Comparing spatio-temporal clusters of arthropod-borne infections using administrative medical claims and state reported surveillance data

Tularemia cases increase in the USA from 2011 through 2019

Tularemia is a rare but potentially serious bacterial zoonosis, which has been reported in the 47 contiguous states of the USA during 2001-2010. This report summarizes the passive surveillance data of tularemia cases reported to the Centers for Disease Control and Prevention from 2011 through 2019. There were 1984 cases reported in the USA during this period. The average national incidence was 0.07 cases per 100,000 person-years (PY), compared to 0.04 cases per 100,000 PY during 2001-2010. The highest statewide reported case 2011-2019 was in Arkansas (374 cases, 20.4% of total), followed by Missouri (13.1%), Oklahoma (11.9%), and Kansas (11.2%). Regarding race, ethnicity, and sex, tularemia cases were reported more frequently among white, non-Hispanic, and male patients. Cases were reported in all age groups; however, individuals 65 years-old and older exhibited the highest incidence. The seasonal distribution of cases generally paralleled the seasonality of tick activity and human outdoor activity, increasing during spring through mid-summer and decreasing through late summer and fall to winter lows. Improved surveillance and education of ticks and tick- and water-borne pathogens should play a key role in efforts to decrease the incidence of tularemia in the USA.

Healthcare claims-based Lyme disease case-finding algorithms in the United States: A systematic literature review

BACKGROUND AND OBJECTIVE

Lyme disease (LD) is the fifth most commonly reported notifiable infectious disease in the United States (US) with approximately 35,000 cases reported in 2019 via public health surveillance. However, healthcare claims-based studies estimate that the number of LD cases is >10 times larger than reported through surveillance. To assess the burden of LD using healthcare claims data and the effectiveness of interventions for LD prevention and treatment, it is important to use validated well-performing LD case-finding algorithms ("LD algorithms"). We conducted a systematic literature review to identify LD algorithms used with US healthcare claims data and their validation status.

METHODS

We searched PubMed and Embase for articles published in English since January 1, 2000 (search date: February 20, 2021), using the following search terms: (1) "Lyme disease"; and (2) "claim*" or "administrative* data"; and (3) "United States" or "the US*". We then reviewed the titles, abstracts, full texts, and bibliographies of the articles to select eligible articles, i.e., those describing LD algorithms used with US healthcare claims data.

RESULTS

We identified 15 eligible articles. Of these, seven studies used LD algorithms with LD diagnosis codes only, four studies used LD diagnosis codes and antibiotic dispensing records, and the remaining four studies used serologic test order codes in combination with LD diagnosis codes and antibiotics records. Only one of the studies that provided data on algorithm performance: sensitivity 50% and positive predictive value 5%, and this was based on Lyme disease diagnosis code only.

CONCLUSIONS

US claims-based LD case-finding algorithms have used diverse strategies. Only one algorithm was validated, and its performance was poor. Further studies are warranted to assess performance for different algorithm designs and inform efforts to better assess the true burden of LD.

Increasing Incidence of Spotted Fever Group Rickettsioses in the United States, 2010-2018

Spotted fever group Rickettsia species are intracellular bacteria transmitted by tick or mite vectors and that cause human diseases referred to as spotted fever group rickettsioses, or spotted fevers. In the United States, the most recognized and commonly reported spotted fevers are Rocky Mountain spotted fever (RMSF) (Rickettsia rickettsii), Rickettsia parkeri rickettsiosis, Pacific Coast tick fever (Rickettsia species 364D), and rickettsialpox (Rickettsia akari). In this study, we summarize and evaluate surveillance data on spotted fever cases reported to the Centers for Disease Control and Prevention (CDC) through the National Notifiable Diseases Surveillance System from 2010 to 2018. During this period, there were 36,632 reported cases of spotted fevers with 95.83% (N = 35,104) reported as meeting the case definition as probable and 4.17% (N = 1528) reported as meeting the case definition as confirmed. The average national incidence of total cases, both probable and confirmed, was 12.77 cases per million persons per year. The highest statewide incidence was in Arkansas, with 256.84 per million per year, whereas the lowest incidence occurred in California, with 0.32 per million per year (note that spotted fevers were not notifiable in Hawaii and Alaska). Cases of spotted fevers were reported more frequently among males by gender, White by race, and non-Hispanic by ethnicity. The incidence of spotted fevers increased significantly from 2010 to 2018, but it is uncertain how many of the reported cases were RMSF and how many developed from more moderate spotted fevers. Improvement of the ability to differentiate between spotted fever group Rickettsia species is needed.

