riate provision of routine medication and management of medical co-morbidity are needed to promote fast recovery.There are widespread anecdotal reports of seemingly successful treatment among the early (three to seven days from symptoms) stage coronavirus disease 2019 (COVID-19) patients with the drug hydroxychloroquine (HCQ), and randomized placebo-controlled trials of HCQ in outpatient settings are underway. In this note, we (1) report observational evidence and present scientific reasoning as to why early treatment with HCQ may succeed while treatment later in the disease progression is likely to fail and (2) hypothesize a public health regime under which HCQ may be used to mitigate the impact of the current pandemic.

Before October 2015, pregnancy cohorts assembled from US health insurance claims have relied on medical encounters with International Classification of Diseases-ninth revision-clinical modification (ICD-9-CM) codes. We aimed to extend existing pregnancy identification algorithms into the ICD-10-CM era and evaluate performance.

We used national private insurance claims data (2005-2018) to develop and test a pregnancy identification algorithm. We considered validated ICD-9-CM diagnosis and procedure codes that identify medical encounters for live birth, stillbirth, ectopic pregnancy, abortions, and prenatal screening to identify pregnancies. We then mapped these codes to the ICD-10-CM system using general equivalent mapping tools and reconciled outputs with literature and expert opinion. c-Met inhibitor Both versions were applied to the respective coding period to identify pregnancies. We required 45 weeks of health plan enrollment from estimated conception to ensure the capture of all pregnancy endpoints.

We identified D transition period. New codes for gestational age can potentially improve the precision of conception estimates and minimize measurement biases.

We aimed to test if blood transfusion is a risk factor for the prevalence of cancer.

We conducted secondary analyses using the NHANES database from 1999 to 2016. We included all individuals who received a blood transfusion with known cancer comorbidity (diseased or not). We used univariate logistic regression to identify any possible association between history of blood transfusion and the prevalence of cancer with adjustment for different co-founders was done. Regression results were expressed as odds ratios (ORs) and 95% confidence interval (95% CI) for both adjusted and unadjusted models.

A total of 48,796 individuals were included in the final analysis 6333 of them received a blood transfusion, while the other 42,463 individuals did not. In individuals who received a blood transfusion, the most prevalent cancer was breast cancer (3.4%), followed by prostate (3.0%), non-melanoma skin (2.4%) cancers, while non-melanoma skin (1.2%), prostate (1.1%) and breast (1.1%) cancers were the most prevalent in the no transfusion individuals. There was a significant association between the reported history of blood transfusion and the overall prevalence of cancer in both the unadjusted (OR= 3.47; 95% CI= 3.23-0.72; P-value< 0.001) and adjusted model (OR= 1.86; 95% CI= 1.72-0.2.01; P-value< 0.001). On the level of individual cancers, a significant reduction in cancer prevalence was found in patients with breast, cervix, larynx, Hodgkin’s lymphoma, melanoma, prostate, skin (non-melanoma), skin (unspecified), soft tissue, testicular, thyroid, and uterine cancers.

Results did not imply any concrete association between cancer risk and history of blood transfusion. These findings would help in debunking the myth of increased cancer risk following blood transfusion.

Results did not imply any concrete association between cancer risk and history of blood transfusion. These findings would help in debunking the myth of increased cancer risk following blood transfusion.

The Swedish National Patient Register was validated only for a few diagnoses in the field of trauma. In this study, we calculated the positive predictive values (PPV) of the diagnosis of open tibial fracture and corresponding E-codes (cause of injury).

Out of 2845 cases from a 10-year period (2007-2016), a random sample of 300 cases was selected for review of medical records. The accuracy of the diagnosis and cause of injury was calculated and presented as PPV. We divided the study population into two subgroups (moderate and severe injury) that were analyzed separately. Severe injury was defined as when a patient had an amputation and/or reconstructive surgical procedures, indicated by corresponding ICD-codes.

The PPV of the diagnosis of open tibial fracture was 87% (95% CI 86-88%) overall, 86% (95% CI 79-91%) for moderate injuries and 96% (95% CI 91-98%) for severe injuries. The PPV for E-codes was 74% (95% CI 65-81%). The majority of injuries were caused by falls (47%) or transport accidents (38%). Most of these injuries were caused by high-energy trauma (60%).

The PPV of the diagnosis of open tibial fracture in the Swedish National Patient Register is high (87%). The PPV of E-codes was lower (79%). The results imply that the register is well suited for healthcare evaluation and research purposes regarding trauma diagnoses. Most open tibial fractures are high-energy injuries.

The PPV of the diagnosis of open tibial fracture in the Swedish National Patient Register is high (87%). The PPV of E-codes was lower (79%). The results imply that the register is well suited for healthcare evaluation and research purposes regarding trauma diagnoses. Most open tibial fractures are high-energy injuries.

Electronic health records are widely used in cardiovascular disease research. We appraised the validity of stroke, acute coronary syndrome and heart failure diagnoses in studies conducted using European electronic health records.

Using a prespecified strategy, we systematically searched seven databases from dates of inception to April 2019. Two reviewers independently completed study selection, followed by partial parallel data extraction and risk of bias assessment. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value estimates were narratively synthesized and heterogeneity between sensitivity and PPV estimates were assessed using I

.

We identified 81 studies, of which 20 validated heart failure diagnoses, 31 validated acute coronary syndrome diagnoses with 29 specifically recording estimates for myocardial infarction, and 41 validated stroke diagnoses. Few studies reported specificity or negative predictive value estimates. Sensitivity was ≤66% in all but one heart failure study, ≥80% for 91% of myocardial infarction studies, and ≥70% for 73% of stroke studies.