Development and Application of a Data-Driven Signal Detection Method for Surveillance of Adverse Event Variability Across Manufacturing Lots of Biologics.

INTRODUCTION: Postmarketing drug safety surveillance research has focused on the product-patient interaction as the primary source of variability in clinical outcomes. However, the inherent complexity of pharmaceutical manufacturing and distribution, especially of biologic drugs, also underscores the importance of risks related to variability in manufacturing and supply chain conditions that could potentially impact clinical outcomes. We propose a data-driven signal detection method called HMMScan to monitor for manufacturing lot-dependent changes in adverse event (AE) rates, and herein apply it to a biologic drug. METHODS: The HMMScan method chooses the best-fitting candidate from a family of probabilistic Hidden Markov Models to detect temporal correlations in per lot AE rates that could signal clinically relevant variability in manufacturing and supply chain conditions. Additionally, HMMScan indicates the particular lots most likely to be related to risky states of the manufacturing or supply chain condition. The HMMScan method was validated on extensive simulated data and applied to three actual lot sequences of a major biologic drug by combining lot metadata from the manufacturer with AE reports from the US FDA Adverse Event Reporting System (FAERS). RESULTS: Extensive method validation on simulated data indicated that HMMScan is able to correctly detect the presence or absence of variable manufacturing and supply chain conditions for contiguous sequences of 100 lots or more when changes in these conditions have a meaningful impact on AE rates. Applying the HMMScan method to FAERS data, two of the three actual lot sequences examined exhibited evidence of potential manufacturing or supply chain-related variability. CONCLUSIONS: HMMScan could be utilized by both manufacturers and regulators to automate lot variability monitoring and inform targeted root-cause analysis. Broad application of HMMScan would rely on a well-developed data input pipeline. The proposed method is implemented in an open-source GitHub repository.

Human fertility after a disaster: a systematic literature review.

Fertility is a key demographic parameter influenced by disaster. With the growing risk of disasters, interest in the fertility response to a disaster is increasing among the public, policy makers and researchers alike. However, a systematic literature review on how disaster affects live birth counts does not yet exist. We reviewed 50 studies retrieved from a systematic search based on a pre-registered protocol. We found an overall negative impact of disasters on fertility. If any, increases in fertility were mostly linked with weather-related physical disasters. We also identified 13 distinct mechanisms which researchers have considered as underlying the fertility effects of disaster. By contrast to the common belief that disasters are more likely to increase fertility in contexts with already high fertility, we found little evidence to suggest that the total fertility rate of the studied populations was an important predictor of the direction, timing or size of fertility impacts. While this may be because no relationship exists, it may also be due to biases we observed in the literature towards studying high-income countries or high-cost disasters. We summarize the methodological limitations identified from the reviewed studies into six practical recommendations for future research. Our findings inform both the theories behind the fertility effects of disasters and the methods for studying them.

Characteristics of prehospital ST-segment elevation myocardial infarctions.

INTRODUCTION: Despite attention directed at treatment times of ST-segment elevation myocardial infarctions (STEMIs), little is known about the types of STEMIs presenting to the emergency department (ED). OBJECTIVE: The purpose of this study was to determine the relative frequencies and characteristics of emergency medical services (EMS) STEMIs compared with those in patients who present to the ED by walk-in. This information may be applied in EMS training, system planning, and public education. METHODS: This was a query of a prospectively gathered database of all STEMIs in patients presenting to Summa Akron City Hospital ED in 2009 and 2010. We collected demographic information, chief complaint, mode and time of arrival, and STEMI pattern (anterior, lateral, inferior, or posterior). We excluded transfers and in-hospital STEMIs. We calculated means, percentages, significance, and 95% confidence intervals (CIs) ± 10%. RESULTS: We analyzed data from 308 patients. Most patients (241/308, 78%, CI 73%-83%) arrived by EMS, were male (203/308, 66%, CI 60%-71%), and were white (286/308, 93%, CI 89%-96%). Patients arriving by EMS were older (average 63 years, range 35-95) than walk-in patients (average 57 years, range 24-92). Two percent (5/241, 2%, CI 1%-5%) of EMS STEMI patients were under 40 years of age, compared with 10% (7/67, 10%, CI 4%-20%) of walk-in patients (p = 0.0017). The most common chief complaint was chest pain (278/308, 90%, CI 86%-93%). Inferior STEMIs were most common (167/308, 54%, CI 49%-60%), followed by anterior (127/308, 41%, CI 48%-60%), lateral (8/308, 3%, CI 1%-5%), and posterior (6/308, 2%, CI 1%-4%). A day-of-the-week analysis showed that no specific day was most common for STEMI presentation. Forty percent (122/308, 40%, CI 34%-45%) of patients presented during open catheterization laboratory hours (Monday through Friday, 0730-1700 hours). There was no significant statistical difference between EMS and walk-in patients with regard to STEMI pattern or patient demographics. CONCLUSIONS: In this study, 95% (294/308) of all STEMIs were inferior or anterior infarctions, and these types of presentations should be stressed in EMS education. Most STEMI patients at this institution arrived by ambulance and during off-hours. Younger patients were more likely to walk in. We need further study, but we may have identified a target population for future interventions. Key words: emergency medical services; allied health personnel; electrocardiography; myocardial infarction; heart catheterization; STEMI.

The Effect of Fertility Reduction on Economic Growth.

We assess quantitatively the effect of exogenous reductions in fertility on output per capita. Our simulation model allows for effects that run through schooling, the size and age structure of the population, capital accumulation, parental time input into child-rearing, and crowding of fixed natural resources. The model is parameterized using a combination of microeconomic estimates, data on demographics and natural resource income in developing countries, and standard components of quantitative macroeconomic theory. We apply the model to examine the effect of a change in fertility from the UN medium-variant to the UN low-variant projection, using Nigerian vital rates as a baseline. For a base case set of parameters, we find that such a change would raise output per capita by 5.6 percent at a horizon of 20 years, and by 11.9 percent at a horizon of 50 years.