Space Time Trends of Community Onset Staphylococcus Aureus Infections in Children Living in Southeastern United States: 2002-2010

Space Time Trends of Community Onset Staphylococcus Aureus Infections in Children Living in Southeastern United States: 2002-2010 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Space Time Trends of Community Onset Staphylococcus Aureus Infections in Children Living in Southeastern United States: 2002-2010 Lilly Immergluck, Ruijin Geng, Chaohua Li, Mike Edelson, Lance Waller, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-59691/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Staphylococcus aureus ( S. aureus ) remains a serious cause of infections in the U.S. and worldwide. Non antibiotic resistant Staphylococcus aureus (methicillin susceptible or MSSA) is the cause of half of all health care–associated staphylococcal infections, and methicillin resistant Staphylococcus aureus (MRSA) still is the leading cause of community onset skin and soft tissue infections in the U.S. This is the first study to spatially look at trends of both community onset MRSA and MSSA infections over nine years and determine ‘best’ to ‘worst’ infection trends over a nine year period (2002-2010),which spanned when community onset MRSA infections were occurring in epidemic proportions across the U.S. Methods Retrospective study from 2002-2010, using electronic health records of children living in the southeastern U.S. (Atlanta, Georgia) with S. aureus infections and relevant U.S. census data (at the census tract level). The Proc Traj for SAS was applied to generate community onset MRSA and MSSA trajectory infection groups (low, high, very high, or deviant trends), and then, mapping of these trajectory groups using census tract boundaries. Results From community onset MRSA infection trend patterns (low, high, very high), only 0.8% of the census tracts showed a dramatic increase from 2002-2007 and then a gradual decline from 2008 to 2010. From community onset MSSA infection trend patterns (low and high), 85.7% of ‘high infection’ group persisted throughout the nine year period, compared to 14.3% of ‘low infection’ group over this same period. Low community onset MRSA and MSSA trend patterns were seen throughout the 20 counties of Atlanta, Georgia’s metropolitan statistical area, but more often seen in those counties less densley populated. Census tracts reflecting Atlanta’s ‘innercity’ had the highest proportion of the worst infection trend pattern (community onset MRSA-Very High-CO-MSSA-High or community onset MRSA-High-CO-MSSA-High). The deviant trend of community onset MRSA Very High- CO-MSSA Low infection were in census tracts east of downtown Atlanta. Conclusions ‘Trends’ of S. aureus infection patterns, stratified by antibiotic resistance, over geographic areas and time identify communities with higher risks for community onset MRSA infection compared to community onset MSSA infection. Health Economics & Outcomes Research Health Policy Community onset methicillin-resistant Staphylococcus aureus community onset methicillin susceptible Staphylococcus aureus group-based trajectory modeling hotspot mapping Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Staphylococcus aureus ( S. aureus ) is a major cause of community onset and health care–associated infections, ranging from superficial skin and soft tissue infections (SSTI) to invasive infections, sepsis, and death ( 1 ). In 2017, U.S. Centers for Disease Control and Prevention reported the antibiotic resistant bacteria, methicillin resistant S. aureus (MRSA) was the cause of more than 11,000 deaths per year in the U.S. and the cause of over 80,000 invasive forms of infection( 2 ). The non-antibiotic resistant form, methicillin susceptible S. aureus (MSSA), also is a serious cause of infections and is estimated to be the cause of half of all health care–associated S. aureus infections( 1 ). Hospital onset MSSA infection rates have remained at a steady rate since 2012, whereas community onset MSSA (CO-MSSA) infection rates continue to increase ( 1 ). Given U.S. Department of Health and Human Services’ goal for a fifty percent reduction of S. aureus invasive infections by 2020 ( 3 , 4 ), community based primary and secondary prevention efforts to prevent spread of these infections seem more than ever important to put in place. Majority of efforts have focused on reducing infections within healthcare settings, rather than in community settings. Risks associated with community onset staphylococcal infections have been widely reported over the last decade ( 5 – 10 ), but few studies have shown the relationship of ‘place-based’ or community level risks from a spatial or geographic lens ( 9 , 11 ). Spatial analyses factoring geographic areas allow for visualization of how socio- and built environments might contribute to occurrence of S. aureus infections. Little has been published on how rates of infections may differ between different geographic areas over a longitudinal time period, especially during a period when community onset MRSA (CO-MRSA) infections were occurring in epidemic proportions ( 1 , 12 ). Analyses of trends in population health data have mostly been determined by comparing rate differences between two time points and changes in the linear or log linear rates over time ( 13 ). Another way to analyze trends of population is through group based trajectory models (GBTM), which incorporates information from all time points and examines non linear (quadratic, cubic, and other higher order) rate trends. In this manner, GBTM determine if groups of study units have similar trajectory patterns and can predict outcome trends of individual units which are grouped together into patterns ( 14 – 16 ). There are currently no studies to model or analyze multiyear trends of MRSA or MSSA rates over a longitudinal time period, identifying high or low infection trends based on location. We sought to determine trends of CO-MRSA and CO-MSSA infections and specifically, to identify particular trends (trajectory patterns) of infections rates which reveal areas with continued low rates of infections over nine years, relatively high rates of infection throughout this period, or ‘deviant’ trends (high rate with one type of S. aureus infection and low rate with the other.) We also sought to determine areas where clusters of trajectory patterns occurred and specifically, to identify geographic deviants, where ‘cluster’ of census tracts had one type of trend, which differed significantly from the trajectory patterns of surrounding or neighboring census tracts. This is the first study to group S. aureus infection rates into community-onset MRSA and MSSA infection ‘trajectory’ group patterns at the community level and then, map spatially the trajectory patterns (ranging from ‘best’ to ‘worst’ trends of staphylococcal infections, stratified by their resistance to methicillin). Methods Overview. A retrospective study was conducted of children < 19 years who were treated for S. aureus infections from January 1, 2002 through December 31, 2010 at two pediatric hospitals in Atlanta, Georgia (Scottish Rite Children’s Hospital and Egleston Children’s Hospital are part of Children’s Healthcare of Atlanta (CHOA), a large pediatric healthcare system in Atlanta, Georgia, which consistently provides more than 80% of the pediatric hospitalizations for the Atlanta metropolitan statistical area (MSA)) ( 12 ). Patients < 19 years of age diagnosed with S. aureus infection who had emergency department visit (ED) and/or inpatient admission with residential addresses within the 20 counties of the Atlanta MSA were included. All study participants had an International Classification of Diseases, Clinical Modification (ICD- 9-CM) code compatible with S. aureus infection and a positive S. aureus culture as previously described ( 12 , 17 ). We included only those patients who met the definition for community-onset infection (patients with positive S. aureus culture within 48 hours from the time patient was initially evaluated or admitted) ( 18 – 20 ). Individual Level Patient Data from Electronic Health Records. Demographic data (race/ethnicity, gender, type of health insurance, and place of residence); relevant laboratory information ( S. aureus culture site(s), antibiotic susceptibility phenotype patterns); and relevant clinical information (clinical diagnoses, prior hospitalizations or ED visits within 12 months of the date of admission, number and source of cultures positive for S. aureus , recorded past medical history of chronic illnesses) were abstracted from patients’ electronic health records (EHR). Georeferencing of the patients’ U.S. postal addresses was performed after excluding addresses which were confirmed to be out-of-state or U.S. post office box numbers. All georeferencing was performed using ArcGIS 10.6 (ESRI, Redlands CA). See Fig. 1. Enrollment Scheme . Table 1. Area-based Characteristics of Population Variable Definition Data Source Median Household Income Median distribution of the total number of households and families including those with no income** ACS* Education Attainment Percent of people above 25 years of age with high school degree, bachelor’s degree and professional degree ACS Kindergarten to 12th Grades Enrollment Percent of people above 3 years of age who attended kindergarten to 12th grades ACS Employment Status Percent of people above 16 years of age in labor force ACS Population under 18 years old Percent of youth under 18 years of age*** ACS Median House Value Median houseing value in USD ACS Race Percent Black in Population, Percent White in Population ACS Ethnicity Percent Hispanic and Non-Hispanic in Population ACS Household Crowding Percent of occupied households with > 1 person/room ACS Poverty Percent of population living below poverty level in past 12 months as defined by the U.S. Census Bureau ACS Income inequality index Gini Coefficient measures inequality among values of a frequency distribution of income, where a coefficient of zero expresses perfect equality ACS *ACS: American Community Survey (2000, and 2010) **For Median Household Income, and Median House Value, data obtained from US Census 2000 and US Census 2010 (ACS) were used. Values from US 2000 were adjusted to 2010 dollar values, using inflation calculator. (21) ***No ACS or Census data for 2000 or 2010 available for ‘<19 years of age’ Area Level Variables from US Census Tracts. Demographic, socio-economic and socio-environmental characteristics at the census tract level were obtained from U.S. Census 2000 and 2010, American Community Survey (ACS) 2000 and 2010 as outlined in Table 1 . Variables were selected if previously reported as determinants of health or risk factors associated with community associated MRSA ( 12 , 22 ). Based on the inclusion criteria for selecting patients, area variables included: race, ethnicity, median household income, education attainment, population enrolled in grades K-12, proportion of population < 18 years of age, proportion of population employed, median housing value, household crowding, Gini coefficient index for income inequality, and proportion of population living below poverty. Median household income and median house values for 2000 were adjusted to 2010 values using an inflation calculator( 21 ). Area level data was limited to census tracts associated with CO-MRSA and/or CO- MSSA patients. Difference of means in 2000 and 2010 were calculated. U.S. 2000 Census Data Adjusted to U.S. 2010 Census Tract Boundaries. Census tracts are fundamental enumeration units for the U.S. decennial censuses and their boundaries change over time. Area data obtained from U.S. Census 2000 and U.S. Census 2010 were adjusted through longitudinal tract data base (LTDB) in the same way as previously described by Logan JR et al ( 23 ), which used 2010 census tract geographic boundaries. Estimates of < 19 years of age population were then made for each census tract per year from 2002 to 2009 via linear extrapolation and actual population size for 2010 were then used in the trajectory model as a time varying covariate. Statistical analyses . Group-based trajectory modeling : We included all children with S. aureus infections (2002–2010), stratified by antibiotic resistant cases (CO-MRSA) and non-antibiotic resistant controls (CO-MSSA) per year across all census tracts located within Atlanta’s MSA 20 counties boundaries. The yearly case counts of CO-MRSA and CO-MSSA infected patients < 19 years of age in each census tract from 2002 to 2010 were converted to rate of infection per 1,000 children. The yearly CO-MRSA or CO-MSSA infection rate was the outcome measure used to generate rate trajectories using Proc Traj for SAS, version 9.4 (SAS Institute, Inc). In group-based trajectory modeling, discrete underlying groups within the population are assumed to have their own case intercept, slope and possibly higher order terms. Proc Traj requires specification of the number of groups the model will fit. We used a process of evaluating model fit while simultaneously identifying informative similar trajectory groups as previously described by Baltrus et al( 16 ). We estimated a zero-inflated Poisson model with a first order, quadratic term for each outcome, and an independent variable of time (years) for each group. We continued adding groups and assessed the change in the Bayesian information criterion (BIC) as an evaluation of model fit. Since Proc Traj has the limitation that does not allow for an offset variable in order to generate rates, we adjusted for each census tract’s < 19 years of age population as a time varying covariate. (The CO-MSSA and CO-MRSA overall model fit did not improve with the addition of more than two groups. The CO-MRSA model produced a small “very high infection” group of census tracts with the addition of a third group into the model with only a slight decline in the fit of overall model.) We next added or removed second and higher-order terms from each group’s model based on significance (p < 0.05). Descriptive analyses by individual-level and area-level variables across the trajectory groups : Characteristics from individual level data were stratified by group -based trajectory patterns for CO-MRSA and CO-MSSA. We grouped the individual level data into two periods, designated as early period (2002–2005) and late period (2006–2010). The early period individual level data was linked to area data from U.S. Census 2000, and the late period individual level data was linked to U.S. Census 2010. Since spatial factors, including the ecological conditions and neighborhood spatial boundaries differed during the 10-year time span between U.S. Census 2000 and 2010, we used information from both time periods in order to best characterize the neighborhoods at the time of CO-MRSA or CO-MSSA infection occurrence. Chi-square test was applied to compare CO-MRSA and CO-MSSA population characteristics, stratified by the CO-MRSA trajectory groups and CO-MSSA trajectory groups, respectively. For area level variables, normality of distribution was determined using Shapiro-Wilk test. For normally distributed population, ANOVA was used when comparing three trend groups and T-test when comparing two trend groups. Kruskal-Wallis test was used for three groups, and Mann-Whitney test for two groups when population was not normally distributed. All statistical analyses were done by SAS version 9.4 (SAS Institute, Inc). Spatial Patterns for Community Onset MRSA and MSSA Trajectory Trends. Thematic mapping of different CO-MRSA and CO-MSSA infection trajectories was performed and assessed for spatial autocorrelation, which confirmed whether adjacent observations were related (and not independent occurrences). The degree of spatial autocorrelation was measured using the Global Moran’s I statistic (ESRI ArcGIS Pro, Redlands CA). Census tracts that had CO-MRSA or CO-MSSA were grouped into six group based trajectories: 1) Very High MRSA–High MSSA clusters representing all census tracts with very high CO-MRSA and high CO-MSSA infection rates, the worst trajectory group across the nine years of the study period; 2) Very High CO-MRSA–Low CO-MSSA clusters representing census tracts in the worst trajectory CO-MRSA group near census tracts in the most favorable trajectory CO-MSSA group (deviant trend); 3) High CO-MRSA–High CO-MSSA clusters representing census tracts in the second worst trajectory CO-MRSA group near census tracts in the worst trajectory CO-MSSA group; 4) High CO- MRSA–Low CO-MSSA clusters of census tracts in the second worst trajectory CO-MRSA group near census tracts in the most favorable trajectory CO-MSSA group (deviant trend); 5) Low CO-MRSA-High CO-MSSA clusters representing census tracts in the most favorable trajectory CO-MRSA group near census tracts in the worst trajectory CO-MSSA group (deviant trend); and 6) Low CO-MRSA-Low CO-MSSA clusters representing all census tracts with low infection rates, the most favorable trajectory group. Local ‘clusters’ of aggregated patients’ point data were determined by applying the local indicators of spatial association (LISA) technique as previously described ( 16 , 24 , 25 ). From this, hot and cold spot analyses were performed using GetisOrd Gi* statistic. The output is a surface raster layer depicting the different degrees of ‘hot spots’ and ‘cold spots’ of CO-MRSA or CO-MSSA occurrences. Significant clusters were those areas, where an occurrence of infection (CO-MRSA or CO-MSSA) and its neighboring points all had high Getis-Ord Gi* values (hot spots). Geographic deviants were defined as clusters that had much higher or much lower values than neighboring occurrence points (cold spots). Spatial Analyses. Optimized Hot Spot Analysis (OHPA) tool (Esri ArcGIS Pro, Redlands, CA) was performed to assess spatial patterns in our data. This allowed us to visualize statistically significant patterns, where clustering of cases (CO-MRSA) or controls (CO-MSSA) exist, which are not likely due to random occurrence. The results of the applying this tool showed us areas within the 20 counties where ‘hot spots’ occur within a ‘cluster’ in a non random way. (A simple ‘hot spot’ analysis will not tell necessarily inform “why” a particular event is occurring in a statistically significant cluster, only that it is occurring in a non random manner). We ran the analysis using all unique patients’ point locations from 2002 to 2010, < 19 years, with CO-MRSA and then repeated these steps for CO-MSSA patients within the same time period and for the same ages. With the OHSA tool, we set our ‘Study Area’ to the boundaries of Atlanta’s MSA 20 counties. We then assigned ‘neighborhood’ variables as 1-mile hexgon patterns. We chose the 1-mile hexagon neighborhood size based on Tober’s First Law of Geography, which follows the spatial diffusion theory for infectious pathogens, whereby the risk for infection increases with closer distance to source of infection. In our model, we postulate that the closer an individual’s neighborhood is geographically near to an occurrence of CO-MRSA or CO-MSSA, the more likely the persons within the neighborhood are likely to contract CO-MRSA or CO-MSSA. In this model, we accounted for various sizes of neighborhoods, apartment complexes, and other living or housing units. We estimated that a neighborhood is within