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. 2021 Aug 19;16(8):e0255593. doi: 10.1371/journal.pone.0255593

Seasonality and weather dependance of Acinetobacter baumannii complex bloodstream infections in different climates in Brazil

Sebastião Pires Ferreira Filho 1, Milca Severino Pereira 2, Jorge Luiz Nobre Rodrigues 3, Raul Borges Guimarães 4, Antônio Ribeiro da Cunha 5, José Eduardo Corrente 6, Antônio Carlos Campos Pignatari 7, Carlos Magno Castelo Branco Fortaleza 1,*
Editor: Aleksandra Barac8
PMCID: PMC8376013  PMID: 34411123

Abstract

Recent studies report seasonality in healthcare-associated infections, especially those caused by Acinetobacter baumannii complex. We conducted an ecologic study aimed at analyzing the impact of seasons, weather parameters and climate control on the incidence and carbapenem-resistance in A. baumannii complex bloodstream infections (ABBSI) in hospitals from regions with different climates in Brazil. We studied monthly incidence rates (years 2006–2015) of ABBSI from hospitals in cities from different macro-regions in Brazil: Fortaleza (Ceará State, Northeast region), Goiânia (Goiás State, Middle-west) and Botucatu (São Paulo State, Southeast). Box-Jenkins models were fitted to assess seasonality, and the impact of weather parameters was analyzed in Poisson Regression models. Separate analyses were performed for carbapenem-resistant versus carbapenem-susceptible isolates, as well as for infections occurring in climate-controlled intensive care units (ICUs) versus non-climate-controlled wards. Seasonality was identified for ABSSI ICUs in the Hospitals from Botucatu and Goiânia. In the Botucatu hospital, where there was overall seasonality for both resistance groups, as well as for wards without climate control. In that hospital, the overall incidence was associated with higher temperature (incidence rate ratio for each Celsius degree, 1.05; 95% Confidence Interval, 1.01–1.09; P = 0.006). Weather parameters were not associated with ABBSI in the hospitals from Goiânia and Fortaleza. In conclusion, seasonality was found in the hospitals with higher ABBSI incidence and located in regions with greater thermal amplitude. Strict temperature control may be a tool for prevention of A. baumanii infections in healthcare settings.

Introduction

Despite their specific characteristics, healthcare-associated infections (HCAIs) can share some epidemiological determinants with those that occur in the community. Seasonality, increasingly identified in recent studies, is one example [1]. It is prominent in bloodstream infections caused by Gram-negative bacilli (GNB), which have been linked to proximity to the equator [2], summer season [3] and high environmental temperatures measured either within [4] or outside hospitals [5].

This latter aspect is one of the gaps in our current understanding of HCAIs seasonality, since GNB incidence increases during warm periods even within units that are climate-controlled (and thus expected not to present relevant temperature variations) [1,5–7]. Some authors have theorized an influx from reservoirs outside healthcare settings, on the basis of greater seasonality of multidrug-susceptible (as opposed to multidrug resistant, supposedly “hospital-borne”) GNB [8] or on molecular heterogeneity of summer strains (which suggests multiple sources) [9]. However, those findings were not supported by other studies, especially those pointing to relevant “summer peaks” of multidrug-resistant GNB infections [6,7,10].

Acinetobacter baumannii complex bloodstream infections (ABBSI) stand out among GNB HCAIs for their striking seasonal pattern [11]. They pose therefore unique opportunities to assess HCAIs seasonality. With that in mind, we conducted an ecological study aimed at analyzing how that seasonality of ABBSI varies in different climates, as well as its association with antimicrobial resistance and climate control in hospital units.

Methods

Ethical statement

This study adheres to the Helsinki declaration guidelines and was approved by Committee for Ethic in Research (CAAE # 81985517.0.1001.5411) from Botucatu School of Medicine. Since individual data were only used for building time series and most analyses were performed for aggregate data, there was waiver of the application of informed consent forms.

