Polycystic Ovary Syndrome

We designed algorithms for application to primary and secondary care data to establish incident diabetes cases. Our focus was on type 2 diabetes, given the age of UKB participants at recruitment. To assist generalisability to the UKB population, we restricted CPRD data to those on whom we had linked secondary care data, people aged 40–69 years on 1^st January 2006, (to reflect age entry criteria for UKB) Primary care algorithms were derived based on four types of evidence: 1) Diabetes diagnostic codes (considered separately as any diagnostic code and the more specific C10E [type 1 diabetes] or C10F [type 2 diabetes] codes, these are a requirement for the Quality Outcomes Framework [QOF] system[14 (link)]), 2) Diabetes medication, (excluding those on metformin only as this has other prescribing indications e.g. pre-diabetes, polycystic ovarian syndrome and is therefore not wholly diabetes specific), 3) Hyperglycaemia on blood results (defined as HbA_1c≥6.5% or 48 mmol/mol, or fasting/ random/ unspecified glucose≥11.1 mmol/l) and 4) Presence of diabetes process of care codes (restricted to those routinely recorded for QOF monitoring purposes, e.g. retinopathy screening, foot checks etc.). The threshold for glucose was chosen because primary care records frequently do not specify whether glucose is fasting or not, and we wished to avoid false positives from a non-fasting glucose in the 7.0–11.1 mmol/l range. Using CPRD and the linked Welsh UKB sub-cohort, we used an iterative approach, cross-tabulating evidence at each step, to determine the logical steps to include in the algorithm and in what order. We then applied the final incidence algorithm to both databases. For CPRD, we excluded prevalent diabetes according to pre-existing C10 diabetes-specific Read codes, and for the Welsh dataset, we removed all those with prevalent diabetes according to our UKB algorithm.
When developing the incidence algorithms intended for secondary care data, we defined incident diabetes type based on ICD-10 codes (E10 = type 1 diabetes, E11 = type 2 diabetes, E13/E14 = unspecified diabetes). Prevalent diabetes was excluded as above.
For both primary and secondary care incidence algorithms, we derived event dates by taking the mid-point between the last primary care consultation/ hospital admission without diabetes and the date of the first diabetes Read code/ ICD code/ diabetes medication/ hyperglycaemic blood test/ fifth process of care code. If there were no previous consultations or admissions, we used the UK Biobank inception date. The date of the first diabetes Read code/ ICD code/ diabetes medication/ hyperglycaemic blood test/ fifth process of care code will be available to researchers separately if they wish to calculate the event date in an alternative manner.

