Extensive Preoperative Work Is Required for Revision Hip and Knee Arthroplasty.

BACKGROUND: Determining the clinical effort associated with preparing for revision total hip and knee arthroplasty is necessary to maintain the appropriate work relative value unit rating. We have investigated the work done by the orthopedic surgical team in the days and weeks prior to revision hip and knee arthroplasty using a count of time by team members in the electronic medical record (EMR). METHODS: EMR audit logs were generated, and preoperative work (POW) was calculated for members of the surgical team for 200 sequential revision cases. Independent samples t-tests were conducted to compare total POW for procedure, age, gender, insurance, and health literacy; significance threshold was set at P = .05. RESULTS: POW was 97.7 minutes (standard deviation [SD] 53.1). Surgeon POW accounted for 10.5 minutes (SD 9.3), nurses for 29.9 minutes (SD 34.2), mid-level providers for 22.1 minutes (SD 17.0), and office technicians for 34.1 minutes (SD 35.2). There was no difference in total POW based on procedure (hip vs knee), age, gender, insurance type, or health literacy. CONCLUSION: Revision arthroplasty requires substantial preoperative preparation from the surgical team. Most of this is by nurses, mid-level providers, and office staff. This does not seem to be different for hip or knee revisions or by age and gender. EMR audit logs capture the bare minimum POW required to prepare a patient for revision arthroplasty.

Electronic Medical Record Audit Time Logs as a Measure of Preoperative Work Before Total Joint Arthroplasty.

BACKGROUND: In order to achieve rapid recovery total joint arthroplasty, surgeons and their teams are spending more time in the weeks before surgery to prepare patients. This study aims to quantify total knee and hip prearthroplasty work using retrospective electronic medical record (EMR) activity audit log analysis. METHODS: EMR activity in 100 elective knee and 100 elective hip arthroplasty cases was performed using audit logs. Each mouse click and action in the EMR was recorded. The time between mouse clicks was calculated and summed for each member of the clinical team. Descriptive statistics and independent samples t-tests were conducted to quantify and compare total preoperative work (POW) between groups defined by gender, procedure, age, insurance type, or health literacy (P < .05). RESULTS: The mean number of days defined in the prearthroplasty time period was 69.1 days (standard deviation [SD] 42.8; range 8-191). The mean time spent in each patient's chart in the prearthroplasty period was 76.8 (SD 47.8) minutes. Surgeon's work in the medical record accounted for 7.9 (SD 7.9) minutes, registered nurses 46.7 minutes (SD 39.1), physician extenders 10.8 minutes (SD 16.9), and licensed practical nurses and patient care technicians 9.8 minutes (SD 13.0). A significant difference was observed when groups were dichotomized based on age <65 and insurance provider type. CONCLUSION: A considerable amount of POW is required to prepare patients for surgery from the clinic date one decides to pursue total joint arthroplasty and the day prior to surgery. Retrospective electronic time stamps from the EMR should represent the minimum time required for surgical preparation.

Infectious disease implications of large-scale migration of Venezuelan nationals.

Background: The ongoing economic and political crisis in Venezuela has resulted in a collapse of the healthcare system and the re-emergence of previously controlled or eliminated infectious diseases. There has also been an exodus of Venezuelan international migrants in response to the crisis. We sought to describe the infectious disease risks faced by Venezuelan nationals and assess the international mobility patterns of the migrant population. Methods: We synthesized data on recent infectious disease events in Venezuela and among international migrants from Venezuela, as well as on current country of residence among the migrant population. We used passenger-level itinerary data from the International Air Transport Association to evaluate trends in outbound air travel from Venezuela over time. We used two parameter-free mobility models, the radiation and impedance models, to estimate the expected population flows from Venezuelan cities to other major Latin American and Caribbean cities. Results: Outbreaks of measles, diphtheria and malaria have been reported across Venezuela and other diseases, such as HIV and tuberculosis, are resurgent. Changes in migration in response to the crisis are apparent, with an increase in Venezuelan nationals living abroad, despite an overall decline in the number of outbound air passengers. The two models predicted different mobility patterns, but both highlighted the importance of Colombian cities as destinations for migrants and also showed that some migrants are expected to travel large distances. Despite the large distances that migrants may travel internationally, outbreaks associated with Venezuelan migrants have occurred primarily in countries proximate to Venezuela. Conclusions: Understanding where international migrants are relocating is critical, given the association between human mobility and the spread of infectious diseases. In data-limited situations, simple models can be useful for providing insights into population mobility and may help identify areas likely to receive a large number of migrants.

Intimate partner violence increases adverse outcomes at birth and in early infancy.