approximately a mile of the residence of any CO-MRSA or CO-MSSA occurrence. This study was approved by Institutional Review Boards of hospitals and affiliated academic institutions. Results There were 12,120 children with S. aureus infections from 1,969 census tracts during 2002-2010 who were seen at Children’s Healthcare of Atlanta’s two pediatric hospitals. For Atlanta’s 20 counties’ MSA, there were 9,095 patients, < 19 years of age, from 901 census tracts. Overall, the number of infections increased steadily from 2002 to 2010, with the highest rates occurring in 2009 for CO-MRSA and 2008 for CO-MSSA; CO-MRSA rates increased at a higher rate compared to CO-MSSA. Of six possible combinations for trends of CO-MRSA and CO-MSSA infections, there were five group-based trajectory patterns for CO-MRSA (3) and CO-MSSA (2) with overlapping U.S. census tracts as shown in Table 2 ; no census tracts fell into the group-based trajectory pattern of Very High CO-MRSA - High CO-MSSA infections. Figure 2 shows the three CO-MRSA group-based trajectory trends and the two CO-MSSA group-based trajectory trends over this nine-year period: Of the three CO-MRSA group-based trajectory trends, only 0.8% of the census tracts showed a dramatic increase over the first six years followed by a gradual decline in the last three years; over two thirds (67.2%) remained in the low infection trend across all years. In contrast, for CO-MSSA, 85.7% of census tracts fell into the high infection group-based trajectory pattern which lasted throughout the nine-year period, compared to 14.3% of census tracts which belonged to a low infection trend over this same period . ( Supplemental Section: Table A shows the coefficients for the terms used in the three group-based CO-MRSA trajectory models and the two group-based CO-MSSA trajectory models. For the three CO-MRSA group-based models, the equations all contained a quadratic term. For the two CO-MSSA group-based models, the equations contained a quadratic term, and a slope term that was relatively flat.) Table 2. US Census Tracts with Community onset MRSA- / MSSA- Infection Group-Based Trajectory Patterns, 2002-2010 CO-MRSA Infection Category CO-MSSA Infection Category Low High Total N N (%) N (%) Low 682 (75.7%) 29 (3.1%) 711 High 170 (18.9%) 14 (1.6%) 184 Very High 6 (0.7%) 0 6 Total 858 43 901 From 901 census tracts with S. aureus infections, 682 (75.7%) were in the low infection group-based trajectory for both CO-MRSA and CO-MSSA (this is the ‘best’ trend category); deviant trajectory census tracts included 6 (0.7%) with Low CO-MSSA infection rate but Very High CO-MRSA infection rate, 170 (18.9%) Low CO-MSSA and High CO-MRSA infection rates and 29 (3.2%) High CO-MSSA and Low CO-MRSA infection rates. High CO- MSSA- High CO-MRSA (worst group-based trajectory) were seen in 14 (1.6%) census tracts. In 2002, there were 38 cases (0.03 infection per 1,000 children) in the Low CO-MRSA infection trajectory group, but this trend over time increased steadily to peak at 334 cases (0.48 infection per 1,000 children) in 2008, before declining to 257 cases (0.43 infection per 1,000 children) in 2010. Similarly, CO-MSSA low infection trajectory group had a baseline of 192 cases (0.18 infection per 1,000 children) in 2002, but this trend peaked later in 2009 with 560 cases (0.51 infection per 1,000 children) before dipping slightly to 482 cases (0.47 infection per 1,000 children) in 2010. Census tracts that started with High CO-MRSA infection trajectory followed a similar pattern of increasing with each year, beginning with 29 cases (0.15 infection per 1,000 children) in 2002, peaking to 454 cases (1.25 infection per 1,000 children) in 2008, before dropping to 422 cases (0.99 infection per 1,000 children) in 2010. Comparatively, High CO-MSSA infection trajectory more than tripled in infections over six years from 32 cases (0.46 infections per 1,000 children) to 102 cases (1.26 infections per 1,000 children). Like other trajectories, its rates also dropped after 2008 with 88 cases (1.02 infection per 1,000 children) in 2010. (Table 3) Table 3. Counts of Community Onset MRSA and MSSA And Average Infection Rates by Group-Based Trajectory Patterns Year Type of Trajectory Pattern CO-MSSA-Low CO-MSSA-High CO-MRSA-Low CO-MRSA-High CO-MRSA-Very High Count (Rate) Count (Rate) Count (Rate) Count (Rate) Count (Rate) 2002 192 0.18 32 0.47 38 0.03 29 0.15 0 0.00 2003 362 0.36 58 0.79 74 0.10 69 0.34 1 0.14 2004 332 0.32 50 0.63 122 0.24 165 0.49 3 0.40 2005 382 0.35 61 0.76 219 0.30 274 0.80 6 0.71 2006 476 0.43 83 1.07 269 0.49 386 0.99 19 2.24 2007 419 0.38 89 1.14 312 0.47 416 1.16 24 2.91 2008 525 0.47 102 1.27 334 0.48 454 1.26 27 3.24 2009 560 0.51 87 1.09 312 0.48 445 1.15 25 2.54 2010 482 0.47 88 1.02 257 0.43 422 0.99 13 1.71 For each census tract with S. aureus occurence, we determined the number of CO-MSSA or CO- MRSA infections per 1,000 children <19 yearst . The actual number of CO-MSSA or CO-MRSA by year is provided along with the rate (infections per 1,000). From aggregated individual level data of early and late time periods (2002-2005 and 2006-2010), we identified significant differences in both time periods, across the all three CO-MRSA infection group-based trajectories and also differences between the high and low CO-MSSA infection group-based trajectories (Table 4) : Rates of blacks increased for CO-MRSA group-based trajectories as the CO-MRSA group-based trajectory moved from low to very high infection trend patterns, and overall, were relatively higher than rates of whites with MRSA infections. This finding remained consistent for both early and late time periods. Health insurance status also was significantly different across the three CO-MRSA infection trajectories with public health insurance ranking highest (p=0.0002) in CO-MRSA-High and CO-MRSA-Very High groups. Having prior hospitalization was significantly different among the three CO-MRSA infection trend patterns (p <0.05) in 2002-2005 but not significant in 2006-2010. Having a skin and soft tissue infection (SSTI), the most common clinical presentation for S. aureus in the community or outpatient setting, was not significant among the three CO-MRSA infection trend patterns in 2002-2005 or 2006-2010. In contrast, rates of whites increased for CO- MSSA group-based trajectories from low to high infection trend patterns, and overall, were relatively higher than rates of blacks with CO-MSSA infections. Rates among females were much higher in CO-MSSA High infection trend group, and the difference was statistically significant in the early period, 2002-2005 (p=0.0076). Rates of private health insurance was significantly higher than public health insurance in CO-MSSA low and high infection trajectory groups for both 2002-2005 (p=0.04140, and 2006-2010 (p=0.0057) periods. History of prior hospitalization was increased with CO-MSSA trajectory groups moving from low to high, in both 2002-2005 (p<0.001) and 2006-2010 (p<0.001). Rates of skin and soft tissues infections decreased in CO-MSSA trajectory groups moving from low to high, for both 2002-2005 (p=0.004) and 2006-2010 (p<0.001). Table 4. Patient-level Characteristics of Group-Based Trajectory Patterns of MRSA and MSSA for 2002-2005/ 2006-2010 Patient Characteristic MRSA-low MRSA-High MRSA-VeryHigh MSSA-low MSSA-high (N= 2660) (N=1937 ) (N= 118) (N= 3730) (N=650) 2002-2005, N= 537 2002-2005, N=453 2002-2005, N= 10 p-value 2002-2005, N= 1268 2002-2005, N= 201 p-value 2006-2010, N= 2123 2006-2010, N= 1484 2006-2010, N= 108 2006-2010, N= 2462 2006-2010, N= 449 Race** 2002-2005 <.0001* 0.0003* Black 163(31.17%) 215(48.53%) 7(70%) 286 (22.95%) 25 (12.63%) White 46(8.8%) 51(11.51%) 2(20%) 797 (63.96%) 155 (78.28%) Other 314(60.04%) 177(39.95%) 1(10%) 163 (13.08%) 18 (9.09%) 2006-2010 <.0001* <.0001* Black 758(36.32%) 878(60.05%) 87(80.56%) 816 (34.1%) 94 (21.76%) White 1113(53.33%) 455(31.12%) 14(12.96%) 1287 (53.78%) 286 (66.2%) Other 216(10.35%) 129(8.82%) 7(6.48%) 290 (12.12%) 52 (12.04%) Ethnicity (Hispanic)*** 2002-2005 7(2.18%) 11(5.85%) 0(0) 0.078 n/a n/a n/a 2006-2010 175(13.09%) 118(17.05%) 7(19.44%) 0.0392 251 (16%) 51 (17.47%) 0.5321 Gender (Female) 2002-2005 262(48.79%) 245(54.08%) 3(30%) 0.0972 535 (42.19%) 105 (52.24%) 0.0076* 2006-2010 1073(50.54%) 782(52.7%) 68(62.96%) 0.0272* 1137 (46.18%) 220 (49%) 0.2714 Health Insurance 2002-2005 0.0002* 0.0414* Private 269(50.09%) 179(39.51%) 0(0) 776 (61.2%) 140 (69.65%) Public 250(46.55%) 258(56.95%) 10(100%) 456 (35.96%) 54 (26.87%) Other**** 18(3.35%) 16(3.53%) 0(0) 36 (2.84%) 7 (3.48%) 2006-2010 <.0001* 0.0057* Private 935(44.04%) 464(31.27%) 17(15.74%) 1163 (47.24%) 246 (54.79%) Public 1080(50.87%) 906(61.05%) 86(79.63%) 1163 (47.24%) 188 (41.87%) Other**** 108(5.09%) 114(7.68%) 5(4.63%) 136 (5.52%) 15 (3.34%) SSTI Diagnosis 2002-2005 336 (62.57%) 252 (55.63%) 7 (70%) 0.0632 352 (27.76%) 32 (15.92%) 0.0004* 2006-2010 1495 (70.42%) 1077(72.57%) 79 (73.15%) 0.3389 1082 (43.95%) 141 (31.4%) <.0001* History of Hospitalizations 2002-2005 29 (5.4%) 55 (12.14%) 0 0.0005 210 (16.56%) 70 (34.83%) <.0001 2006-2010 100 (4.71%) 63 (4.25%) 2 (1.85%) 0.373 214 (8.69%) 100 (22.27%) <.0001* *p-value <0.05, means statistically significant. ** Other races include: Asian, Native Hawaii, Alaskan, and Multiple-races (self identified by patients as more than one race). ***Other health insurance types include: Self-pay, no insurance, and those with both private and public. Patient level data aggregated into two periods, 2002-2005 and 2006-2010, and then stratified by MRSA and MSSA population characteristics respectively based on MRSA and MSSA trajectory groups. (There are 9095 patients that accounted for the five different group-based trajectory patterns who resided within the 20 counties of Atlanta MSA across all years, <19 years.) Comparing population data from US Census 2000 and 2010, we found significant differences among socio- economic and demographic variables, stratified by CO-MRSA- and CO-MSSA- group-based trajectory patterns ( Table 5) : Similar to what was seen with individual level data, we also found rates of whites decreased as the CO-MRSA group-based trajectories moved from low infection to very high infection in both US Census 2000 and 2010. In contrast, the rates of blacks increased as the CO-MRSA infection trajectories moved from low to very high for both U.S. Census 2000 and 2010. For CO-MSSA group-based trajectories, a similar pattern was also seen whereby, rates of whites increased as CO-MSSA moved from low to high both in U.S. Census 2000 (p=0.054) and U.S. Census 2010 (p=0.0096). In CO-MSSA group-based trajectories, proportions of blacks decreased as infection trends increased; these trends were significant for both U.S. Census 2000 (p=0.0158) and 2010 areas (p=0.001). Although the proportion of census tracts with children <19 years of age increased as infection trends increased for both CO-MRSA and CO-MSSA group-based trajectories, and relatively higher in U.S. Census 2010 compared to 2000, none of the CO-MRSA or CO-MSSA infection trend patterns were significant except for U.S. Census 2010 census tracts representing the CO-MSSA-High infection trend pattern. Table 5. Population Area-level Characteristics of Group-Based Trajectory Patterns of MRSA and MSSA in Atlanta Population Characteristic MRSA–Low MRSA-High MRSA-Very High p-value MSSA-Low MSSA-High p-value N (%) N (%) N (%) N (%) N (%) Census tracts N=901 (%) 711 (67.2%) 184 (32%) 6 (0.8%) 858 (85.7%) 43 (14.3%) Race/Ethnicity, % White, % 2000 68.4 46.5 31.4 <.0001* 63.4 69.5 0.054 2010 59.4 39.8 21.5 <.0001* 54.6 65.3 0.0096* Difference in Periods, % -8.7 -6.2 -9.1 0.1188 -8.4 -4.2 0.0349* Black, % 2000 24.3 44.9 63.3 <.0001* 29.1 21.8 0.0158* 2010 29.2 49.9 71.9 <.0001* 34.2 22.3 0.001* Difference in Periods, % 4.8 4.1 8.2 0.981 4.9 0.8 0.0293* Hispanic, % 2000 5.77 7.48 4.44 0.2315 6.02 8.04 0.6623 2010 9.8 10.8 5.7 0.3007 9.8 13.4 0.6361 Difference in Periods, % 3.8 3.6 1.7 0.8551 3.6 5.6 0.4351 <18 years, % 2000 26.4 27.1 29.5 0.4065 26.5 27.9 0.2359 2010 28.6 29.2 29.8 0.7528 28.6 30.8 0.0253 Difference in Periods, % 2.4 2.3 -0.9 0.3644 2.3 2.9 0.4148 K-12 th Grades, % 2000 71.3 72.6 76.1 0.2774 71.4 73.9 0.6034 2010 67.9 69.0 69.6 0.8499 67.9 73.9 0.0042* Difference in Periods, % -2.9 -3.0 -6.8 0.6411 -3.1 -0.2 0.0415 Nursery to preschool, % 2000 8.9 8.5 7.2 0.177 8.8 9.3 0.2455 2010 7.9 7.6 5.6 0.401 7.7 8.6 0.115 Difference in Periods, % -1.0 -1.0 -2.0 0.9299 -1.0 -0.6 0.2138 High School, % 2000 24.6 25.8 26 0.3111 25.0 21.4 0.0295* 2010 24.7 28.3 29.7 0.0002* 25.7 22.1 0.0431* Difference in Periods % 0.2 2.4 3.8 0.0002* 0.7 1.0 0.5 Bachelor, % 2000 21.7 18.5 14.5 0.0085* 20.8 25.6 0.0255* 2010 23.7 19.1 16.2 <.0001* 22.5 26.9 0.0327* Difference in Periods, % 1.91 0.45 1.7 0.0022* 1.6 1.0 0.8262 Graduate or professional, % 2000 10.4 9.0 8.8 0.0839 9.9 12.6 0.0801 2010 12.4 10.3 9.8 0.01* 11.8 14.1 0.208 Difference in Periods, % 2.0 1.4 -0.7 0.1914 1.9 1.0 0.2307 Labor Force, % 2000 70.6 69.35 69.35 0.2486 70.3 70.9 0.7147 2010 70.0 70.65 70.68 0.6528 70.2 70.2 0.7211 Difference in Periods, % -0.6 1.2 0.3 0.0464* -0.2 -0.7 0.7159 Median Annual Household Income^, $ 2000 71,537.44 64,354.33 60,191.23 0.0048* 69,118.34 87,486.73 0.0043* 2010 63,983.19 54,450.01 45,369.67 <.0001* 61,026.01 79,598.74 0.0090* Difference in Periods, $ -7,554.25 -9,904.32 -14,821.6 0.1179 -8,092.33 -7,887.98 0.4704 Median House Value^, $ 2000 193,740.38 170,922.67 145,859.5 0.023* 186,692.81 230,044.26 0.0178* 2010 217,349.72 197,003.26 155,783.33 0.0365* 210,287.79 262,673.81 0.0158* Difference in Periods, $ 25,719.34 26,080.59 9,923.83 0.8035 24,761.96 44,466.64 0.0262* Household Crowding**, % 2000 1.3 1.1 1.0 <.0001* 1.2 0.8 0.1311 2010 3.9 5.6 7.3 0.9937 4.3 4.1 0.0823 Difference in Periods, % -2.7 -4.5 -6.1 <.0001* -3.1 -3.3 0.6453 Poverty*** % 2000 9.1 10.7 10.1 <.0001* 9.5 7.40 0.0727 2010 18.3 19 17.0 0.0236* 18.6 14.1 0.0202* Difference in Periods, % 8.8 7.6 6.1 0.7673 8.6 6.8 0.5399 Gini Index-2010 0.40 0.4 0.40 0.1912 0.4 0.4 0.858 A total of 901 census tracts from 20 counties in Atlanta (2000 and 2010) represent the patients with S. aureus infections who were included in the study. *p-value 1 person/room. ***People living under poverty level in past 12 months. ^Median Annual Household Income and Median House Value for 2000 were adjusted to 2010, using an inflation calcuator. Socio-economic conditions of the neighborhoods where CO-MRSA infection trends were higher differed from CO-MSSA infection trends over the same time period. For example, median household income and housing values decreased as CO-MRSA infection group-based trajectories moved from low to very high infection trends. Proportion of the population living below the federal poverty level also increased significantly in areas where CO-MRSA infection trends moved from low to very high infection rates. This trend was not seen with CO-MSSA group-based trajectories, where the median household income and housing values increased, and the proportion of the population living below the federal poverty level decreased in areas where it was clearly a CO-MSSA high infection trend pattern. Since crowding is a known risk associated with community-associated MRSA infections, we calculated the percentage of U.S. 2000 census tracts with evidence of household crowding and found crowding increased significantly as trends of CO-MRSA infection moved from low infection to very high infection pattern (p<0.0001). However, rates of crowding in U.S. 2010 census tracts were not significantly different in those areas where CO-MRSA infection trends moved from low to very high (p=0.9937). In contrast, for CO-MSSA group-based trajectories, household crowding was not significantly increased over the different categories of CO-MSSA infection trends, nor was the change in rates between the two time periods significantly different (p=0.6453). Spatial analyses of census tract group-based trajectories for MRSA and MSSA. Spatial variations of the different community onset MRSA-MSSA infection group-based trajectories were identified (Figure 3): The most favorable group-based trajectory (CO-MRSA Low infection and CO-MSSA Low infection) and second most favorable trajectory (CO-MRSA High-CO-MSSA Low) were scattered with higher proportion of census tracts in counties where the population is less dense relative to inner city Atlanta. There were also ‘pockets’ within Gwinnett, Henry and Douglas counties, where there were census tracts with CO-MRSA-Very High or CO-MRSA-High, even though most of the county fell into a more ‘favorable’ or lower infection group-based trajectory pattern. In contrast, counties representing the inner city (areas within DeKalb County and Fulton County) had the highest proportion of the worst trajectories (CO-MRSA-Very High-CO-MSSA-High or CO-MRSA-High-CO-MSSA-High). Seven of these census tracts, representing CO-MRSA Very High-CO-MSSA Low or worst group-based trajectory combination, were from areas located east of downtown Atlanta (DeKalb County) and a single census tract southeast of downtown Atlanta (Henry County). To identify areas where infections intensified with each year in a particular census tract and also in neighboring census tracts (‘hot spots’) compared to areas where the infections were much lower than neighboring census tracts across each year (‘cold spots’), we found CO-MRSA and CO-MSSA ‘hot’ and ‘cold’ spots overlapped in a number of areas over the 2002-2010 period. ‘Hot spots’ for CO-MRSA and CO-MSSA overlapped for a large number of census tracts located primarily in the most heavily populated counties (Dekalb, Fulton and Gwinnett), but CO-MRSA ‘hot spots’ extended much further down into the southern counties of Rockdale, Henry and Clayton compared to CO-MSSA ‘hot spots’. ‘Cold spots’ followed the perimeter of the ‘hot spots’ for both CO-MRSA and CO-MSSA. Interestingly, ‘cold spots’ were seen southeast of Fulton county, where there were CO-MRSA ‘hot spots’. There was a large band of cold spots, located in the northwestern portion of 20 counties boundaries, which ran along the perimeter of hot spots seen in Cobb and Cherokee counties (northwest of the most densely populated counties) (Figure 4). In order to discern what group-based trajectory patterns might fall within ‘hot spots’, we overlaid the group-based trajectory map with hot spots and found that the ‘worst’ group-based trajectory was located in areas found to be ‘hot spots’ for CO-MRSA and CO-MSSA, and the best trajectory groups tended to occur where there were neither cold nor hot spots. Interestingly, Bartow county (northwest of Atlanta’s downtown area) had a number of census tracts which had a CO-MRSA-high – CO-MSSA- low designation, yet no CO-MSSA or CO-MRSA ‘hot’ or ‘cold’ spots. Discussion We applied group-based trajectory modeling to identify Atlanta MSA census tracts with ‘worst’ and ‘best’ temporal trends of community onset MRSA and MSSA infection rates over nine years. We identified three distinct CO-MRSA group-based trajectory infection patterns and two CO-MSSA group-based trajectory infection patterns over this period, which included the time when community associated MRSA infections were occurring in epidemic proportions around the U.S. We found ~ 94% of all the census tracts with S. aureus occurrence during this time period belonged to the best trend categories of low