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Biological, Social, and Behavioral Factors Associated With Premenstrual Syndrome
Patricia A. Deuster, PhD, MPH;
Tilahun Adera, MPH, PhD;
Jeannette South-Paul, MD
Arch Fam Med. 1999;8:122-128.
ABSTRACT
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Objective To investigate the influence of various biological, socioeconomic, and behavioral factors on premenstrual syndrome (PMS).
Design Random-digit dialing technique. Of 7900 calls from all area codes, exchanges, and 2 digits known to be open in Virginia, with a pair of random digits, 1700 women were eligible for telephone interviews. A total of 874 women completed interviews, for a response rate of 67%.
Setting State of Virginia.
Patients or Other Participants All women between the ages of 18 and 44 years and living in Virginia between August 1 and September 15, 1994, were eligible.
Main Outcome Measures Scores on Menstrual Distress Questionnaire, biological variables, lifestyle behaviors, socioeconomic status, and menstrual and reproductive history.
Results Of the 874 women, 8.3% (95% confidence interval, 6.4%-10.2%) experienced PMS. Adjusted prevalence odds ratios for perceived stress and alcohol intake were 3.7 and 2.5, respectively, in women with PMS. Women with PMS were 2.9 times more likely to be physically active than women without PMS. Younger women, black women, and women with longer menses were more likely to have PMS.
Conclusions Scores on the stress scale and alcohol intake support the concept that PMS is stress related; intervention strategies to cope with stress may be effective. Further study will be required to determine the influence of race on PMS and whether women with PMS exercise more regularly than women without PMS because they believe exercise is effective in attenuating their symptoms.
INTRODUCTION
PREMENSTRUAL syndrome (PMS) is a cluster of physical and emotional symptoms that appear on a regular basis before the onset of menstrual bleeding. Symptoms include bloating, breast pain, ankle swelling, a sense of increase in body weight, irritability, aggressiveness, depression, lethargy, and food cravings. Although the true prevalence of PMS is unknown, approximately 75% of women complain of some premenstrual symptoms.1 Criteria for the diagnosis of PMS have recently been developed, and when these criteria are used, 3% to 8% of women are diagnosed as having PMS.2-4 Women with severe symptoms report that PMS interferes with their daily functioning, be it in personal, social, or work activities. To elucidate underlying factors contributing to this syndrome, numerous studies have been conducted. Because many of the observed symptoms appear to be centrally mediated, neuroendocrine links have been sought. However, no specific abnormality or defect has yet been identified.
In an effort to develop effective treatment approaches, investigators have also examined the influence of various socioeconomic and lifestyle factors. Behavioral and social factors have included use of medications, smoking, alcohol and caffeine intake, dietary patterns, oral contraceptive use, affective state, marital status, and education.1-2,5-14 Moreover, the effects of regular exercise on PMS have been examined.11, 15-18 Finally, the relevance of various biological factors, such as age, anthropometric characteristics, and reproductive and menstrual history, has been evaluated.10 Despite the wealth of studies, the etiology of PMS remains elusive.
Of the many studies conducted to date, only a few have been population based,3-4 and most have used primarily white samples derived from women attending a university or seeking treatment for PMS at health clinics.6, 8-13 Results from such studies are not always generalizable to other, more diverse populations of women. Accordingly, it was considered important to conduct a population-based sample of women of reproductive age. Our goal was to obtain a racially and economically diverse sample so that the influence of various biological, socioeconomic, and behavioral factors on the manifestation of PMS could be assessed in the general population. Biological variables of interest included age, body type, and menstrual and reproductive histories, whereas the behavioral factors we were particularly interested in evaluating included stress, stress-related behaviors (smoking and alcohol intake), and dietary and physical activity patterns. To this end, telephone interviews were conducted in a random sample of women aged 18 to 44 years living in the state of Virginia.
