Evaluating the association of physical activity and weight gain in pregnancy

Previous research has shown that physical activity in pregnancy decreases the risk of poor pregnancy outcomes including development of gestational hypertension, pre-eclampsia, gestational diabetes, and the need for unplanned cesarean section. Research has also shown that excessive weight gain in pregnancy increases the risk of poor pregnancy outcomes. Tracking accurate physical activity in pregnancy is difficult using patient-reported data, however with commercially available and accurate physical activity monitors, objective data is more readily available. Our study is a feasibility study using objective data to track physical activity and weight gain in pregnancy

Using Search Volume for Surveillance of Medication Prescribing

OBJECTIVE: To validate search volume estimation for outpatient medication prescribing. INTRODUCTION: Investigators have used the volume of internet search queries to model disease incidence, especially influenza and general consumer behavior [1]. Our group has used search volume to model interest in FDA safety alerts and adverse drug event incidence. We found evidence of changes in search behavior following warnings and the expected relationship between search volume and adverse drug event incidence. Thus, search volume may help provide near real time surveillance of drug use patterns to help monitor and mitigate risk to the population from adverse drug events. However, the use of search query volume as a proxy for drug use has yet to be validated. We attempt to validate search volume estimation of drug utilization in three ways: 1) explore seasonal variations in search volume and outpatient utilization, 2) monitor change between substitute drugs following patent expirations and 3) use search volume estimation methods to estimate TB incidence. METHODS: Google Insights normalized search share was used to characterize interest in a drug. The estimates of drug utilization were derived from the Medical Expenditure Panel Survey (MEPS), a nationally representative sample of the US population. Substitute drugs and notable patent expirations between 2004 and 2011 were obtained via pharmacist review. TB incidence was derived from the MMWR yearly Summary of Notifiable Diseases. To validate the assumption that search volume relates to drug utilization, we estimated weekly utilization for 9 drugs (amoxicillin, azelastine, azithromycin, benzomatate, cefdinir, ciprofloxacin, levofloxacin, moxifloxacin and olopatadine) using MEPS for 2004–2009. The weekly utilization volume was cross-correlated with the Google Insights series with lags ranging from −6 to +6 months. To compare the rate of substitution between name brand and generic drugs following the expiration of a patent, we treated the generic drug search volume as the independent variable and the name brand as the dependent variable. Using OLS, we calculated the marginal rate of substitution between the name brand and generic search queries. Preliminary work has focused on substitution of generic simvastatin for branded Zocor. As TB treatment regimens usually include a fixed set of medications (isoniazid, rifampin, pyrazinamide, ethambutol), the utilization of these drugs should correspond with TB incidence. We modeled national TB incidence using OLS with search volume and an indicator for the month of December. The number of reported cases in December is inconsistent with the seasonality of TB in the US and is a significant departure from the expected value given the rest of the series. We suspect this is due to a reporting artifact and include the indicator variable in our model to mitigate the effects of this inconsistency. RESULTS: The seasonality of drug use is reflected in search volume. Only 3 of the 9 drugs (33%, amoxicillin, azithromycin and cefdinir) had enough volume in the MEPS to create a reasonable time series. All 3 drugs had statistically significant positive correlations at lags near 0 and significant negative correlations at lags of +/− 6 months. Amoxicillin, for example, had a significant correlation at lags around 0 of 0.55–0.60 and correlations at a lag of −5 or +5 months of −0.4. The magnitude of this correlation coefficient would suggest that the two series track closely. Patent expirations (and the resulting emergence of generic medications with new names) are apparent in search volume as well. We find a strong negative relationship between search volume for simvastatin and Zocor. Specifically, a one unit increase in search volume for ‘simvastatin’ is associated with a 0.96 (p < 0.0001) unit decrease in the search volume for ‘Zocor.’ The simple model for TB incidence demonstrates the utility of using drugs as queries for disease. Search volume was a significant (p = 0.006) and positive predictor of TB incidence controlling for the December aberrations. CONCLUSIONS: The Google Insights search volume for a set of highly seasonal drugs is highly correlated with community utilization as measured by seasonal variance in utilization, change in search and prescribing patterns and expected prescribing following TB. The ability to estimate use of drugs from search volume presents a new method for keyword selection in search based incidence models and a method to monitor changes in the pharmaceutical market

Optimal screening strategies for healthcare associated infections in a multi-institutional setting.