infection rates for both CO-MRSA and CO-MSSA. With group-based trajectory models, we used all the rates generated over the nine year span instead of relying on change between just two rates at the beginning and end of this period. This allowed us to detect and examine the trajectory shape over the entire time period, including non-linear shapes which are common for the epidemic curves associated with communicable diseases. Given how this bacteria, S. aureus , can have many strains with various virulent factors that may contribute to infection, understanding these epidemic curves within any community is important. It provides some insight as to which communities may have higher proportion of its population, with risks for infection. A recent worldwide review of S. aureus nasal carriage estimated that the average prevalence of nasal colonization in the general population is 24%( 26 ) but the possible place based risks which contribute to movement from colonized to infected population have not been elucidated. We have clearly delineated population level crowding is associated with worst trajectory trends for CO-MRSA infection in specific communities at the census tract level. Furthermore, we also identified the groups which make up the trajectory patterns from both individual and area level data instead of defining group categories a priori. This study highlights not only individual and area level risks which may contribute to why certain census tracts over time have either a high or low rate of infection, but also the location-based relationships which may be explanatory of these staphylococcal trend patterns of infection. Our main findings demonstrated that variations in spatial trend patterns of CO-MRSA infection and CO-MSSA infection rates across these 20 counties include both rural and urban communities. We mapped out the geographic clustering of the group-based trajectory patterns, with ‘worst’ trajectories clustering mainly in Dekalb and northwest counties of Bartow, Cherokee, Paulding and Cobb, and ‘best’ trajectories clustering in the northeast, southeast, and southwest of the inner-city area. Identification of these clusters of census tracts with similar rate trajectories over time will direct us to better understand what ‘place based’ conditions or factors might contribute to these findings that are location specific. For example, population density of the ‘worst’ trajectories, along with demographic factors including race and age distribution have been demonstrated to partially explain the unfavorable trajectory seen in DeKalb County. However, the southwest section of Bartow County has a significant group of census tracts which are geographically deviant from the surrounding ‘low infection’ census tracts. Socioeconomic factors (e.g., poverty, inequality index, employment status, etc.) may be more of a ‘driver’ than population density or household crowding in these rural communities. When considering demographic characteristics of the 901 census tracts and the distribution based on five distinct S. aureus infection group-based trajectories, we found that race differences were seen in census tracts where infection trends were high for both MRSA and MSSA infection trend patterns. Areas with low S. aureus infection trends had higher proportion of whites compared to blacks, whereas high infection trend patterns occurred in areas where there were higher proportion of blacks. (In Gwinnett county, 29.3% of the population are black and 54.5% are white, but the pockets with much higher trends of CO-MRSA correspond to census tracts with higher proportion of blacks.) Many of the census tracts found to have the ‘worst’ CO-MRSA infection trajectories were in areas with lower housing value, lower household income, and higher poverty rates. These findings are consistent with findings published from CDC researchers in their two year population surveillance study of 33 counties across nine states, which also demonstrated higher incidence of MRSA infection from census tracts with low-household income, persons living under the poverty level, and low education ( 27 ). They also found that ‘crowding’ was associated with increased MRSA rates. Our analyses, which focused on ‘trend’ patterns of MRSA infections over a nine-year period and not incidence rates, did not find crowding to persist over this nine-year time period. This may be related to the fact that our data compared a longer time interval (nine years compared to two years) and was based on analyses of multi-level data, including individual patient level data. Another explanation maybe related to communities in our catchment area may have improved housing conditions between the early and late time periods, e.g., higher percentage of people living in less crowded housing situations over almost a decade of time. Profiles of socioeconomic conditions in areas where CO-MSSA trajectories were high differed from the profiles where CO-MRSA trajectories were high: In areas where CO-MSSA infections trended high, the proportion of whites was higher than proportion of blacks. This relationship was reversed in areas of high or very high CO-MRSA group-based infection trajectories, where proportions of blacks were higher than whites. Higher household income and housing values and lower individual poverty levels were seen in areas with high CO- MSSA- infection trend patterns, compared to low CO-MSSA infection trend patterns. Whether or not this is a temporal effect related in part by the fact that some of these CO-MSSA-high infection census tracts crossover with CO-MRSA-low infection, is unclear. Overall, multi-level factors contribute to the explanation of geographically ‘deviant’ communities whose trajectory group is surrounded by very different types of infection trend patterns. Our study did not look at the changes of demographic composition as a dependent variable or other factors, e.g., changes in socioeconomic conditions, housing conditions, etc. which may potentially contribute to the geographic deviation found. Interestingly, we did not see a ‘Hispanic paradox’( 28 ), where census tracts with higher proportion of Hispanics were in areas with the best infection trajectories. In our analyses, we did not see significant differences among Hispanics for any of the CO-MRSA group based trajectories and in fact, found worsening infection trend rates among Hispanics in the low and high CO-MSSA group based trajectories over the nine-year period. The use of this novel method of group-based trajectory analysis (GBTA) of communicable disease trends holds some advantages over traditional hot-spot analysis. Hot spot analysis is good for spotting where concentrations of cases have occurred over time. However, hot spot analyses may not always identify geographic areas where residents are at highest risk, since the analyses does not take into account the population size where cases are actually occurring. For instance, a tract that has 5 cases arising over the nine year period may not show up in a hot spot analysis; however, if that tract has a relatively low population it may show up as a high infection rate trajectory tract in GBTA. This may be why some tracts in the more suburban areas in our study were found to be high infection rate trajectory tracts, yet did not show up as ‘hot spots’. The practical clinical application of using GBTA can be demonstrated, when estimating whether or not a patient who presents with a skin or soft tissue infection has CO-MRSA or CO-MSSA, based on the patient’s assigned group based trajectory infection category. In this scenario, the treating healthcare provider could factor location based CO-MRSA or CO-MSSA infection trends as part of the management strategy. Another advantage is that with GBTA, we can ascertain a general idea of the rate pattern experienced over the study period by tracts without having to examine the hot spot maps for each year. Limitations. There are several limitations to this analyses: First we were not able to determine causal association (relationship) between the population characteristics of an area and CO-MRSA and CO-MSSA infections, since the study draws from area level data which were collected in two different time periods (2000 and 2010). Although we have included children seen for all staphylococcal infections within one healthcare system, our findings may not necessarily be generalizable to children who did not access care from this single pediatric healthcare system or applicable to other geographically distinct locations where the place based factors are different from this catchment area. Future Directions. We plan to look at geographic influences from changes which are place based, using particular ‘neighborhoods’ as boundaries, and not census tracts. These ‘neighborhood’ boundaries tend to outline areas where the sociocultural influences are similar among the residents of the neighborhood, e.g., types of healthcare access, density and types of daycare centers, mode and availability of transportation, and racial and ethnic groups including various immigrant populations or cultural influences. Our study clearly outlines which census tracts have the greatest disparities in temporal S. aureus infection trends, and specifically, which census tracts are located in the ‘worst’ or ‘best’ group-based S. aureus infection trajectory areas. It is these ‘geographic deviants’ that deserve more attention. Identifying the potential remediable factors may pave the path towards eliminating spread of these types of infections or at least, mitigate contributing factors to the spread. Similarly, looking at why certain census tracts perform consistently better than their surrounding areas will provide insights on how to prevent the spread of this bacterial infection, and thereby, improve outcomes for other census tracts which have failed to have a positive infection trajectory trends over time. Deciphering the primary multi-level factors, driven by place and time elements, for both antibiotic resistant and non-antibiotic resistant S. aureus infections is paramount in the effort to develop effective prevention models that effectively and efficiently decrease transmission and infections at the neighborhood or community level. Conclusions. We demonstrated S. aureus infection trends over a nine-year period, factoring in time and space for both community onset MRSA and MSSA. We identified specific areas which are unique or overlapping at the census tract level for these staphylococcal infections, and specifically, identified unique areas which consistently proved to have high infection rates and performed the ‘worst’ over the nine year period. Group-based trajectory modelling allows us to not only identify areas which perform better, worse, or remain essentially, unchanged, but also identify the spatial relatedness of these areas. There is a continued need to develop strategies aimed at primary and secondary prevention, especially at the community level. Therefore understanding ‘trends’ of infection patterns, stratified by antibiotic resistance, over geographic areas and time can provide evidence-based information, necessary for community-specific prevention guidelines. Abbreviations S. aureus Staphylococcus aureus MRSA Methicillin Resistant S. aureus MSSA Methicillin Suspeptable S. aureus CO-MRSA Community Onset Methicillin Resistant S. aureus CO-MSSA Community Onset Methicillin Suspeptable S. aureus SSTI Skin and Soft Tissue Infection ED Emergency Department CHOA Children’s Healthcare Of Atlanta MSA Motropolitan Statistical Area EHR Electronic Health Record GBTA Group Based Trajectory Analysis Declarations Ethics approval and consent to participate This study is approved by Children’s Healthcare of Atlanta Institutional Review Board (IRB), Morehouse School of Medicine and Emory University. The individual data was from Electronic Health Record, so there was no consent needed to conduct this study. Consent for publication All authors listed have consented to the publication of this manuscript. Availability of data and materials This study used datasets from Children’s Healthcare of Atlanta- Electronic Health Record, which is governed by HIPPA guidelines. There is no link to access patient level information. Competing interests All authors declare that they have no competing interests of BMC Public Health. Funding LCI receives funding from the following sources: National Library Medicine, Grant #1G08LM013190-01; K-08 AHRQ-Mentored Clinical Scientist Career Research Development Award- HS024338-01. LCI, RG supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health." Authors' contributions Lilly Immergluck contributed to the hypothesis, study design, data analyses and interpretation and preparation of the manuscript. Ruijin Geng contributed to the data analysis, data interpretation and preparation of the manuscript; Peter Blatrus contributed to the study design, and oversaw the statistical analysis, data interpretation of the manuscript; Choahua Li contributed to the statistical analysis and data interpretation of the manuscript; Junjun Xu contributed to the overall statistical analysis; Mike Edelson contributed the spatial analysis methodology design and GIS mapping. Lance Waller, Traci Leong, and George Rust contributed to the interpretation of data analyses and preparation of the manuscript. All authors reviewed the final manuscript prior to submission. All authors read and approved the final manuscript. Acknowledgements Children's Healthcare of Atlanta - IS&T Department, especially Mr. Tal Senior, Mr. Robert Massey. Director of Clinical Microbiology Scottish Rite and Egleston Children's Healthcare of Atlanta—Dr. Robert C. Jerris, and Clinical Microbiology Laboratory Morehouse School of Medicine, Clinical Research Center, Ms. Jacquelyn Ali. Authors information Morehouse School of Medcine, Department of Microbiology/Biochemistry/Immunology; Clinical Research Center -Pediatric Clinical & Translational Research Unit, Atlanta, GA, USA. Morehouse School of Medicine, National Center for Primary Care; Department of Community Health & Preventive Medicine, Atlanta, GA USA Emory University, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Atlanta, GA, USA. Florida State University, Center for Medicine and Public Health, College of Medicine, Tallahassee, FL, USA InterDev, Roswell, GA, USA. Ningbo Consulting Inc., Atlanta, GA, USA. References Kourtis AP, Hatfield K, Baggs J, Mu Y, See I, Epson E, et al. Vital Signs: Epidemiology and Recent Trends in Methicillin-Resistant and in Methicillin-Susceptible Staphylococcus aureus bloodstream infections - United States. 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WorldMinds: Geographical Perspectives on 100 Problems : Commemorating the 100th anniversary of the Association of American Geographers 1904–2004 2004:5. Zhou Y, Hallisey EJ, Freymann GR. Identifying perinatal risk factors for infant maltreatment: an ecological approach. Int J Health Geogr. 2006;5:53. Kim MW, Greenfield BK, Snyder RE, Steinmaus CM, Riley LW. The association between community-associated Staphylococcus aureus colonization and disease: a meta-analysis. BMC Infect Dis. 2018;18(1):86. See I, Wesson P, Gualandi N, Dumyati G, Harrison LH, Lesher L, et al. Socioeconomic factors explain racial disparities in invasive community-associated methicillin-resistant Staphylococcus aureus disease rates. Clin Infect Dis. 2017;64(5):597-604. The Hispanic paradox. Lancet. 2015;385(9981):1918. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-59691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":2081688,"identity":"211358fd-a9d7-4366-9d87-e42ec6cb7600","order_by":0,"name":"Lilly Immergluck","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACPmYILcPAwMb4AMjg4SOkhQ2qhQfENAAx2AhqYUBoYZNAFsGthZ07dcPHPbU8BsePpVV+zbGTYWNgfvjoBl6H8W67OePZcR6DM2nHbstuSwY6jM3YOIeAlts8B47xGNxgb7stuY0ZqIWHTZqglj9QLcWS2+qJ1MJwoAaohe0Y48dth4nTcrPnwAEeyTNpydKM247zsDET8As//9ltN34cqJPjO37M8OPPbdX2/OzNDx/j0wIFh8EkMw+YJKwcBOrAJOMP4lSPglEwCkbBCAMARIFDfP8F5KUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8542-896X","institution":"Morehouse School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lilly","middleName":"","lastName":"Immergluck","suffix":""},{"id":2081689,"identity":"f856756a-92c6-470d-9e0f-c554d781c3c0","order_by":1,"name":"Ruijin Geng","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruijin","middleName":"","lastName":"Geng","suffix":""},{"id":2081690,"identity":"68a412aa-b032-4524-b80c-932b8759e17c","order_by":2,"name":"Chaohua Li","email":"","orcid":"","institution":"Morehouse School of Medicine, National Center for Primary Care","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chaohua","middleName":"","lastName":"Li","suffix":""},{"id":2081691,"identity":"0a01f7cf-f004-42c9-afaa-237fd30c5223","order_by":3,"name":"Mike Edelson","email":"","orcid":"","institution":"InterDev LLC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mike","middleName":"","lastName":"Edelson","suffix":""},{"id":2081692,"identity":"95582d18-0023-4776-a474-7302f7486c7f","order_by":4,"name":"Lance Waller","email":"","orcid":"","institution":"Emory University School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lance","middleName":"","lastName":"Waller","suffix":""},{"id":2081693,"identity":"a6a1af30-91ea-4b50-b9a5-91fa35c135ef","order_by":5,"name":"George S. Rust","email":"","orcid":"","institution":"Florida State University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"George","middleName":"S.","lastName":"Rust","suffix":""},{"id":2081694,"identity":"7fe54bb9-1a36-4b48-89bf-b536a743cd13","order_by":6,"name":"Junjun Xu","email":"","orcid":"","institution":"Ningbo Consulting Inc.