The ongoing monitoring of mortality is crucial to detect and estimate the magnitude of deaths during epidemics, emergence of new diseases (for example, seasonal or pandemic influenza, AIDS, SARS), and the impact of extreme environmental conditions on a population such as heat waves or other relevant public health events or threats [1
]. The surveillance of vital statistics is not a novel idea; mortality surveillance has played an integral part in public health since the London Bills of Mortality were devised in the seventeenth century [3
]. The Bills served as an early warning tool against bubonic plague by monitoring deaths from the 1635 to the 1830s. Today, mortality surveillance continues to be a critical activity for public health agencies throughout the world [4
Pneumonia and influenza are serious public health threats and are a cause of substantial morbidity and mortality worldwide; for instance, the World Health Organization (WHO) estimates seasonal influenza causes between 250,000 to 500,000 deaths worldwide each year [8
] while pneumonia kills more than 4 million people worldwide every year [9
]. Worldwide, the morbidity and mortality of influenza and pneumonia have a considerable economic impact in the form of hospital and other health care costs. Each year in the United States approximately 3 million persons acquire pneumonia and, depending on the severity of the influenza season, 15 to 61 million people in the US contract influenza [9
]. These numbers contribute to approximately 1.3 million hospitalizations, of which 1.1 million are pneumonia cases [10
] and the remainder for influenza [11
]. Moreover, pneumonia cases and influenza together cost the American economy 40.2 billion dollars in 2005 [12
]. In The Netherlands it has been estimated that influenza accounts for 3713 and 744
days of hospitalization per 100,000 high-risk and low-risk elderly, respectively [13
]. Due to the public health burden and the unpredictability of an influenza season, strong pneumonia and influenza surveillance systems are a priority for health authorities.
Mortality monitoring is an important tool for the surveillance of pneumonia and influenza which can aid in the rapid detection and estimates of excess deaths and inform and evaluate the effect of vaccination and control programs. Traditionally, influenza mortality surveillance often uses the category of “pneumonia and influenza” (P-I) on death certificates as an indicator of the severity of an influenza season or to identify trends within a season; however, only a small proportion of these deaths are influenza related. It has been reported that only 8.5–9.8% of all pneumonia and influenza deaths are influenza related [14
]. The non-influenza-related pneumonia deaths tend to be stable from year to year and fluctuations in this category are largely driven by the prevalence and severity of seasonal influenza. As a result, the P-I category is an important sentinel indicator.
In the US, death certificates are the primary data source for mortality surveillance whose findings are widely used to exemplify epidemics and measure the severity of influenza seasons [16
]. Currently, there are three systems to monitor influenza-related mortality; one system in particular, the 122 Cities Mortality Reporting System, provides a rapid assessment of pneumonia and influenza mortality [6
]. Each week, this system summarizes the total number of death certificates filed in 122 US cities, as well as the number of deaths due to pneumonia and influenza. However, even these data can be delayed by approximately 2–3
weeks from the times of death. This delay can be attributed to one of the following reasons: 1) timeliness of death registration and 2) reviewing of the death certificates to identify pneumonia and influenza deaths [6
]. The registration and reviewing of death certificates varies by states and, as a result, there is variability in length of time to report a death to CDC. For instance, states with paper-based death registration system typically perform manual reviews of the death certificates which can take up to 3
weeks; however states with electronic death registration systems (EDRS) may perform automatic reviews which can decrease this time significantly.
The current 122 Cities Mortality Reporting System surveillance system also lacks flexibility for expanding the number of conditions and/or the geographic distribution. Moreover, the unavailability of coded death records due to the complexity of the National Center of Health Statistics (NCHS) coding process results in multiple strategies to identify common outbreaks such as pneumonia and influenza deaths, which greatly vary by jurisdiction. To bypass the lengthy NCHS process, a variety of approaches have been attempted that are close to ‘real- time’ but less than optimal. For instance, in Utah keyword searching is used to identify pneumonia and influenza deaths; although this method is fast and easy to implement, it can easily result in the over or under estimation of cases. This can occur by missing cases due to misspelled terms, synonyms, variations, or the selection of strings containing the search term.
Other research groups [18
] have demonstrated the feasibility of using mortality data for real time surveillance but all used “free text” search for the string “pneumonia”, “flu” or “influenza.” As noted earlier, although this method can provide the semi quantitative measurements for disease surveillance purposes, keyword searches can also result in an array of problems that result from complexities of human language such as causal relationships and synonyms [20
]. Therefore, the lack of coded death data that may not be available for months [21
] seriously limits the use of death records in automated systems. At this time, there is little published on the automatic assignment of codes to death certificates for automatic case detection.
