| Literature DB >> 34749716 |
Samar Karout1, Lama Soubra2, Deema Rahme1, Lina Karout3, Hani M J Khojah4, Rania Itani5.
Abstract
BACKGROUND: Primary dysmenorrhea (PD) is one of the most common gynecological conditions among young females, which has a significant negative impact on health-related quality of life and productivity. Despite its high prevalence, the evidence is limited regarding the management-seeking practices and its perceived effectiveness among females with PD.Entities:
Keywords: Lebanon; Management-seeking practices; Menstrual pain; Non-steroidal anti-inflammatory drugs; Prevalence; Primary dysmenorrhea; Risk factors
Mesh:
Year: 2021 PMID: 34749716 PMCID: PMC8576974 DOI: 10.1186/s12905-021-01532-w
Source DB: PubMed Journal: BMC Womens Health ISSN: 1472-6874 Impact factor: 2.809
Socio-demographic characteristics, lifestyle habits and the menstrual pattern of the study participants (N = 550)
| Characteristic | n (%)a | Dysmenorrhea | ||
|---|---|---|---|---|
| No | Yes | |||
| Academic specialization | 0.003d | |||
| Medical | 275 (50) | 39 (14.2) | 236 (85.8) | |
| Non-medical | 275 (50) | 66 (24) | 209 (76) | |
| Academic level | 0.46 | |||
| Undergraduate | 478 (86.9) | 89 (18.6) | 389 (81.4) | |
| Postgraduate | 72 (13.1) | 16 (22.3) | 56 (77.7) | |
| Age (mean = 21.82, SD = ± 2.77, range = 18–30) | 0.11 | |||
| 18–24 | 463 (84.2) | 83 (17.9) | 380 (82.1) | |
| > 25 | 87 (15.8) | 22 (25.2) | 65 (74.8) | |
| BMI category (Kg/m2, mean = 22.2, SD = ± 3.38, range = 15.2–37.5) | 0.21 | |||
| Underweight (< 18.5) | 59 (10.7) | 7 (11.8) | 52 (88.2) | |
| Normal weight (18.5–24.9) | 394 (71.6) | 84 (21.3) | 310 (78.7) | |
| Overweight (25–29.9) | 78 (14.2) | 11 (14.1) | 67 (85.9) | |
| Obese (≥ 30) | 19 (3.5) | 3 (15.8) | 16 (84.2) | |
| Marital status | 0.01 | |||
| Single | 514 (93.5) | 92 (17.9) | 422 (82.1) | |
| Married | 36 (6.5) | 13 (36.1) | 23 (63.9) | |
| Parity | < 0.001d | |||
| No | 522 (94.9) | 93 (17.8) | 429 (82.2) | |
| Yes | 28 (5.1) | 12 (42.8) | 16 (57.2) | |
| Smoke | 0.21 | |||
| No | 413 (75.1) | 84 (20.3) | 329 (79.7) | |
| Yes | 137 (24.9) | 21 (15.3) | 116 (84.7) | |
| Alcohol | 0.84 | |||
| No | 502 (91.3) | 95 (18.9) | 407 (81.1) | |
| Yes | 48 (8.7) | 10 (20.8) | 38 (79.2) | |
| Family history of dysmenorrhea | < 0.001d | |||
| No | 262 (47.6) | 73 (27.8) | 189 (72.2) | |
| Yes | 288 (52.4) | 32 (11.2) | 256 (88.8) | |
| History of weight loss attempt within the last year | 0.001d | |||
| No | 251 (45.6) | 63 (25.1) | 188 (74.9) | |
| Yes | 299 (54.4) | 42 (14) | 257 (86) | |
| Exercise (mean time = 1.32 h/week, SD = ± 2.55, range = 0–21) | 0.01d | |||
| No | 295 (53.6) | 55 (18.6) | 240 (81.4) | |
| Once to twice weekly | 158 (28.7) | 32 (20.2) | 126 (79.8) | |
| Three to four times weekly | 63 (11.5) | 8 (12.7) | 55 (87.3) | |
| Five to six times weekly | 18 (3.3) | 2 (11.2) | 16 (88.8) | |
| On daily basis | 16 (2.9) | 8 (50) | 8 (50) | |
| Menarcheal age (mean = 12.35 years, SD = ± 1.34) | 0.32 | |||
| 9–11 (early onset) | 129 (23.5) | 20 (15.5) | 109 (84.5) | |
| 12–14 (Normative onset) | 395 (71.8) | 80 (20.2) | 315(79.8) | |
| 15–16 (Late onset) | 26 (4.7) | 5 (19.2) | 21 (80.8) | |
| Period regularity | 0.43 | |||
| Irregular | 77 (14) | 12 (15.5) | 65 (84.5) | |