SUBJECTS AND METHODS
All women between the ages of 18 and 44 years and living in Virginia during the period from August 1 through September 15, 1994, were eligible. The random-digit dialing technique was used as the sampling method19; thus, households without telephones (<6%) could not be covered by this method. The sampling frame was established by forming "blocks," which consisted of all unique combinations of the area code, exchange, and the following 2 digits known to be open in Virginia. Each block was analyzed for the number of known residential working telephone numbers present in the block. Only blocks with at least 1 known residential working telephone number were included in the sampling frame. Once the sampling frame was established, the telephone numbers in each block were created by attaching a random pair of digits. Each block contributed a number of telephone numbers to the sample equal to the block's proportion of known telephone numbers in the state. After the sample was drawn from the selected blocks, it was matched against a computerized list of known business and nonworking telephone numbers; the numbers that matched were excluded from the sample.
A minimum sample size of 830 women was estimated under the assumption that (1) the prevalence of PMS is 5% in women without the exposure of interest; (2) the closest value that will be distinguishable in women without the exposure is 1.0%, ie, any value of 1.0% or lower would give a P value of .05 or less; and (3) the participation rate is 80%. Of the total of 7900 telephone numbers called by random-digit dialing, 1700 women were eligible for interview. Of these eligible subjects, 826 were not selected for an interview because of refusal to complete (n=422), language difficulties (n=76 not English speaking), illnesses (n=23), and unavailability (n=305). Our final interview sample consisted of 874 women. Subjects were interviewed by means of computer-assisted telephone interviewing techniques. The cooperation rate (completed interviews divided by the sum of completed interviews and refusals) was 67%.
The interview was conducted on the basis of a 5-part questionnaire. Part 1 consisted of the Shortened Premenstrual Assessment Form (SPAF) and was used to gather information on PMS symptoms.20 Parts 2 through 5 gathered sociodemographic, physical activity, stress, and dietary intake information, respectively. Sociodemographic questions related to income, education, marital status, employment, church attendance, menstrual and reproductive history, and various other factors.
The physical activity questions were standardized questions used previously in the Framingham Study.21 The subject was probed on time and frequency of participation in any or all recreational and work-related physical activities, the number of hours spent in a sedentary state, walking pace, stairs climbed per day, and minutes walked per day. From these responses, a global score was created as an index of overall physical activity.
The Cohen Perceived Stress Scale was used as an indicator of perceived stress.22 This scale consists of 14 questions relating to thoughts and feelings during the last month. The interviewer asked the subject how often in the last month (never, 0; almost never, 1; sometimes, 2; fairly often, 3; or very often, 4) she felt or thought specific ways (such as upset because of something that happened unexpectedly, unable to control the important things in life, nervous and stressed) during the preceding month. The score (0-4) of each of the 14 responses was summed to obtain a total stress score (possible range, 0-54). Cronbach  coefficient of internal consistency was reported to be 0.85, and test-retest reliability during a short retest interval (several days) was 0.85.22
Finally, the dietary questions were adapted from the Health Habits and History Questionnaire.23 This is a food frequency instrument, and subjects were asked the number of times per day, week, month, or year they consumed various foods (dairy products, vegetables, eggs and meats, breads, grains and starches, sweets or baked goods, and various beverages); no data on portion size were obtained. The frequency data were then converted into a weekly rate and assigned codes on the basis of their overall nutrient content. A nutrition quotient was calculated for each individual.
CASE DEFINITION
Cases were determined by responses to the SPAF, a self-report questionnaire that allows for the classification of premenstrual changes into 10 categories, which reflects alterations in mood and physical condition. The SPAF is a shortened form of the 95-item Premenstrual Assessment Form originally developed by Halbreich et al24 and has been shown to be reliable and valid when the study design precludes the use of the original Premenstrual Assessment Form.20 Cronbach coefficient of internal consistency was 0.95, and test-retest reliability averaged 0.60. All women were asked to recall their experiences during the week before their menstrual period. A series of 10 symptoms were presented, and the women were asked to rate the symptoms on a scale from 1 to 6, where 1 indicates no change and 6, extreme change. A total score based on summing the responses to each symptom was then obtained. Women were classified as having PMS if they rated 5 or more of the 10 symptoms in the Premenstrual Assessment Form as severe (score of 5) or extremely severe (score of 6), with at least 1 of the symptoms being questions 2 (feel that I just can't cope or am overwhelmed by ordinary demands), 3 (feel under stress), 4 (have outbursts of irritability or bad temper), or 5 (feel sad or blue). This ensured that changes were noted in all 3 subscales of the SPAF: affect, water retention, and pain.20 The noncases, or comparison women, were all women who did not meet these criteria.
STATISTICAL ANALYSES
The association between categorical data and the outcome measure was examined by subdividing the sample according to the suspected predisposing factors being studied, and comparing the prevalence, prevalence rate ratios, and prevalence odds ratios (PORs) in each group. In the case of the categorical variables, such as income, education, smoking habits (yes or no; packs per week; years smoked; and pack-years, the product of packs per week and years), church attendance, and others, the variables were dichotomized when the trend across categories appeared binary. Otherwise, the variables were maintained as selected categories based on what appeared to be appropriate groupings. Relationships between continuous variables and the outcome variable were examined by creating grouping variables based on means and quartiles of the variable of interest. In particular, quartiles for activity levels, caffeine and alcohol intake, and nutritional habits were examined. If no striking trend across quartiles was noted, the variables were recoded in categories that most effectively represented the trend. Prevalence and confidence intervals were calculated as described by Fleiss.25
Univariate analyses were first used to evaluate the strength of the associations between each independent variable and the outcome variable. When the contribution of the most important risk factors or predictors for the outcome variable was evaluated, variables whose univariate analysis indicated a P value of less than .25 were selected for initial inclusion in the multiple logistic regression model.26 Subsequently, only variables that served as confounders or effect modifiers, or yielded a Wald  2 test with a P value of less than .10, were retained.
RESULTS
Of the 874 women interviewed, complete SPAF scores were obtained from 821. Of these, 68 met the case definition. Therefore, approximately 8.3% (95% confidence interval, 6.4%-10.2%) of those interviewed were subsequently found to experience PMS. The general characteristics of the entire sample of women are presented in Table 1, and indicated that the sample was quite diverse. The mean±SEM score on the SPAF for the entire sample of women was 26.0±0.4, with a range of 10 to 60. When mean scores for the cases and noncases were compared, scores of 45.4±0.8 and 24.3±0.3, respectively, were noted. Figure 1 presents the distribution of PMS symptoms grouped as no change to minimal change, mild to moderate change, and severe to extreme change. Overall, the symptoms experienced by most women as severe or extreme were irritability (17.4%), backaches or muscle pain (14.2%), and bloating (13.2%).
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Table 1. General Characteristics of Subject Sample
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Percentage of the sample reporting various premenstrual symptoms from the Shortened Premenstrual Assessment Form. The 6 possible ratings were no change at all, only a slight or minimal change, mild change, moderate change, severe change, or extreme change during the week before the menstrual cycle. These ratings were collapsed into 3 groups as indicated.
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Table 2 presents the prevalence of PMS for various socioeconomic and biological characteristics. The crude prevalence of PMS was 10.4% for black women, 7.4% for white women, and 4.3% for women of other races. Women between 35 and 44 years of age were less likely to experience PMS (4.5%) as compared with younger women (9.4%); the prevalence of PMS was highest in women between 25 and 34 years of age (10.7%). With respect to income, the prevalence of PMS in those with an income of less than $20,000 per year was only slightly higher (8.4%) than for those with an income greater than $20,000 (6.5%). In contrast, women with less than a high school education were more likely to report symptoms (19.7%) than were those with some college and (7.9%) and those with a college education (6.9%). Whereas the prevalence of PMS appeared independent of marital status, those who were employed (6.7%) had a lower prevalence than those not employed (12.6%).
