Menstrual disorders are a group of conditions that impair the quality of life of many women who are otherwise healthy.
These conditions include heavy menstrual bleeding (HMB), painful periods (dysmenorrhea) and endometriosis, and are estimated to affect 25%, 20% and 10% of women of reproductive age, respectively.
Previous studies have highlighted concerns about how effectively menstrual disorders are managed and treated in primary care, but the number of patients seeking help from GPs for heavy and/or painful periods is not well understood and neither are their healthcare needs.
In this project we will develop code lists to determine patients who go to their GP about heavy and/or painful periods. We will then calculate the incidence and prevalence of these conditions and describe how this varies by age, ethnicity and deprivation status. Finally, we will describe how often patients with menstrual disorders use the health service, which treatments they are getting and how many of them are referred to secondary care services.
It is important to know how many women are affected by menstrual disorders and understand the way in which they are using the NHS so that we can identify opportunities to improve access to existing treatments and highlight any disparities that exist in the care that they are given.
Objectives
This study aims to support GPs, gynaecologists and commissioners to understand the health service needs of patients with menstrual disorders and how these vary by clinical and socio-demographic factors.
This study will estimate the incidence and prevalence of common menstrual disorders among women of reproductive age and describe the use of primary and secondary health care by women with these conditions by age, ethnicity, socioeconomic deprivation and time.
Methods and data analysis
First, codelists (overall and by sub-condition) will be developed following best practice guidance,drawing on clinical expertise and previously published studies. This methodological work is not only an essential preparation for data analysis, but it will also guide recommendations on how diagnoses and procedures related to menstrual disorders can be better recorded into electronic databases in the future.
The finalised codelists will be applied to a random sample of 500,000 female patients (aged 8 to 60 years in 2013) with linked CPRD-HES records. This dataset will be used to:
- Estimate the incidence and prevalence of menstrual disorders in CPRD in 2013, overall and by sub-condition
- Describe the characteristics of patients who attend their GP with a menstrual disorder, by age; ethnic and deprivation profiles; type of menstrual disorder.
- Describe the treatments these patients receive in primary care over a 5-year follow-up period (2014 to 2019) and how patterns of referral to secondary care services vary according to age, ethnicity, level of deprivation and type of menstrual disorder.
- Describe and compare and levels of primary and secondary care utilisation (as captured in HES) over a 5-year follow-up period (2014 to 2019) between patients with and without a menstrual disorder.
Number of patients with a likely, probable or possible menstrual disorder
Proportion of patients with a likely, probable or possible menstrual disorder who are referred to secondary care gynaecology services
Overall healthcare utilisations as measured by:
o Number and duration of primary care consultations
o Number of prescribed products
o Number of outpatient visits
o Number of emergency department visits
o Number and duration of hospital admissions
Hannah Knight - Chief Investigator - The Health Foundation
Elizabeth Crellin - Corresponding Applicant - The Health Foundation
Anne Connolly - Collaborator - NHS England
Christina Pagel - Collaborator - University College London ( UCL )
Ipek Gurol-Urganci - Collaborator - Imperial College London
Sarah Deeny - Collaborator - The Health Foundation
Richard Welpton - Corresponding Applicant - The Health Foundation
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation