in 73.7% and 35.3% (OR 5.14, 95% CI 2.98 to 8.86), history of occupational stress in 50.5% and 42.1% (OR 1.40, 95% CI 0.85 to 2.30), job dissatisfaction 15.8% and 12.6% (OR 1.29, 95% CI 0.64 to 2.60), and practicing a contact sport in 14.7% and 10% (OR 1.55, 95% CI 0.74 to 3.25). Complementary data are shown in Table II.

The LRM included schooling, history of handling heavy objects, comorbidity, history of dyslipidemia, and BMI (as continuous data). The goodness of fit test (Hosmer-Lemeshow) had a p-value (Chi-squared) of 0.68, which eliminated collinearity among the variables, especially between BMI and dyslipidemia, identifying as clinically significant variables secondary-level schooling (OR 0.25, 95% CI 0.08 to 0.81), history of dyslipidemia (not having it, OR 0.26, 95% CI 0.12 to 0.56), a history of handling heavy objects (not having it, OR 0.22, 95% CI 0.12 to 0.42) and gradually increased BMI (OR 1.22, 95% CI 1.12 to 1.32), the model excluding other categories of schooling, and comorbidity of some kind. Complementary data are shown in Table III.

Discussion

In the absence of proper data on what could be the factors associated with CLBP in the local enrolled population served by the IMSS, part of the variables analyzed were taken from various reports that identify them, associated or not, with the syndromic process. One, and perhaps the least analyzed for lack of a direct pathophysiological effect, is schooling at various levels, although its role as a risk factor has been controversial due to finding both a reducing effect,^19,20 and an increased risk.¹⁷ In this study "secondary" schooling, relative to other educational levels together, specifically the higher levels, was the variable whose effect was "protective", because it reduced the risk of CLBP up to 75% with OR < 1 with 95% CI, which also excluded the value "1" of no difference.

The interpretation of the finding would be that those with a history of having reached this educational level (the variable category with the most controls) would have no more than a 25% chance of having CLBP, compared to those with higher education levels, as happened with the cases. Zavala-Gonzalez¹⁹ reports similar findings to these in their study, which includes the population treated at a social security hospital; the variable "student" is identified as one of several factors that reduce the risk of CLBP up to 78% (OR 0.18), while Dionne et al.,²⁰ in the opposite way, also report an association between schooling and CLBP, when making a systematic review of the literature they found that the lower the educational level, the more likely the patient is to have back pain.

Taking the association as valid, the pathophysiological explanation would support the findings of the Korean authors,¹⁷ showing that individuals with lower-middle schooling, called "blue-collar workers", have a chance of CLBP up 2.4 times higher because they perform tasks of greater physical difficulty (potential causes of back injury), compared to those with more schooling called "white-collar workers". Furthermore, they find that the variable even has a predictive role in the frequency of episodes of CLBP and their future outcome.

For its relevance, one of the variables included in the study was having or having had dyslipidemia (versus not having had or not having it), either alone or from increased serum triglycerides, total cholesterol or its subtypes, from lowering high-density cholesterol, or a combination of lipid abnormalities, and although the association was documented, this had a protective effect since not having dyslipidemia (the variable category that also prevailed among controls) reduced the risk by up to 74% according to the OR expressed by the logistic model; in other words, the presence of dyslipidemia of some sort would be a real CLBP risk-modifying factor, but in this study the composition of the sample would have changed the trend of the variable to be protective, implying for the control patient a low probability (26%) of having it. 

This finding partly differs with the results of some studies,^19,21,22 and agrees partly with the results of others.^23,24 On the first point, the cohort population study from Heuch et al.²¹ prospectively analyzed the association between serum lipids and CLBP in two groups of patients, one that does not have the syndrome at baseline but who develops it at this point, and one that has CLBP since the start of follow-up. Among their findings they report finding inconsistencies in the relationship between the variables, because the association exists with a particular lipid but not with others, and when it exists it is weak and loses effect when adjusting the data by gender and specifically by BMI, a variable that gives a confounding effect. The association is found between high-density cholesterol and the group with CLBP since the start of monitoring, although it is protective, as for every mmol/L of that lipid, the risk is reduced by 10 to 15%. In the same context Ha et al.²² showed an association between cardiovascular disease and the development of CLBP, but not between this and lipid profile, while in the study from Mexican authors,¹⁹ the logistic analysis also excludes dyslipidemia as a risk factor, even though their OR is 2.58 but with 9% CI that includes the value of non-difference.

