1. A new approach for the determination of the lag and size of effect of the relationship between antimicrobial use and antimicrobial resistance: Time Series Analysis José-María López-Lozano Dominique L. Monnet Hospital Vega Baja Orihuela-Alicante (Spain) Statens Serum Institut Copenhagen (Denmark)
2. Static Model The effect of each factor on current patients is contemporaneous, and independent of precedent and followings months Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Resistance Antimicrobial use Hospital hygiene Bacterial flora diversity
4. If there where no such thing as gravity…. Source: “The Seven Year Itch”, Billy Wilder, 1955 this scene would be… impossible
5. “ If there where no such thing as time, everything would happen all at once ” George Carlin, comedian Source: Forecasting with Dynamic Regression Models , Pankratz, A. Wiley & Sons, New York, 1993
6. Dynamic Model All factor sizes varies while time pass Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Resistance Antimicrobial use Hospital hygiene Bacterial flora diversity
7. Dynamic Model Antibiotic effect is delayed and it decay progressively Resistance Antimicrobial use Hospital hygiene Bacterial flora diversity Month 1 Month 2 Month 3 Month 4 Month 5
8. Dynamic Model Relationship between antibiotic use and resistance is retarded Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Resistance Antimicrobial use Hospital hygiene Bacterial flora diversity
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10. Lagged Reduction of extended-spectrum beta-lactamase-producing Klebsiella pneumoniae (ESBL-KP) incidence after a reduction of antimicrobial use. Hospital Bellvitge, Barcelona, 1993-95 Source: Pena et al. 1998. Antimicrob Agents Chemother 42 :53-8.
11. Lagged Reduction of Acinetobacter baumannii incidence after a reduction of carbapenems . Hospital Bellvitge, Barcelona, 1997-98 Source: Corbella et al.. 2000. J Clin Microbiol 38 :4086-95.
12. Gentamicin Use and Percent Gentamicin-Resistant Gram-Negative Bacilli Isolates, Brussels, 1979-1986 R = 0.90 p < 0.005 % Gentamicin-resistant gram-negative bacilli Gentamicin use same year (g/year) Source: Goossens H, et al. Lancet 1986;2:804. Gentamicin use previous year (g/year)
30. Cross-correlation Function Concept Significant relationship between the use of ciprofloxacin and resistance: - 7 month later - exponential decay Max. relation
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32. Autocorrelation Concept for Univariate Series Ciprofloxacin use is related to its previous values 1 and 3 month before
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43. Study of resistance series 1. Graphical Examination Source: López-Lozano JM, et al. Int J Antimicrob Agents 2000;14:21-31. Hospital ceftazidime use (DDD/1,000 patient-days ) % Ceftazidime-resistant/intermediate gram-negative bacilli 16 16 14 12 10 8 6 4 2 0 14 12 10 8 6 4 2 Jul. 1991 0 Nov. 1991 Mar. 1992 Jul. 1992 Nov. 1992 Mar. 1993 Nov. 1993 Mar. 1994 Jul. 1993 Jul. 1994 Mar. 1995 Nov. 1994 Jul. 1995 Nov. 1995 Mar. 1996 Jul. 1996 Nov. 1996 Mar. 1997 Jul. 1997 Nov. 1997 Jul. 1998 Mar. 1998 Nov. 1998 Mar. 1999 Jul. 1999 Monthly Percent Ceftazidime-Resistant/Intermediate Gram-Negative Bacilli and Hospital Ceftazidime Use, Hospital Vega Baja, Spain, 1991-1998
70. Tranfer Function model for Monthly Percent Ceftazidime-Resistant/Intermediate Gram-Negative Bacilli and Hospital Ceftazidime Use, Hospital Vega Baja, Spain, July 1991-April 2002
