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Background: Worldwide the rate of unplanned pregnancies is more than 40%. Identifying women at risk of pregnancy can help prevent negative outcomes and also reduce healthcare costs of potential complications. It can also allow the investigation of the natural history of pregnancy outcomes, such as ectopic pregnancies or miscarriages. The use of medical records databases has been a crucial development in the field of pharmacoepidemiology - e.g. The Health Improvement Network (THIN) database is a validated database representative of the UK population. This project aimed to test the feasibility of identifying a population of women of childbearing age who are at risk of pregnancy not using any contraception in THIN database.Methods: First a cohort of women of childbearing age (15-45yo) was identified. By applying a computer-based algorithm, containing codes for contraception methods or other suggestion of contraception, the risk of pregnancy was then ascertained. Next, two validation steps were implemented: 1) Revision of medical records/free text and 2) Questionnaires were sent to primary care practitioners (PCP) of women whose medical records had been reviewed. Positive predicted values (PPV) were calculated.Results: A total of 266,433 women were identified in THIN. For the first validation step, 123 records were reviewed, with a PPV of 99.2% (95%CI: 95.5-99.9). For the questionnaires step, the PPV was of 82.3% (95%CI: 70-91.1). Information on sexual behaviour and attitudes towards conception was not captured by THIN.Conclusion: This study shows that by applying a comprehensive computer-based algorithm, THIN can be used to identify women at risk of pregnancy. [ABSTRACT FROM AUTHOR]

Background: Primary care is likely to see the highest number of Lyme disease patients. Despite this, there is limited published data regarding Lyme disease patients accessing primary care in the UK. We aim to describe trends in the incidence of a new diagnosis, and demographics of patients identified in a primary care electronic health database.Methods: A descriptive epidemiological study of Lyme disease coded patients in UK primary care. 3725 patients coded for Lyme disease during 1998-2016 were identified within The Health Improvement Network (THIN). Incidence rates and the demographics of cases identified were described. Poisson regression was used to analyse socio-demographic characteristics of the cases.Results: There was an increase in annual crude incidence rates, peaking in 2015 at 5.47 (95% CI 4.85-6.14) cases per 100,000 population per year. Multivariable analysis showed there were significant differences in the ages of those affected, incidence of a new diagnosis rose as deprivation levels improved, and that there was a higher incidence of cases living in rural areas compared to urban areas. There was no significant difference between sexes for the UK. Cases were significantly more likely to identify with being white compared to the national population.Conclusions: An increasing incidence of patients newly coded with Lyme disease related Read codes was identified using data from a UK national primary care database. By comparing these incidence figures with national laboratory-confirmed surveillance data, a multiplication factor of 2.35 (95%CI 1.81-2.88) can be calculated in order to estimate the annual number of cases seen in primary care. The significant socio-demographic variables associated with a Lyme disease diagnosis likely reflect a complex interplay of socio-economic issues, which needs to be further explored. Future work is needed to examine the treatment and management of patients within this database. [ABSTRACT FROM AUTHOR]