Factors associated with premenstrual symptoms: a study among graduate-level students of the institute of science and technology, Tribhuvan university | BMC Women’s Health

Study area

The study was conducted on all the females who enrolled in the Institute of Science and Technology, Tribhuvan University, Kirtipur, Kathmandu, Nepal, of academic years BS 2077 (AD 2020) and BS 2079 (AD 2022). The participants enrolled at the Institute of Science and Technology, Tribhuvan University, came from various geographical regions of Nepal, which supports the generalizability of the results. Therefore, this study area was selected.

Study design

The exclusion criteria were currently pregnant, consuming medication related to heart disease, diabetes, depression, anxiety, and other psychometric disorders and presently suffering from Polycystic Ovarian Disease (PCOD), which may cause irregular periods. The irregular periods may be the result of different causes such as thyroid, PCOS, incidence of chronic diseases, anxiety, depression and other illnesses. Therefore, only those females whose menstrual cycle lies between 21 and 35 days and who menstruate regularly for two consecutive months before the data collection duration [7] were taken as participants in this study. The inclusion criteria of this study were females with regular menstruation for at least two consecutive months before data collection duration and females of age between 15 and 49 years. Before filling out the questionnaire, participants were informed about the inclusion and exclusion criteria, and the questionnaire was provided only to those who met the inclusion criteria. Out of the total 429 female population at IOST, TU, only 285 met the inclusion criteria; therefore, further data were collected from these participants. The number of female participants who met the inclusion and exclusion criteria is illustrated in the flowchart presented in Fig. 1.

Data collection

A cross-sectional research approach was adopted, which is entirely based on primary data directly collected from individuals using a self-administered questionnaire. The data collection began on 13 February 2024 and ended on 29 February 2024 after obtaining ethical clearance from the Institutional Review Committee (IRC) and the registration number was IRCIOST-24-0002. This study includes a questionnaire on demographic, socioeconomic, menstrual-health-related and lifestyle habit variables.

Questionnaire

While preparing the questionnaire, demographic variables, socio-economic variables, menstrual-health related variables and lifestyle habit variables were prepared with the help of different literature, to measure the PMS standard tool PSST [8] was used.

PMS

The status of Premenstrual Symptoms (PMS) was measured using a standard tool called the PSST [8]. This tool was selected to measure PMS because it was previously used among medical and dental students at a medical college in Kathmandu, and was found suitable for this type of population [1]. PSST is a scale for measuring Premenstrual Symptoms (PMS) that includes a list of premenstrual symptoms and impairment. The PMDD was assessed using the criteria given in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). PSST consists of 19 questions listed in two domains, which are measured on a 4-point Likert scale including not at all, mild, moderate and severe. In the first domain, symptoms of the premenstrual phase were listed and asked females, “Do you experience some or any of the following premenstrual symptoms that start before your period and stop within a few days after bleeding?” The symptoms are anger, anxiety/tension, tearfulness, moods of depression, decreased interest in work activities, home activities, social activities, a distraction from work, fatigue/lack of energy, overeating/food craving, insomnia, hypersomnia, feeling overwhelmed or out of control and other physical symptoms such as breast tenderness, headaches, joint/muscles pain, bloating, weight gain. In the second domain, the symptoms of the first domain interfere with or do not, which is also termed as impairments.

All the symptoms were measured according to the perceptions of the participants, but anxiety and depression status (normal, mild, moderate and severe) were measured using another standard tool termed DASS-21.

DASS-21

DASS-21 is a standard tool that contains 21 questions measured on a 4-point Likert scale ranging from 0 to 3 for measuring anxiety, depression, and stress levels among participants [9]. It contains 21 questions on depression, anxiety and stress each with 7 questions. Total scores were obtained for each and multiplied by 2 as the DASS-21 is the short form of DASS-42 to measure the status of depression, anxiety and stress to measure its status whether it is normal, mild, moderate or severe. The status of depression, anxiety and stress using the score is listed below.

Internal consistency test

A total of 35 participants were selected for the pilot survey to test the clarity, reliability, and feasibility of the questionnaire before the main study. Selecting 35 participants strikes a balance between being large enough to provide meaningful feedback and small enough to be manageable in terms of time and resources. During the pilot survey, the internal consistency of the PSST and DASS-21 tools was found to be 0.968 and 0.775 respectively, indicating good reliability. To confirm this in the actual study population, internal consistency was reassessed after final data collection. Among the 285 participants, the PSST and DASS-21 showed Cronbach’s alpha values of 0.961 and 0.848, respectively, demonstrating strong reliability and consistency of both tools within the study population.

Covariates

A questionnaire includes demographic variables (age, weight), socioeconomic variables (monthly family income in Nepalese rupees, monthly personal expenditure in Nepalese rupees), menstrual-health-related variables (age at menarche, menstrual flow, menstrual cycle in days, use of pads per day, bleeds last in days, consumption of menstrual delaying medicine and dysmenorrhea) and lifestyle habit variables (alcohol consumption, smoking habit, tea/coffee consumption, sleeping (hours), physical exercise, meditation and stress).

Data processing and analysis

This study involved both descriptive and inferential statistical analysis. The dependent variable in the study had a dichotomous nature, with the absence of PMS and the presence of PMS. For the categorical independent variable, the Chi-square test was applied to the variable that has a minimum expected cell frequency of 5 or greater than 5, but Fisher’s exact test was applied instead of Yates’ correction of Chi-square when the minimum expected cell frequency is less than 5. For the continuous independent variables, an independent t-test is not applied due to the non-normality of the data; therefore, a non-parametric test Mann-Whitney U test was applied. In multivariate analysis, the nature of data is ordinal, but while using ordinal logistic regression, the data doesn’t follow the proportional odds (parallel line) assumption, which is an important assumption of the ordinal logistic regression model. Therefore, the idea to use ordinal logistic regression was dropped. After violating the assumption of the ordinal logistic regression model, multinomial regression was not applied due to the smaller sample size in the category of the dependent variable. To overcome these problems, the ordinal variable was converted into binary, and hence, multiple binary logistic regression was applied. The Hosmer and Lemeshow test was applied for the goodness of fit of the model. The level of significance in this study was 5%. Data was analyzed using SPSS version 20 and R programming version 4.3.1.

Confidence interval of odds ratio

In this study, 95% confidence intervals (CIs) for the odds ratios (ORs) were calculated using the standard error from the logistic regression analysis. The formula used was:

CI = exp(β ± 1.96 × SE), where β represents the estimated regression coefficient.

A 95% confidence interval (CI) that does not include 1 was considered statistically significant. In logistic regression, an odds ratio (OR) of 1 indicates no association between the independent variable and the outcome. Therefore, a CI that lies entirely above or below 1 suggests a meaningful association unlikely to have occurred by chance.