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

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

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

Use of outcomes to evaluate surveillance systems for bioterrorist attacks

<p>Abstract</p> <p>Background</p> <p>Syndromic surveillance systems can potentially be used to detect a bioterrorist attack earlier than traditional surveillance, by virtue of their near real-time analysis of relevant data. Receiver operator characteristic (ROC) curve analysis using the area under the curve (AUC) as a comparison metric has been recommended as a practical evaluation tool for syndromic surveillance systems, yet traditional ROC curves do not account for timeliness of detection or subsequent time-dependent health outcomes.</p> <p>Methods</p> <p>Using a decision-analytic approach, we predicted outcomes, measured in lives, quality adjusted life years (QALYs), and costs, for a series of simulated bioterrorist attacks. We then evaluated seven detection algorithms applied to syndromic surveillance data using outcomes-weighted ROC curves compared to simple ROC curves and timeliness-weighted ROC curves. We performed sensitivity analyses by varying the model inputs between best and worst case scenarios and by applying different methods of AUC calculation.</p> <p>Results</p> <p>The decision analytic model results indicate that if a surveillance system was successful in detecting an attack, and measures were immediately taken to deliver treatment to the population, the lives, QALYs and dollars lost could be reduced considerably. The ROC curve analysis shows that the incorporation of outcomes into the evaluation metric has an important effect on the apparent performance of the surveillance systems. The relative order of performance is also heavily dependent on the choice of AUC calculation method.</p> <p>Conclusions</p> <p>This study demonstrates the importance of accounting for mortality, morbidity and costs in the evaluation of syndromic surveillance systems. Incorporating these outcomes into the ROC curve analysis allows for more accurate identification of the optimal method for signaling a possible bioterrorist attack. In addition, the parameters used to construct an ROC curve should be given careful consideration.</p

Gestational weight gain and child adiposity at age 3 years

OBJECTIVE The purpose of this study was to examine the associations of gestational weight gain with child adiposity. STUDY DESIGN Using multivariable regression, we studied associations of total gestational weight gain and weight gain according to 1990 Institute of Medicine guidelines with child outcomes among 1044 mother-child pairs in Project Viva. RESULTS Greater weight gain was associated with higher child body mass index z-score (0.13 units per 5 kg [95% CI, 0.08, 0.19]), sum of subscapular and triceps skinfold thicknesses (0.26 mm [95% CI, 0.02, 0.51]), and systolic blood pressure (0.60 mm Hg [95% CI, 0.06, 1.13]). Compared with inadequate weight gain (0.17 units [95% CI, 0.01, 0.33]), women with adequate or excessive weight gain had children with higher body mass index z-scores (0.47 [95% CI, 0.37, 0.57] and 0.52 [95% CI, 0.44, 0.61], respectively) and risk of overweight (odds ratios, 3.77 [95% CI: 1.38, 10.27] and 4.35 [95% CI: 1.69, 11.24]). CONCLUSION New recommendations for gestational weight gain may be required in this era of epidemic obesity

Correlations among adiposity measures in school-aged children

BACKGROUND: Given that it is not feasible to use dual x-ray absorptiometry (DXA) or other reference methods to measure adiposity in all pediatric clinical and research settings, it is important to identify reasonable alternatives. Therefore, we sought to determine the extent to which other adiposity measures were correlated with DXA fat mass in school-aged children. METHODS: In 1110 children aged 6.5-10.9 years in the pre-birth cohort Project Viva, we calculated Spearman correlation coefficients between DXA (n=875) and other adiposity measures including body mass index (BMI), skinfold thickness, circumferences, and bioimpedance. We also computed correlations between lean body mass measures. RESULTS: 50.0% of the children were female and 36.5% were non-white. Mean (SD) BMI was 17.2 (3.1) and total fat mass by DXA was 7.5 (3.9) kg. DXA total fat mass was highly correlated with BMI (r(s)=0.83), bioimpedance total fat (r(s)=0.87), and sum of skinfolds (r(s)=0.90), and DXA trunk fat was highly correlated with waist circumference (r(s)=0.79). Correlations of BMI with other adiposity indices were high, e.g., with waist circumference (r(s)=0.86) and sum of subscapular plus triceps skinfolds (r(s)=0.79). DXA fat-free mass and bioimpedance fat-free mass were highly correlated (r(s)=0.94). CONCLUSIONS: In school-aged children, BMI, sum of skinfolds, and other adiposity measures were strongly correlated with DXA fat mass. Although these measurement methods have limitations, BMI and skinfolds are adequate surrogate measures of relative adiposity in children when DXA is not practical

Duration of Lactation and Maternal Adipokines at 3 Years Postpartum

Objective: Lactation has been associated with reduced maternal risk of type 2 diabetes, the metabolic syndrome, and cardiovascular disease. We examined the relationship between breastfeeding duration and maternal adipokines at 3 years postpartum. Research Design and Methods: We used linear regression to relate the duration of lactation to maternal leptin, adiponectin, ghrelin, and peptide YY (PYY) at 3 years postpartum among 570 participants with 3-year postpartum blood samples (178 fasting), prospectively collected lactation history, and no intervening pregnancy in Project Viva, a cohort study of mothers and children. Results: A total of 88% of mothers had initiated breastfeeding, 26% had breastfed $\geq 12$ months, and 42% had exclusively breastfed for $\geq 3$ months. In multivariate analyses, we found that duration of total breastfeeding was directly related to PYY and ghrelin, and exclusive breastfeeding duration was directly related to ghrelin (predicted mean for never exclusively breastfeeding: 790.6 pg/mL vs. $\geq 6$ months of exclusive breastfeeding: 1,008.1 pg/mL; P < 0.01) at 3 years postpartum, adjusting for pregravid BMI, gestational weight gain, family history of diabetes, parity, smoking status, and age. We found a nonlinear pattern of association between exclusive breastfeeding duration and adiponectin in multivariate-adjusted models. Conclusions: In this prospective cohort study, we found a direct relationship between the duration of lactation and both ghrelin and PYY at 3 years postpartum

Syndromic surveillance using minimum transfer of identifiable data: the example of the National Bioterrorism Syndromic Surveillance Demonstration Program

Several health plants and other organizations are collaborating with the Centers for Disease Control and Prevention to develop a syndromic surveillance system with national coverage that includes more than 20 million people. A principal design feature of this system is reliance on daily reporting of counts of individuals with syndromes of interest in specified geographic regions rather than reporting of individual encounter-level information. On request from public health agencies, health plans and telephone triage services provide additional information regarding individuals who are part of apparent clusters of illness. This reporting framework has several advantages, including less sharing of protected health information, less risk that confidential information will be distributed inappropriately, the prospect of better public acceptance, greater acceptance by health plans, and less effort and cost for both health plans and public health agencies. If successful, this system will allow any organization with appropriate data to contribute vital information to public health syndromic surveillance systems while preserving individuals’ privacy to the greatest extent possible