OBJECTIVE: To determine the effect of intimate partner violence (IPV) on birth outcomes and infant hospitalization. STUDY DESIGN: Hospitalization records for the first 4 months of life for infants born in the Military Health System in 2006-2007 were linked to Family Advocacy Program-substantiated cases of IPV among military parents. Adverse outcomes were identified using International Classification of Diseases, Ninth Revision codes. Logistic regression modeling calculated the OR of children exposed to IPV experiencing adverse outcomes. RESULTS: A total of 204,546 infants were born during the study period. Among these, 173,026 infants (85%) were linked to active duty military parents. 31,603 infants (18%) experienced adverse outcomes, and 3059 infants (1.8%) were born into families with IPV. The infants exposed to IPV had a 31% increased odds of experiencing adverse outcomes compared with infants without known IPV exposure. IPV exposure increased the odds of the following outcomes: prematurity (OR, 1.45; 95% CI, 1.29-1.62), low birth weight (OR, 1.57; 95% CI, 1.25-1.97), respiratory problems (OR, 1.17; 95% CI, 1.04-1.32), neonatal hospitalization (OR, 1.39; 95% CI, 1.20-1.61), and postneonatal hospitalization (OR, 1.52; 95% CI, 1.29-1.81). After controlling for prematurity and demographic variables, IPV exposure was associated with low birth weight (OR, 1.52; 95% CI, 1.16-1.99), neonatal hospitalization (OR, 1.24; 95% CI, 1.02-1.49), and postneonatal hospitalization (OR, 1.27; 95% CI, 1.03-1.56). CONCLUSION: Infants exposed to IPV are more likely to experience adverse birth outcomes and infant hospitalization. Routinely addressing IPV during prenatal and early pediatric visits may potentially prevent these adverse outcomes.

Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches.

Haiti has been in the midst of a cholera epidemic since October 2010. Rainfall is thought to be associated with cholera here, but this relationship has only begun to be quantitatively examined. In this paper, we quantitatively examine the link between rainfall and cholera in Haiti for several different settings (including urban, rural, and displaced person camps) and spatial scales, using a combination of statistical and dynamic models. Statistical analysis of the lagged relationship between rainfall and cholera incidence was conducted using case crossover analysis and distributed lag nonlinear models. Dynamic models consisted of compartmental differential equation models including direct (fast) and indirect (delayed) disease transmission, where indirect transmission was forced by empirical rainfall data. Data sources include cholera case and hospitalization time series from the Haitian Ministry of Public Health, the United Nations Water, Sanitation and Health Cluster, International Organization for Migration, and Hôpital Albert Schweitzer. Rainfall data was obtained from rain gauges from the U.S. Geological Survey and Haiti Regeneration Initiative, and remote sensing rainfall data from the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission. A strong relationship between rainfall and cholera was found for all spatial scales and locations examined. Increased rainfall was significantly correlated with increased cholera incidence 4-7 days later. Forcing the dynamic models with rainfall data resulted in good fits to the cholera case data, and rainfall-based predictions from the dynamic models closely matched observed cholera cases. These models provide a tool for planning and managing the epidemic as it continues.

Cholera epidemic in Haiti, 2010: using a transmission model to explain spatial spread of disease and identify optimal control interventions.

BACKGROUND: Haiti is in the midst of a cholera epidemic. Surveillance data for formulating models of the epidemic are limited, but such models can aid understanding of epidemic processes and help define control strategies. OBJECTIVE: To predict, by using a mathematical model, the sequence and timing of regional cholera epidemics in Haiti and explore the potential effects of disease-control strategies. DESIGN: Compartmental mathematical model allowing person-to-person and waterborne transmission of cholera. Within- and between-region epidemic spread was modeled, with the latter dependent on population sizes and distance between regional centroids (a "gravity" model). SETTING: Haiti, 2010 to 2011. DATA SOURCES: Haitian hospitalization data, 2009 census data, literature-derived parameter values, and model calibration. MEASUREMENTS: Dates of epidemic onset and hospitalizations. RESULTS: The plausible range for cholera's basic reproductive number (R(0), defined as the number of secondary cases per primary case in a susceptible population without intervention) was 2.06 to 2.78. The order and timing of regional cholera outbreaks predicted by the gravity model were closely correlated with empirical observations. Analysis of changes in disease dynamics over time suggests that public health interventions have substantially affected this epidemic. A limited vaccine supply provided late in the epidemic was projected to have a modest effect. LIMITATIONS: Assumptions were simplified, which was necessary for modeling. Projections are based on the initial dynamics of the epidemic, which may change. CONCLUSION: Despite limited surveillance data from the cholera epidemic in Haiti, a model simulating between-region disease transmission according to population and distance closely reproduces reported disease patterns. This model is a tool that planners, policymakers, and medical personnel seeking to manage the epidemic could use immediately.