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Junjun","middleName":"","lastName":"Xu","suffix":""},{"id":2081695,"identity":"7b127ac7-b794-41b0-9781-00629d7d2fee","order_by":7,"name":"Traci Leong","email":"","orcid":"","institution":"Emory University School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Traci","middleName":"","lastName":"Leong","suffix":""},{"id":2081696,"identity":"5184acb9-b5ea-428d-a110-f2f41ba93d22","order_by":8,"name":"Peter Baltrus","email":"","orcid":"","institution":"Morehouse School of Medicine, Department of Community Health \u0026 Preventive Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Baltrus","suffix":""}],"badges":[],"createdAt":"2020-08-14 12:24:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-59691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-59691/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":2295964,"identity":"3e35e83b-fc5e-4c3c-8afb-c552f78f2f20","added_by":"auto","created_at":"2020-09-08 14:51:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59105,"visible":true,"origin":"","legend":"Enrollment Scheme –Unique Patients with Community Onset MRSA and MSSA Infections","description":"","filename":"1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-59691/v1/1.JPG"},{"id":2295965,"identity":"a9caa7b2-f73c-4260-b88c-01666f143f96","added_by":"auto","created_at":"2020-09-08 14:51:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102790,"visible":true,"origin":"","legend":"Trends of Community-Onset Staphylococcus aureus Infection Rates Based on Group-based Trajectory Models","description":"","filename":"2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-59691/v1/2.JPG"},{"id":2295966,"identity":"f3c39536-ef37-46da-8520-1a10e60edb65","added_by":"auto","created_at":"2020-09-08 14:51:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78805,"visible":true,"origin":"","legend":"Geographic Locations of Community Onset MRSA and MSSA Infection Group-Based Trajectory Patterns, 2002-2010\nNote: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.","description":"","filename":"3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-59691/v1/3.JPG"},{"id":2295967,"identity":"3c4ba648-186c-4ccc-bd4f-d635a7c759af","added_by":"auto","created_at":"2020-09-08 14:51:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":86780,"visible":true,"origin":"","legend":"Hot Spot Analyses for Community Onset MRSA and MSSA Infection Group-Based Trajectory Patterns\nFigure Legend. Optimized hot spot analyses of community onset MRSA (Left Panel) and community onset MSSA (Right Panel) for the 20 counties within Atlanta’s MSA, 2002-2010. Hot spots are depicted in red or orange pixels with varying shades representing confidence intervals, ranging 90-99%. All pixels in white represent areas where there were no significant hot spots detected. The maps are created by co-author (M Edelson) using data from Atlanta Regional Commission and U.S. Census (both data sources are open access and available online). Software used to create maps include: ArcPro, 2.5.0; URL link: https://www.esri.com/. \nNote: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.","description":"","filename":"4.JPG","url":"https://assets-eu.researchsquare.com/files/rs-59691/v1/4.JPG"},{"id":15668860,"identity":"cc80d56c-81c1-4353-a849-db83b591dcef","added_by":"auto","created_at":"2021-11-18 13:50:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":951695,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-59691/v1/4aade8e0-1b1b-442d-ace3-7094535efd7c.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eSpace Time Trends of Community Onset \u003cem\u003eStaphylococcus Aureus\u003c/em\u003e Infections in Children Living in Southeastern United States: 2002-2010\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003e \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (\u003cem\u003eS. aureus\u003c/em\u003e) is a major cause of community onset and health care\u0026ndash;associated infections, ranging from superficial skin and soft tissue infections (SSTI) to invasive infections, sepsis, and death (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2017, U.S. Centers for Disease Control and Prevention reported the antibiotic resistant bacteria, methicillin resistant \u003cem\u003eS. aureus\u003c/em\u003e (MRSA) was the cause of more than 11,000 deaths per year in the U.S. and the cause of over 80,000 invasive forms of infection(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The non-antibiotic resistant form, methicillin susceptible \u003cem\u003eS. aureus\u003c/em\u003e (MSSA), also is a serious cause of infections and is estimated to be the cause of half of all health care\u0026ndash;associated \u003cem\u003eS. aureus\u003c/em\u003e infections(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Hospital onset MSSA infection rates have remained at a steady rate since 2012, whereas community onset MSSA (CO-MSSA) infection rates continue to increase (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Given U.S. Department of Health and Human Services\u0026rsquo; goal for a fifty percent reduction of \u003cem\u003eS. aureus\u003c/em\u003e invasive infections by 2020 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), community based primary and secondary prevention efforts to prevent spread of these infections seem more than ever important to put in place. Majority of efforts have focused on reducing infections within healthcare settings, rather than in community settings.\u003c/p\u003e \u003cp\u003eRisks associated with community onset staphylococcal infections have been widely reported over the last decade (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), but few studies have shown the relationship of \u0026lsquo;place-based\u0026rsquo; or community level risks from a spatial or geographic lens (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Spatial analyses factoring geographic areas allow for visualization of how socio- and built environments might contribute to occurrence of \u003cem\u003eS. aureus\u003c/em\u003e infections. Little has been published on how rates of infections may differ between different geographic areas over a longitudinal time period, especially during a period when community onset MRSA (CO-MRSA) infections were occurring in epidemic proportions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalyses of trends in population health data have mostly been determined by comparing rate differences between two time points and changes in the linear or log linear rates over time (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Another way to analyze trends of population is through group based trajectory models (GBTM), which incorporates information from all time points and examines non linear (quadratic, cubic, and other higher order) rate trends. In this manner, GBTM determine if groups of study units have similar trajectory patterns and can predict outcome trends of individual units which are grouped together into patterns (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). There are currently no studies to model or analyze multiyear trends of MRSA or MSSA rates over a longitudinal time period, identifying high or low infection trends based on location.\u003c/p\u003e \u003cp\u003eWe sought to determine trends of CO-MRSA and CO-MSSA infections and specifically, to identify particular trends (trajectory patterns) of infections rates which reveal areas with continued low rates of infections over nine years, relatively high rates of infection throughout this period, or \u0026lsquo;deviant\u0026rsquo; trends (high rate with one type of \u003cem\u003eS. aureus\u003c/em\u003e infection and low rate with the other.) We also sought to determine areas where clusters of trajectory patterns occurred and specifically, to identify geographic deviants, where \u0026lsquo;cluster\u0026rsquo; of census tracts had one type of trend, which differed significantly from the trajectory patterns of surrounding or neighboring census tracts. This is the first study to group \u003cem\u003eS. aureus\u003c/em\u003e infection rates into community-onset MRSA and MSSA infection \u0026lsquo;trajectory\u0026rsquo; group patterns at the community level and then, map spatially the trajectory patterns (ranging from \u0026lsquo;best\u0026rsquo; to \u0026lsquo;worst\u0026rsquo; trends of staphylococcal infections, stratified by their resistance to methicillin).\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003e \u003cb\u003eOverview.\u003c/b\u003e A retrospective study was conducted of children\u0026thinsp;\u0026lt;\u0026thinsp;19\u0026nbsp;years who were treated for \u003cem\u003eS. aureus\u003c/em\u003e infections from January 1, 2002 through December 31, 2010\u0026nbsp;at two pediatric hospitals in Atlanta, Georgia (Scottish Rite Children\u0026rsquo;s Hospital and Egleston Children\u0026rsquo;s Hospital are part of Children\u0026rsquo;s Healthcare of Atlanta (CHOA), a large pediatric healthcare system in Atlanta, Georgia, which consistently provides more than 80% of the pediatric hospitalizations for the Atlanta metropolitan statistical area (MSA)) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Patients\u0026thinsp;\u0026lt;\u0026thinsp;19\u0026nbsp;years of age diagnosed with \u003cem\u003eS. aureus\u003c/em\u003e infection who had emergency department visit (ED) and/or inpatient admission with residential addresses within the 20 counties of the Atlanta MSA were included. All study participants had an International Classification of Diseases, Clinical Modification (ICD- 9-CM) code compatible with \u003cem\u003eS. aureus\u003c/em\u003e infection and a positive \u003cem\u003eS. aureus\u003c/em\u003e culture as previously described (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). We included only those patients who met the definition for community-onset infection (patients with positive \u003cem\u003eS. aureus culture\u003c/em\u003e within 48 hours from the time patient was initially evaluated or admitted) (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndividual Level Patient Data from Electronic Health Records.\u003c/b\u003e Demographic data (race/ethnicity, gender, type of health insurance, and place of residence); relevant laboratory information (\u003cem\u003eS. aureus\u003c/em\u003e culture site(s), antibiotic susceptibility phenotype patterns); and relevant clinical information (clinical diagnoses, prior hospitalizations or ED visits within 12 months of the date of admission, number and source of cultures positive for \u003cem\u003eS. aureus\u003c/em\u003e, recorded past medical history of chronic illnesses) were abstracted from patients\u0026rsquo; electronic health records (EHR). Georeferencing of the patients\u0026rsquo; U.S. postal addresses was performed after excluding addresses which were confirmed to be out-of-state or U.S. post office box numbers. All georeferencing was performed using ArcGIS 10.6 (ESRI, Redlands CA). See \u003cb\u003eFig.\u0026nbsp;1. Enrollment Scheme\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;1. Area-based Characteristics of Population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDefinition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eData Source\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Household Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian distribution of the total number of households and families including those with no income**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of people above 25\u0026nbsp;years of age with high school degree, bachelor\u0026rsquo;s degree and professional degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKindergarten to 12th Grades Enrollment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of people above 3\u0026nbsp;years of age who attended kindergarten to 12th grades\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of people above 16\u0026nbsp;years of age in labor force\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation under 18\u0026nbsp;years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of youth under 18\u0026nbsp;years of age***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian House Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian houseing value in USD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent Black in Population, Percent White in Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent Hispanic and Non-Hispanic in Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Crowding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of occupied households with \u0026gt;\u0026thinsp;1 person/room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of population living below poverty level in past 12 months as defined by the U.S. Census Bureau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome inequality index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGini Coefficient measures inequality among values of a frequency distribution of income, where a coefficient of zero expresses perfect equality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e*ACS: American Community Survey (2000, and 2010)\u003c/p\u003e \u003cp\u003e**For Median Household Income, and Median House Value, data obtained from US Census 2000 and US Census 2010 (ACS) were used. Values from US 2000 were adjusted to 2010 dollar values, using inflation calculator. (21)\u003c/p\u003e \u003cp\u003e***No ACS or Census data for 2000 or 2010 available for \u0026lsquo;\u0026lt;19\u0026nbsp;years of age\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eArea Level Variables from US Census Tracts.\u003c/b\u003e Demographic, socio-economic and socio-environmental characteristics at the census tract level were obtained from U.S. Census 2000 and 2010, American Community Survey (ACS) 2000 and 2010 as outlined in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. Variables were selected if previously reported as determinants of health or risk factors associated with community associated MRSA (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Based on the inclusion criteria for selecting patients, area variables included: race, ethnicity, median household income, education attainment, population enrolled in grades K-12, proportion of population\u0026thinsp;\u0026lt;\u0026thinsp;18\u0026nbsp;years of age, proportion of population employed, median housing value, household crowding, Gini coefficient index for income inequality, and proportion of population living below poverty. Median household income and median house values for 2000 were adjusted to 2010 values using an inflation calculator(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Area level data was limited to census tracts associated with CO-MRSA and/or CO- MSSA patients. Difference of means in 2000 and 2010 were calculated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eU.S. 2000 Census Data Adjusted to U.S. 2010 Census Tract Boundaries.\u003c/b\u003e Census tracts are fundamental enumeration units for the U.S. decennial censuses and their boundaries change over time. Area data obtained from U.S. Census 2000 and U.S. Census 2010 were adjusted through longitudinal tract data base (LTDB) in the same way as previously described by Logan JR et al (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), which used 2010 census tract geographic boundaries. Estimates of \u0026lt;\u0026thinsp;19\u0026nbsp;years of age population were then made for each census tract per year from 2002 to 2009 via linear extrapolation and actual population size for 2010 were then used in the trajectory model as a time varying covariate.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analyses\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGroup-based trajectory modeling\u003c/span\u003e: We included all children with \u003cem\u003eS. aureus\u003c/em\u003e infections (2002\u0026ndash;2010), stratified by antibiotic resistant cases (CO-MRSA) and non-antibiotic resistant controls (CO-MSSA) per year across all census tracts located within Atlanta\u0026rsquo;s MSA 20 counties boundaries. The yearly case counts of CO-MRSA and CO-MSSA infected patients\u0026thinsp;\u0026lt;\u0026thinsp;19\u0026nbsp;years of age in each census tract from 2002 to 2010 were converted to rate of infection per 1,000 children. The yearly CO-MRSA or CO-MSSA infection rate was the outcome measure used to generate rate trajectories using Proc Traj for SAS, version 9.4 (SAS Institute, Inc). In group-based trajectory modeling, discrete underlying groups within the population are assumed to have their own case intercept, slope and possibly higher order terms. Proc Traj requires specification of the number of groups the model will fit. We used a process of evaluating model fit while simultaneously identifying informative similar trajectory groups as previously described by Baltrus et al(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). We estimated a zero-inflated Poisson model with a first order, quadratic term for each outcome, and an independent variable of time (years) for each group. We continued adding groups and assessed the change in the Bayesian information criterion (BIC) as an evaluation of model fit. Since Proc Traj has the limitation that does not allow for an offset variable in order to generate rates, we adjusted for each census tract\u0026rsquo;s\u0026thinsp;\u0026lt;\u0026thinsp;19\u0026nbsp;years of age population as a time varying covariate. (The CO-MSSA and CO-MRSA overall model fit did not improve with the addition of more than two groups. The CO-MRSA model produced a small \u0026ldquo;very high infection\u0026rdquo; group of census tracts with the addition of a third group into the model with only a slight decline in the fit of overall model.) We next added or removed second and higher-order terms from each group\u0026rsquo;s model based on significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDescriptive analyses by individual-level and area-level variables across the trajectory groups\u003c/span\u003e: Characteristics from individual level data were stratified by group -based trajectory patterns for CO-MRSA and CO-MSSA. We grouped the individual level data into two periods, designated as early period (2002\u0026ndash;2005) and late period (2006\u0026ndash;2010). The early period individual level data was linked to area data from U.S. Census 2000, and the late period individual level data was linked to U.S. Census 2010. Since spatial factors, including the ecological conditions and neighborhood spatial boundaries differed during the 10-year time span between U.S. Census 2000 and 2010, we used information from both time periods in order to best characterize the neighborhoods at the time of CO-MRSA or CO-MSSA infection occurrence. Chi-square test was applied to compare CO-MRSA and CO-MSSA population characteristics, stratified by the CO-MRSA trajectory groups and CO-MSSA trajectory groups, respectively. For area level variables, normality of distribution was determined using Shapiro-Wilk test. For normally distributed population, ANOVA was used when comparing three trend groups and T-test when comparing two trend groups. Kruskal-Wallis test was used for three groups, and Mann-Whitney test for two groups when population was not normally distributed. All statistical analyses were done by SAS version 9.4 (SAS Institute, Inc).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatial Patterns for Community Onset MRSA and MSSA Trajectory Trends.\u003c/b\u003e Thematic mapping of different CO-MRSA and CO-MSSA infection trajectories was performed and assessed for spatial autocorrelation, which confirmed whether adjacent observations were related (and not independent occurrences). The degree of spatial autocorrelation was measured using the Global Moran\u0026rsquo;s I statistic (ESRI ArcGIS Pro, Redlands CA). Census tracts that had CO-MRSA or CO-MSSA were grouped into six group based trajectories: 1) Very High MRSA\u0026ndash;High MSSA clusters representing all census tracts with very high CO-MRSA and high CO-MSSA infection rates, the worst trajectory group across the nine years of the study period; 2) Very High CO-MRSA\u0026ndash;Low CO-MSSA clusters representing census tracts in the worst trajectory CO-MRSA group near census tracts in the most favorable trajectory CO-MSSA group (deviant trend); 3) High CO-MRSA\u0026ndash;High CO-MSSA clusters representing census tracts in the second worst trajectory CO-MRSA group near census tracts in the worst trajectory CO-MSSA group; 4) High CO- MRSA\u0026ndash;Low CO-MSSA clusters of census tracts in the second worst trajectory CO-MRSA group near census tracts in the most favorable trajectory CO-MSSA group (deviant trend); 5) Low CO-MRSA-High CO-MSSA clusters representing census tracts in the most favorable trajectory CO-MRSA group near census tracts in the worst trajectory CO-MSSA group (deviant trend); and 6) Low CO-MRSA-Low CO-MSSA clusters representing all census tracts with low infection rates, the most favorable trajectory group.