Public health surveillance data are systematically collected on an ongoing basis and are essential to help characterize disease and measure trends over time. Surveillance data for all diseases and conditions are especially useful to monitor case counts, emerging populations at risk of disease, and geographic distribution and expansion. In the United States, reportable status is designated at the state level and mandates healthcare professionals and laboratories to notify public health departments of diagnosed cases or positive laboratory tests for given diseases. However, not all fungal diseases are reportable, and of those that are, they are not reportable in all states, which may limit geographic representation [20]. While standardized surveillance case definitions are intended to facilitate comparison across jurisdictions, some jurisdictions do not follow these standardized definitions.

Surveillance data are not always available in a timely manner; although provisional data are typically presented at regular intervals (e.g., weekly and monthly), reconciled data may lag by months to years. The breadth, quality, and completeness of the data also depend on the information reported by the healthcare provider or laboratory. Key data elements for evaluating health disparities, such as race, ethnicity, disease severity, health outcomes, and factors related to social determinants of health, are frequently incomplete or not collected.

Surveillance systems are meant for continuous data collection, and therefore, opportunities to add or adjust data elements are often limited. However, surveillance practices and case definitions may change over time based on improved disease knowledge or changes in laboratory diagnostic methods (e.g., advances in new diagnostic tools and availability of laboratory tests). These artifacts of reporting should be considered, if applicable, in analyses of surveillance data.

Hospital discharge and survey data can be used to explore health disparities in fungal diseases through healthcare encounters. Hospital discharge datasets enable researchers to assess patient interactions with the healthcare system, including diagnosis, treatment, disease management, and health outcomes. These data are especially well-suited for studying severe fungal diseases that require hospitalization and may be particularly influenced by the presence of underlying medical conditions and social determinants of health. Hospital discharge data generally comprise large sample sizes that can be nationally representative, and data are collected longitudinally, though availability may lag by several years, limiting timeliness.

Demographic information, including race and ethnicity, is often available and fairly complete, though accuracy often differs by racial and ethnic groups [21]. Payer type (e.g., private insurance, Medicare, and Medicaid) provides insight into insurance status, which may be associated with IFI, likely due to its connection with socioeconomic status and access to quality healthcare [3, 4]. Healthcare charges and associated costs are also included and can be used to analyze financial burden in connection with health disparities. Hospital survey datasets may also contain facility-level data related to social determinants of health and health equity, particularly around measures related to healthcare quality.

Hospital discharge and survey datasets almost certainly underestimate the true number of healthcare visits associated with fungal diseases. Disease classification based on medical coding (e.g., International Classification of Diseases, Tenth Revision [ICD-10] codes) can be inaccurate or nonspecific. For example, a recent study found that only 44% of culture-confirmed candidemia cases received an ICD-10 code for candidiasis [22]. Hospital discharge data may also disproportionately represent more severe cases of IFI, which can potentially misrepresent health disparities and limit generalizability to all cases.

Insurance claims databases offer similar benefits and are subject to similar limitations as hospital discharge datasets. They are made up of large samples and may contain longitudinal data related to healthcare encounters, including diagnosis, treatment, outcomes, and cost. Insurance information can help identify disparities in fungal disease rates and outcomes through comparison across insurance types. However, information about social determinants of health may be limited.

Data may also be skewed toward specific types of insurance, and therefore, researchers should be cautious about generalizing results to the total population.

Laboratory testing data provide insights into diagnostic testing practices and results for fungal diseases and associated trends over time, though the utility for measuring health disparities may be restricted. These datasets often contain limited demographic data, and do not include information about socioeconomic status, treatment, or health outcomes, therefore identification of associated disparities is not feasible. However, geographic and facility data points may be beneficial, particularly when used in conjunction with other data sources, such as the census or location-based population datasets, to further assess differences. For researchers who use laboratory testing data, information on diagnostic testing is detailed and often up-to-date in near real-time, though referral bias and limited insight into contributing laboratories may hinder generalizability.

Electronic medical records (EMR) are the digital versions of medical charts, including patient information such as demographics and medical history, as well as clinical information related to diagnoses, medications, and immunizations. EMR data are generated for most people (88%) who interact with the healthcare system [23], resulting in large sample sizes, though population representation and inclusion bias is dependent on the healthcare centers included in the analysis. Information is detailed, high-quality, and often available in close to real-time.