Using Data Surveillance to Understand the Rising Incidence of Babesiosis in the United States, 2011-2018

Babesiosis is a tick-borne disease that is caused by intraerythrocytic protozoan parasites of the genus Babesia. Common symptoms of babesiosis are generally characterized as nonspecific flu-like symptoms, such as fever or chills. Human infections are reported to the Centers for Disease Control and Prevention (CDC) through the National Notifiable Diseases Surveillance System (NNDSS). This study summarizes data of Babesia infections reported to the CDC by the NNDSS from 2011 to 2018. In total, there were 14,159 reported cases of babesiosis, and the incidence rate was 5.55 cases per million persons per year, displaying an increasing trend during the study period. The demographic group most affected was middle-aged and elderly white males. Infections were most abundant in the New England and the Mid-Atlantic regions of the United States. Our study provides useful results for a basic understanding of incidence, spatial and temporal distribution, and severity of babesiosis.

Increasing Incidence of Anaplasmosis in the United States, 2012 Through 2016

Anaplasmosis is a tick-borne disease that is primarily caused by the rickettsial bacterium Anaplasma phagocytophilum. Anaplasmosis is a febrile disease with common symptoms, including headaches, fever, and lethargy, but it can cause serious organ failure and even death if left untreated. Human infections are reported annually to the Centers for Disease Control and Prevention (CDC) through the National Notifiable Diseases Surveillance System (NNDSS). This report analyzed the cases of anaplasmosis reported by the NNDSS from 2012 to 2016. In total, there were 15,778 reported A. phagocytophilum infections, and the incidence rate was 7.27 cases per million persons per year, with the number of reported cases increasing each year. The demographic group most affected was middle-aged and elderly white males. Infections were most abundant in the coastal northeast and northern midwest regions. Our study provides useful results for a basic understanding of incidence, distribution, and severity of A. phagocytophilum infections.

Increased Incidence of Ehrlichia chaffeensis Infections in the United States, 2012 Through 2016

Human ehrlichioses are tick-borne diseases that have been increasing in incidence in the United States during recent years. Ehrlichia chaffeensis is one of the primary bacteria that cause ehrlichiosis in humans, which typically results in fever-like symptoms, but may also be fatal if left untreated. E. chaffeensis infections are reported to the Centers for Disease Control and Prevention (CDC) through the National Notifiable Diseases Surveillance System (NNDSS). This study analyzed the cases of E. chaffeensis infections reported by the NNDSS from 2012 through 2016. There were 6786 cases and the incidence rate was 4.46 cases per million persons per year. The demographic group most commonly infected was white males between the ages of 40 and 64. Infections were most abundant in the southeast and midwest, particularly in Arkansas, Missouri, Tennessee, and Oklahoma, as well as much of the east coast. The number of cases reported each year from 2012 through 2016 was higher than the number reported in any of the previous 4 years. Ongoing surveillance and reporting of tick-borne diseases are critical to inform public health practice and guide disease treatment and prevention efforts.

A framework for adaptive surveillance of emerging tick-borne zoonoses

Significant global ecological changes continue to drive emergence of tick-borne zoonoses around the world. This poses an important threat to both human and animal health, and highlights the need for surveillance systems that are capable of monitoring these complex diseases effectively across different stages of the emergence process. Our objective was to develop an evidence-based framework for surveillance of emerging tick-borne zoonoses. We conducted a realist review to understand the available approaches and major challenges associated with surveillance of emerging tick-borne zoonoses. Lyme disease, with a specific focus on emergence in Canada, was used as a case study to provide real-world context, since the process of disease emergence is ongoing in this country. We synthesize the results to propose a novel framework for adaptive surveillance of emerging tick-borne zoonoses. Goals for each phase of disease emergence are highlighted and approaches are suggested. The framework emphasizes the needs for surveillance systems to be inclusive, standardized, comprehensive and sustainable. We build upon a growing body of infectious disease literature that is advocating for reform to surveillance systems. Although our framework has been developed for tick-borne zoonoses, it is flexible and has the potential to be applied to a variety of other vector-borne and zoonotic diseases.

Where did I get dengue? Detecting spatial clusters of infection risk with social network data

Typical spatial disease surveillance systems associate a single address to each disease case reported, usually the residence address. Social network data offers a unique opportunity to obtain information on the spatial movements of individuals as well as their disease status as cases or controls. This provides information to identify visit locations with high risk of infection, even in regions where no one lives such as parks and entertainment zones. We develop two probability models to characterize the high-risk regions. We use a large Twitter dataset from Brazilian users to search for spatial clusters through analysis of the tweets' locations and textual content. We apply our models to both real-world and simulated data, demonstrating the advantage of our models as compared to the usual spatial scan statistic for this type of data.

Spatial, temporal and spatio-temporal clusters of measles incidence at the county level in Guangxi, China during 2004-2014: flexibly shaped scan statistics

Background

Outbreaks of measles re-emerged in Guangxi province during 2013–2014, where measles again became a major public health concern. A better understanding of the patterns of measles cases would help in identifying high-risk areas and periods for optimizing preventive strategies, yet these patterns remain largely unknown. Thus, this study aimed to determine the patterns of measles clusters in space, time and space-time at the county level over the period 2004–2014 in Guangxi.

