Information Brief: Surveillance of Vector-borne Disease in the U.S. Military Asha Riegodedios Staff Epidemiologist Navy and Marine Corps Public Health Center x3048 The views expressed in this presentation are those of the author and do not necessarily reflect the official policy or position of the Department of the Navy, Department of Defense, or the U. S. Government.

Objectives Why do we do surveillance How do we do surveillance Malaria burden in the DOD/DON Lyme Disease burden in the DOD/DON Conclusion

Surveillance – Why do we do it For situational awareness locally and up the chain To inform decisions To support changes in intervention, treatment, control, and policy To enlist assistance as needed beyond already existing published resources SOFA agreements

Surveillance – What do we do Exposure versus Outcome –Vector-borne diseases Reportable Medical Event Surveillance –Man Med, BUMED INST, Triservice guidelines –Applies to all Navy and Marine ships, stations and units providing outpatient or inpatient medical care –Required reporting of 70+ diseases, injuries, outbreaks among active duty and family members –Urgent events, report within 24 hours –Information collected: prevention measures, severity of disease, exposure, epi-link

Reportable Medical Events Dengue Tick-borne encephalitis Filariasis Tick-borne hemorrhagic fever Mosquito-borne fever Leishmaniasis Lyme disease Malaria Plague RMSF Trypanosomiasis Tularemia Typhus Yellow Fever

Supplemental Data Sources Clinical lab results –Daily collection of lab results recorded in CHCS Clinical pharmacy transactions –Daily collection of meds dispensed in military MTFs Clinical encounters –Weekly collection of outpatient visits and diagnoses –Monthly collection of inpatient discharge data

Malaria Seeing a shift in disease burden over time

Malaria Trends - DOD Cases, US Military AD and RC Army (76%), Navy (12%), MC (6%), AF (6%) Highest case counts = age Species –21.7% falciparum; 31.3% vivax Of Cases with available exposure info (n=??) –90% likely acquired in Afghanistan Seasonal trends – July peak of cases Cases, US Military AD and RC Army (53%), Navy (26%), MC (12%), AF (8%) Highest case counts = age Species –41.7% falciparum; 18.3% vivax Of cases with available exposure information (n=48) –35.0% likely acquired in Afghanistan –41.7% likely acquired in Africa Seasonal trends - July peak of cases Source: AFHSC MSMR, publication pending, January 2010

Figure. Malaria Cases Among US Service Members, by plasmodium species, by calendar year of diagnosis/report,

Figure. Diagnoses and Reported Cases of Malaria, by Location of Acquisition of Infection, by Cumulative Month of Clinical Presentation/Diagnosis, US Armed Forces, January 2002-December 2009

Malaria Trends - Afghanistan Exposure in Afghanistan continues to occur 336 cases, US Military, Highest case counts among age group Majority of cases were Army (90.5%) Breakout by occupational specialty

Malaria Trends - Afghanistan Source: Data provided by AFHSC, September 2009

Malaria Trends - Afghanistan Table. Occupational Specialty Distribution of Malaria Cases Acquired in Afghanistan, Source: Data provided by AFHSC, September 2009

Malaria Trends – Navy/MC 73 cases, Active Duty, –7 to 19 cases per year –Rate: 1-6 cases per 100,000 persons per year Marine Corps showed higher rates than Navy Navy showed higher rates than Marine Corps 2008 & 2009 Rates were highest among persons age 40 and older Species –30% unknown –47% falciparum; 20% vivax; 4% ovale Of cases with available exposure information (n=30) –53% were duty related –63% were likely acquired in Africa Seasonal trends not readily apparent

Malaria Trends – Navy/MC Source: NMCPHC EpiData Center, February 2010

Malaria Trends – Navy/MC Source: NMCPHC EpiData Center, February 2010

Lyme Disease Difficult disease to monitor due to non- specific clinical presentation and laboratory testing Estimated burden of disease is not easily attainable Trends over time are the focus of our surveillance analyses

Lyme Disease Trends - DOD US military AD, 2001 to 2008 –694 confirmed cases –1803 possible cases > 120 locations No clear trend over time Seasonal trend, majority of cases in May-Aug Most cases occurring in endemic areas, East Coast US and Germany in bases supporting basic and advanced training environments Majority of cases coming from Navy and Army locations Germany confirming a high percentage of suspect cases while VA and NC bases are confirming a low percentage –Reflection of access to confirmatory testing capabilities? –!!Underestimation of true burden of disease!! Source: AFHSC MSMR, Vol. 16/No. 7, p.2

Figure. Numbers and rates of “confirmed” and “possible” cases of Lyme disease, active component, US Armed Forces,

Source: AFHSC MSMR, Vol. 16/No. 7, p.4 Figure. Confirmed cases* of Lyme disease, by month, active and Reserve components,

Source: AFHSC MSMR, Vol. 16/No. 7, p.4 Table. Lyme disease by location of diagnosis, active and Reserve components, US Armed Forces, *Cases diagnosed in multiple states.

Source: AFHSC MSMR, Vol. 16/No. 7, p.4 Table. Lyme disease by location of diagnosis, active and Reserve components, US Armed Forces,

Lyme Disease Trends – Navy/MC 2335 cases, DON ben, 2004-Oct 2009 Bencat –35% Active Duty –2% Recruit/Cadet –63% are family, retirees, and others Seasonal trend, peak in Jun-Jul San Diego CA among the top three facilities diagnosing lyme dz, yet biggest burden of dz is seen in East US

Source: NMCPHC EpiData Center, January 2010

Table. Lyme Disease by Location of Diagnosis and Bencat, DON beneficiaries, Source: NMCPHC EpiData Center, January 2010

Conclusions Surveillance data coupled with existing data sources provide good idea of burden of disease Can and is being used to inform leadership decisions Malaria burden in the DOD is shifting –Decrease in Korea acquired cases while Afghanistan acquired cases continue to occur, increase in Africa acquired cases –While malaria in MC appears to be controlled, Navy malaria burden is increasing, likely attributed to Africa exposure Lyme Disease –Clinical course and lack of lab testing present a challenge in describing burden –Trends using all existing datasources don’t show unexpected trends

Acknowledgements LCDR Brian Prendergast Mrs. Gosia Nowak Ms. Ashleigh McCabe Ms. Jessica Sharkey Ms. Rosa Ergas Ms. Laura Horne Maj Chris Perdue CDR Annette Von Thun NMCPHC Epi Data Center staff AFHSC staff