Health institutions may choose to screen newly admitted patients for the presence of disease in order to reduce disease prevalence within the institution. Screening is costly, and institutions must judiciously choose which patients they wish to screen based on the dynamics of disease transmission. Since potentially infected patients move between different health institutions, the screening and treatment decisions of one institution will affect the optimal decisions of others; an institution might choose to "free-ride" off the screening and treatment decisions of neighboring institutions. We develop a theoretical model of the strategic decision problem facing a health care institution choosing to screen newly admitted patients. The model incorporates an SIS compartmental model of disease transmission into a game theoretic model of strategic decision-making. Using this setup, we are able to analyze how optimal screening is influenced by disease parameters, such as the efficacy of treatment, the disease recovery rate and the movement of patients. We find that the optimal screening level is lower for diseases that have more effective treatments. Our model also allows us to analyze how the optimal screening level varies with the number of decision makers involved in the screening process. We show that when institutions are more autonomous in selecting whom to screen, they will choose to screen at a lower rate than when screening decisions are more centralized. Results also suggest that centralized screening decisions have a greater impact on disease prevalence when the availability or efficacy of treatment is low. Our model provides insight into the factors one should consider when choosing whether to set a mandated screening policy. We find that screening mandates set at a centralized level (i.e. state or national) will have a greater impact on the control of infectious disease

Tuberculosis Unseen - Missed Opportunities in Diagnosis

This study estimates the number of tuberculosis cases that are potentially misdiagnosed as alternative respiratory illnesses in months prior to receiving a correct diagnosis. Inpatient and emergency department records in the state of California, from 2005 to 2011, were analyzed for patients that had an initial tuberculosis diagnosis along with a previous recorded visit. Tuberculosis patients were far more likely to receive a respiratory diagnosis in a window 5 to 90 days prior to their initial tuberculosis diagnosis than were uninfected patients. Findings suggest that more than 20% of tuberculosis cases are potentially missed in inpatient and emergency department settings

Using Search Volume for Surveillance of Medication Prescribing

Over the past five years, the potential for web search volume to provide insight into the present has become increasingly apparently. To date, much of these methods have focused on syndromic keywords and are not directly suitable for surveillance of less common or highly variable diseases. The more unique mapping between drug therapy had disease provides a potential workout this problem. We demonstrate the high degree of correlation between search volume and drug utilization and apply this method of keyword generation to modeling drug utilization, patent expirations and TB incidence in the US

Population Based Trends in the Incidence of Hospital Admission for the Diagnosis of Hepatorenal Syndrome: 1998–2011

Background and Objectives. Hepatorenal syndrome carries a high risk of mortality. Understanding the incidence and mortality trends in hepatorenal syndrome will help inform future studies regarding the safety and efficacy of potential therapeutic interventions. Design and Methods. We conducted a retrospective cohort study using the Nationwide Inpatient Sample. We identified hospitalizations from January 1998–June 2011 with a primary diagnosis of hepatorenal syndrome. To characterize the incidence trends in monthly hepatorenal syndrome hospitalizations, we fit a piecewise linear model with a change point at January 2008. We examined hospital and patient characteristics before and after the change point. Results. Hospital admissions with a diagnosis of hepatorenal syndrome increased markedly between September of 2007 and March of 2008. Comparing patients who were admitted with a diagnosis of hepatorenal syndrome prior to 2008 with those after 2008, we found that length of stay increased while the mortality of patients admitted for hepatorenal syndrome decreased. Conclusion. The revision of the diagnostic criteria for hepatorenal syndrome may have contributed to the increase in the incidence of admissions for hepatorenal syndrome. However, the changes in the principles of hepatorenal syndrome management may have also contributed to the increase in incidence and lower mortality

No screening equilibria.

<p>Under some disease parameter values, when treatment is less effective and the best response curve is upward sloping, increasing the degree of screening autonomy may lead a “no-screening” equilibrium, where institutions simply choose not to screening for disease. In both figures, screening takes place at a positive value when the level of screening autonomy is low () but no screening occurs as autonomy increases (). In Figure A, decreasing autonomy from five to two DUs results in 35% of patients being screening instead of none, while in Figure B, the same decrease in autonomy results in all patients being screened.</p

Changing the level of treatment efficacy.

<p>The shape of the best response curve and the equilibrium screening level are influenced by the level of treatment efficacy <i>τ</i>. As treatment becomes more effective (<i>τ</i> increases), the best response curve shifts from an increasing concave function to a decreasing convex function. The equilibrium screening level decreases as treatment becomes more effective from to .</p