\u003c/p\u003e \u003cp\u003eLocal \u0026lsquo;clusters\u0026rsquo; of aggregated patients\u0026rsquo; point data were determined by applying the local indicators of spatial association (LISA) technique as previously described (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). From this, hot and cold spot analyses were performed using GetisOrd Gi* statistic. The output is a surface raster layer depicting the different degrees of \u0026lsquo;hot spots\u0026rsquo; and \u0026lsquo;cold spots\u0026rsquo; of CO-MRSA or CO-MSSA occurrences. Significant clusters were those areas, where an occurrence of infection (CO-MRSA or CO-MSSA) and its neighboring points all had high Getis-Ord Gi* values (hot spots). Geographic deviants were defined as clusters that had much higher or much lower values than neighboring occurrence points (cold spots).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatial Analyses.\u003c/b\u003e Optimized Hot Spot Analysis (OHPA) tool (Esri ArcGIS Pro, Redlands, CA) was performed to assess spatial patterns in our data. This allowed us to visualize statistically significant patterns, where clustering of cases (CO-MRSA) or controls (CO-MSSA) exist, which are not likely due to random occurrence. The results of the applying this tool showed us areas within the 20 counties where \u0026lsquo;hot spots\u0026rsquo; occur within a \u0026lsquo;cluster\u0026rsquo; in a non random way. (A simple \u0026lsquo;hot spot\u0026rsquo; analysis will not tell necessarily inform \u0026ldquo;why\u0026rdquo; a particular event is occurring in a statistically significant cluster, only that it is occurring in a non random manner). We ran the analysis using all unique patients\u0026rsquo; point locations from 2002 to 2010, \u0026lt;\u0026thinsp;19 years, with CO-MRSA and then repeated these steps for CO-MSSA patients within the same time period and for the same ages. With the OHSA tool, we set our \u0026lsquo;Study Area\u0026rsquo; to the boundaries of Atlanta\u0026rsquo;s MSA 20 counties. We then assigned \u0026lsquo;neighborhood\u0026rsquo; variables as 1-mile hexgon patterns. We chose the 1-mile hexagon neighborhood size based on Tober\u0026rsquo;s First Law of Geography, which follows the spatial diffusion theory for infectious pathogens, whereby the risk for infection increases with closer distance to source of infection. In our model, we postulate that the closer an individual\u0026rsquo;s neighborhood is geographically near to an occurrence of CO-MRSA or CO-MSSA, the more likely the persons within the neighborhood are likely to contract CO-MRSA or CO-MSSA. In this model, we accounted for various sizes of neighborhoods, apartment complexes, and other living or housing units. We estimated that a neighborhood is within approximately a mile of the residence of any CO-MRSA or CO-MSSA occurrence. This study was approved by Institutional Review Boards of hospitals and affiliated academic institutions.\u003c/p\u003e "},{"header":"Results","content":"\u003cp\u003eThere were 12,120 children with \u003cem\u003eS. aureus\u003c/em\u003e infections from 1,969 census tracts during 2002-2010 who were seen at Children\u0026rsquo;s Healthcare of Atlanta\u0026rsquo;s two pediatric hospitals. For Atlanta\u0026rsquo;s 20 counties\u0026rsquo; MSA, there were 9,095 patients, \u0026lt; 19 years of age, from 901 census tracts. Overall, the number of infections increased steadily from 2002 to 2010, with the highest rates occurring in 2009 for CO-MRSA and 2008 for CO-MSSA; CO-MRSA rates increased at a higher rate compared to CO-MSSA. Of six possible combinations for trends of CO-MRSA and CO-MSSA infections, there were five group-based trajectory patterns for CO-MRSA (3) and CO-MSSA (2) with overlapping U.S. census tracts as shown in \u003cstrong\u003eTable \u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e; no census tracts fell into the group-based trajectory pattern of Very High CO-MRSA - High CO-MSSA infections. \u003cstrong\u003eFigure 2\u003c/strong\u003e shows the three CO-MRSA group-based trajectory trends and the two CO-MSSA group-based trajectory trends over this nine-year period: Of the three CO-MRSA group-based trajectory trends, only 0.8% of the census tracts showed a dramatic increase over the first six years followed by a gradual decline in the last three years; over two thirds (67.2%) remained in the low infection trend across all years. In contrast, for CO-MSSA, 85.7% of census tracts fell into the high infection group-based trajectory pattern which lasted throughout the nine-year period, compared to 14.3% of census tracts which belonged to a low infection trend over this same period\u003cstrong\u003e. \u003c/strong\u003e(\u003cstrong\u003eSupplemental Section: Table A \u003c/strong\u003eshows the coefficients for the terms used in the three group-based CO-MRSA trajectory models and the two group-based CO-MSSA trajectory models. For the three CO-MRSA group-based models, the equations all contained a quadratic term. For the two CO-MSSA group-based models, the equations contained a quadratic term, and a slope term that was relatively flat.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. US Census Tracts with Community onset MRSA- / MSSA- Infection Group-Based Trajectory Patterns, 2002-2010\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCO-MRSA Infection \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCO-MSSA Infection \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eLow \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eTotal \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e682 (75.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e29 (3.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e711\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e170 (18.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e14 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e184\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eVery High\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e6 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e858\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e901\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFrom 901 census tracts with \u003cem\u003eS. aureus\u003c/em\u003e infections, 682 (75.7%) were in the low infection group-based trajectory for both CO-MRSA and CO-MSSA (this is the \u0026lsquo;best\u0026rsquo; trend category); deviant trajectory census tracts included 6 (0.7%) with Low CO-MSSA infection rate but Very High CO-MRSA infection rate, 170 (18.9%) Low CO-MSSA and High CO-MRSA infection rates and 29 (3.2%) High CO-MSSA and Low CO-MRSA infection rates. High CO- MSSA- High CO-MRSA (worst group-based trajectory) were seen in 14 (1.6%) census tracts. In 2002, there were 38 cases (0.03 infection per 1,000 children) in the Low CO-MRSA infection trajectory group, but this trend over time increased steadily to peak at 334 cases (0.48 infection per 1,000 children) in 2008, before declining to 257 cases (0.43 infection per 1,000 children) in 2010. Similarly, CO-MSSA low infection trajectory group had a baseline of 192 cases (0.18 infection per 1,000 children) in 2002, but this trend peaked later in 2009 with 560 cases (0.51 infection per 1,000 children) before dipping slightly to 482 cases (0.47 infection per 1,000 children) in 2010. Census tracts that started with High CO-MRSA infection trajectory followed a similar pattern of increasing with each year, beginning with 29 cases (0.15 infection per 1,000 children) in 2002, peaking to 454 cases (1.25 infection per 1,000 children) in 2008, before dropping to 422 cases (0.99 infection per 1,000 children) in 2010. Comparatively, High CO-MSSA infection trajectory more than tripled in infections over six years from 32 cases (0.46 infections per 1,000 children) to 102 cases (1.26 infections per 1,000 children). Like other trajectories, its rates also dropped after 2008 with 88 cases (1.02 infection per 1,000 children) in 2010. \u003cstrong\u003e(Table 3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Counts of Community Onset MRSA and MSSA And Average Infection Rates by Group-Based Trajectory Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eType of Trajectory Pattern\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCO-MSSA-Low \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCO-MSSA-High\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCO-MRSA-Low\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCO-MRSA-High\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCO-MRSA-Very High \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Count (Rate)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCount (Rate)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCount (Rate)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCount (Rate)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCount (Rate)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e2002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e2003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e362\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e2004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e2005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e382\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e274\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e2006\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e386\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e2007\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e416\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e3.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e2009\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e2.54\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003e2010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e422\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e1.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor each census tract with \u003cem\u003eS. aureus \u003c/em\u003eoccurence, we determined the number of CO-MSSA or CO- MRSA infections per 1,000 children \u0026lt;19 yearst\u003cstrong\u003e. \u003c/strong\u003eThe actual number of CO-MSSA or CO-MRSA by year is provided along with the rate (infections per 1,000).\u003c/p\u003e\n\u003cp\u003eFrom aggregated individual level data of early and late time periods (2002-2005 and 2006-2010), we identified significant differences in both time periods, across the all three CO-MRSA infection group-based trajectories and also differences between the high and low CO-MSSA infection group-based trajectories \u003cstrong\u003e(Table 4)\u003c/strong\u003e: Rates of blacks increased for CO-MRSA group-based trajectories as the CO-MRSA group-based trajectory moved from low to very high infection trend patterns, and overall, were relatively higher than rates of whites with MRSA infections. This finding remained consistent for both early and late time periods. Health insurance status also was significantly different across the three CO-MRSA infection trajectories with public health insurance ranking highest (p=0.0002) in CO-MRSA-High and CO-MRSA-Very High groups. Having prior hospitalization was significantly different among the three CO-MRSA infection trend patterns (p \u0026lt;0.05) in 2002-2005 but not significant in 2006-2010. Having a skin and soft tissue infection (SSTI), the most common clinical presentation for \u003cem\u003eS. aureus\u003c/em\u003e in the community or outpatient setting, was not significant among the three CO-MRSA infection trend patterns in 2002-2005 or 2006-2010. In contrast, rates of whites increased for CO- MSSA group-based trajectories from low to high infection trend patterns, and overall, were relatively higher than rates of blacks with CO-MSSA infections. Rates among females were much higher in CO-MSSA High infection trend group, and the difference was statistically significant in the early period, 2002-2005 (p=0.0076). Rates of private health insurance was significantly higher than public health insurance in CO-MSSA low and high infection trajectory groups for both 2002-2005 (p=0.04140, and 2006-2010 (p=0.0057) periods. History of prior hospitalization was increased with CO-MSSA trajectory groups moving from low to high, in both 2002-2005 (p\u0026lt;0.001) and 2006-2010 (p\u0026lt;0.001). Rates of skin and soft tissues infections decreased in CO-MSSA trajectory groups moving from low to high, for both 2002-2005 (p=0.004) and 2006-2010 (p\u0026lt;0.001).\u003c/p\u003e\n\u003ctable border=\"1\" width=\"900\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" width=\"900\"\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e \u003cstrong\u003ePatient-level Characteristics of Group-Based Trajectory Patterns of MRSA and MSSA for 2002-2005/ 2006-2010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Characteristic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003eMRSA-low\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003eMRSA-High\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003eMRSA-VeryHigh\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003eMSSA-low\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003eMSSA-high\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003e(N= 2660)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003e(N=1937 )\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003e(N= 118)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003e(N= 3730)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u003cstrong\u003e(N=650)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2002-2005, N= 537\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2002-2005, N=453\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2002-2005, N= 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"69\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2002-2005, N= 1268\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2002-2005, N= 201\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"68\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2006-2010, N= 2123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2006-2010, N= 1484\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2006-2010, N= 108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2006-2010, N= 2462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2006-2010, N= 449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace**\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003e2002-2005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.0003*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e163(31.17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e215(48.53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e7(70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e286 (22.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e25 (12.63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e46(8.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e51(11.51%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2(20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e797 (63.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e155 (78.28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e314(60.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e177(39.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1(10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e163 (13.08%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e18 (9.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003e2006-2010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e758(36.32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e878(60.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e87(80.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e816 (34.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e94 (21.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1113(53.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e455(31.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e14(12.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1287 (53.78%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e286 (66.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e216(10.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e129(8.82%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e7(6.48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e290 (12.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e52 (12.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"251\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthnicity (Hispanic)***\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2002-2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e7(2.18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e11(5.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2006-2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e175(13.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e118(17.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e7(19.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.0392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e251 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e51 (17.47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.5321\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"251\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender (Female)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2002-2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e262(48.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e245(54.08%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e3(30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.0972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e535 (42.19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e105 (52.24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.0076*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2006-2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1073(50.54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e782(52.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e68(62.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.0272*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1137 (46.18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e220 (49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.2714\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"251\"\u003e\n\u003cp\u003e\u003cstrong\u003eHealth Insurance\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003e2002-2005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.0002*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.0414*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003ePrivate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e269(50.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e179(39.51%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e776 (61.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e140 (69.65%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003ePublic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e250(46.55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e258(56.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e10(100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e456 (35.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e54 (26.87%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eOther****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e18(3.