Study settings

This study was conducted in three hospitals from cities located in different macro-regions of Brazil: Fortaleza (Ceará State, Northeast region), Goiânia (Goiás State, Middle-west) and Botucatu (São Paulo State, Southeast). In all hospitals, Intensive Care Units (ICUs) were climate-controlled, while other wards were not climatized. Fig 1 presents the geographic distribution of those cities within the map of Brazil. Geographic and climate aspects of those cities are presented in Table 1. The study cities differed from each other in the Köppen-Geiger climate classification, according to the Brazil’s “National Institute for Spatial Research” (INPE, www.inpe.br) [12].

Fig 1. Map of Brazil, showing the cities that host the study hospitals.

Fig 1

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Note. This figure copyrights belong to the authors. Figure was drawn in ArCGis 10 (ESRI, Redlands, CA).

Table 1. Climatic and geographic characteristics of the municipalities where the study hospitals are located.

Hospital Botucatu Medical School Hospital Goiás Federal University Hospital Ceará Federal University Hospital
City Botucatu Goiânia Fortaleza
State São Paulo Goiás Ceará
Macro-region Southeast Middle-west Northeast
Latitude, longitude 22°53′25″S, 48°27′19″W  16°40’48’’S, 49°15’18’’W 3°43’6’’S, 38°32’36’’W
Altitude (meters above sea level) 828 764 14
Geographic distance to the Atlantic Ocean (in Kilometers) 246 868 0
Köppen-Geiger climate classification * Cfa Aw As
Mean monthly temperature in Celsius degrees, average (SE) 22.3±3.4 23.5±1.5 27.4±0.7
Mean monthly relative humidity, average (SE) 72.7±8.7 65.8±12.5 71.9±6.1
Monthly total rainfall in millimeters, average (SE) 119.4±100.6 120.4±110.7 103.2±124.9
Biomes ** Atlantic forest “Cerrado” Mangrove/Sandbank
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*According to Köppen-Geiger classification11: Cfa, subtropical climate, humid; Aw, tropical climate with dry winter; As, tropical with dry summer.

**According to the classification of Brazilian Institute for Geography and Statistics (IBGE, www.ibge.gov.br).

Study data

We searched the study hospitals laboratory blood culture databases from years 2006 through 2015. The data were validated in comparison to the hospitals’ infection control committees. Briefly, ABBSI were defined as A. baumannii recovered from blood cultures collected after 48 hours of patients’ admission to the hospitals. Duplicate data (two or more blood cultures from the same patient within a 30-day period) were discarded. After that, time series of monthly incidence (per 10,000 patient-days) were generated for each hospital. Separate rates were calculated for carbapenem-susceptible and carbapenem-resistant A. baumannii, as well as for units with or without climate control. Weather parameters (monthly average temperature and humidity, monthly aggregated rainfall) were obtained from INPE climate database. Of note, raw data for our study are included as S1 File.

Time series analysis

Incidence rate series were fitted to stochastic, Box-Jenkins models (Seasonal Autoregressive Integrated Moving Average [SARIMA]). Briefly, those models fit time series data for time trends, autoregression (correlation of each value with that recorded in preceding time units) and seasonality (correlation of each value with that recorded in the same period/season from the preceding years) [13]. Therefore, those models allowed us to assess statistically the correlations both with data from immediately preceding months and seasons in preceding years. Models were configured in the usual SARIMA(p,d,q)(P,D,Q)m format using the following trend elements: trend autoregression order (p), 1, i.e., testing the fitness of time series for correlation of each month value with the value recorded one month before; trend difference order (d), 0; trend moving average order (q), 0; seasonal autoregressive order (P), 1 i.e., testing the fitness of time series for correlation of each month value with the value recorded in the same month one year before; seasonal difference order (D), 0; seasonal moving average order (Q), 0; number of time steps (m), 12 (seasonality investigated on a monthly basis). Therefore, we attempted to fit our times series to the models parameters: SARIMA (1,0,0)(1,0,0)12.

Models of weather dependence

Multivariable Poisson regression models (which are particularly appropriate for outcomes presented as incidence rates) were analyzed with average monthly temperature and humidity, as well as aggregate monthly rainfall as independent variables and monthly rates as outcomes. Both Poisson regression an time series analyses were conducted using STATA 14 software (StataCorp, College Station, TX).

