BackgroundGoogle Trends is a novel, freely accessible tool that allows users to interact with Internet search data, which may provide deep insights into population behavior and health-related phenomena. However, there is limited knowledge about its potential uses and limitations. We therefore systematically reviewed health care literature using Google Trends to classify articles by topic and study aim; evaluate the methodology and validation of the tool; and address limitations for its use in research.Methods and FindingsPRISMA guidelines were followed. Two independent reviewers systematically identified studies utilizing Google Trends for health care research from MEDLINE and PubMed. Seventy studies met our inclusion criteria. Google Trends publications increased seven-fold from 2009 to 2013. Studies were classified into four topic domains: infectious disease (27% of articles), mental health and substance use (24%), other non-communicable diseases (16%), and general population behavior (33%). By use, 27% of articles utilized Google Trends for casual inference, 39% for description, and 34% for surveillance. Among surveillance studies, 92% were validated against a reference standard data source, and 80% of studies using correlation had a correlation statistic ≥0.70. Overall, 67% of articles provided a rationale for their search input. However, only 7% of articles were reproducible based on complete documentation of search strategy. We present a checklist to facilitate appropriate methodological documentation for future studies. A limitation of the study is the challenge of classifying heterogeneous studies utilizing a novel data source.ConclusionGoogle Trends is being used to study health phenomena in a variety of topic domains in myriad ways. However, poor documentation of methods precludes the reproducibility of the findings. Such documentation would enable other researchers to determine the consistency of results provided by Google Trends for a well-specified query over time. Furthermore, greater transparency can improve its reliability as a research tool.
This first comprehensive combined Australia and New Zealand audit of ACS care identified variations in the application of the ACS evidence base and varying rates of inhospital clinical events. A focus on integrated clinical service delivery may provide greater translation of evidence to practice and improve ACS outcomes in Australia and New Zealand.
ObjectiveTo evaluate the proportion of patients hospitalised with acute coronary syndrome (ACS) in Australia and New Zealand who received optimal inpatient preventive care and to identify factors associated with preventive care.MethodsAll patients hospitalised bi-nationally with ACS were identified between 14–27 May 2012. Optimal in-hospital preventive care was defined as having received lifestyle advice, referral to rehabilitation, and prescription of secondary prevention pharmacotherapies. Multilevel multivariable logistic regression was used to determine factors associated with receipt of optimal preventive care.ResultsFor the 2299 ACS survivors, mean (SD) age was 69 (13) years, 46% were referred to rehabilitation, 65% were discharged on sufficient preventive medications, and 27% received optimal preventive care. Diagnosis of ST elevation myocardial infarction (OR: 2.64 [95% CI: 1.88–3.71]; p<0.001) and non-ST elevation myocardial infarction (OR: 1.99 [95% CI: 1.52–2.61]; p<0.001) compared with a diagnosis of unstable angina, having a percutaneous coronary intervention (PCI) (OR: 4.71 [95% CI: 3.67–6.11]; p<0.001) or coronary bypass (OR: 2.10 [95% CI: 1.21–3.60]; p=0.011) during the admission or history of hypertension (OR:1.36 [95% CI: 1.06–1.75]; p=0.017) were associated with greater exposure to preventive care. Age over 70 years (OR:0.53 [95% CI: 0.35–0.79]; p=0.002) or admission to a private hospital (OR:0.59 [95% CI: 0.42–0.84]; p=0.003) were associated with lower exposure to preventive care.ConclusionsOnly one-quarter of ACS patients received optimal secondary prevention in-hospital. Patients with UA, who did not have PCI, were over 70 years or were admitted to a private hospital, were less likely to receive optimal care.
Isuru Ranasinghe and colleagues compare readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia in adults aged 18 to 64 years with readmissions in those aged 65 and older.
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Background
Young women (<65 years) experience a 2–3-fold greater mortality risk than younger men after acute myocardial infarction (AMI). However, it is unknown whether they are at higher risk for 30-day readmission, and if this association varies by age. We examined sex differences in the rate, timing and principal diagnoses of 30-day readmissions, including the independent effect of sex following adjustment for confounders.
Methods and Results
We included patients aged 18–64 years with a principal diagnosis of AMI. Data was utilized from the Healthcare Cost and Utilization Project-State Inpatient Database for California (07–09). Readmission diagnoses were categorized using an aggregated version of the Centers for Medicare and Medicaid Services’ Condition Categories, and readmission timing was determined from the day after discharge. Of 42,518 younger patients with AMI (26.4% female), 4,775 (11.2%) had at least one readmission. The 30-day all-cause readmission rate was higher for women (15.5% vs. 9.7%, P<0.0001). For both sexes, readmission risk was highest on days 2–4 after discharge and declined thereafter, and women were more likely to present with non-cardiac diagnoses (44.4% vs. 40.6%, P=0.01). Female sex was associated with a higher rate of 30-day readmission, which persisted after adjustment (HR=1.22, 95% CI 1.15, 1.30). There was no significant interaction between age and sex on readmission.
Conclusions
Compared with men, younger women have a higher risk for readmission, even after adjustment for confounders. The timing of 30-day readmission was similar in women and men, and both sexes were susceptible to a wide range of causes for readmission.
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