A SaTScan™ Macro Accessory for Cartography (SMAC) Package Implemented with SAS® Software

Background: SaTScan is a software program written to implement the scan statistic; it can be used to find clusters in space and/or time. It must often be run multiple times per day when doing disease surveillance. Running SaTScan frequently via its graphical user interface can be cumbersome, and the output can be difficult to visualize. Results: The SaTScan Macro Accessory for Cartography (SMAC) package consists of four SAS macros and was designed as an easier way to run SaTScan multiple times and add graphical output. The package contains individual macros which allow the user to make the necessary input files for SaTScan, run SaTScan, and create graphical output all from within SAS software. The macros can also be combined to do this all in one step. Conclusion: The SMAC package can make SaTScan easier to use and can make the output more informative

A SaTScan™ macro accessory for cartography (SMAC) package implemented with SAS(® )software

BACKGROUND: SaTScan is a software program written to implement the scan statistic; it can be used to find clusters in space and/or time. It must often be run multiple times per day when doing disease surveillance. Running SaTScan frequently via its graphical user interface can be cumbersome, and the output can be difficult to visualize. RESULTS: The SaTScan Macro Accessory for Cartography (SMAC) package consists of four SAS macros and was designed as an easier way to run SaTScan multiple times and add graphical output. The package contains individual macros which allow the user to make the necessary input files for SaTScan, run SaTScan, and create graphical output all from within SAS software. The macros can also be combined to do this all in one step. CONCLUSION: The SMAC package can make SaTScan easier to use and can make the output more informative

Type I Error Control for Cluster Randomized Trials Under Varying Small Sample Structures

BackgroundLinear mixed models (LMM) are a common approach to analyzing data from cluster randomized trials (CRTs). Inference on parameters can be performed via Wald tests or likelihood ratio tests (LRT), but both approaches may give incorrect Type I error rates in common finite sample settings. The impact of different combinations of cluster size, number of clusters, intraclass correlation coefficient (ICC), and analysis approach on Type I error rates has not been well studied. Reviews of published CRTs find that small sample sizes are not uncommon, so the performance of different inferential approaches in these settings can guide data analysts to the best choices.MethodsUsing a random-intercept LMM stucture, we use simulations to study Type I error rates with the LRT and Wald test with different degrees of freedom (DF) choices across different combinations of cluster size, number of clusters, and ICC.ResultsOur simulations show that the LRT can be anti-conservative when the ICC is large and the number of clusters is small, with the effect most pronouced when the cluster size is relatively large. Wald tests with the between-within DF method or the Satterthwaite DF approximation maintain Type I error control at the stated level, though they are conservative when the number of clusters, the cluster size, and the ICC are small.ConclusionsDepending on the structure of the CRT, analysts should choose a hypothesis testing approach that will maintain the appropriate Type I error rate for their data. Wald tests with the Satterthwaite DF approximation work well in many circumstances, but in other cases the LRT may have Type I error rates closer to the nominal level

in silico Surveillance: evaluating outbreak detection with simulation models

Background Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors’ objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols. Methods A detailed representation of the Boston area was constructed, based on data about individuals, locations, and activity patterns. Influenza-like illness (ILI) transmission was simulated, producing 100 years ofin silico ILI data. Six different surveillance systems were designed and developed using gathered cases from the simulated disease data. Performance was measured by inserting test outbreaks into the surveillance streams and analyzing the likelihood and timeliness of detection. Results Detection of outbreaks varied from 21% to 95%. Increased coverage did not linearly improve detection probability for all surveillance systems. Relaxing the decision threshold for signaling outbreaks greatly increased false-positives, improved outbreak detection slightly, and led to earlier outbreak detection. Conclusions Geographical distribution can be more important than coverage level. Detailed simulations of infectious disease transmission can be configured to represent nearly any conceivable scenario. They are a powerful tool for evaluating the performance of surveillance systems and methods used for outbreak detection

The Effect of Cluster Size Variability on Statistical Power in Cluster-Randomized Trials