Results

The aggregate incidence of ABSSI in the study period (for each type of hospital unit and pattern of carbapenem-resistance) is presented in Table 2, and time series of monthly incidence is presented in Fig 2. Briefly, the Botucatu hospital presented higher overall incidence, but also a greatest proportion of carbapenem-susceptible isolates (especially in years before 2011), when compared to the other two hospitals.

Table 2. Characteristics of hospitals included in this study, alongside with aggregate incidence of Acinetobacter baumanii bloodstream infections (per 10,000 patient-days).

Hospital Botucatu Medical School Hospital Goiás Federal University Hospital Ceará Federal University Hospital
Teaching hospital Yes Yes Yes
Beds in climate-controlled units 98 86 21
Beds in non-climate-controlled units 462 634 165
ABBSI, climate-controlled units *
Carbapenem-susceptible 10.20 1.51 0.56
Carbepenem-resistant 12.96 16.60 6.88
ABBSI, non-climate-controlled units *
Carbapenem-susceptible 1.27 1.83 0.34
Carbepenem-resistant 1.30 3.00 0.87
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*In all study hospitals, Intensive Care Units (ICU) were climate-controlled, while other others were not. ABBSI, Acinetobacter baumannii complex bloodstream infections.

Fig 2. Monthly incidence of Acinetobacter baumannii bloodstream infections in three hospitals from different places in Brazil.

Fig 2

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Note. ICU, intensive care units; Wards, units for non-critically ill patients; Carb-R, carbapenem-resistant; CARB-S, carbapenem-susceptible.

Results from the Box-Jenkins models are presented in Table 3. Briefly seasonality was identified for ABSSI in ICUs in the Hospitals from Botucatu and Goiânia. In the Botucatu hospital, there was also overall seasonality for both resistance groups, as well as for wards without climate control.

Table 3. Seasonal autoregressive coefficients from Box-Jenkins models for incidence of Acinetobacter baumannii complex bloodstream infections, according to type of hospital unit and resistance.

Autoregressive coefficient (95% Confidence Interval)
Hospital Unit, carbapenem susceptibility Botucatu Goiânia Fortaleza
ICU, carbapenem-susceptible +0.24 (+0.06 to +0.42) -0.03 (-11.34 to +11.29) +0.09 (-0.05 to +0.23)
ICU, carbapenem-resistant +0.19 (-0.01 to +0.39) +0.32 (+0.21 to +0.43) +0.09 (-0.05 to +0.22)
ICU, total +0.25 (+0.09 to +0.41) +0.34 (+0.24 to +0.44) +0.09 (- 0.05 to +0.23)
Wards, carbapenem-susceptible -0.01 (-0.16 to +0.17) + 0.08 (-0.10 to +0.25) +0.06 (-0.12 to +0.25)
Wards, carbapenem-resistant +0.14 (-0.04 to +0.33) - 0.06 (-0.42 to + 0.31) -0.12 (-0.22 to +0.21)
Wards, total +0.27 (+0.12 to +0.41) +0.04 (-0.14 to +0.23) +0.07 (-0.14 to +0.28)
Overall ABBSI +0.26 (+0.11 to +0.41) + 0.14 (-0.06 to +0.33) +0.08 (-0.10 to +0.26)
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Note. Time series were tested for fitting a Box Jenkins Seasonal Autoregressive Moving Average (SARIMA) model. Statistically significant results (P < .05) are presented in boldface. All Intensive Care Units (ICU) were climate controlled, while other wards were not.

In the Poisson regression analysis (Table 4), we found significant association of temperature with carbapenem-resistant and overall ABBSI in non-climate-controlled units in the Botucatu hospital. There was also a similar association when we used overall hospital incidence of ABBSI as outcome. No association with weather was found for the other study hospitals.

Table 4. Poisson regression analysis of the impact of weather on monthly incidence of Acinetobacter baumannii complex bloodstream infections, according to type of hospital unit and resistance.

