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How Important was the Jail in CA-MRSA Spread? Diane S. Lauderdale University of Chicago Backcasting MRSA in Chicago 1 Supported by the NIH - National Institute.

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Presentation on theme: "How Important was the Jail in CA-MRSA Spread? Diane S. Lauderdale University of Chicago Backcasting MRSA in Chicago 1 Supported by the NIH - National Institute."— Presentation transcript:

1 How Important was the Jail in CA-MRSA Spread? Diane S. Lauderdale University of Chicago Backcasting MRSA in Chicago 1 Supported by the NIH - National Institute of General Medical Sciences (MIDAS Network) U01GM087729.

2 MRSA History Methicillin-resistant Staphylococcus aureus 1960-1990s: Exclusively healthcare pathogen Late 1990s: Reports of novel strains spreading in the community (CA-MRSA) 2001: Strain USA300 identified; predominant strain by 2005; primarily SSTIs 2001 & later: Jail health services among first to report serious CA-MRSA problem 2

3 Reports of CA-MRSA In Urban Jails Before 2000, no reports of MRSA in jails LA County Jail – 1700 MRSA SSTIs over 18 months in 2001-2002 – 8,448 cases were reported between 2002 and 2005 SF County Jail system – SSTIs 74% MRSA in 2002 Cook County Jail (Chicago) – SSTIs 63.5% MRSA in 2004-5 85% of staph SSTIs were MRSA MMWR Morb. Mortal. Wkly. Rep. 2003 52:88. Pan et al. 2003. Clin. Infect. Dis. 37:1384–1388. David et al. 2008. J. Infect. Dis. 197:1235–1243. 3

4 Colonization Colonization reference data for national population from NHANES – 0.8% nasal colonization 2001-2002 – 1.5% nasal colonization 2003-2004 Hospitalized Baltimore City Jail detainees – 13% colonized in 2003-4 MMWR Morb. Mortal. Wkly. Rep. 2001 50:919–922. Wright et al. 2007. Control Hosp. Epidemiol. 28:877–879. 4

5 Plausibility of High Rates of Jail Transmission Poor hygiene – Shower reluctance – Inadequate laundry – Alcohol-based handrubs not allowed Crowding – Reported detainees lancing boils of others Mixing populations Possibly poor access to healthcare 5

6 Temporal Trend CA-MRSA Dukic, Lauderdale, Wilder, Daum, David (2013) PLoS ONE 8(1): e52722. 6

7 Causal? Did jail transmission fuel the rapid rise citywide? Alternate explanations: 1.Jail might cause INFECTIONS, but not colonizations If individuals enter jail colonized, being arrested & detained could cause minor traumas that become infected 2.High rates of colonization at the time of arrest: risk factors for MRSA are the same as risk factors for incarceration Cook County Hospital & Clinics patient population 2000-2005 risk factors: Recent jail incarceration (self or family) BUT ALSO -- African American race and public housing residence Hota et al. 2007. Arch. Intern. Med. 167: 1026–1033. 7

8 High MRSA Rates among Groups at High Risk of Incarceration San Francisco 1999 – 6.1% nasal colonization among IVDUs San Francisco 2002 – 6.2% colonization among homeless and runaway youths (12 to 24 years old) Cleveland, OH 2009 – 25.6% nasal colonization in 215 homeless men and women Pan et al. 2005 J. Infect. Dis. 192:811–818. Landers et al. 2009. Infect. Control Hosp. Epidemiol. 30:801–803. Kuehnert et al. 2006. J. Infect. Dis. 193:172–179. Charlebois et al. 2004. Clin. Infect. Dis. 39:47–54. 8

9 Length of Stay& Colonization Baltimore City Jail 2006 – 15.8% of NEW detainees were colonized Dallas County Jail 2009 – No association with length of incarceration – Non-significant trend of LESS colonization with longer detention Farley et al. 2008. Am. J. Infect. Control 36:644–650. Daum et al. unpublished data 9

10 Study Question 10 To what extent did jail transmission fuel the rapid rise in CA-MRSA citywide from 2001 to 2006 Cannot be directly tested Use a simulation

11 Approach Construct ABM of Chicago – empirically derived features relevant for CA-MRSA transmission Backcast CA-MRSA in Chicago from 2001-2010 Experimentally vary jail transmission rates and compare Chicago-wide “epidemic curve” with different levels of jail transmission, including – No jail transmission 11