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e16(3.53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e36 (2.84%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e7 (3.48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003e2006-2010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.0057*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003ePrivate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e935(44.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e464(31.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e17(15.74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1163 (47.24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e246 (54.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003ePublic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1080(50.87%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e906(61.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e86(79.63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1163 (47.24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e188 (41.87%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eOther****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e108(5.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e114(7.68%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e5(4.63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e136 (5.52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e15 (3.34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"251\"\u003e\n\u003cp\u003e\u003cstrong\u003eSSTI Diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2002-2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e336 (62.57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e252 (55.63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e7 (70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.0632\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e352 (27.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e32 (15.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.0004*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2006-2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1495 (70.42%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1077(72.57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e79 (73.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.3389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e1082 (43.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e141 (31.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"251\"\u003e\n\u003cp\u003e\u003cstrong\u003eHistory of Hospitalizations\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2002-2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e29 (5.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e55 (12.14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.0005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e210 (16.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e70 (34.83%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2006-2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e100 (4.71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e63 (4.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e2 (1.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.373\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e214 (8.69%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"128\"\u003e\n\u003cp\u003e100 (22.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p-value \u0026lt;0.05, means statistically significant.\u003c/p\u003e\n\u003cp\u003e** Other races include: Asian, Native Hawaii, Alaskan, and Multiple-races (self identified by patients as more than one race).\u003c/p\u003e\n\u003cp\u003e***Other health insurance types include: Self-pay, no insurance, and those with both private and public.\u003c/p\u003e\n\u003cp\u003ePatient level data aggregated into two periods, 2002-2005 and 2006-2010, and then stratified by MRSA and MSSA population characteristics respectively based on MRSA and MSSA trajectory groups. (There are 9095 patients that accounted for the five different group-based trajectory patterns who resided within the 20 counties of Atlanta MSA across all years, \u0026lt;19 years.)\u003c/p\u003e\n\u003cp\u003eComparing population data from US Census 2000 and 2010, we found significant differences among socio- economic and demographic variables, stratified by CO-MRSA- and CO-MSSA- group-based trajectory patterns \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eTable 5)\u003c/strong\u003e: Similar to what was seen with individual level data, we also found rates of whites decreased as the CO-MRSA group-based trajectories moved from low infection to very high infection in both US Census 2000 and 2010. In contrast, the rates of blacks increased as the CO-MRSA infection trajectories moved from low to very high for both U.S. Census 2000 and 2010. For CO-MSSA group-based trajectories, a similar pattern was also seen whereby, rates of whites increased as CO-MSSA moved from low to high both in U.S. Census 2000 (p=0.054) and U.S. Census 2010 (p=0.0096). In CO-MSSA group-based trajectories, proportions of blacks decreased as infection trends increased; these trends were significant for both U.S. Census 2000 (p=0.0158) and 2010 areas (p=0.001). Although the proportion of census tracts with children \u0026lt;19 years of age increased as infection trends increased for both CO-MRSA and CO-MSSA group-based trajectories, and relatively higher in U.S. Census 2010 compared to 2000, none of the CO-MRSA or CO-MSSA infection trend patterns were significant except for U.S. Census 2010 census tracts representing the CO-MSSA-High infection trend pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp; Population Area-level Characteristics of Group-Based Trajectory Patterns of MRSA and MSSA in Atlanta\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"720\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 59px;\"\u003e\n\u003ctd style=\"height: 59px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003eMRSA\u0026ndash;Low\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"89\"\u003e\n\u003cp\u003e\u003cstrong\u003eMRSA-High\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"126\"\u003e\n\u003cp\u003e\u003cstrong\u003eMRSA-Very High\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 94px;\" rowspan=\"2\" width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003eMSSA-Low\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003eMSSA-High\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 94px;\" rowspan=\"2\" width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 59px;\"\u003e\n\u003ctd style=\"height: 59px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003eCensus tracts \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN=901 (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"84\"\u003e\n\u003cp\u003e711 (67.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"89\"\u003e\n\u003cp\u003e184 (32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"126\"\u003e\n\u003cp\u003e6 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"84\"\u003e\n\u003cp\u003e858 (85.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"84\"\u003e\n\u003cp\u003e43 (14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace/Ethnicity, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003eWhite, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e68.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e46.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e31.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e63.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e69.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e59.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e39.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e21.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e54.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e65.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0096*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-8.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e-6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e-9.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.1188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-8.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-4.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.0349*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003eBlack, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e24.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e44.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e63.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e29.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e21.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0158*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e29.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e49.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e71.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e34.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e22.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e4.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e4.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e8.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.981\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e4.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.0293*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003eHispanic, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e5.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e7.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e4.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.2315\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e6.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e8.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.6623\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e9.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e10.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e5.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.3007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e9.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e13.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.6361\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e3.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e3.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.8551\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e3.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.4351\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;18 years, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e26.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e27.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e29.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.4065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e26.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e27.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.2359\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e28.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e29.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e29.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.7528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e28.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e30.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0253\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e2.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e-0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.3644\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e2.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.4148\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 37px;\"\u003e\n\u003ctd style=\"height: 37px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003eK-12\u003csup\u003eth\u003c/sup\u003e Grades, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e71.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e72.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e76.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.2774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e71.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e73.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.6034\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e67.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e69.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e69.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.8499\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e67.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e73.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0042*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-2.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e-3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e-6.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.6411\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-3.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.0415\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"205\"\u003e\n\u003cp\u003e\u003cstrong\u003eNursery to preschool, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e8.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.177\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e8.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e9.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.2455\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.401\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e8.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.115\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e-1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e-2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.9299\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.2138\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003eHigh School, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e24.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e25.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.3111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e25.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e21.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0295*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e24.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e28.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e29.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0002*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e25.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e22.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0431*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e2.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e3.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.0002*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003eBachelor, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e21.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e14.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0085*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e20.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e25.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0255*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e23.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e19.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e16.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e22.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e26.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0327*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods,\u0026nbsp; %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e1.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.0022*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.8262\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"205\"\u003e\n\u003cp\u003e\u003cstrong\u003eGraduate or professional, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e10.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e8.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0839\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e9.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e12.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0801\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e12.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e10.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e9.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.01*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e11.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e14.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.208\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e1.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e-0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.1914\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.2307\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003eLabor Force, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e70.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e69.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e69.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.2486\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e70.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e70.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.7147\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e70.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e70.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e70.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.6528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e70.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e70.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.7211\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e1.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.0464*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.7159\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"3\" width=\"294\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian Annual Household Income^, $\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e71,537.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e64,354.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e60,191.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0048*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e69,118.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e87,486.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0043*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e63,983.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e54,450.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e45,369.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e61,026.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e79,598.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0090*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, $\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-7,554.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e-9,904.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e-14,821.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.1179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-8,092.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-7,887.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.4704\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"205\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian House Value^, $\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e193,740.