Analysis of population-level determinants of legionellosis: spatial and geovisual methods for enhancing classification of high-risk areas

Abstract

BackgroundAlthough the incidence of legionellosis throughout North America and Europe continues to increase, public health investigations have not been able to identify a common exposure in most cases. Over 80% of cases are sporadic with no known source. To better understand the role of the macro-environment in legionellosis risk, a retrospective ecological study assessed associations between population-level measures of demographic, socioeconomic, and environmental factors and high-risk areas.MethodsGeographic variability and clustering of legionellosis was explored in our study setting using the following methods: unadjusted and standardized incidence rate and SaTScan™ cluster detection methods using default scanning window of 1 and 50% as well as a reliability score methodology. Methods for classification of “high-risk” census tracts (areas roughly equivalent to a neighborhood with average population of 4000) for each of the spatial methods are presented. Univariate and multivariate logistic regression analyses were used to estimate associations with sociodemographic factors: population ≥ 65 years of age, non-white race, Hispanic ethnicity, poverty, less than or some high school education; housing factors: housing vacant, renter-occupied, and built pre-1950 and pre-1970; and whether drinking water is groundwater or surface water source.ResultsCensus tracts with high percentages of poverty, Hispanic population, and non-white population were more likely to be classified as high-risk for legionellosis versus a low-risk census tract. Vacant housing, renter-occupied housing, and homes built pre-1970 were also important positively associated risk factors. Drinking water source was not found to be associated with legionellosis incidence.DiscussionCensus tract level demographic, socioeconomic, and environmental characteristics are important risk factors of legionellosis and add to our understanding of the macro-environment for legionellosis occurrence. Our findings can be used by public health professionals to target disease prevention communication to vulnerable populations. Future studies are needed to explore the exact mechanisms by which these risk factors may influence legionellosis clustering.

Rising Incidence of Legionnaires’ Disease and Associated Epidemiologic Patterns, United States, 1992–2018

“…Social determinants of health also likely contributed to disparities in incidence ( 23 ). Black or African American persons had the lowest median household income relative to other races ( 24 ), and areas of poverty were associated with a higher incidence of LD ( 25 , 26 ). Residence in areas with more vacant housing, more renter-occupied homes, more homes built before 1970, and lower education levels were also identified as risk factors for LD ( 26 ).…”

“…Black or African American persons had the lowest median household income relative to other races ( 24 ), and areas of poverty were associated with a higher incidence of LD ( 25 , 26 ). Residence in areas with more vacant housing, more renter-occupied homes, more homes built before 1970, and lower education levels were also identified as risk factors for LD ( 26 ). Certain occupations (transportation, repair, protective services, cleaning services, and construction) were found to carry a higher risk for LD, but the associations with race and socioeconomic status were unclear ( 25 ).…”

“…Several studies found temperature, precipitation, and humidity to be associated with LD cases, although the mechanics are not completely understood ( 27 – 31 ). Aging infrastructure might also have played a role, because residing in areas with older homes has been identified as a risk factor for LD ( 26 ). Median population age varied by jurisdiction; the Northeast region had the highest median population age, followed by the Midwest, South, and West regions ( 17 ).…”

Development and Evaluation of Statewide Prospective Spatiotemporal Legionellosis Cluster Surveillance, New Jersey, USA

“…Users should select parameters on the basis of their jurisdiction’s needs because those choices can meaningfully vary results ( 16 ). No one model will be most effective for the surveillance needs of all urban or rural, or city, county, or state jurisdictions, so investigators would benefit from piloting and exploring a variety of different options and performing continued surveillance using different parameters, baseline periods, and geographic units.…”

Racial Disparities in Incidence of Legionnaires’ Disease and Social Determinants of Health: A Narrative Review

“…Percentage of the population with income below the federal poverty level was also strongly associated with a census tract being designated as a high-risk legionellosis cluster area in New Jersey (OR = 7.21; 95% CI, 4.04-12.86). 20 Publications on pneumonia also identified population income as being associated with disease incidence. Impoverished areas had the highest incidence of bacteremic pneumonia, invasive pneumococcal disease, and pneumonia deaths.…”