The warmer the weather, the more gram-negative bacteria - impact of temperature on clinical isolates in intensive care units

Abstract

Background: We investigated the relationship between average monthly temperature and the most common clinical pathogens causing infections in intensive care patients.

Methods: A prospective unit-based study in 73 German intensive care units located in 41 different hospitals and 31 different cities with total 188,949 pathogen isolates (102,377 Gram-positives and 86,572 Gram-negatives) from 2001 to 2012. We estimated the relationship between the number of clinical pathogens per month and the average temperature in the month of isolation and in the month prior to isolation while adjusting for confounders and long-term trends using time series analysis. Adjusted incidence rate ratios for temperature parameters were estimated based on generalized estimating equation models which account for clustering effects.

Results: The incidence density of Gram-negative pathogens was 15% (IRR 1.15, 95%CI 1.10-1.21) higher at temperatures ≥ 20°C than at temperatures below 5°C. E. cloacae occurred 43% (IRR=1.43; 95%CI 1.31-1.56) more frequently at high temperatures, A. baumannii 37% (IRR=1.37; 95%CI 1.11-1.69), S. maltophilia 32% (IRR=1.32; 95%CI 1.12-1.57), K. pneumoniae 26% (IRR=1.26; 95%CI 1.13-1.39), Citrobacter spp. 19% (IRR=1.19; 95%CI 0.99-1.44) and coagulase-negative staphylococci 13% (IRR=1.13; 95%CI 1.04-1.22). By contrast, S. pneumoniae 35% (IRR=0.65; 95%CI 0.50-0.84) less frequently isolated at high temperatures. For each 5°C increase, we observed a 3% (IRR=1.03; 95%CI 1.02-1.04) increase of Gram-negative pathogens. This increase was highest for A. baumannii with 8% (IRR=1.08; 95%CI 1.05-1.12) followed by K. pneumoniae, Citrobacter spp. and E. cloacae with 7%.

Conclusion: Clinical pathogens vary by incidence density with temperature. Significant higher incidence densities of Gram-negative pathogens were observed during summer whereas S. pneumoniae peaked in winter. There is increasing evidence that different seasonality due to physiologic changes underlies host susceptibility to different bacterial pathogens. Even if the underlying mechanisms are not yet clear, the temperature-dependent seasonality of pathogens has implications for infection control and study design.

Figure 1. Time series of monthly aggregated incidence densities of Gram-positive pathogens (bold red line) and temperature (thin blue line) in 73 German ICUs, January 2001 to December 2012.

Figure 2. Time series of monthly aggregated incidence density of Gram-negative pathogens (bold red line) and temperature (thin blue line) in 73 German ICUs, January 2001 to December 2012.

Figure 3. Adjusted incidence rate ratios for Gram-positive and Gram-negative pathogens by temperature in the month of isolation.

As reference we defined temperatures in the month of isolation <5°C. For the calculation we used generalized estimating equations (GEE) models using negative binomial distribution and logarithmic transformed patient days as offset parameter. Data based on 5965 monthly data sets of 73 different German ICUs, January 2001 to December 2012. The adjusted models accounts for cluster effects within an ICU and for significant confounding parameters from the first model building step: S.aureus ^{(a, d, f)}, coagulase negative staphylococci ^{(a, b)}, E. faecalis ^(b), E. faecium ^{(a, c, d, e)}, S. pneumoniae ^{(a, b)}, E. coli ^(e), P. aeruginosa ^{(a, c)}, K. pneumoniae ^{(no confounder)}, E. cloacae ^{(a, b, c, d)}, S. maltophilia ^{(a, b, c, f)}, S. marcescens ^{(a, f)}, Citrobacter spp. ^{(no confounder)}, A. baumannii ^{(a, b, f, g)} in which: ^a, autoregressive factor AR(1) number pathogens in the previous month; ^b, linear time trend; ^c, quadratic time trend; ^d, cubic time trend; ^e, ICU is located in a university hospital; ^f, hospital size > = 1000 beds; ^g, type of ICU medical; ^h, type of ICU surgical. *P<0.05 in chi-square test with 4 degrees of freedom (type III test) in the adjusted GEE model; Whiskers represent 95% confidence intervals.

Figure 4. Adjusted incidence rate ratios for Gram-positive and Gram-negative pathogens by temperature in the month prior to isolation.

As reference we defined temperatures in the month prior to isolation <5°C. For the calculation we used generalized estimating equations (GEE) models using negative binomial distribution and logarithmic transformed patient days as offset parameter. Data based on 5965 monthly data sets of 73 different German ICUs, January 2001 to December 2012. The adjusted models accounts for cluster effects within an ICU and for significant confounding parameters from the first model building step: S.aureus ^{(a, d, f)}, coagulase negative staphylococci ^{(a, b)}, E. faecalis ^(b), E. faecium ^{(a, c, d, e)}, S. pneumoniae ^{(a, b)}, E. coli ^(e), P. aeruginosa ^{(a, c)}, K. pneumoniae ^{(no confounder)}, E. cloacae ^{(a, b, c, d)}, S. maltophilia ^{(a, b, c, f)}, S. marcescens ^{(a, f)}, Citrobacter spp. ^{(no confounder)}, A. baumannii ^{(a, b, f, g)} in which: ^a, autoregressive factor AR(1) number pathogens in the previous month; ^b, linear time trend; ^c, quadratic time trend; ^d, cubic time trend; ^e, ICU is located in a university hospital; ^f, hospital size > = 1000 beds; ^g, type of ICU medical; ^h, type of ICU surgical. *P<0.05 in chi-square test with 4 degrees of freedom (type III test) in the adjusted GEE model; Whiskers represent 95% confidence intervals.