 Type of unit/Carbapenem-resistance Average temperature (oC) Average relative Humidity (%) Total rainfall
IRR (95%CI) P RR (IC95CI%) P IRR (95%CI) P
Botucatu hospital
ICU, carbapenem-susceptible 1.06 (0.99–1.12) 0.08 0.99 (0.96–1.03) .96 0.99 (0.99–1.01) 0.22
ICU, carbapenem-resistant 1.01 (0.95–1.07) 0.86 0.99 (0.96–1.02) .48 0.99 (0.99–1.00) 0.97
ICU, total ABBSI 1.03 (0.99–1.08) 0.19 0.99 (0.97–1.01) .57 0.99 (0.99–1.00) 0.39
Wards, carbapenem-susceptible 1.03 (0.96–1.11) 0.43 1.00 (0.96–1.04) .84 1.00 (0.99–1.01) 0.87
Wards, carbapenem-resistant 1.07 (1.01–1.15) 0.04 1.00 (0.96–1.04) .99 0.99 (0.99–1.00) 0.78
Wards, total ABBSI 1.05 (1.01–1.10) 0.04 1.00 (0.98–1.03) .81 0.99 (0.99–1.00) 0.89
Overall ABBSI 1.05 (1.01–1.09) 0.006 0.99 (0.98–1.01) .32 0.99 (0.99–1.00) 0.85
Goiânia hospital
ICU, carbapenem-susceptible 2.25 (0.96–5.26) 0.06 1.11 (0.96–1.28) .15 0.98 (0.96–1.03) 0.09
ICU, carbapenem-resistant 1.01 (0.79–1.28) 0.96 1.02 (0.98–1.07) .29 0.99 (0.99–1.00) 0.24
ICU, total ABBSI 1.08 (0.86–1.36) 0.48 1.03 (0.99–1.07) .17 0.99 (0.98–1.00) 0.09
Wards, carbapenem-susceptible 1.23 (0.82–1.87) 0.31 0.96 (0.89–1.02) .16 1.01 (0.99–1.02) 0.06
Wards, carbapenem-resistant 0.79 (0.57–1.08) 0.14 0.99 (0.93–1.04) .61 1.00 (0.99–1.01) 0.33
Wards, total ABBSI 0.92 (0.73–1.18) 0.54 0.97 (0.93–1.01) 0.17 1.01 (0.99–1.01) 0.09
Overall ABBSI 1.00 (0.84–1.17) 0.97 0.99 (0.97–1.03) 0.97 1.00 (0.99–1.00) 0.89
Fortaleza hospital
ICU, carbapenem-susceptible 0.67 (0.11–4.02) 0.67 0.86 (0.62–1.21) 0.41 0.99 (0.97–1.02) 0.51
ICU, carbapenem-resistant 1.27 (0.79–20.07) 0.32 0.98 (0.89–1.08) 0.72 0.99 (0.99–1.00) 0.74
ICU, total ABBSI 1.21 (0.77–1.93) 0.41 0.98 (0.89–1.06) 0.57 0.99 (0.99–1.00) 0.67
Wards, carbapenem-susceptible 1.28 (0.57–2.85) 0.55 0.97 (0.86–1.08) 0.56 1.00 (0.99–1.01) 0.31
Wards, carbapenem-resistant 1.17 (0.74–1.88) 0.49 0.99 (0.91–1.08) 0.88 0.99 (0.99–1.01) 0.81
Wards, total ABBSI 1.19 (0.80–1.79) 0.38 0.98 (0.91–1.06) 0.64 1.00 (0.99–1.01) 0.75
Overall ABBSI 1.20 (0.88–1.63) 0.24 0.98 (0.93–1.03) 0.45 0.99 (0.99–1.00) 0.99
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Note. Results with P<0.05 are presented in boldface. ABSSI, Acinetobacter baumannii complex bloodstream infections; ICU, intensive care units; IRR, incidence rate ratio.