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Table 2. Prevalence of Premenstrual Syndrome in Virginia Women by Sociodemographic and Biological Characteristics
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In addition to age, several other biological features were examined. The prevalence was higher in women with a body mass index (BMI; calculated as weight in kilograms divided by square of height in meters: weight (kg)/[height (m)]2) of 27 or higher (12.0% vs 5.9%) and women whose age at menarche was 12 years or younger (10.2% vs 7.2%), but these variables did not achieve significance. The biological factor most prevalent in women with PMS was that of menses lasting longer than 6 days, with prevalence rates of 19.0% and 6.8% for greater than 6 days and 6 days or less, respectively. In contrast, the prevalence of PMS in those who had been pregnant (8.1%) was not different from that in those who had not been pregnant (7.2%).
Table 3 presents the prevalence of PMS for various behavioral and lifestyle characteristics. The prevalence of PMS across groupings by caffeine intake did not reveal any significant trends. Although the prevalence of PMS was not different as a function of alcohol consumption, there was a slight trend for an increased prevalence among those who reported drinking alcohol. Physical activity and nutrition scores were related to PMS prevalence. These variables were examined first as continuous variables and then by quartiles. Because the PORs for the 2 upper quartiles were similar, as were the PORs for the lower 2 quartiles, the data were dichotomized. Those who were physically active (prevalence, 10.5%) and diet conscious (prevalence, 10.1%) were more likely to be classified as having PMS than those who were sedentary (5.2%) and eating a less healthy diet (5.5%). In addition, the degree of perceived, or reported, stress was strongly associated with PMS. Stress scores were separated into quartiles on the basis of scores: the low-stress group included those with a score of 17 or lower (quartile 1), moderate stress was for those with a score between 18 and less than 23 (quartiles 2 and 3), and high stress was for those with a score of 24 or greater (quartile 4). The second and third quartiles were combined, as the prevalence of PMS for these groups was the same as that for the low-stress group. On the basis of these divisions, those reporting low, moderate, and high stress had prevalence rates for PMS of 1.9%, 6.7%, and 13.7%, respectively. Attendance at church was not significantly associated with PMS.
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Table 3. Prevalence of Premenstrual Syndrome in Virginia Women by Behavioral and Lifestyle Characteristics
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A number of smoking-related variables, including current smoking status, number of years smoked, number of cigarette packs per day, and pack-years, were examined. Before adjustments, the prevalence of PMS was significantly higher in women who had been smoking for more than 5 years. Prevalence rate ratios for the smoking-related variables ranged from 1.6 to 2.2 for women with as compared with women without PMS. Because pack-years was considered the most descriptive, it was used in subsequent logistic regression analyses. Finally, the prevalence of PMS was similar whether or not the subject was taking birth control pills.
Table 4 presents the crude PORs and the adjusted PORs from the final logistic regression model. Complete data for the variables maintained in the final equation (race, age, age at menarche, length of menses, BMI, education, intake of alcoholic beverages, pack-years of smoking, stress score, and nutrition and physical activity scores) were available for 715 women. The POR for blacks compared with whites increased from 1.5 to 2.1 after adjusting for all other variables. It should be noted that there were only 2 cases of PMS for the "other" races; thus, the confidence interval is quite wide.
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Table 4. Comparisons of Crude and Adjusted Prevalence Odd Ratios (PORs) and 95% Confidence Intervals (CIs) for Final Factors Associated With Premenstrual Syndrome (n = 715)
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Whereas the influences of age, age at menarche, and smoking were not markedly changed by controlling for the other variables, the adjusted PORs for length of menses, BMI, and the 2 levels of education were 84.5%, 86.4%, 73.3%, and 43.2% of their crude PORs, respectively. For example, those who had not completed high school seemed to be at greatest risk for PMS in the univariate analysis (crude POR, 4.4), but after adjustment, the POR (1.9) was reduced by 43%. Whereas the POR for the univariate analysis of alcohol intake was not significant, when included in the full model, this variable became significant, with a 78.6% increase in the adjusted POR. In the end, the greatest risk factor for PMS appeared to be scores on the Cohen Perceived Stress Scale: the PORs for those reporting moderate and high stress levels after controlling for all other factors were 2.4 and 3.7, respectively. As expected, the influence of perceived stress was less when all other variables were included. Of interest was the consistent finding that those who exercised most regularly were also more likely to report PMS. A comparable POR was maintained when other variables reflecting activity level were used. Interestingly, the adjusted POR for physical activity changed minimally from the crude POR, an indication of limited confounding by other factors. It is also important to note that education, caffeine intake, and employment were omitted from the final equation because they were explained by other variables remaining in the equation.