12 ABM Computational simulation Agents – Characteristics, e.g., sociodemographics – Behaviors, e.g., propensity to seek healthcare – States, e.g., no MRSA, MRSA-colonized, MRSA- infected Places Agents have rules that determine their behavior and states in reaction to what happens to them and around them 12

13 ABM of Chicago People – Synthetic population based on the 2000 census (created for MIDAS) – Agents are in households and have age, sex, race/ethnicity, education, HH income Places – Geographic coordinates for households, workplaces, schools Hourly Activities – Extracted from nationally representative surveys with 24 hour time diaries BLS American Time Use Survey & Panel Study of Income Dynamics Activities & locations assigned risk of direct physical contact (high, moderate, negligible) – Activity schedules probabilistically linked to agents by sociodemographics 13

14 Enhanced with Activities not in the Time Diaries Risk of hospitalization – Based on demographic analysis of hospital days in the 2010 NHIS – For each year, # hospital days randomly drawn from demographically matched individuals for each agent Risk of being detained in the Cook County Jail 14

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16 Risk of Incarceration in the Jail Each agent assigned probability of incarceration based on demographics & zip code – Annual probabilities range from 0 to.25 – Probabilities calibrated to sum to ~75,000 individuals per year incarcerated Demographic determinants – 2006 National Jail Survey and 2008 Arrestee Drug Abuse Monitoring (ADAM) program: age, race, sex Zip code – Cook County jail data on zip code distribution of released detainees after conviction 16

17 17 Agent Disease State Chart An agents risk of transitioning from U to C depends on PAR & TIP. An agents risk of transitioning from C to I depends on AIP.

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19 Simulation Plan ABM implemented in Repast at ANL Open source platform runs on a range of computing hardware Test models run on desktop 4 sets of 32 runs to explore the space of stochastic variability – 10-year runs (1 hour ticks) – run in parallel on the Fusion Cluster, 320 node computing cluster at Argonne National Laboratory 19

20 Temporal Trend CA-MRSA Dukic VM, Lauderdale DS, Wilder J, Daum RS, David MZ (2013) PLoS ONE 8(1): e52722. 20

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24 Study Design Scenarios Moderate Transmission (like a school) – Consistent with recent studies of jail colonization High Transmission (like a household) Very High Transmission (2 x household) NO transmission – The hypothetical epidemic curve with no jail transmission 24

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27 Summary & Next Steps Reasonably good fit for backcasting – Further testing of sensitivity to transmission parameters The model strongly suggests that there would have been a CA-MRSA problem in Chicago even with NO jail transmission. Further uses of platform Test interventions Affect of heterogeneity in risk of colonization Adapt to other diseases with person-to-person transmission 27

28 MIDAS University of Chicago-Argonne Modeling MRSA TEAM Diane S. Lauderdale, PhD Charles Macal, PhD Robert Daum, MD Michael Z. David, MD, PhD Vanja Dukic, PhDJames Evans, PhD Michael J. North MBA, PhD Phil Schumm, MA Duane T. Wegener, PhD Jocelyn R. Wilder, MS MPH Roberta Davidson, M.S. 28

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30 Incarceration Results (Model Test Run) 30

31 Previous Jail Modeling Examined 1 st 9 months of LACJ outbreak Determined R0 for infection in jail for CA- MRSA < 1 Concluded: “The modeling also revealed that the outbreak was only sustained because of the continuous inflow of colonized and infected individuals from the community, and not by within-jail transmission.” Kajita et al. 2007. Nature Reviews Microbiology 5, 700-709 31

32 Estimated Increase in Total Chicago Cases/100,000 From 2001-2006 in Each Scenario 32 2001 cases/100,0002006 cases/100,000Difference No Jail Transmission103462+ 359/100,000 Moderate106491+ 385/100,000 High107493+ 386/100,000 Super High114546+ 432/100,000

33 Limitations Next Steps Explore space of transmission parameters consistent with empirical data to determine the sensitivity of the results and determine whether there is a better fit to meta-curve data Introduce heterogeneity among agents in risk of persistent colonization risk – Clustered in households to reflect range of likely genetic contribution 33

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