Discussion

Brazil is a huge country with an area of 8.5 million square kilometers and 200 million inhabitants, distributed 27 states located in five macro-regions. Climates range from equatorial in the Amazon basin to temperate in the South region. There are great socioeconomic differences, with poorer areas in the North/Northeast and more developed states in the South/Southeast [14]. Some community-associated infectious diseases are restricted to a climate or biome (e.g., malaria in Amazon), while other occur seasonally in most areas (e.g., Dengue fever) [15]. Among all this complexity, one may wonder how geography and climate impact on the epidemiology of HCAIs occurring in circa 6,000 hospitals in Brazil.

A. baumannii complex infections are hyperendemic in Brazilian hospitals [16]. As in other countries, they preferentially affect critically ill patients, undergoing invasive procedures or carrying invasive devices [11,17]. Our study aimed, therefore, at comparing the impact of season and weather on ABBSI incidence in areas that were both geographically distant and presented different climates.

Counterintuitively, ABBSI incidence was higher in the hospital located in the area presenting lower temperatures [2]. Two aspects highlight differences in the epidemiology of ABBSI in the Botucatu hospital. First, continuous incidence is detected, while in other hospitals there were gaps during which A. baumannii was not recovered from cultures. (Second, as already mentioned, there was hyperendemicity of carbapenem-susceptible isolates up to year 2010, while in other hospitals more than 90% of isolates recovered all through the study period were carbapenem-resistant.

Seasonality results did not exactly match findings from Poisson regression analysis, using weather parameters as predictors. Some aspects may account for those findings. First, one must notice that weather data were obtained from meteorological stations, located in the same city of (but not inside or nearby) the study hospitals. Therefore, they do not necessarily reflect, for instance, temperature and humidity inside hospital wards. This difference is obviously more relevant for the climate-controlled ICUs, though previous studies from our group have found association of temperature outside hospitals with ABBSI incidence in those units [5,6].

Another confounding aspect is the overall difference in the incidence of ABBSI among study hospitals. As one may infer from the Box-Jenkins models presented in Table 3, lower incidences hindered subgroup analyses and may have impacted on statistical power of regression models. Also, the variation of temperature among months (and seasons) was greater in the Botucatu (standard error [SE], 3.3) than in Goiânia (SE, 1.5) and Fortaleza (SE, 0.7). That means that the lower the latitude, the lesser the temperature changes though the year.

Finally, there are potential drivers of seasonality which are not directly associated with weather, such summer understaffing [1], the presence of new students and resident doctors or even changes in diseases leading to hospital admission. All those factors require further investigation, though a previous study found no impact of patients’ severity-of-illness or comorbidities on the seasonality of Gram-negative (including A. baumannii) bloodstream infections [7].

Our study was limited for not individually assessing the average severity-of-illness of patients admitted to different hospitals, as well as their structure for infection control and the quality of microbiology laboratory. However, the three hospitals were included in a recent multistate survey, with similar performance [18]. The same study found minor differences on overall HCAIs incidence among different regions in Brazil [19]. Other limitation was not assessing the incidence of pathogens (e.g., MRSA and GNB) that may compete ecologically for human and inanimate reservoirs inside hospitals [20]. It is therefore possible that the greater incidence of GNB such as Klebsiella spp in hospitals close to the Equator line [2] could have impacted on the lower incidence of A. baumannii found in our study. Finally, we cannot draw a representative picture for the whole county with data from only three hospitals. Unfortunately, countrywide surveillance system for nosocomial pathogens was only implemented in 2013, so we did not have access to data from a great number of settings. Rather than providing an overview of A. baumannii seasonality in Brazil, we were interested in investigating differences in ABBSI seasonal behavior in hospitals from different areas.

In conclusion, we found seasonality of A. baumannii infections in hospitals located in areas with climates ranging from tropical to temperate. Both incidence, seasonality and association with weather were greater in the hospital located in the area with fresher climate and greater temperature range. Those findings reinforce evidence on the seasonal nature of that pathogen. Also, although incidence was greater in climate-controlled ICUs (where patients have severe disease and are exposed to invasive devices) than in non-climate-controlled wards for non-critically ill patients, the association of incidence with higher temperatures was sound. Appropriate climate control is (uncommon in low-to-middle income countries [21]) may be a tool for infection control and prevention.

