IHP 330 Module 5 Surveillance Milestone example

Counting a Fungus: A Surveillance Milestone on Candida auris in the United States, 2019 to 2022

[Student Name]

Southern New Hampshire University

IHP 330: Principles of Epidemiology

Module Five Final Project Milestone

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Counting a Fungus: A Surveillance Milestone on Candida auris in the United States, 2019 to 2022

The Health Problem

Candida auris is a yeast first reported in the United States in 2016. It can cause bloodstream, wound and ear infections, mainly in people with serious underlying illness, long hospital stays, invasive devices such as central lines and ventilators, or residence in long-term acute care and ventilator-capable skilled nursing facilities. It is often resistant to one or more classes of antifungal drugs, survives on surfaces for weeks and spreads from patient to patient on hands and equipment. Many people are colonized, carrying the organism on their skin without infection, and can transmit it without anyone knowing. A composite regional hospital system with four hospitals and two affiliated long-term care facilities has not yet identified a case, and its infection prevention team wants to know how quickly the organism is spreading nationally and what that implies for its own preparedness.

How Cases Are Reported

Surveillance for Candida auris combines passive and active elements. Clinical laboratories that identify the organism in a specimen collected to diagnose or treat illness report it to the state or local health department, and health departments report cases to the Centers for Disease Control and Prevention (CDC). These are counted as clinical cases. When a case is found, health departments often screen other patients on the same unit or facility with skin swabs, and positive swabs from people without infection are counted as screening cases. Surveillance of this kind depends on laboratories being able to identify the organism correctly, which older identification methods sometimes could not do (CDC, 2012; Lyman et al., 2023).

What the Reported Data Show

The table below shows reported clinical cases by year and the percentage change from the previous year.

Table 1

Reported Clinical Cases of Candida auris in the United States

Note. Counts for 2019 to 2021 from Lyman et al. (2023); 2022 count from CDC tracking data. Percentage change calculated as (current year minus previous year) divided by previous year.

Interpreting the Trend

Clinical cases roughly quintupled between 2019 and 2022 (CDC, 2024), and national surveillance through 2021 counted 3,270 clinical cases and 7,413 screening cases since the first reports. From 2019 to 2021, 17 states identified their first case, and the number of cases resistant to echinocandins, the first-line antifungal class, in 2021 was about three times that of each of the two previous years (Lyman et al., 2023). Some of the rise reflects more testing and better laboratory identification, but the growth in clinical cases, which are found through routine care rather than targeted screening, points to real spread. The authors linked the 2021 acceleration partly to pressures on infection control during the COVID-19 pandemic, including staffing shortages and changes in personal protective equipment use.

Limitations of the Surveillance Data

Several limitations affect how the data should be read. Screening cases depend on how much screening each state does, which varies with resources, so the true number of colonized people is underestimated and comparisons between states are unreliable. Counts are not rates: without the number of hospitalized or high-risk patients as a denominator, states with larger health care systems will naturally report more cases. Laboratory capacity to identify the organism has improved over time, which inflates early trends. Finally, national counts hide local concentration; much of the burden has been in a limited number of states and in long-term acute care and ventilator-capable facilities.

Evaluating the Surveillance System

Guidelines for evaluating public health surveillance systems ask about attributes such as sensitivity, timeliness, data quality, representativeness and acceptability (German et al., 2001). Applied to Candida auris surveillance, the system's sensitivity for clinical infections is probably moderate and improving as laboratories adopt accurate identification methods, but its sensitivity for colonization is low and uneven because it depends on screening. Timeliness is a strength in states with rapid laboratory reporting and containment responses, where screening of contacts can begin within days of a first case. Representativeness is limited, since the data overrepresent states and facility types that screen heavily. Acceptability to facilities may be strained, because a reported case triggers screening, precautions and public attention that facilities may perceive as costly. Naming these attributes makes clear that the reported numbers are shaped by the system that produces them, which is the central lesson of this milestone for the final project.

Using the Data to Plan

For the regional hospital system, the key message is that absence of reported cases is not proof of absence. The organism spreads through transfers between facilities, especially from long-term acute care and ventilator units, and colonized patients are only found if someone looks. The trend, the spread to new states and rising antifungal resistance all argue for preparation before a first case appears. The team should also ask the state health department for regional data, since national counts cannot show whether facilities that regularly send patients to the system have reported cases, and that local picture determines the system's real risk.

Recommendations

The hospital system should take four steps. First, confirm that its laboratories can accurately identify Candida auris and that they report every identification to the health department promptly. Second, adopt admission screening for patients transferred from long-term acute care hospitals, ventilator-capable nursing facilities and facilities in areas with known transmission. Third, review environmental cleaning products to ensure they are effective against the organism, since some common disinfectants are not. Fourth, add Candida auris status to interfacility transfer forms so receiving facilities know when to use contact precautions. Progress should be measured by the number of screens performed, time from identification to reporting, and any cases detected.

Conclusion

National surveillance shows Candida auris clinical cases rising roughly fivefold from 2019 to 2022, spreading to new states and becoming more drug resistant. Understanding how the surveillance system finds cases shows that part of the trend reflects better detection, but clinical cases point to real spread. For a hospital system that has not yet seen a case, the data support building detection and prevention now rather than waiting for the first positive culture.

References

Centers for Disease Control and Prevention. (2012). Principles of epidemiology in public health practice: An introduction to applied epidemiology and biostatistics (3rd ed.). U.S. Department of Health and Human Services.

Centers for Disease Control and Prevention. (2024). Tracking C. auris. https://www.cdc.gov/candida-auris/tracking-c-auris/index.html

German, R. R., Lee, L. M., Horan, J. M., Milstein, R. L., Pertowski, C. A., & Waller, M. N. (2001). Updated guidelines for evaluating public health surveillance systems: Recommendations from the Guidelines Working Group. MMWR Recommendations and Reports, 50(RR-13), 1-35.

Lyman, M., Forsberg, K., Sexton, D. J., Chow, N. A., Lockhart, S. R., Jackson, B. R., & Chiller, T. (2023). Worsening spread of Candida auris in the United States, 2019 to 2021. Annals of Internal Medicine, 176(4), 489-495. https://doi.org/10.7326/M22-3469