COMMENT
This is one of the first population-based studies to examine the interrelationships among factors that might relate to the presence of PMS. Whereas many of the variables examined in the present study have already been postulated to be associated with PMS, typically each factor has been examined independently, without regard to confounding. The present results indicate that approximately 8.3% of women experience PMS, and that one of the primary factors associated with PMS is overall perception of life stresses. Those with the highest scores on the Cohen Perceived Stress Scale were more likely to be classified as having PMS. Furthermore, PMS was more prevalent in blacks, women younger than 34 years, and women whose menses lasted longer than 6 days. Finally, women with PMS consumed alcoholic beverages more frequently, but ate healthier diets and were more physically active than women without PMS.
THE ROLE OF STRESS and major life events has received considerable attention in terms of potential associations with somatic health.27 With respect to PMS, the influence of chronic stress, coping mechanisms, depression, anxiety, and other such descriptors of affective state has been widely studied.3, 8, 28-30 Universally, women reporting significant life stresses are more likely to rate premenstrual symptoms as severe and, hence, be classified as having PMS. We found similar associations in the present study; in particular, after controlling for a variety of biological, social, and behavioral factors, perceived stress was still the strongest predictor of PMS. This raises the possibility that PMS may actually be a stress-related syndrome that is most prominent when somatic symptoms of menses are being manifested. Further work will be required to confirm this issue.
Behavioral variables that may relate to stress and therefore PMS are alcohol consumption, smoking, and caffeine intake. Alcohol intake typically has been found to be higher in women with PMS,5-6,13-14 but not all studies support this finding.3 In the present study, univariate analysis of alcohol intake suggested a slightly, but not significantly greater, prevalence of PMS among women who consumed more than 1 drink per day as compared with nondrinkers. Interestingly, when we controlled for other variables, the influence of alcohol intake assumed greater importance. Thus, women with PMS may in fact be heavier drinkers than women who do not experience PMS. In contrast to alcohol intake, caffeine intake and smoking habits were not found to be significantly associated with PMS when multivariate analyses were conducted. Importantly, these findings strongly indicate the need to examine the influence of various lifestyle variables simultaneously so that the interpretation can be done with great care. Such variables need to be examined simultaneously as interacting factors, and not in isolation.
In addition to other lifestyle factors, physical activity and dietary patterns have long been postulated to influence the severity of PMS. Although definitive studies have not been conducted, several studies suggest that the symptoms of PMS may be attenuated by engaging in exercise.10-11,15-18 Johnson et al10 noted in a survey that 41% of women used exercise as a treatment to minimize premenstrual symptoms. Moreover, other investigators have shown in longitudinal studies that emotional and somatic symptoms associated with menses can be significantly improved by a regular aerobic exercise program.17-18 In cross-sectional surveys, the findings are mixed. Ramcharan et al3 noted that more women with PMS engaged in regular exercise than women without PMS, whereas Gannon et al29 found that women with PMS participated in less exercise. In the present study, a significantly greater prevalence of PMS was noted in women who reported exercising on a regular basis than in sedentary women. Interestingly, the POR for this particular variable changed minimally by inclusion of other variables, an indication of limited or no confounding. Unfortunately, the question as to whether exercise was used therapeutically, as a means of minimizing premenstrual symptoms, was not asked. Nonetheless, we speculate that women with PMS were aware that exercise may be effective in attenuating their symptoms and had initiated exercise for this reason. Clearly this possibility remains to be documented, but it serves to explain the finding that women with PMS were 3 times more likely to exercise than women without PMS.
Of additional interest were the number of biological characteristics that assumed importance with respect to PMS. These included age, age at menarche, length of menses, and race. In the present study, women younger than 34 years were 2.3 times more likely to have PMS. This finding is consistent with the work of other investigators who have reported that PMS is more prevalent in younger women.3-4,8, 29 Earlier work had suggested otherwise,28 but the bulk of the evidence indicates that PMS is more common among younger women.
