Quantifying Contraceptive Side-Effects: A Prospective Cohort Study of Symptom Burden, Risk Factors, and Daily Life Disruption in South-Central Ethiopia
Bibliographic record
Abstract
Abstract Unwanted side-effects are the leading cause of dissatisfaction and discontinuation of hormonal contraceptives worldwide. Yet contraceptive side-effects are commonly dismissed as minor and/or misconceptions within global health, in part due to the paucity of quantitative data on side-effects symptoms. This research aimed to (1) compare changes in symptom number and severity among hormonal contraceptive users and a control group over a 3-months period, (2) identify risk factors for such changes, and (3) evaluate their impact on women’s daily lives. We conducted an observational baseline-controlled prospective cohort study among injectable and implant users and a control group of non-users in Central Oromia, Ethiopia. Sociodemographic, diet, activity data and monthly side-effect symptoms were collected from pre-initiation to three months. Multilevel models adjusted for temporal autocorrelation were used to evaluate change in the number and severity of symptoms. Minimally adjusted models were used to identify risk factors for increased negative symptoms among contraceptive users and evaluate the impact of experiencing symptoms on women’s daily activities. A total of 278 participants (106 injectable, 72 implant, 100 non-users) were included for analysis. Compared to pre-initiation, injectable users experienced 28% more symptoms at month 3 (adjusted incident rate ratio (IRR): 1.28, 95% CI: 1.05 – 1.57 p = 0.015), implant users experienced a peak of 41% more symptoms at month 2 (adjusted IRR 1.41, 95% CI: 1.15 – 1.73, p = 0.002), and non-users experienced no changes over a similar time period. Contraceptive users with physically demanding occupations, food insecurity, and a history of recent infection experienced the greatest symptom severity, also associated with negative impacts on women’s activities, including work, chores, and relationships. These findings indicate that reducing the burden of contraceptive side effects requires addressing underlying health stressors and considering the significant impact of side-effects on women’s daily lives, rather than relying solely on dispelling misconceptions. Key messages What is already know on this topic Existing research lacks the data necessary to both identify risk factors for contraceptive side effects and assess the extent of daily disruptions caused by these symptoms. What this study adds The design of this study enables us to demonstrate that side-effects are: (1) significant: users report an increase in symptoms after initiating contraception, unlike non-users who do not exhibit such changes; (2) predictable: women experiencing health stressors (nutritional, physical and infectious) prior to initiation report the greatest number and severity of side-effects when using hormonal contraception; (3) disruptive: higher symptom severity is associated with a decreased ability to carry out key daily activities pertaining to work, relationships, and house chores. How this study might affect research, practice and/or policy Contraceptive counselling should be sensitive to variation in risk of side-effects and support women with high symptom burdens with management options or method switching, rather than dismissing concerns as misconceptions. Our findings highlight the need for further research confirming predictive drivers of side-effect experiences to guide counselling and to move towards personalised contraceptive technology development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".