| Regular | 473 (86) | 93 (19.6) | 380 (80.4) | |
| Menstrual cycle length (mean = 28.52 days, SD = ± 3.51, range = 20–45) | 0.09 | |||
| 21–35 days | 520 (94.5) | 103 (19.8) | 417 (80.2) | |
| < 21 or > 35 days | 30 (5.5) | 2 (6.6) | 28 (93.4) | |
| Length of menstrual flow (mean = 6.04 days, SD = ± 1.35, range = 2–14) | 0.56 | |||
| Normal (3–7 days) | 504 (91.6) | 98 (19.4) | 406 (80.6) | |
| Abnormal (< 3 or > 7 days) | 46 (8.4) | 7 (15.2) | 39 (80.8) | |
| The intensity of menstrual blood flow | < 0.001* | |||
| Light | 31 (5.6) | 13 (41.9) | 18 (58.1) | |
| Moderate | 412 (74.9) | 86 (20.8) | 326 (79.1) | |
| Heavy | 107 (19.5) | 6 (5.6) | 101 (94.4) | |
BMI body mass index, SD standard deviation
aPercentages for the columns
bPercentages for the row
cUnivariate analysis was conducted to test the associations between variables with PD
dStatistically significant (P < 0.05)
Logistic regression analysisa of significant risk factors associated with primary dysmenorrhea
| Risk factor | B | SE | Wald | AOR | 95% CI | |
|---|---|---|---|---|---|---|
| Constant | − 2.4 | 0.55 | 18.78 | 0.08 | < 0.001b | |
| Academic specialization (reference: non-medical) | ||||||
| Medical | 0.50 | 0.25 | 4.10 | 1.66 | 1.01–2.72 | 0.04b |
| Parity (reference: nulliparous) | ||||||
| Parous | − 1.68 | 0.45 | 13.79 | 0.18 | 0.07–0.45 | < 0.001b |
| Family history of dysmenorrhea (reference: no) | ||||||
| Yes | 0.92 | 0.25 | 13.29 | 2.52 | 1.53–4.16 | < 0.001b |
| Weight loss attempt (reference: no) | ||||||
| Yes | 0.72 | 0.24 | 8.47 | 2.05 | 1.26–3.34 | 0.004b |
| The intensity of blood flow (reference: light) | ||||||
| Moderate (average) | 1.06 | 0.43 | 5.96 | 2.89 | 1.23–6.79 | 0.01b |
| Heavy | 2.33 | 0.60 | 14.95 | 10.28 | 3.15–33.49 | < 0.001b |
AOR adjusted odds ratio, B coefficient for the constant in the null model, CI confidence interval, SE standard error, Wald Wald chi-square test that tests the null hypothesis that the constant equals 0
aBinary logistic regression analysis was conducted using significant variables associated with dysmenorrhea, using backward stepwise analysis. Hosmer and Lemeshow test: 11.857
bStatistically significant (P < 0.05)
Multiple linear regression analysisa of the efficacy of non-pharmacological measures
| Non-pharmacological measures | Unstandardized coefficients | Standardized coefficients | t | 95% CI | ||
|---|---|---|---|---|---|---|
| B | SE | β | ||||
| Sleeping | 0.33 | 0.16 | 0.10 | 2.05 | 0.01–0.65 | 0.04b |
| Heating pads | 0.66 | 0.17 | 0.19 | 3.71 | 0.31–1.01 | < 0.001b |
| Ginger | 0.74 | 0.35 | 0.11 | 2.08 | 0.04–1.43 | 0.03b |
| Anise | 0.62 | 0.21 | 0.15 | 2.97 | 0.21–1.03 | 0.003b |
B coefficient for the constant in the null model, β the standardized odds ratio, CI confidence interval, SE standard error, t the parameter estimate divided by its standard error
aMultiple linear regression analysis was performed using backward stepwise analysis, and the outcome was: Pain difference intensity I = (pain score 1) − (pain score 2). R: 0.372 and R2: 0.138, thus the model detects 13% of the variation in the mean of different pain score post-non-pharmacological measures. ANOVA F: 8.697, P value < 0.001
bStatistically significant (P < 0.05)