Supporting information

S1 File. Anonymized monthly rates for time series analysis.

(XLSX)

Click here for additional data file. (40KB, xlsx)

Acknowledgments

This study is part of the PhD Thesis of SPFF, with CMCBF as his advisor.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The authors received no specific funding for this work.

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Decision Letter 0

Aleksandra Barac

11 May 2021

PONE-D-21-05493

Seasonality and weather dependance of Acinetobacter baumannii complex bloodstream infections in different climates in Brazil.

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #3: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

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**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Many hospital-acquired blood stream infections worldwide have been attributed to Acinetobacter

baumannii complex. The focus of this study was to assess the impact of seasons, weather parameters

and climate control on the incidence as well as carbapenem-resistance in A. baumannii complex

bloodstream infections in hospitals from three regions of Brazil with different climatic conditions. A

major strength of this manuscript is the use of Box-Jenkins models, which allows for identifying

seasonality in the dependent series (seasonally differencing it if necessary) from even retrospective

datasets. Notwithstanding, the manuscript has a number of flaws that makes it not suitable for

publication in the present state, but of major concern is the methodology section. The data sources

from which the entire results are based on are missing. The author’s stated that monthly incidence rates

between 2006 and 2015 were studied, however failed to show yearly distribution of ABBSI in hospitals

from the study sites. Much emphasis was laid on these raw data but none was showed, not in terms of

charts or tables. Other minor issues have been highlighted in the manuscript.

Reviewer #2: PONE-D-21-05493

Report

In this manuscript the authors analyzed the seasonal changes of ABBSI in different climates, and its relationship with antibiotic resistance and climate control in hospitals. The manuscript seems scientifically sound, and contains some interesting results that can be considered for publication in PONE. However, before the decision of acceptance for publication is running, a very major revison of the manuscript is required. Specifically, the following points should be addressed by the authors:

1、In general, there is a lack of explanation of replicates and statistical methods used in the study. It is expected that the author can give a more detailed explanation or basis why SARIMA is selected in the time series analysis and multivariate Poisson regression models, is selected in the weather-dependent model.

2、Another major question needs to be explained by the author: why only three hospitals are selected, and is the argument for the conclusion sufficient? In addition, whether it is possible to increase the number of hospital cases selected in the same city (that is, under the same climatic conditions) to see if more interesting results can be obtained, and further confirm the conclusion.

Reviewer #3: Review on “Seasonality and weather dependence of Acinetobacter baumannii complex

bloodstream infections in different climates in Brazil.”

The study aims to determine the seasonal weather control of Acinetobacter baumannii complex bloodstream infections (ABBSI) in hospitals from different climatic regions of Brazil. The study conducted hospitals in three climatic zones of Brazil viz. Cfa, subtropical climate, humid (Botucatu São Paulo State, Southeast); Aw, tropical climate with dry winter (Goiânia Goiás State, Middle-west); and As, tropical with dry summer (Fortaleza, Ceará State, Northeast region). The values of monthly averaged climate parameters viz. temperature, relative humidity, and rainfall have been obtained from the INPE climate dataset. The monthly incidence rates of ABBSI (per 10,000 patient-days) have been calculated for Carbapenem-susceptible and Carbapenem-resistant cases separately and for climate control units and non-climate control units. The analysis using Seasonal Autoregressive Integrated Moving Average has been made. The study is novel and valuable. It would have a wider readership if it published in PLOS-One. Having said that, I feel the study requires major revision. My comments are mentioned below.

1. It is not clearly stated in the manuscript that why the authors intended to do such a study. The motivation and significance of the study need to be brought out clearly in the manuscript before its publication.

2. The authors have selected the hospitals in different climatic zone to understand the impact of the weather on ABBSI. The elevation and proximity of the ocean differ for all these stations. The author needs to justify how their experiment design does not affect by these factors in the revised manuscript. For the better readership of this paper, it will be helpful if authors supplement a detailed description of the SARIMA model in the revised manuscript.

