Using data from primary care to investigate the epidemiology of motor vehicle crashes

Background Motor Vehicle Crashes (MVCs) are a major cause of morbidity and mortality worldwide. This thesis explores the potential use of large databases of primary care medical records to investigate the epidemiology of MVCs in the United Kingdom and to supplement the data available from national statistics, which are believed to understate both the number of crashes, and the number of injuries which occur as a result. Methods Details of all individuals enrolled in The Health Improvement Network (THIN) database whose primary care records indicated involvement in a MVC were used to calculate a series of summary measures describing the burden and consequences of MVCs. These were compared with data available from police accident reports and from hospital admissions. Data from THIN were used to conduct a series of studies of the impact of health and healthcare-related factors on the risk of involvement in MVCs. Specifically: a case-control study of the impact of modifiable lifestyle factors on the risk of MVC; case-crossover and self-controlled case-series studies of the effect of exposure to prescribed medications on the risk of MVC; a case-control study investigating the impact of disordered sleep on the risk of MVC; a case-control study of the risk of involvement in MVC among individuals with diabetes relative to the general population; and; a cohort study assessing whether there is evidence to suggest that involvement in a MVC may indicate the presence of undiagnosed disease which may impair driving performance. Results The socio-demographic characteristics of individuals involved in MVCs recorded in THIN differ markedly from those recorded in police accident reports and hospital admissions data. There was no evidence of consistent trends in MVC incidence over time in the three data sources. Differences in data collection methodology and the severity and scope of crashes recorded may account for these variations. Evidence was found of an association between having a high Body Mass Index and involvement in MVCs, and between past (but not current) smoking and involvement in MVCs, however the recording of data on lifestyle-related exposures such as smoking and alcohol consumption in the age-groups most likely to be involved in MVCs was poor, complicating interpretation of these results. Current exposure to benzodiazepines and preparations containing opioid analgesics was found to increase the risk of involvement in MVCs, as was longer-term use of non-benzodiazepine hypnotics, selective serotonin reuptake inhibitors and antihistamines. No increased risk of MVC was observed with exposure to beta-blockers or tricyclic antidepressants. Individuals reporting insomnia or snoring to their primary care practitioner were found to be at increased risk of MVC, as were individuals with diagnosed sleep apnoea. This association was independent the use of sedative or antidepressant medications. Individuals with diabetes were not found to be at an increased risk of MVC compared with the general population, and there was no difference in risk between those receiving different forms of treatment. Involvement in a MVC was associated with an increased risk of being diagnosed with cardiac disease in the two years following the crash. Conclusions Current sources of data about MVCs in the UK use different data collection methodologies, none of which is likely to accurately describe the overall burden of MVCs in the population. Primary care data remain a useful resource for those wishing to study the epidemiology of MVCs, but care must be taken to ensure that the uses to which the data are put are appropriate. Studies investigating lifestyle-related exposures are unlikely to produce reliable results as primary care recording of such factors is poor in the age-groups most likely to be involved in MVCs. Primary care data are more useful when studying the time course of pharmacological effects, or the effects of diagnosed illness, and can successfully detect previously observed associations. Primary care data is currently of little use in the study of injuries associated with involvement in MVCs as it is rare for both an injury and its proximate cause to be recorded. The investigation of methods by which this problem might be resolved is an important avenue for future research

Using data from primary care to investigate the epidemiology of motor vehicle crashes

Background Motor Vehicle Crashes (MVCs) are a major cause of morbidity and mortality worldwide. This thesis explores the potential use of large databases of primary care medical records to investigate the epidemiology of MVCs in the United Kingdom and to supplement the data available from national statistics, which are believed to understate both the number of crashes, and the number of injuries which occur as a result. Methods Details of all individuals enrolled in The Health Improvement Network (THIN) database whose primary care records indicated involvement in a MVC were used to calculate a series of summary measures describing the burden and consequences of MVCs. These were compared with data available from police accident reports and from hospital admissions. Data from THIN were used to conduct a series of studies of the impact of health and healthcare-related factors on the risk of involvement in MVCs. Specifically: a case-control study of the impact of modifiable lifestyle factors on the risk of MVC; case-crossover and self-controlled case-series studies of the effect of exposure to prescribed medications on the risk of MVC; a case-control study investigating the impact of disordered sleep on the risk of MVC; a case-control study of the risk of involvement in MVC among individuals with diabetes relative to the general population; and; a cohort study assessing whether there is evidence to suggest that involvement in a MVC may indicate the presence of undiagnosed disease which may impair driving performance. Results The socio-demographic characteristics of individuals involved in MVCs recorded in THIN differ markedly from those recorded in police accident reports and hospital admissions data. There was no evidence of consistent trends in MVC incidence over time in the three data sources. Differences in data collection methodology and the severity and scope of crashes recorded may account for these variations. Evidence was found of an association between having a high Body Mass Index and involvement in MVCs, and between past (but not current) smoking and involvement in MVCs, however the recording of data on lifestyle-related exposures such as smoking and alcohol consumption in the age-groups most likely to be involved in MVCs was poor, complicating interpretation of these results. Current exposure to benzodiazepines and preparations containing opioid analgesics was found to increase the risk of involvement in MVCs, as was longer-term use of non-benzodiazepine hypnotics, selective serotonin reuptake inhibitors and antihistamines. No increased risk of MVC was observed with exposure to beta-blockers or tricyclic antidepressants. Individuals reporting insomnia or snoring to their primary care practitioner were found to be at increased risk of MVC, as were individuals with diagnosed sleep apnoea. This association was independent the use of sedative or antidepressant medications. Individuals with diabetes were not found to be at an increased risk of MVC compared with the general population, and there was no difference in risk between those receiving different forms of treatment. Involvement in a MVC was associated with an increased risk of being diagnosed with cardiac disease in the two years following the crash. Conclusions Current sources of data about MVCs in the UK use different data collection methodologies, none of which is likely to accurately describe the overall burden of MVCs in the population. Primary care data remain a useful resource for those wishing to study the epidemiology of MVCs, but care must be taken to ensure that the uses to which the data are put are appropriate. Studies investigating lifestyle-related exposures are unlikely to produce reliable results as primary care recording of such factors is poor in the age-groups most likely to be involved in MVCs. Primary care data are more useful when studying the time course of pharmacological effects, or the effects of diagnosed illness, and can successfully detect previously observed associations. Primary care data is currently of little use in the study of injuries associated with involvement in MVCs as it is rare for both an injury and its proximate cause to be recorded. The investigation of methods by which this problem might be resolved is an important avenue for future research