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e170,922.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e145,859.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.023*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e186,692.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e230,044.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0178*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e217,349.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e197,003.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e155,783.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0365*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e210,287.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e262,673.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0158*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods,\u0026nbsp; $\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e25,719.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e26,080.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e9,923.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.8035\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e24,761.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e44,466.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.0262*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"205\"\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold Crowding**, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e1.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.1311\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e3.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e7.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.9937\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e4.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e4.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0823\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e-4.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e-6.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-3.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e-3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.6453\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003ePoverty*** %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e9.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e10.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e10.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e\u0026lt;.0001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e9.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e7.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0727\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e18.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e17.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0236*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e18.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e14.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.0202*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"121\"\u003e\n\u003cp\u003eDifference in Periods, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e8.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"89\"\u003e\n\u003cp\u003e7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"126\"\u003e\n\u003cp\u003e6.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.7673\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e8.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"84\"\u003e\n\u003cp\u003e6.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"66\"\u003e\n\u003cp\u003e0.5399\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"121\"\u003e\n\u003cp\u003e\u003cstrong\u003eGini Index-2010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"126\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.1912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"84\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"66\"\u003e\n\u003cp\u003e0.858\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr /\u003eA total of 901 census tracts from 20 counties in Atlanta (2000 and 2010) represent the patients with \u003cem\u003eS. aureus \u003c/em\u003einfections who were included in the study.\u003c/p\u003e\n\u003cp\u003e*p-value \u0026lt;0.05, means statistically significant.\u003c/p\u003e\n\u003cp\u003e**Occupied households with \u0026gt;1 person/room.\u003c/p\u003e\n\u003cp\u003e***People living under poverty level in past 12 months.\u003c/p\u003e\n\u003cp\u003e^Median Annual Household Income and Median House Value for 2000 were adjusted to 2010, using an inflation calcuator.\u003c/p\u003e\n\u003cp\u003eSocio-economic conditions of the neighborhoods where CO-MRSA infection trends were higher differed from CO-MSSA infection trends over the same time period. For example, median household income and housing values decreased as CO-MRSA infection group-based trajectories moved from low to very high infection trends. Proportion of the population living below the federal poverty level also increased significantly in areas where CO-MRSA infection trends moved from low to very high infection rates. This trend was not seen with CO-MSSA group-based trajectories, where the median household income and housing values increased, and the proportion of the population living below the federal poverty level decreased in areas where it was clearly a CO-MSSA high infection trend pattern.\u003c/p\u003e\n\u003cp\u003eSince crowding is a known risk associated with community-associated MRSA infections, we calculated the percentage of U.S. 2000 census tracts with evidence of household crowding and found crowding increased significantly as trends of CO-MRSA infection moved from low infection to very high infection pattern (p\u0026lt;0.0001). However, rates of crowding in U.S. 2010 census tracts were not significantly different in those areas where CO-MRSA infection trends moved from low to very high (p=0.9937). In contrast, for CO-MSSA group-based trajectories, household crowding was not significantly increased over the different categories of CO-MSSA infection trends, nor was the change in rates between the two time periods significantly different (p=0.6453).\u003c/p\u003e\n\u003cp\u003eSpatial analyses of census tract group-based trajectories for MRSA and MSSA. Spatial variations of the different community onset MRSA-MSSA infection group-based trajectories were identified (Figure 3): The most favorable group-based trajectory (CO-MRSA Low infection and CO-MSSA Low infection) and second most favorable trajectory (CO-MRSA High-CO-MSSA Low) were scattered with higher proportion of census tracts in counties where the population is less dense relative to inner city Atlanta. There were also \u0026lsquo;pockets\u0026rsquo; within Gwinnett, Henry and Douglas counties, where there were census tracts with CO-MRSA-Very High or CO-MRSA-High, even though most of the county fell into a more \u0026lsquo;favorable\u0026rsquo; or lower infection group-based trajectory pattern. In contrast, counties representing the inner city (areas within DeKalb County and Fulton County) had the highest proportion of the worst trajectories (CO-MRSA-Very High-CO-MSSA-High or CO-MRSA-High-CO-MSSA-High). Seven of these census tracts, representing CO-MRSA Very High-CO-MSSA Low or worst group-based trajectory combination, were from areas located east of downtown Atlanta (DeKalb County) and a single census tract southeast of downtown Atlanta (Henry County). To identify areas where infections intensified with each year in a particular census tract and also in neighboring census tracts (\u0026lsquo;hot spots\u0026rsquo;) compared to areas where the infections were much lower than neighboring census tracts across each year (\u0026lsquo;cold spots\u0026rsquo;), we found CO-MRSA and CO-MSSA \u0026lsquo;hot\u0026rsquo; and \u0026lsquo;cold\u0026rsquo; spots overlapped in a number of areas over the 2002-2010 period. \u0026lsquo;Hot spots\u0026rsquo; for CO-MRSA and CO-MSSA overlapped for a large number of census tracts located primarily in the most heavily populated counties (Dekalb, Fulton and Gwinnett), but CO-MRSA \u0026lsquo;hot spots\u0026rsquo; extended much further down into the southern counties of Rockdale, Henry and Clayton compared to CO-MSSA \u0026lsquo;hot spots\u0026rsquo;. \u0026lsquo;Cold spots\u0026rsquo; followed the perimeter of the \u0026lsquo;hot spots\u0026rsquo; for both CO-MRSA and CO-MSSA. Interestingly, \u0026lsquo;cold spots\u0026rsquo; were seen southeast of Fulton county, where there were CO-MRSA \u0026lsquo;hot spots\u0026rsquo;. There was a large band of cold spots, located in the northwestern portion of 20 counties boundaries, which ran along the perimeter of hot spots seen in Cobb and Cherokee counties (northwest of the most densely populated counties) (Figure 4). In order to discern what group-based trajectory patterns might fall within \u0026lsquo;hot spots\u0026rsquo;, we overlaid the group-based trajectory map with hot spots and found that the \u0026lsquo;worst\u0026rsquo; group-based trajectory was located in areas found to be \u0026lsquo;hot spots\u0026rsquo; for CO-MRSA and CO-MSSA, and the best trajectory groups tended to occur where there were neither cold nor hot spots. Interestingly, Bartow county (northwest of Atlanta\u0026rsquo;s downtown area) had a number of census tracts which had a CO-MRSA-high \u0026ndash; CO-MSSA- low designation, yet no CO-MSSA or CO-MRSA \u0026lsquo;hot\u0026rsquo; or \u0026lsquo;cold\u0026rsquo; spots.\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eWe applied group-based trajectory modeling to identify Atlanta MSA census tracts with \u0026lsquo;worst\u0026rsquo; and \u0026lsquo;best\u0026rsquo; temporal trends of community onset MRSA and MSSA infection rates over nine years. We identified three distinct CO-MRSA group-based trajectory infection patterns and two CO-MSSA group-based trajectory infection patterns over this period, which included the time when community associated MRSA infections were occurring in epidemic proportions around the U.S. We found\u0026thinsp;~\u0026thinsp;94% of all the census tracts with \u003cem\u003eS. aureus\u003c/em\u003e occurrence during this time period belonged to the best trend categories of low infection rates for both CO-MRSA and CO-MSSA. With group-based trajectory models, we used all the rates generated over the nine year span instead of relying on change between just two rates at the beginning and end of this period. This allowed us to detect and examine the trajectory shape over the entire time period, including non-linear shapes which are common for the epidemic curves associated with communicable diseases. Given how this bacteria, \u003cem\u003eS. aureus\u003c/em\u003e, can have many strains with various virulent factors that may contribute to infection, understanding these epidemic curves within any community is important. It provides some insight as to which communities may have higher proportion of its population, with risks for infection. A recent worldwide review of \u003cem\u003eS. aureus\u003c/em\u003e nasal carriage estimated that the average prevalence of nasal colonization in the general population is 24%(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) but the possible place based risks which contribute to movement from colonized to infected population have not been elucidated. We have clearly delineated population level crowding is associated with worst trajectory trends for CO-MRSA infection in specific communities at the census tract level. Furthermore, we also identified the groups which make up the trajectory patterns from both individual and area level data instead of defining group categories \u003cem\u003ea priori.\u003c/em\u003e This study highlights not only individual and area level risks which may contribute to why certain census tracts over time have either a high or low rate of infection, but also the location-based relationships which may be explanatory of these staphylococcal trend patterns of infection.\u003c/p\u003e \u003cp\u003eOur main findings demonstrated that variations in spatial trend patterns of CO-MRSA infection and CO-MSSA infection rates across these 20 counties include both rural and urban communities. We mapped out the geographic clustering of the group-based trajectory patterns, with \u0026lsquo;worst\u0026rsquo; trajectories clustering mainly in Dekalb and northwest counties of Bartow, Cherokee, Paulding and Cobb, and \u0026lsquo;best\u0026rsquo; trajectories clustering in the northeast, southeast, and southwest of the inner-city area. Identification of these clusters of census tracts with similar rate trajectories over time will direct us to better understand what \u0026lsquo;place based\u0026rsquo; conditions or factors might contribute to these findings that are location specific. For example, population density of the \u0026lsquo;worst\u0026rsquo; trajectories, along with demographic factors including race and age distribution have been demonstrated to partially explain the unfavorable trajectory seen in DeKalb County. However, the southwest section of Bartow County has a significant group of census tracts which are geographically deviant from the surrounding \u0026lsquo;low infection\u0026rsquo; census tracts. Socioeconomic factors (e.g., poverty, inequality index, employment status, etc.) may be more of a \u0026lsquo;driver\u0026rsquo; than population density or household crowding in these rural communities.\u003c/p\u003e \u003cp\u003eWhen considering demographic characteristics of the 901 census tracts and the distribution based on five distinct \u003cem\u003eS. aureus\u003c/em\u003e infection group-based trajectories, we found that race differences were seen in census tracts where infection trends were high for both MRSA and MSSA infection trend patterns. Areas with low \u003cem\u003eS. aureus\u003c/em\u003e infection trends had higher proportion of whites compared to blacks, whereas high infection trend patterns occurred in areas where there were higher proportion of blacks. (In Gwinnett county, 29.3% of the population are black and 54.5% are white, but the pockets with much higher trends of CO-MRSA correspond to census tracts with higher proportion of blacks.)\u003c/p\u003e \u003cp\u003eMany of the census tracts found to have the \u0026lsquo;worst\u0026rsquo; CO-MRSA infection trajectories were in areas with lower housing value, lower household income, and higher poverty rates. These findings are consistent with findings published from CDC researchers in their two year population surveillance study of 33 counties across nine states, which also demonstrated higher incidence of MRSA infection from census tracts with low-household income, persons living under the poverty level, and low education (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). They also found that \u0026lsquo;crowding\u0026rsquo; was associated with increased MRSA rates. Our analyses, which focused on \u0026lsquo;trend\u0026rsquo; patterns of MRSA infections over a nine-year period and not incidence rates, did not find crowding to persist over this nine-year time period. This may be related to the fact that our data compared a longer time interval (nine years compared to two years) and was based on analyses of multi-level data, including individual patient level data. Another explanation maybe related to communities in our catchment area may have improved housing conditions between the early and late time periods, e.g., higher percentage of people living in less crowded housing situations over almost a decade of time.\u003c/p\u003e \u003cp\u003eProfiles of socioeconomic conditions in areas where CO-MSSA trajectories were high differed from the profiles where CO-MRSA trajectories were high: In areas where CO-MSSA infections trended high, the proportion of whites was higher than proportion of blacks. This relationship was reversed in areas of high or very high CO-MRSA group-based infection trajectories, where proportions of blacks were higher than whites.\u003c/p\u003e \u003cp\u003eHigher household income and housing values and lower individual poverty levels were seen in areas with high CO- MSSA- infection trend patterns, compared to low CO-MSSA infection trend patterns. Whether or not this is a temporal effect related in part by the fact that some of these CO-MSSA-high infection census tracts crossover with CO-MRSA-low infection, is unclear.\u003c/p\u003e \u003cp\u003eOverall, multi-level factors contribute to the explanation of geographically \u0026lsquo;deviant\u0026rsquo; communities whose trajectory group is surrounded by very different types of infection trend patterns. Our study did not look at the changes of demographic composition as a dependent variable or other factors, e.g., changes in socioeconomic conditions, housing conditions, etc. which may potentially contribute to the geographic deviation found. Interestingly, we did not see a \u0026lsquo;Hispanic paradox\u0026rsquo;(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), where census tracts with higher proportion of Hispanics were in areas with the best infection trajectories. In our analyses, we did not see significant differences among Hispanics for any of the CO-MRSA group based trajectories and in fact, found worsening infection trend rates among Hispanics in the low and high CO-MSSA group based trajectories over the nine-year period.\u003c/p\u003e \u003cp\u003eThe use of this novel method of group-based trajectory analysis (GBTA) of communicable disease trends holds some advantages over traditional hot-spot analysis. Hot spot analysis is good for spotting where concentrations of cases have occurred over time. However, hot spot analyses may not always identify geographic areas where residents are at highest risk, since the analyses does not take into account the population size where cases are actually occurring. For instance, a tract that has 5 cases arising over the nine year period may not show up in a hot spot analysis; however, if that tract has a relatively low population it may show up as a high infection rate trajectory tract in GBTA. This may be why some tracts in the more suburban areas in our study were found to be high infection rate trajectory tracts, yet did not show up as \u0026lsquo;hot spots\u0026rsquo;. The practical clinical application of using GBTA can be demonstrated, when estimating whether or not a patient who presents with a skin or soft tissue infection has CO-MRSA or CO-MSSA, based on the patient\u0026rsquo;s assigned group based trajectory infection category. In this scenario, the treating healthcare provider could factor location based CO-MRSA or CO-MSSA infection trends as part of the management strategy. Another advantage is that with GBTA, we can ascertain a general idea of the rate pattern experienced over the study period by tracts without having to examine the hot spot maps for each year.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLimitations.\u003c/span\u003e There are several limitations to this analyses: First we were not able to determine causal association (relationship) between the population characteristics of an area and CO-MRSA and CO-MSSA infections, since the study draws from area level data which were collected in two different time periods (2000 and 2010). Although we have included children seen for all staphylococcal infections within one healthcare system, our findings may not necessarily be generalizable to children who did not access care from this single pediatric healthcare system or applicable to other geographically distinct locations where the place based factors are different from this catchment area.