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.

In a period of dynamic change in health care technology, delivery, and behaviors, tracking trends in health and health care can provide a perspective on what is being achieved.OBJECTIVE To comprehensively describe national trends in mortality, hospitalizations, and expenditures in the Medicare fee-for-service population between 1999 and 2013. DESIGN, SETTING, AND PARTICIPANTS Serial cross-sectional analysis of Medicare beneficiaries aged 65 years or older between 1999 and 2013 using Medicare denominator and inpatient files. MAIN OUTCOMES AND MEASURESFor all Medicare beneficiaries, trends in all-cause mortality; for fee-for-service beneficiaries, trends in all-cause hospitalization and hospitalizationassociated outcomes and expenditures. Geographic variation, stratified by key demographic groups, and changes in the intensity of care for fee-for-service beneficiaries in the last 1, 3, and 6 months of life were also assessed. RESULTSThe sample consisted of 68 374 904 unique Medicare beneficiaries (fee-for-service and Medicare Advantage). All-cause mortality for all Medicare beneficiaries declined from 5.30% in 1999 to 4.45% in 2013 (difference, 0.85 percentage points; 95% CI, 0.83-0.87).

IMPORTANCE Little contemporary information is available about comparative performance between Veterans Affairs (VA) and non-VA hospitals, particularly related to mortality and readmission rates, 2 important outcomes of care.OBJECTIVE To assess and compare mortality and readmission rates among men in VA and non-VA hospitals. DESIGN, SETTING, AND PARTICIPANTSCross-sectional analysis involving male Medicare fee-forservice beneficiaries aged 65 years or older hospitalized between 2010 and 2013 in VA and non-VA acute care hospitals for acute myocardial infarction (AMI), heart failure (HF), or pneumonia using the Medicare Standard Analytic Files and Enrollment Database together with VA administrative claims data. To avoid confounding geographic effects with health care system effects, we studied VA and non-VA hospitals within the same metropolitan statistical area (MSA).EXPOSURES Hospitalization in a VA or non-VA hospital in MSAs that contained at least 1 VA and non-VA hospital. MAIN OUTCOMES AND MEASURESFor each condition, 30-day risk-standardized mortality rates and risk-standardized readmission rates for VA and non-VA hospitals. Mean aggregated within-MSA differences in mortality and readmission rates were also assessed. RESULTSWe studied 104 VA and 1513 non-VA hospitals, with each condition-outcome analysis cohort for VA and non-VA hospitals containing at least 7900 patients (men; Ն65 years), in 92 MSAs. Mortality rates were lower in VA hospitals than non-VA hospitals for AMI (13.5% vs 13.7%, P = .02; −0.2 percentage-point difference) and HF (11.4% vs 11.9%, P = .008; −0.5 percentage-point difference), but higher for pneumonia (12.6% vs 12.2%, P = .045; 0.4 percentage-point difference). In contrast, readmission rates were higher in VA hospitals for all 3 conditions (AMI, 17.8% vs 17.2%, 0.6 percentage-point difference; HF, 24.7% vs 23.5%, 1.2 percentage-point difference; pneumonia, 19.4% vs 18.7%, 0.7 percentage-point difference, all P < .001). In within-MSA comparisons, VA hospitals had lower mortality rates for AMI (percentage-point difference, −0.22; 95% CI, −0.40 to −0.04) and HF (−0.63; 95% CI, −0.95 to −0.31), and mortality rates for pneumonia were not significantly different (−0.03; 95% CI, −0.46 to 0.40); however, VA hospitals had higher readmission rates for AMI (0.62; 95% CI, 0.48 to 0.75), HF (0.97; 95% CI, 0.59 to 1.34), or pneumonia (0.66; 95% CI, 0.41 to 0.91).CONCLUSIONS AND RELEVANCE Among older men with AMI, HF, or pneumonia, hospitalization at VA hospitals, compared with hospitalization at non-VA hospitals, was associated with lower 30-day risk-standardized all-cause mortality rates for AMI and HF, and higher 30-day risk-standardized all-cause readmission rates for all 3 conditions, both nationally and within similar geographic areas, although absolute differences between these outcomes at VA and non-VA hospitals were small.

Epithelial-mesenchymal transition (EMT) is a critical process for embryogenesis but is abnormally activated during cancer metastasis and recurrence. This process enables epithelial cancer cells to acquire mobility and traits associated with stemness. It is unknown whether epithelial stem cells or epithelial cancer stem cells are able to undergo EMT, and what molecular mechanism regulates this process in these specific cell types. We found that Epithelial Ovarian Cancer Stem cells (EOC stem cells) are the source of metastatic progenitor cells through a differentiation process involving EMT and Mesenchymal-Epithelial Transition (MET). We demonstrate both in vivo and in vitro the differentiation of EOC stem cells into mesenchymal spheroid-forming cells (MSFCs) and their capacity to initiate an active carcinomatosis. Furthermore, we demonstrate that human EOC stem cells injected i.p in mice are able to form ovarian tumors, suggesting that the EOC stem cells have the ability to “home” to the ovaries and establish tumors. Most interestingly, we found that TWIST1 is constitutively degraded in EOC stem cells, and that the acquisition of TWIST1 requires additional signals that will trigger the differentiation process. These findings are relevant for understanding the differentiation and metastasis process in EOC stem cells.