3. Table 2 shows that ABBSI per (10000 patients-days) are more in climate control units than non-climate control units for Carbapenem-susceptible and Carbapenem-resistant cases. However, in conclusion, they have stated that “Strict temperature control may be a tool for prevention of A. baumanii infections in healthcare settings.” – It is confusing and need proper analysis and explanation.

4. The temperature, relative humidity, and rainfall have been analyzed to understand their impact on ABBSI incidences. Relative humidity and rainfall do not vary significantly over all three stations, as seen from the monthly average dataset. Therefore the variation of temperature has importance. However, it will be valuable if authors perform this study for non-rainy months and rainy months.

The author needs to check spelling and grammar before submitting the revised manuscript. There are some typo-grammatical errors in the manuscript.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachment

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Attachment

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PLoS One. 2021 Aug 19;16(8):e0255593. doi: 10.1371/journal.pone.0255593.r002

Author response to Decision Letter 0

13 Jul 2021

RESPONSE TO REVIEWERS

To the Editor and Reviewers –

We thank you for extensively reading our manuscript and for your excellent comments. Whenever possible, we followed your recommendations and changed the manuscript accordingly. Changes in the revised manuscript are presented in red types (in the “track changes version”). We address each comment and recommendation bellow.

Editorial team

We note that Figure 1 in your submission contain map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

Authors’ response: Figure 1 was generated in ArcGis (ESRI, Redlands, CA) and its copyrights belong to the authors. We included that information as a note in the figure.

Reviewer #1

Reviewer #1: Many hospital-acquired blood stream infections worldwide have been attributed to Acinetobacter baumannii complex. The focus of this study was to assess the impact of seasons, weather parameters and climate control on the incidence as well as carbapenem-resistance in A. baumannii complex bloodstream infections in hospitals from three regions of Brazil with different climatic conditions. A major strength of this manuscript is the use of Box-Jenkins models, which allows for identifying seasonality in the dependent series (seasonally differencing it if necessary) from even retrospective datasets. Notwithstanding, the manuscript has a number of flaws that makes it not suitable for publication in the present state, but of major concern is the methodology section. The data sources from which the entire results are based on are missing. The author’s stated that monthly incidence rates between 2006 and 2015 were studied, however failed to show yearly distribution of ABBSI in hospitals from the study sites. Much emphasis was laid on these raw data but none was showed, not in terms of charts or tables. Other minor issues have been highlighted in the manuscript.

Authors’ response: We thank Reviewer #1 for her/his comments. The data used for analysis were obtained in laboratory files in the study hospitals, and validated in accordance to databases from the infection control committee’s in every study hospital. We clarified that topic in the methodology section. A new figure (Figure 2) presenting time series of monthly ABBSI incidence was included. Also, we included a statement informing that raw data are submitted as supplementary file. We included the type of study in the abstract (ecologic study).

Reviewer #2

In this manuscript the authors analyzed the seasonal changes of ABBSI in different climates, and its relationship with antibiotic resistance and climate control in hospitals. The manuscript seems scientifically sound, and contains some interesting results that can be considered for publication in PONE. However, before the decision of acceptance for publication is running, a very major revison of the manuscript is required. Specifically, the following points should be addressed by the authors: 1、In general, there is a lack of explanation of replicates and statistical methods used in the study. It is expected that the author can give a more detailed explanation or basis why SARIMA is selected in the time series analysis and multivariate Poisson regression models, is selected in the weather-dependent model. 2、Another major question needs to be explained by the author: why only three hospitals are selected, and is the argument for the conclusion sufficient? In addition, whether it is possible to increase the number of hospital cases selected in the same city (that is, under the same climatic conditions) to see if more interesting results can be obtained, and further confirm the conclusion.

Authors’ response: We expanded explanations on the reasons for using Box-Jenkins models and Poisson regression. We used only three hospitals because, previously to 2013, there was no countrywide data on incidence of healthcare-associated pathogens. Those three hospitals agreed to share their data. We are aware that this does not provide a wide or representative sample of the country, but those were the data available for our analysis. We attempted to compare those hospitals from different areas rather than providing a representative overview of ABBSI incidence in Brazil. We included this limitation in the discussion section.