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFuture Directions.\u003c/span\u003e We plan to look at geographic influences from changes which are place based, using particular \u0026lsquo;neighborhoods\u0026rsquo; as boundaries, and not census tracts. These \u0026lsquo;neighborhood\u0026rsquo; boundaries tend to outline areas where the sociocultural influences are similar among the residents of the neighborhood, e.g., types of healthcare access, density and types of daycare centers, mode and availability of transportation, and racial and ethnic groups including various immigrant populations or cultural influences.\u003c/p\u003e \u003cp\u003eOur study clearly outlines which census tracts have the greatest disparities in temporal \u003cem\u003eS. aureus\u003c/em\u003e infection trends, and specifically, which census tracts are located in the \u0026lsquo;worst\u0026rsquo; or \u0026lsquo;best\u0026rsquo; group-based \u003cem\u003eS. aureus\u003c/em\u003e infection trajectory areas. It is these \u0026lsquo;geographic deviants\u0026rsquo; that deserve more attention. Identifying the potential remediable factors may pave the path towards eliminating spread of these types of infections or at least, mitigate contributing factors to the spread. Similarly, looking at why certain census tracts perform consistently better than their surrounding areas will provide insights on how to prevent the spread of this bacterial infection, and thereby, improve outcomes for other census tracts which have failed to have a positive infection trajectory trends over time. Deciphering the primary multi-level factors, driven by place and time elements, for both antibiotic resistant and non-antibiotic resistant \u003cem\u003eS. aureus\u003c/em\u003e infections is paramount in the effort to develop effective prevention models that effectively and efficiently decrease transmission and infections at the neighborhood or community level.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions.\u003c/b\u003e We demonstrated \u003cem\u003eS. aureus\u003c/em\u003e infection trends over a nine-year period, factoring in time and space for both community onset MRSA and MSSA. We identified specific areas which are unique or overlapping at the census tract level for these staphylococcal infections, and specifically, identified unique areas which consistently proved to have high infection rates and performed the \u0026lsquo;worst\u0026rsquo; over the nine year period. Group-based trajectory modelling allows us to not only identify areas which perform better, worse, or remain essentially, unchanged, but also identify the spatial relatedness of these areas. There is a continued need to develop strategies aimed at primary and secondary prevention, especially at the community level. Therefore understanding \u0026lsquo;trends\u0026rsquo; of infection patterns, stratified by antibiotic resistance, over geographic areas and time can provide evidence-based information, necessary for community-specific prevention guidelines.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eS. aureus\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eStaphylococcus aureus\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMethicillin Resistant \u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMethicillin Suspeptable \u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCO-MRSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity Onset Methicillin Resistant \u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCO-MSSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity Onset Methicillin Suspeptable \u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSkin and Soft Tissue Infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmergency Department\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHOA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChildren\u0026rsquo;s Healthcare Of Atlanta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMotropolitan Statistical Area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Health Record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGroup Based Trajectory Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cul\u003e\n\u003cli\u003eEthics approval and consent to participate\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study is approved by Children\u0026rsquo;s Healthcare of Atlanta Institutional Review Board (IRB), Morehouse School of Medicine and Emory University. The individual data was from Electronic Health Record, so there was no consent needed to conduct this study.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eConsent for publication\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors listed have consented to the publication of this manuscript.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAvailability of data and materials\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study used datasets from Children\u0026rsquo;s Healthcare of Atlanta- Electronic Health Record, which is governed by HIPPA guidelines. There is no link to access patient level information.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCompeting interests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors declare that they have no competing interests of BMC Public Health.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFunding\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLCI receives funding from the following sources: National Library Medicine, Grant #1G08LM013190-01; K-08 AHRQ-Mentored Clinical Scientist Career Research Development Award- HS024338-01. LCI, RG supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\"\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors' contributions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLilly Immergluck contributed to the hypothesis, study design, data analyses and interpretation and preparation of the manuscript. Ruijin Geng contributed to the data analysis, data interpretation and preparation of the manuscript; Peter Blatrus contributed to the study design, and oversaw the statistical analysis, data interpretation of the manuscript; Choahua Li contributed to the statistical analysis and data interpretation of the manuscript; Junjun Xu contributed to the overall statistical analysis; Mike Edelson contributed the spatial analysis methodology design and GIS mapping. Lance Waller, Traci Leong, and George Rust contributed to the interpretation of data analyses and preparation of the manuscript. All authors reviewed the final manuscript prior to submission. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eChildren's Healthcare of Atlanta - IS\u0026amp;T Department, especially Mr. Tal Senior, Mr. Robert Massey.\u003c/li\u003e\n\u003cli\u003eDirector of Clinical Microbiology Scottish Rite and Egleston Children's Healthcare of Atlanta\u0026mdash;Dr. Robert C. Jerris, and Clinical Microbiology Laboratory\u003c/li\u003e\n\u003cli\u003eMorehouse School of Medicine, Clinical Research Center, Ms. Jacquelyn Ali.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors information\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eMorehouse School of Medcine, Department of Microbiology/Biochemistry/Immunology; Clinical Research Center -Pediatric Clinical \u0026amp; Translational Research Unit, Atlanta, GA, USA.\u003c/li\u003e\n\u003cli\u003eMorehouse School of Medicine, National Center for Primary Care; Department of Community Health \u0026amp; Preventive Medicine, Atlanta, GA USA\u003c/li\u003e\n\u003cli\u003eEmory University, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Atlanta, GA, USA.\u003c/li\u003e\n\u003cli\u003eFlorida State University, Center for Medicine and Public Health, College of Medicine, Tallahassee, FL, USA\u003c/li\u003e\n\u003cli\u003eInterDev, Roswell, GA, USA.\u003c/li\u003e\n\u003cli\u003eNingbo Consulting Inc., Atlanta, GA, USA.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKourtis AP, Hatfield K, Baggs J, Mu Y, See I, Epson E, et al. Vital Signs: Epidemiology and Recent Trends in Methicillin-Resistant and in Methicillin-Susceptible \u003cem\u003eStaphylococcus aureus\u003c/em\u003e bloodstream infections - United States. MMWR Morb Mortal Wkly Rep. 2019;68(9):214-9.\u003c/li\u003e\n\u003cli\u003eCalfee DP. Trends in Community Versus Healthcare-acquired methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections. Curr Infect Dis Rep. 2017;19(12):48.\u003c/li\u003e\n\u003cli\u003eKavanagh KT, Abusalem S, Calderon LE. The incidence of MRSA infections in the United States: is a more comprehensive tracking system needed? Antimicrob Resist Infect Control. 2017;6:34.\u003c/li\u003e\n\u003cli\u003eU.S. Department of Health \u0026amp; Human Services. National Action Plan to Prevent Health Care-Associated Infections: Road Map to Elimination: Office of Disease Prevention and Health Promotion; 2017 [updated Mar. 30. Available from: \u003ca href=\"https://health.gov/hcq/prevent-hai-action-plan.asp\"\u003ehttps://health.gov/hcq/prevent-hai-action-plan.asp\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eKlevens RM, Morrison MA, Nadle J, Petit S, Gershman K, Ray S, et al. Invasive methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections in the United States. JAMA. 2007;298(15):1763-71.\u003c/li\u003e\n\u003cli\u003eSuaya JA, Mera RM, Cassidy A, O'Hara P, Amrine-Madsen H, Burstin S, et al. Incidence and cost of hospitalizations associated with \u003cem\u003eStaphylococcus aureus\u003c/em\u003e skin and soft tissue infections in the United States from 2001 through 2009. BMC Infect Dis. 2014;14:296.\u003c/li\u003e\n\u003cli\u003eKnox J, Uhlemann AC, Lowy FD. \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections: transmission within households and the community. Trends Microbiol. 2015;23(7):437-44.\u003c/li\u003e\n\u003cli\u003eTong SY, Davis JS, Eichenberger E, Holland TL, Fowler VG, Jr. \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections: epidemiology, pathophysiology, clinical manifestations, and management. Clin Microbiol Rev. 2015;28(3):603-61.\u003c/li\u003e\n\u003cli\u003eBaker P, Cohen B, Liu J, Larson E. Incidence and risk factors for community-associated methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in New York City, 2006-2012. Epidemiol Infect. 2016;144(5):1014-7.\u003c/li\u003e\n\u003cli\u003eSattler CA, Mason EO, Jr., Kaplan SL. Prospective comparison of risk factors and demographic and clinical characteristics of community-acquired, methicillin-resistant versus methicillin-susceptible \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infection in children. Pediatr Infect Dis J. 2002;21(10):910-7.\u003c/li\u003e\n\u003cli\u003eTirabassi MV, Wadie G, Moriarty KP, Garb J, Konefal SH, Courtney RA, et al. Geographic information system localization of community-acquired MRSA soft tissue abscesses. J Pediatr Surg. 2005;40(6):962-5.\u003c/li\u003e\n\u003cli\u003eImmergluck LC, Leong T, Malhotra K, Parker TC, Ali F, Jerris RC, et al. Geographic surveillance of community associated MRSA infections in children using electronic health record data. BMC Infect Dis. 2019;19(1):170.\u003c/li\u003e\n\u003cli\u003eVaughan AS, Kramer MR, Waller LA, Schieb LJ, Greer S, Casper M. Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010. Ann Epidemiol. 2015;25(5):329-35 e3.\u003c/li\u003e\n\u003cli\u003eBen-Assuli O, Padman R, Bowman M, Leshno M, Shabtai I. On analyzing readmissions using a trajectory model: evidence from Israel. Studies Health Tech \u0026amp; Informat. 2015;216:1063.\u003c/li\u003e\n\u003cli\u003eHybels CF, Bennett JM, Landerman LR, Liang J, Plassman BL, Wu B. Trajectories of depressive symptoms and oral health outcomes in a community sample of older adults. Int J Geriatr Psychiatry. 2016;31(1):83-91.\u003c/li\u003e\n\u003cli\u003eBaltrus P, Malhotra K, Rust G, Levine R, Li C, Gaglioti AH. Identifying county-level all-cause mortality rate trajectories and their spatial distribution across the United States. Prev Chronic Dis. 2019;16:E55.\u003c/li\u003e\n\u003cli\u003eICD9Data.com. 2012 ICD-9-CM Diagnosis Code 041.10, Staphylococcus infection in conditions classified elsewhere and of unspecified site, \u003cem\u003eStaphylococcus\u003c/em\u003e, unspecified 2012 [Available from: \u003ca href=\"http://www.icd9data.com/2012/Volume1/001-139/030-041/041/041.10.htm\"\u003ehttp://www.icd9data.com/2012/Volume1/001-139/030-041/041/041.10.htm\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eMorrison MA, Hageman JC, Klevens RM. Case definition for community-associated methicillin-resistant \u003cem\u003eStaphylococcus aureu\u003c/em\u003es. J Hosp Infect. 2006;62(2):241.\u003c/li\u003e\n\u003cli\u003eDavid MZ, Daum RS. Community-associated methicillin-resistant\u003cem\u003e Staphylococcus aureus\u003c/em\u003e: epidemiology and clinical consequences of an emerging epidemic. Clin Microbiol Rev. 2010;23(3):616-87.\u003c/li\u003e\n\u003cli\u003eAli F, Immergluck LC, Leong T, Waller L, Malhotra K, Jerris RJ, Parker TC, Edelson M, and Rust GS. A spatial analyses of health disparities associated with antibiotic resistant infections in children living in Atlanta (2002-2010). Healthcare J Deliv Sci Innov 2019;7(1):1-14.\u003c/li\u003e\n\u003cli\u003eUS Bureau of Labor Statistics. Databases, Tables \u0026amp; Calculators by Subject-- CPI Inflation Calculator: United States Department of Labor; [Available from: \u003ca href=\"https://www.bls.gov/data/inflation_calculator.htm\"\u003ehttps://www.bls.gov/data/inflation_calculator.htm\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eBraveman P, Gottlieb L. The social determinants of health: it's time to consider the causes of the causes. Public Health Rep. 2014;129 Suppl 2:19-31.\u003c/li\u003e\n\u003cli\u003eLogan J, Stults B, Xu Z. Census geography: Bridging data for census tracts across time [Available from: \u003ca href=\"https://s4.ad.brown.edu/projects/diversity/Researcher/Bridging.htm\"\u003ehttps://s4.ad.brown.edu/projects/diversity/Researcher/Bridging.htm\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eGetis A. A Geographic Approach to Identifying Disease Clusters. WorldMinds: Geographical Perspectives on 100 Problems : Commemorating the 100th anniversary of the Association of American Geographers 1904\u0026ndash;2004 2004:5.\u003c/li\u003e\n\u003cli\u003eZhou Y, Hallisey EJ, Freymann GR. Identifying perinatal risk factors for infant maltreatment: an ecological approach. Int J Health Geogr. 2006;5:53.\u003c/li\u003e\n\u003cli\u003eKim MW, Greenfield BK, Snyder RE, Steinmaus CM, Riley LW. The association between community-associated \u003cem\u003eStaphylococcus aureus\u003c/em\u003e colonization and disease: a meta-analysis. BMC Infect Dis. 2018;18(1):86.\u003c/li\u003e\n\u003cli\u003eSee I, Wesson P, Gualandi N, Dumyati G, Harrison LH, Lesher L, et al. Socioeconomic factors explain racial disparities in invasive community-associated methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e disease rates. Clin Infect Dis. 2017;64(5):597-604.\u003c/li\u003e\n\u003cli\u003eThe Hispanic paradox. Lancet. 2015;385(9981):1918.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Community onset methicillin-resistant Staphylococcus aureus, community onset methicillin susceptible Staphylococcus aureus, group-based trajectory modeling, hotspot mapping","lastPublishedDoi":"10.21203/rs.3.rs-59691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-59691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003eBackground\u003c/u\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e (\u003cem\u003eS. aureus\u003c/em\u003e) remains a serious cause of infections in the U.S. and worldwide.\u0026nbsp;Non antibiotic resistant \u003cem\u003eStaphylococcus aureus \u003c/em\u003e(methicillin susceptible or MSSA) is the cause of half of all health care–associated \u003cem\u003estaphylococcal \u003c/em\u003einfections, and methicillin resistant \u003cem\u003eStaphylococcus aureus \u003c/em\u003e(MRSA) still is the leading cause of community onset skin and soft tissue infections in the U.S. This is the first study to spatially look at trends of both community onset MRSA and MSSA infections over nine years and determine ‘best’ to ‘worst’ infection trends over a nine year period (2002-2010),which spanned when community onset MRSA infections were occurring in epidemic proportions across the U.S. \u003c/p\u003e\u003cp\u003e\u003cu\u003eMethods\u003c/u\u003e\u003c/p\u003e\u003cp\u003eRetrospective study from 2002-2010, using electronic health records of children living in the southeastern U.S. (Atlanta, Georgia) with \u003cem\u003eS. aureus \u003c/em\u003einfections and relevant U.S. census data (at the census tract level). The Proc Traj for SAS was applied to generate community onset MRSA and MSSA trajectory infection groups (low, high, very high, or deviant trends), and then, mapping of these trajectory groups using census tract boundaries.\u003c/p\u003e\u003cp\u003e\u003cu\u003eResults\u003c/u\u003e\u003c/p\u003e\u003cp\u003eFrom community onset MRSA infection trend patterns (low, high, very high), only 0.8% of the census tracts showed a dramatic increase from 2002-2007 and then a gradual decline from 2008 to 2010. From community onset MSSA infection trend patterns (low and high), 85.7% of ‘high infection’ group persisted throughout the nine year period, compared to 14.3% of ‘low infection’ group over this same period. Low community onset MRSA and MSSA trend patterns were seen throughout the 20 counties of Atlanta, Georgia’s metropolitan statistical area, but more often seen in those counties less densley populated. Census tracts reflecting Atlanta’s ‘innercity’ had the highest proportion of the worst infection trend pattern (community onset MRSA-Very High-CO-MSSA-High or community onset MRSA-High-CO-MSSA-High). The deviant trend of community onset MRSA Very High- CO-MSSA Low infection were in census tracts east of downtown Atlanta. \u003c/p\u003e\u003cp\u003e\u003cu\u003eConclusions\u003c/u\u003e \u003c/p\u003e\u003cp\u003e‘Trends’ of \u003cem\u003eS. aureus\u003c/em\u003e infection patterns, stratified by antibiotic resistance, over geographic areas and time identify communities with higher risks for community onset MRSA infection compared to community onset \u0026nbsp;MSSA infection.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Space Time Trends of Community Onset Staphylococcus Aureus Infections in Children Living in Southeastern United States: 2002-2010","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-08 14:51:16","doi":"10.21203/rs.3.rs-59691/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89cd255c-0012-499f-b081-3ab96b4ebe2b","owner":[],"postedDate":"September 8th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":455261,"name":"Health Economics \u0026 Outcomes Research"},{"id":455262,"name":"Health Policy"}],"tags":[],"updatedAt":"2020-10-30T19:49:47+00:00","versionOfRecord":[],"versionCreatedAt":"2020-09-08 14:51:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-59691","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-59691","identity":"rs-59691","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}