Investigating the detection of adverse drug events in a UK general practice electronic health-care database

Data-mining techniques have frequently been developed for Spontaneous reporting databases. These techniques aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information,under reporting and incorrect entries. This often results in a detection lag or prevents the detection of some adverse drug events. These limitations do not occur in electronic healthcare databases. In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared. The results suggests that the application of existing methods to the general practice database may help find signals that have gone undetected when using the spontaneous reporting system database. In addition the general practice database provides far more supplementary information, that if incorporated in analysis could provide a wealth of information for identifying adverse events more accurately

Attributes for causal inference in electronic healthcare databases

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria

Attributes for causal inference in electronic healthcare databases

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria

The Nondeterministic Waiting Time Algorithm: A Review

We present briefly the Nondeterministic Waiting Time algorithm. Our technique for the simulation of biochemical reaction networks has the ability to mimic the Gillespie Algorithm for some networks and solutions to ordinary differential equations for other networks, depending on the rules of the system, the kinetic rates and numbers of molecules. We provide a full description of the algorithm as well as specifics on its implementation. Some results for two well-known models are reported. We have used the algorithm to explore Fas-mediated apoptosis models in cancerous and HIV-1 infected T cells

Risk of fall in patients with COPD

A matched cohort study was conducted to determine the incidence of falls in patients following a diagnosis of COPD using a UK primary care database. 44 400 patients with COPD and 175 545 non-COPD subjects were identified. The incidence rate of fall per 1000 person-years in patients with COPD was higher (44.9; 95% CI 44.1 to 45.8) compared with non-COPD subjects (24.1; 95% CI 23.8 to 24.5) (P<0.0001). Patients with COPD were 55% more likely to have an incident record of fall than non-COPD subjects (adjusted HR, 1.55; 95% CI 1.50 to 1.59). The greater falls risk in patients with COPD needs consideration and modifiable factors addressed

First trimester exposure to anxiolytic and hypnotic drugs and the risks of major congenital anomalies: a United Kingdom population-based cohort study.

BACKGROUND: Despite their widespread use the effects of taking benzodiazepines and non-benzodiazepine hypnotics during pregnancy on the risk of major congenital anomaly (MCA) are uncertain. The objectives were to estimate absolute and relative risks of MCAs in children exposed to specific anxiolytic and hypnotic drugs taken in the first trimester of pregnancy, compared with children of mothers with depression and/or anxiety but not treated with medication and children of mothers without diagnosed mental illness during pregnancy. METHODS: We identified singleton children born to women aged 15-45 years between 1990 and 2010 from a large United Kingdom primary care database. We calculated absolute risks of MCAs for children with first trimester exposures of different anxiolytic and hypnotic drugs and used logistic regression with a generalised estimating equation to compare risks adjusted for year of childbirth, maternal age, smoking, body mass index, and socioeconomic status. RESULTS: Overall MCA prevalence was 2.7% in 1,159 children of mothers prescribed diazepam, 2.9% in 379 children with temazepam, 2.5% in 406 children with zopiclone, and 2.7% in 19,193 children whose mothers had diagnosed depression and/or anxiety but no first trimester drug exposures. When compared with 2.7% in 351,785 children with no diagnosed depression/anxiety nor medication use, the adjusted odds ratios were 1.02 (99% confidence interval 0.63-1.64) for diazepam, 1.07 (0.49-2.37) for temazepam, 0.96 (0.42-2.20) for zopiclone and 1.27 (0.43-3.75) for other anxiolytic/hypnotic drugs and 1.01 (0.90-1.14) for un-medicated depression/anxiety. Risks of system-specific MCAs were generally similar in children exposed and not exposed to such medications. CONCLUSIONS: We found no evidence for an increase in MCAs in children exposed to benzodiazepines and non-benzodiazepine hypnotics in the first trimester of pregnancy. These findings suggest that prescription of these drugs during early pregnancy may be safe in terms of MCA risk, but findings from other studies are required before safety can be confirmed