The finding that BMI and menstrual history were associated with PMS deserves further work. Our results indicated that women with a BMI greater than 27.0 were 1.9 times more likely to have PMS than women with lower BMIs; this finding is consistent with the work of Gannon et al.29 We also found that women whose age at menarche was 12 years or younger were 1.6 times more likely to have PMS. Interestingly, if one were to break age at menarche into more discrete age groups, 21.3% of women who began cycling before 10 years of age had PMS as compared with 5.7% in women who began cycling at 13 years or older. Whereas Woods et al4 were unable to find such an association, their sample was small and they may not have been able to observe this association. There is justification for such an association. It is well established that increased body mass and BMI accelerate the occurrence of menarche,31 and that adipose tissue cells are active in the peripheral metabolism of steroid hormones.32 Furthermore, dietary intake of fat modifies the metabolism of estrogen and menstrual cycle length.33-34 Thus, the association between BMI, age at menarche, and length of periods may reflect altered hormonal profiles that predispose a woman to exaggerated somatic premenstrual symptoms, or PMS.
Finally, the results of the present study indicate that black women are 2.1 times more likely to have PMS than women of other races. Interestingly, the POR for the univariate analysis was 1.5, but after adjusting for other factors, there was a 40% increase in the POR. To our knowledge, such a finding has not been previously reported, in part because most study samples have consisted primarily of white women. Whether this increased prevalence reflects a specific biological, social, or lifestyle characteristic remains to be determined. Clearly this finding will require further study.
The limitations of this study must also be recognized. First, although this was a population-based study, only women who had telephones were eligible for inclusion. Thus, a portion of the population was automatically excluded. Second, the SPAF used has not been specifically validated on a sample of black women. Although we believe it is unlikely that the questionnaire poses any racial biases, this issue would require additional study for clarification. Other limitations relate to the sensitivity of the instruments and response rate. Although specific criteria were used to classify cases and noncases, it is likely that some women were inappropriately classified. However, since our classification assignment was based on responses to specific questions, it is unlikely there would be a differential classification bias. Finally, all physical activity and dietary questionnaires have inherent limitations in terms of sensitivity and accuracy. However, the questions asked were standardized and have been used in many other studies. Thus, it is unlikely that any systematic error was introduced by using these instruments.
In summary, this population-based study with racial diversity concurrently examined multiple factors and found that both lifestyle and biological factors are related to the prevalence of PMS. Scores on the stress scale and alcohol intake patterns support the concept that PMS is stress related, and that stress-coping strategies may be effective treatments. While it has been suggested that premenstrual symptoms may have more to do with cultural or psychological factors than with biological ones, the term PMS may be inappropriate because syndromes typically describe a particular abnormality. Premenstrual symptoms appear to be more normal than abnormal.35 However, we did find that several biological factors associated with PMS (age, BMI, menstrual history, and race) predispose women to more severe premenstrual symptoms. Further study will be required to determine which biological characteristics are most strongly associated with somatic symptoms. Finally, it is reasonable to speculate that women with PMS were more likely to exercise than women without PMS because they believed exercise to be effective in attenuating their symptoms. However, further study will be required to confirm this possibility.
AUTHOR INFORMATION
Accepted for publication January 14, 1998.
This project was supported by the Defense Women's Health Research Program (LWHM5578) from the US Army Medical Research and Materiel Command, Fort Detrick, Md.
The opinions and assertions expressed herein are those of the authors and should not be construed as reflecting those of the Uniformed Services University of the Health Sciences or the Department of Defense.
Corresponding author: Patricia A. Deuster, PhD, MPH, Department of Military and Emergency Medicine, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814-4799 (e-mail: pdeuster{at}usuhs.mil).
From the Departments of Military and Emergency Medicine (Dr Deuster) and Family Medicine (Dr South-Paul), Uniformed Services University of the Health Sciences, Bethesda, Md, and Department of Preventive Medicine and Community Health, Medical College of Virginia, Richmond (Dr Adera).
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