Massachusetts syndromic surveillance data, nowcast and moving epidemic methods

Learn how we use the Massachusetts syndromic surveillance data for nowcasting and moving epidemic thresholds as applied in the viral respiratory illness dashboards.

Background on syndromic surveillance data

• The data source for this analysis is emergency department (ED) visits from the National Syndromic Surveillance Program (NSSP) ESSENCE platform.
  • NSSP is a collaboration among CDC, federal partners, local and state health departments, and academic and private sector partners to collect, analyze, and share electronic patient encounter data received from multiple health care settings. For more information on NSSP, visit NSSP on cdc.gov.
• Currently, 100% of emergency departments in the Commonwealth are sending data to ESSENCE, allowing for a complete picture of ED visits.
• When a patient is admitted, discharged, or transferred, the hospital’s electronic medical record system triggers real-time HL7 messages, which travel through Mass HIWay to the National Syndromic Surveillance Program.
• Data can then be accessed and analyzed in ESSENCE. These records contain information about the visit, patient, and reason for the visit, including diagnosis codes, but do not include the patient’s name nor the patient’s home address. There is very limited identifiable information about the patient included.
• While ESSENCE receives updates to health records instantaneously, delays in test results, hospital coding, etc. result in often-substantial delays between the visit and the notification of the patient’s diagnosis. This is why we advise caution when interpreting data for the most recent three weeks.  

Nowcasting method 

• To account for delays in recent data, “nowcasting” methods are utilized to give a better and more timely picture of current trends than that based upon initial, not yet fully complete data. This allows us to spot changes in rates of respiratory illnesses in a more timely fashion.  
• Nowcasting methods utilize patterns in reporting delays associated with respiratory visits to estimate a more complete picture of the current data. These methods have been found to reliably estimate the final total counts of respiratory visits from their incomplete (still updating) counts.  

Data 

• Statewide respiratory ED visits from December 29th, 2024 to present are retrieved using the CDC Broad Acute Respiratory DD v1, CDC COVID-Specific DD v, CDC Influenza DD v1, and CDC Respiratory Syncytial Virus DD v1 queries. For more information, visit the NSSP CoP Knowledge Repository Syndrome Library.  
• This recent historic data is assumed to follow similar patterns of reporting and delay as current data, so 6–12 months of data from this time frame are used to train the nowcasting method for the current season. The data used to make these estimates are refreshed regularly to improve the accuracy of the nowcast estimate.

Methods

• Using recent historic data, we compute an estimate of the delay distribution–the distribution of times between when an ED visit was first registered (called the “reference date”) to when the respiratory illness information was considered complete (called the “diagnosis date”). At a high-level, this distribution tells us what proportion of diagnoses arrived within a certain number of days (for example, 30% of respiratory illness diagnoses are complete 1 day after the reference date). 
• The delay distribution is then used to estimate the expected number of respiratory illness diagnoses that will arrive on each of the recent reference dates for which information is incomplete. This is estimated by dividing the current total counts by the proportion estimated to be received by this time (for example if there are 3 respiratory illnesses observed so far and you’ve observed only the same day diagnoses, which are estimated to represent 30% of eventual respiratory illnesses, we expect 3/0.3 =  10 final respiratory illnesses). This provides a point estimate of the final number of respiratory illnesses on each reference date, obtained by summing the estimates for the not yet observed delays and the partial information for each reference date. 
• Prediction intervals for this estimate are calculated by generating many possible outcomes of how different past estimates were from the true final observed counts once all the diagnoses had come in. We report the central 95% prediction interval, which means we expect about 95% of observations to fall within this interval.  
• Find more information about the tool and methods used to generate the nowcast estimates.

Background on the Moving Epidemic Method (MEM) 

• The moving epidemic method is a set of steps for categorizing disease rates into activity levels, based on what was observed in past seasons.
• The method was first introduced by Vega et al. in 2004, developed to categorize influenza activity. A modified version of the MEM was adopted by the CDC in 2015 in order to track seasonal influenza. In recent years, several public health departments have developed versions of the MEM for additional illness categories, such as COVID-19, RSV, and respiratory illness overall.
• The MEM consists of 2 sets of calculations. Each calculation uses data from the 6 most recent waves (for non-COVID-19 syndromes, the 2020-2021 respiratory season is excluded, as no significant respiratory wave was observed):
  • Baseline calculation: Determines the level of activity at which an epidemic (such as the seasonal influenza or a COVID-19 wave) has begun. Once respiratory activity has reached/exceeded this level, the seasonal wave has begun. Respiratory activity below this level is considered “Very Low”.
    • For non-COVID-19 syndromes, the very low incidence periods, the times during which respiratory activity is outside of a peak and remains very low, come regularly each year and are defined as weeks 20-39 of the year. For COVID-19, the very low incidence periods were classified using a wave-identification algorithm, as described by Vega et al.
    • This calculation uses the 5 highest weekly rates from each very low incidence period of the 6 most recent waves. (n=30)
    • Baseline = the upper limit of the one tailed 95% CI of the arithmetic mean of those rates.
  • Activity levels calculation: Categorizes levels as either below baseline or within the epidemic (once activity has passed the baseline). The MA DPH Respiratory Illness Dashboard divides activity into Very Low (below Baseline), Low, Moderate, High, and Very High.
    • For non-COVID-19 syndromes, the respiratory wave was defined as weeks 40-19 of the year. For COVID-19, waves were classified using a wave-identification algorithm, as described by Vega et al.
    • This calculation uses the 5 highest weekly rates from each of the 6 most recent waves. (n=30)
    • The thresholds are calculated based on the upper bounds of the one-sided confidence intervals of the geometric mean of those rates at:
      • Very Low: Less than the Baseline calculation
      • Low: Greater than or equal to the Baseline calculation and less than 50%
      • Moderate: Greater than or equal to 50% and less than 90%
      • High: Greater than or equal to 90% and less than 97.5%
      • Very High: Greater than or equal to 97.5%