Reviewer #3

The study aims to determine the seasonal weather control of Acinetobacter baumannii complex bloodstream infections (ABBSI) in hospitals from different climatic regions of Brazil. The study conducted hospitals in three climatic zones of Brazil viz. Cfa, subtropical climate, humid (Botucatu São Paulo State, Southeast); Aw, tropical climate with dry winter (Goiânia Goiás State, Middle-west); and As, tropical with dry summer (Fortaleza, Ceará State, Northeast region). The values of monthly averaged climate parameters viz. temperature, relative humidity, and rainfall have been obtained from the INPE climate dataset. The monthly incidence rates of ABBSI (per 10,000 patient-days) have been calculated for Carbapenem-susceptible and Carbapenem-resistant cases separately and for climate control units and non-climate control units. The analysis using Seasonal Autoregressive Integrated Moving Average has been made. The study is novel and valuable. It would have a wider readership if it published in PLOS-One. Having said that, I feel the study requires major revision. My comments are mentioned below. 1. It is not clearly stated in the manuscript that why the authors intended to do such a study. The motivation and significance of the study need to be brought out clearly in the manuscript before its publication. 2. The authors have selected the hospitals in different climatic zone to understand the impact of the weather on ABBSI. The elevation and proximity of the ocean differ for all these stations. The author needs to justify how their experiment design does not affect by these factors in the revised manuscript. For the better readership of this paper, it will be helpful if authors supplement a detailed description of the SARIMA model in the revised manuscript. 3. Table 2 shows that ABBSI per (10000 patients-days) are more in climate control units than non-climate control units for Carbapenem-susceptible and Carbapenem-resistant cases. However, in conclusion, they have stated that “Strict temperature control may be a tool for prevention of A. baumanii infections in healthcare settings.” – It is confusing and need proper analysis and explanation. 4. The temperature, relative humidity, and rainfall have been analyzed to understand their impact on ABBSI incidences. Relative humidity and rainfall do not vary significantly over all three stations, as seen from the monthly average dataset. Therefore the variation of temperature has importance. However, it will be valuable if authors perform this study for non-rainy months and rainy months.

The author needs to check spelling and grammar before submitting the revised manuscript. There are some typo-grammatical errors in the manuscript.

Authors’ response: 1. We included the following statement in the discussion: Rather than providing an overview of A. baumannii seasonality in Brazil, we were interested in investigating differences in ABBSI seasonal behavior in hospitals from different areas. We believe that this statement clarifies the intention of our study (as well as its limitations, such as those provided by the study design). 2. We included a more detailed description of the SARIMA model in the methods section (please see response to Reviewer #2). 3. The incidence was higher in climate controlled units because they were intensive care units, therefore harboring more severilly ill patients with invasive devices. However, we changed the conclusion section in order to avoid confusing messages. 4. We did preliminar analyes of rainy vs. non-rainy months and colder vs warmer seasons. They presented no statistical significance and did not relevantly expand our inferences. So we preferred to exclude them. As for typos and other errors, we performed an extensive revision of the manuscript. We thank Reviewer #3 for her/his comments and recommendations.

We believe that the peer review process is a rich opportunity for important discussion on scientific relevance and methodology, and that reviewers’ comments and recommendations helped us improving our manuscript. We thank once again the reviewers and editor.

Yours,

The authors

Attachment

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Decision Letter 1

Aleksandra Barac

21 Jul 2021

Seasonality and weather dependance of Acinetobacter baumannii complex bloodstream infections in different climates in Brazil.

PONE-D-21-05493R1

Dear Dr. Fortaleza,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Aleksandra Barac

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Aleksandra Barac

10 Aug 2021

PONE-D-21-05493R1

Seasonality and weather dependance of Acinetobacter baumannii complex bloodstream infections in different climates in Brazil.

Dear Dr. Fortaleza:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Dr. Aleksandra Barac

Academic Editor

PLOS ONE

Associated Data

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    Supplementary Materials

    S1 File. Anonymized monthly rates for time series analysis.

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