The search strategy was designed to capture studies reporting on women’s and girls’ experiences of menstruation. Searches were undertaken in 11 databases (Applied Social Science Index and Abstracts, Cumulative Index of Nursing and Allied Health Literature, ProQuest Dissertation and Theses, Embase, Global Health, Medline, Open Grey, Popline, PsycINFO, Sociological Abstracts, WHO Global Health Library) using a prespecified, piloted strategy reported in Table 1. Searches were completed in January 2019 with no language of publication or date restrictions applied. Comprehensive grey literature searching and hand searching were undertaken. Organisations attending to menstrual health were identified through participation in reports [7 , 21 ], stakeholder meetings [2 (link)], and online searches. Websites (see list in S1 Text) were searched using relevant terms (e.g., ‘menstrual’, ‘menstruation’). Citations of included studies and reference lists of large menstrual health reports were searched [7 , 21 ]. Results were exported into EPPI-Reviewer 4 (EPPI-Centre; https://eppi.ioe.ac.uk/cms/Default.aspx?tabid=2914). Two authors (JH, AS) independently screened titles and abstracts, followed by full-text screening to determine eligibility (JH).
Studies were eligible if they reported qualitative analysis of the menstrual experiences of women and girls residing in low- or middle-income countries (LMICs) as defined by the World Bank [24 ]. Studies that included women from LMICs now residing in high-income countries, or that combined populations from LMICs with those in high-income settings, were excluded. While these experiences also deserve increased attention, this review sought to synthesise the large set of studies situated in LMICs to inform evolving policy and practice in these regions. Studies exclusively concerning the acceptability of menstrual suppression were excluded. Studies focussed on puberty more broadly, or the use of sanitation infrastructure, were only included when they reported on experiences of menstruation. For example, studies that included lists of puberty education needs that referenced menstruation but did not report on women’s or girls’ lived menstrual experiences were not included. Similarly, studies focussed on menopause, premenstrual syndrome, or polycystic ovary syndrome were not eligible for inclusion. Studies capturing the menstrual experiences of populations with menstrual disorders (e.g., dysmenorrhea, endometriosis) were eligible. Menstruating women and girls were the target population; thus, studies were excluded if they focused exclusively on girls’ premenarche, key informants, or males. Where key informant interviews were analysed alongside women’s and girls’ experiences, studies were included, but analysis focused on the experience of the target population. Qualitative and mixed-methods studies reported in peer-reviewed or grey literature were eligible for inclusion. Studies were excluded if they did not report any qualitative analysis or results (e.g., qualitative responses were back-coded for quantitative description).
Following full-text screening, study research questions were extracted and iteratively grouped. Three groupings emerged: studies broadly focused on menstrual experiences, studies of experiences of menstruation for those with dysmenorrhea or disorders, and studies of experiences of menstrual interventions or products. Because the review aimed to provide a synthesis of menstrual experience and advance problem theory rather than explore the role of interventions, the third grouping was excluded from the present review but was retained for analyses reported elsewhere.

Pre-pregnancy complications were defined based on a history of (i) previous three or more early miscarriages [2 ], (ii) previous late miscarriage (after week 16) including IUFD, or for the current pregnancy (iii) ART (IVF, insemination, or other medically or surgical assistance), or (iv) duration exceeding one year to conceive.
Uncomplicated pre-pregnancy was defined as pregnancies with fewer than three prior early miscarriages, no late miscarriage or IUFD, less than one year to conceive and unassisted conception. If a participant did not answer a question needed for this categorization, the answer was considered uncomplicated.
Questions regarding potential risk factors were chosen from the questionnaire to estimate: (i) general health and health seeking behavior (body mass index (BMI), menstruation, cervical screening attendance, eating disorders and gynecological infections), (ii) lifestyle (age, country of birth, education level, work situation, contact with animals, smoking, mouth tobacco use and diet), (iii) drugs (drug use before pregnancy: asthma and allergy medication, anxiety, antidepressants and sleep medication, prescription free pain medication, opioids and strong pain medication, thyroid medication, blood pressure medication, stomach acid medication and other medication), (iv) reproductive health (first pregnancy and contraceptive use) and (v) comorbidities (diagnosed and suspected endometriosis and polycystic ovary syndrome (PCOS)) (Supplement 1). Participants were sorted as having suspected PCOS if they reported Ferriman-Gallwey score ≥ 8 [14 (link)] or hair loss grade 3, and suspected endometriosis if they reported vaginal spotting before the start of menstruation [15 (link), 16 (link)].

To estimate the effect of general health prior to pregnancy on the risk of pre-pregnancy complications, six categories were created based on reported medical history: (i) Gynecological history (diagnosed or suspected endometriosis or PCOS), (ii) Non-gastrointestinal chronic inflammatory diseases (diagnosed rheumatoid arthritis, autoimmune diseases or multiple sclerosis (MS)), (iii) Gastrointestinal problems (gastric acid medication, diagnosed Crohn’s disease or ulcerative colitis), (iv) Mental health (eating disorder, anti-depressive-, anti-anxiety- or sleeping medication), (v) Chronic respiratory diseases and allergies (asthma, hay fever, allergies, asthma- or allergy medication) and (vi) Endocrine indications (thyroid medication or diagnosed hypothyroidism). These categories were not mutually exclusive. Participants with each comorbidity were compared to those without the specific comorbidity; and the group with any of these comorbidities was also compared to those without.