Data elements in EMR may overlap with other sources, including insurance claims, hospital discharge data, and laboratory testing. The inclusion of these data points highlights the breadth of data available in EMR, though the limitations of those data sources (e.g., poor documentation, miscoding, and changes to diagnostic testing practices) should also be taken into consideration for EMR analyses.

Accessibility to EMR data can range based on the scope of the analysis. Data may be more easily obtained for an individual institution or a narrow geographic area, but access is often more limited at a national scale or across numerous medical centers. Additionally, analysis may require rigorous and laborious data cleaning as there is no standardized data structure across EMR platforms; different systems may use different structures and terminologies for certain data points. For fungal diseases, important information is often included in free text responses (e.g., imaging report notes and physician comments), which would similarly entail substantial data cleaning prior to analysis, though advancements in machine learning and artificial intelligence may offer techniques to more efficiently mine text data.

Surveys offer the opportunity to ask specific questions relevant to the topic of interest and direct those questions to applicable populations (e.g., healthcare providers, patients, and certain racial and ethnic groups). They can therefore help to fill gaps in literature because survey questions can be customized to the needs of a particular analysis; other data sources often necessitate more indirect connections or assumptions to answer research questions. National surveys generally boast large samples, though surveys can also be conducted to a targeted group of participants. Some people may be more likely to respond to a given survey based on the subject matter, and this response bias can influence survey results and may impact whether or not a survey is representative of the population of interest; data are also self-reported and therefore subject to recall bias.

Patient and provider survey datasets may be more likely to contain high-quality demographic and socioeconomic data than the data sources previously mentioned, which is useful for evaluation of health disparities. Some national surveys collect data relevant to social determinants of health (e.g., household size, household income, and education) that can be leveraged as denominators for various groups. In addition, survey data may be available soon after survey completion and therefore timelier than other data sources.

Vital statistics capture data related to births and deaths. For IFI, death data is likely the most useful information within vital statistics records to explore associations between patient characteristics and fungal disease mortality. Vital statistics mortality data comprise the entire deceased population, but reported fungal disease-associated deaths likely underestimate the true number of fungal disease-associated deaths to varying degrees depending on the pathogen. For example, an analysis of deaths related to coccidioidomycosis, an environmental fungal disease endemic to the southwestern United States, found that coccidioidomycosis-associated deaths were underreported in death registry data by two-to sevenfold in Arizona [24]. Underreporting may be a factor of misclassification or nonspecific attribution; approximately 20–30% of death certificates are thought to have issues with completeness, meaning they lack description of a clear chain of events from the immediate to the underlying cause of death, fail to report other conditions that contributed to death, or provide nonspecific information [25]. A previous study estimated that only 50% of IFIs are diagnosed prior to death, and autopsies reached their lowest documented rate in in 2020 (7.4%) [26, 27]. Vital statistics contain fairly complete demographic data, such as sex, age, race, and ethnicity, but data elements regarding socioeconomic status or social determinants of health are limited. Notably, fungal diseases are rarely listed as a primary cause of death; researchers using vital statistics data would benefit from pulling underlying and multiple cause of death data.

Health equity indices are geographic place-based tools that measure a combination of social, and in some cases environmental conditions that affect health at a community level. These indices comprise a variety of indicators (e.g., unemployment, vehicle access, and walkability), which are generally grouped into larger themes or domains (e.g., socioeconomic status, housing type and transportation, and built environment) and then ranked in comparison with other neighborhoods. Indices provide an overall score for each community, usually by census tract or by county, and there are often additional scores for each theme or domain for more detailed analyses.

Two such indices include the Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index (SVI) and the CDC/ATSDR Environmental Justice Index (EJI). These tools aim to help public health officials identify communities at greater risk of poor health outcomes stemming from social determinants of health and environmental burden [28, 29].

Health equity indices can be used to explore potential associations between fungal disease and health equity or environmental justice based on index scores. Their finer geographic scale compared to other datasets also allows researchers to analyze the potential effect of rural–urban status on index scores and fungal disease incidence. The data necessitate analysis at the community level (e.g., county and census tract), and must be interpreted in relative terms since the index scores are generated through comparative rankings across the unit of analysis (county or census tract). This kind of analysis also requires a separate dataset for the disease or condition of interest at the same spatial scale as the index data; notably, the EJI is only available at the census tract level, and corresponding fungal disease data may be difficult to obtain at this level of specificity. Overall, these indices can allow for mapping and detection of communities at greater risk of fungal disease based on social determinants of health and environmental burden.