Methods

Annual data on measles cases and population sizes for each county were obtained from Guangxi CDC and Guangxi Bureau of Statistics, respectively. Epidemic curves and Kulldorff’s temporal scan statistics were used to identify seasonal peaks and high-risk periods. Tango’s flexible scan statistics were implemented to determine irregular spatial clusters. Spatio-temporal clusters in elliptical cylinder shapes were detected by Kulldorff’s scan statistics. Population attributable risk percent (PAR%) of children aged ≤24 months was used to identify regions with a heavy burden of measles.

Results

Seasonal peaks occurred between April and June, and a temporal measles cluster was detected in 2014. Spatial clusters were identified in West, Southwest and North Central Guangxi. Three phases of spatio-temporal clusters with high relative risk were detected: Central Guangxi during 2004–2005, Midwest Guangxi in 2007, and West and Southwest Guangxi during 2013–2014. Regions with high PAR% were mainly clustered in West, Southwest, North and Central Guangxi.

Conclusions

A temporal uptrend of measles incidence existed in Guangxi between 2010 and 2014, while downtrend during 2004–2009. The hotspots shifted from Central to West and Southwest Guangxi, regions overburdened with measles. Thus, intensifying surveillance of timeliness and completeness of routine vaccination and implementing supplementary immunization activities for measles should prioritized in these regions.

Using administrative medical claims data to supplement state disease registry systems for reporting zoonotic infections

OBJECTIVE

To determine what, if any, opportunity exists in using administrative medical claims data for supplemental reporting to the state infectious disease registry system.

MATERIALS AND METHODS

Cases of five tick-borne (Lyme disease (LD), babesiosis, ehrlichiosis, Rocky Mountain spotted fever (RMSF), tularemia) and two mosquito-borne diseases (West Nile virus, La Crosse viral encephalitis) reported to the Tennessee Department of Health during 2000-2009 were selected for study. Similarly, medically diagnosed cases from a Tennessee-based managed care organization (MCO) claims data warehouse were extracted for the same time period. MCO and Tennessee Department of Health incidence rates were compared using a complete randomized block design within a general linear mixed model to measure potential supplemental reporting opportunity.

RESULTS

MCO LD incidence was 7.7 times higher (p<0.001) than that reported to the state, possibly indicating significant under-reporting (∼196 unreported cases per year). MCO data also suggest about 33 cases of RMSF go unreported each year in Tennessee (p<0.001). Three cases of babesiosis were discovered using claims data, a significant finding as this disease was only recently confirmed in Tennessee.

DISCUSSION

Data sharing between MCOs and health departments for vaccine information already exists (eg, the Vaccine Safety Datalink Rapid Cycle Analysis project). There may be a significant opportunity in Tennessee to supplement the current passive infectious disease reporting system with administrative claims data, particularly for LD and RMSF.

CONCLUSIONS

There are limitations with administrative claims data, but health plans may help bridge data gaps and support the federal administration's vision of combining public and private data into one source.

Influence of spatial resolution on space-time disease cluster detection

BACKGROUND

Utilizing highly precise spatial resolutions within disease outbreak detection, such as the patients' address, is most desirable as this provides the actual residential location of the infected individual(s). However, this level of precision is not always readily available or only available for purchase, and when utilized, increases the risk of exposing protected health information. Aggregating data to less precise scales (e.g., ZIP code or county centroids) may mitigate this risk but at the expense of potentially masking smaller isolated high risk areas.

METHODS

To experimentally examine the effect of spatial data resolution on space-time cluster detection, we extracted administrative medical claims data for 122500 viral lung episodes occurring during 2007-2010 in Tennessee. We generated 10000 spatial datasets with varying cluster location, size and intensity at the address-level. To represent spatial data aggregation (i.e., reduced resolution), we then created 10000 corresponding datasets both at the ZIP code and county level for a total of 30000 datasets. Using the space-time permutation scan statistic and the SaTScan™ cluster software, we evaluated statistical power, sensitivity and positive predictive values of outbreak detection when using exact address locations compared to ZIP code and county level aggregations.

RESULTS

The power to detect disease outbreaks did not largely diminish when using spatially aggregated data compared to more precise address information. However, aggregations negatively impacted the ability to more accurately determine the exact spatial location of the outbreak, particularly in smaller clusters (<800 km²).

CONCLUSIONS

Spatial aggregations do not necessitate a loss of power or sensitivity; rather, the relationship is more complex and involves simultaneously considering relative risk within the cluster and cluster size. The likelihood of spatially over-estimating outbreaks by including geographical areas outside the actual disease cluster increases with aggregated data.