On the other hand, the association found between dyslipidemia and CLBP is consistent with the findings of Leino-Arjas et al.,²³ who tested it in patients with localized or diffuse CLBP, finding it positively correlated with the concentration of triglycerides and cholesterol serum predominantly in men, even after adjusting the data for potential confusion from BMI, a finding also reported by Hemingway et al.²⁴ in a prospective study documenting the positive association between triglycerides and low back pain reported by retired patients who had worked as office workers, an effect that is also maintained after adjusting the data for BMI.

Accepting that there is an association between dyslipidemia and CLBP, either increasing or decreasing the risk depending on the composition of the samples analyzed, could be supported with the pathophysiological explanation of the role of dyslipidemia on the development of atheroma of the lumbar arteries, whose effect reducing blood supply to the lumbar region would induce disc degeneration and, therefore, changes in anatomy, an argument also used to explain (and compare) the association between aortal and lumbar vascular degenerative process and coronary heart disease or peripheral vascular disease, whose pathophysiological substrates are characterized precisely by the presence of atherosclerosis in which lipids have an overwhelming causal effect.^25,26

BMI was the only variable that increased the likelihood of CLBP, which it did up to 22% per kg/m² increase in weight compared to the group of patients with normal BMI. This finding on one hand is divergent with that of Violante et al.,⁶ who when analyzing various risk factors among health workers, found no significant association between BMI and CLBP (OR 1.01, 95% CI 0.98 to 1.05), a result also reported by Korean authors,¹⁷ in whose series no BMI category, including obesity, is associated with CLBP. On the other hand, the data would be consistent with the finding of Torres et al.,¹³ reporting that the risk of low back pain is 1.5 times higher if the subjects are obese, regardless of gender (as in > 64% of our patients who acted as cases), data also documented by Fransen et al.¹⁴ in 854 patients for whom obesity increases the risk of CLBP 1.84 times. In the systematic review of Shiri et al.²⁷ evaluating the association of low back pain and overweight or obesity, they report that this increases the risk 1.53 times, a result that remains constant after adjusting for publication bias and limiting the review to studies that control for confounding variables.

Accepting that there is an association between BMI (especially in the category of obesity, as documented in the cases of this study) and CLBP would not be illogical considering that excess weight, like dyslipidemia, is one of the criteria for metabolic syndrome (MS), a set of metabolic and clinical abnormalities²⁸ that has been strongly associated with CLBP,²⁹ and the pathophysiological explanation for the association involves the vascular atherogenic effect that abnormally high BMI may have, without discounting its mechanical effect on the structure of the spinal column.²⁷

Handling heavy objects, specifically not having a prior or current history of this, was another variable that the LRM gave an inverse effect on its association with CLBP, as it reduced the risk by up to 78%. In other words, the low frequency with which control patients reported handling heavy objects (lifting them, pushing them, moving them) would explain the "protective" effect of variable, because in them the probability of CLBP would only be 22%. From the above it can be concluded that the finding could be related to the composition of the sample, because with more patients reporting exposure to handling heavy burdens, the variable would have been positively related, then serving as a risk factor, probably increasing it.

Findings similar to this study are reported by Mexican authors,7 whose study analyzes the role of work tasks involving carrying or moving weight as risk factors for spondylarthrosis, finding that they increase the risk over seven times, which becomes up to 10.4 times when such tasks are carried out routinely. They also identify the volume of loads, cumulative hours of work, and hours of load-bearing as factors that increase risk progressively, data that show the variable’s role, not only a risk factor associated with CLBP, but as a factor triggering the process, and even as a factor inducing the transition from acute to chronic form.¹⁴ Mariconda et al.³⁰ extensively describe how certain occupational physical activities produce structural changes in the lumbosacral region and therefore associate it with CLBP.

Conclusions

Beyond the methodological limitations that this study could have (such as the likelihood of having committed potential case definition bias or even control bias due to accepting or rejecting the CLBP diagnosis made from a medical note from the file or patient interview), the results of this investigation can be considered to have internal validity that could be applied to the source population of the sample, which does not exclude the possibility, and even the need, to plan prospective investigations that take into account and include the variables associated with CLBP we identified, whether reducing or increasing the risk, and at the same time allowing strict control of other biases that the study might have, as well as the variables that could act as data confounders, strategies to give the results external validity.

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