The frequency of cluster-randomized trials (CRTs) in peer-reviewed literature has increased exponentially over the past two decades. CRTs are a valuable tool for studying interventions that cannot be effectively implemented or randomized at the individual level. However, some aspects of the design and analysis of data from CRTs are more complex than those for individually randomized controlled trials. One of the key components to designing a successful CRT is calculating the proper sample size (i.e. number of clusters) needed to attain an acceptable level of statistical power. In order to do this, a researcher must make assumptions about the value of several variables, including a fixed mean cluster size. In practice, cluster size can often vary dramatically. Few studies account for the effect of cluster size variation when assessing the statistical power for a given trial. We conducted a simulation study to investigate how the statistical power of CRTs changes with variable cluster sizes. In general, we observed that increases in cluster size variability lead to a decrease in power

Matching in Cluster Randomized Trials Using the Goldilocks Approach

In group or cluster-randomized trials (GRTs), matching is a technique that can be used to improve covariate balance. When baseline data are available, we suggest a strategy that can be used to achieve the desired balance between treatment and control groups across numerous potential confounding variables. This strategy minimizes the overall within-pair Mahalanobis distance; and involves iteratively: 1) making pairs that minimize the distance between pairs of clusters with respect to potentially confounding variables; 2) visually assessing the potential effects of these pairs and resulting possible randomizations; and 3) reweighting variables of selecting weights to make pairs of clusters. In step 2, we plot the between-arm differences with a parallel-coordinates plot. Investigators can compare plots of different weighting schemes to determine the one that best suits their needs prior to the actual, final, randomization. We demonstrate application of the approach with the Mupirocin-Iodophor Swap Out trial. A webapp is provided

Correlates of Parental Antibiotic Knowledge, Demand, and Reported Use

Clinicians cite parental misconceptions and requests for antibiotics as reasons for inappropriate prescribing. To identify misconceptions regarding antibiotics and predictors of parental demand for antibiotics and to determine if parental knowledge and attitudes are associated with use. Survey of parents in 16 Massachusetts communities. Domains included antibiotic-related knowledge, attitudes about antibiotics, antibiotic use during a 12-month period, demographics, and access to health information. Bivariate and multivariate analyses evaluated predictors of knowledge and proclivity to demand antibiotics. A multivariate model evaluated the associations of knowledge, demand, and demographic factors with parent-reported antibiotic use. A total of 1106 surveys were returned (response rates: 54% and 32% for commercially-insured and Medicaid-insured families). Misconceptions were common regarding bronchitis (92%) and green nasal discharge (78%). Two hundred sixty-five (24%) gave responses suggesting a proclivity to demand antibiotics. Antibiotic knowledge was associated with increased parental age and education, having more than 1 child, white race, and receipt of media information on resistance. Factors associated with a proclivity to demand antibiotics included decreased knowledge, pressure from day-care settings, lack of alternatives offered by clinicians, and lack of access to media information. Among all respondents, reported antibiotic use was associated with younger child age and day-care attendance. Among Medicaid-insured children only, less antibiotic knowledge and tendency to demand antibiotics were associated with higher rates of antibiotic use. Misconceptions regarding antibiotic use are widespread and potentially modifiable by clinicians and media sources. Particular attention should be paid to Medicaid-insured patients in whom such misconceptions may contribute to inappropriate prescribing

Child care center policies and practices for management of ill children

OBJECTIVES: The objectives of this study were to 1) describe child care staff knowledge and beliefs regarding upper respiratory tract infections and antibiotic indications and 2) evaluate child care staff reported reasons for a) exclusion from child care, b) referral to a health care provider, and c) recommending antibiotics for an ill child. METHODS: A longitudinal study based in randomly selected child care centers in Massachusetts. Staff completed a survey to assess knowledge regarding common infections. For six weeks, staff completed a record of absences each day, describing the reason for an absence, and advice given to the parents regarding exclusion, referral to a health care provider, and obtaining antibiotics. Exclusions for the specific illness/symptom were defined as appropriate or inappropriate based on national guidelines. RESULTS: A large proportion of child care staff incorrectly believed that antibiotics are indicated for bronchitis (80.5%) and green rhinorrhea (80.5%) in children. For 82.2% of absences, the circumstances or reasons for the absence were discussed with a child care staff member. Of 538 absences due to illness that child care staff discussed with parents, there were 45 inappropriate exclusions (8.4% of illnesses discussed), 91 appropriate exclusions (16.9% of illnesses discussed), and 402 cases (74.7%) in which no recommendation for exclusion was made. CONCLUSIONS: Misconceptions regarding the need for antibiotics for URIs are common among child care staff. However, day care staff do not pressure parents to seek medical attention or antibiotics