Coding death certificates
Currently the coding of death certificates is a complex process which involves many entities. In the US, where we are focusing this study, the codes on death certificates that are generated by the National Center for Health Statistics (NCHS) depend on information reported on the death certificate
by the medical examiner, coroner, or another certifier, and there is substantial variation in how certifiers interpret and adhere to cause-of-death definitions [22
]. The cause of death literals are coded into International Classification of Diseases Tenth Revision (ICD-10) [23
] and the underlying and multiple-cause-of-death codes are selected based on the World Health Organization coding rules. These coding rules have been automated by CDC with the development the Mortality Medical Data System (MMDS) which consists of four programs: Super Mortality Medical Indexing Classification and Retrieval (SuperMICAR) Data Entry; Mortality Medical Indexing Classification and Retrieval (MICAR); Automated Classification of Medical Entities (ACME) and TRANSAX (Translation Axes). SuperMICAR was designed to facilitate the entry of literal text of causes of death in death certificates and convert them into standardized expressions acceptable by MICAR. It contains a dictionary which assigns an entity reference number (ERN) to statements on the death certificate. These ERNs are fed into MICAR200 which transforms the ERNS into ICD-10 codes by using specific mortality coding rules; the rules require look-up files and a dictionary. ACME and TRANSAX then selects the underlying and multiple causes of death respectively. ICD-10 codes from MICAR200 are fed into ACME which assigns the underlying cause of death using decision tables. The decision table contains all possible pairings of diseases for which the first disease can cause the second. In the latest version of the system, ACME is comprised of eight decision tables including three tables of valid and invalid codes, causal relationships (General Principle and Rule 1), and direct sequel (Rule 3), and three other tables needed by modification rules. Figure
provides the workflow for the MMDS system.
The Mortality Medical Data System (MMDS) Workflow.
Of the 2.3 million deaths that occur each year 80–85 percent are automatically coded through SuperMICAR, and the remaining records are then manually coded by nosologists, a medical classification specialist [24
]; this is a tedious and lengthy process lasting up to 3
months. Although the automation process has decreased the time required for coding death data to 1–2
weeks, the national vital statistics data is not available for at least two years. Therefore, local health department still manually code records or perform basic process techniques to quickly characterize disease patterns [25
Records that were processed through Super-MICAR or were manually coded are then processed through the remaining components (MICAR200, ACME and TRANSAX) of MMDS. In 1999, MICAR200 had a throughput rate of 95–97%, while ACME rate was 98 percent. Moreover, based on a reliability study, ACME error rate for selecting the underlying cause is at one-half percent, while TRANSAX, the multiple cause codes had a one-half percent error rate [26
]. Due to the high processing rates and low error rates, MMDS is considered by practitioners as the gold standard for the processing and coding of death certificates in the US and other countries (such as Canada, the United Kingdom (UK) and Australia). Therefore, we used the codes produced by this system as the “gold standard” when comparing with the methods developed here.
Electronic death registration system
In 1997, the US Steering Committee to Reengineer the Death Registration Process (a task force representing federal agencies, the National Center for Health Statistics and the Social Security Administration, and professional organizations representing funeral directors, physicians, medical examiners, coroners, hospitals, medical records professionals, and vital records and statistics officials (NAPHSIS) published the report “Toward an Electronic Death Registration System in the United States: Report of the Steering Committee to Reengineer the Death Registration Process
.” This report explained the feasibility of developing electronic death registration in the United States [27
] and argued that these electronic death records have the potential to be an effective source of information for nation-wide tracking and detecting of disease outbreaks. However, little actions have been taken to implement such recommendations in a comprehensive manner. As of July 2011, electronic death registration systems were operating in 36 states, the District of Colombia, and in development or planning stage in a dozen others [28
Information representing the ‘cause of death’ field on the death certificates is free text. One major goal of natural language processing (NLP) is to extract and encode data from free- texts. There have been many research groups developing NLP systems to aid in clinical research, decision support, quality assurance, the automation of encoding free text data and disease surveillance [29
]. Although, there have been a few NLP applications to the public health domain [32
], little is known about its capability to automatically code death certificates for outbreak and disease surveillance. Recently, Medical Match Master (MMM) [25
], developed by Riedl et al at the University of California Davis, was used to match unstructured cause of death phrases to concepts and semantic types within the Unified Medical Language System (UMLS). The system annotates each death phrase input with two types of information, the Concept Unique Identifier, CUI, and a semantic type both assigned by the UMLS. MMM was able to identify an exact concept identifier (CUI) from the UMLS for over 50% of ‘cause of death’ phrases. Although, the focus of this study was to use NLP techniques to process death certificates, the description of this system reported in the literature did not show how well coded data from an NLP tool along with predefined rules can detect countable cases for a specific disease or condition.
The purpose of our project is to create a pipeline which automatically encodes death certificates using a NLP tool and identify deaths related to pneumonia and influenza which provides daily and/or weekly counts. We compared the new technique developed here with keyword searching and MMDS as exemplars of the easiest possible approach and the current “gold standard”, respectively. The comparison of the techniques was done by calculating recall, precision, F- measure, positive predictive value and agreement (Cohen’s Kappa).