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72. Imipenem Meropenem Evolution of the Monthly % of Carbapenem Resistant Pseudomonas aeruginosa (%CR-PA) and Monthly Consumption of Carbapenems (imipenem until Dec-97 and meropenem from Jan-97 to Dec-01). Antwerp University Hospital
73. Imipenem Meropenem Evolution of the Monthly % of Carbapenem Resistant Pseudomonas aeruginosa (%CR-PA) and Monthly Consumption of Carbapenems (imipenem until Dec-97 and meropenem from Jan-97 to Dec-01). Antwerp University Hospital
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75. % Imipenem-resistant/intermediate Pseudomonas aeruginosa Hospital imipenem use (DDD/1,000 patient-days) Mar. 1994 Nov. 1994 Mar. 1995 Jul. 1995 Nov. 1995 Mar. 1996 Jul. 1996 Nov. 1996 Mar. 1997 Jul. 1997 Nov. 1997 Mar. 1998 Jul. 1998 Fuente: López-Lozano JM, et al. Int J Antimicrob Agents 2000;14:21-30. 35 30 25 20 15 10 5 0 45 40 35 30 25 20 15 10 5 0 Jul. 1991 Jul. 1992 Nov. 1991 Mar. 1993 Nov. 1993 Jul. 1993 Nov. 1992 Mar. 1992 Jul. 1994 Jul. 1999 Nov. 1998 Mar. 1999 Lag effect = 1 month 1 DDD/1,000 pat-days +0.40% R 5-Month Moving Average Percent Imipenem-Resistant/Intermediate P. aeruginosa and Hospital Imipenem Use, Hospital Vega Baja, Spain, 1991-1998
76. Source: Lepper et al. Antimicrob Agents Chemother, 2002; 46:2920-25. Evolution of the monthly % of Imipenem-Resistant/Intermediate P. aeruginosa and Hospital Imipenem Use, Ulm University Hospital, Germany, 1997-1999
77. Cross-correlation function of the monthly % of Imipenem-Resistant/Intermediate P. aeruginosa and Hospital Imipenem Use, Ulm University Hospital, Germany, 1997-1999 Source: Lepper et al. Antimicrob Agents Chemother, 2002; 46:2920-25. * Significant lag
78. Monthly evolution of %MRSA Aberdeen Royal Infirmary and Woodend Hospital. 1996-2002
80. Evolution of the monthly %MRSA and monthly sum of lagged antimicrobial use: macrolides (lags of 1 to 3 months), third-generation cephalosporins (lags of 4 to 7 months) and fluoroquinolones (lags of 4 and 5 months), Aberdeen Royal Infirmary, January 1996 - December 2000
81. Monthly %MRSA at Aberdeen Royal Infirmary and in the surrounding Grampian Region, January 1996-February 2002 Monthly %MRSA in hospital Monthly % MRSA in community - 90.8% - R2 1.99 (p = 0.051) 0.47 - C 5.12(p < 0.0001) 0.10 1 MRSAARI 4.76(p < 0.0001) 4.66 - D00 -1.89(p = 0’063) -0.32 3 MRSACOMM*D00 2.07(p = 0.042) 0.27 3 MRSACOMM T Statistic (Prob.) Estimated coefficient Lag(months) Explaining variable
82. The Aberdeen MRSA Outbreak 1: Woodend outbreak 2: Woodend transmit MRSA to ARI 3: ARI antimicrobial use select resistance 4: ARI transmit resistance to Woodend 5: ARI transmit resistance to Community
86. SCA EXPORT EXCEL ACCESS RESULTS TIME SERIES ANALISYS EXCEL MACRO Automatic process Semi-automatic process ANTIBIOTIC PROFILE SERIES ISOLATE SERIES RESISTANCE SERIES ANTIBIOTIC USE SERIES ACCESS
87. Expected resistance percentage for each microorganism-antibiotic combination Expected % of imipenem-resistant P.aeruginosa for current month
89. Specimen Microorg. Antibiotic Setting Save graphics Saving results (Excel format) Evolution of monthly percentage of resistance Number of resistant isolates Total number of isolates
90. Evolution of the hospital antimicrobial use Monthly no. DDD per 1,000 patient-days
95. Comparing resistance and antibiotic use series Monthly hospital erythromycin use Monthly hospital clarithromycin use Monthly % erythromycin-resistant coag.-negative staph.
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Editor's Notes
Consider that resistance is a consequence of several independent factors: antimicrobial use that can select resistant strains in a individual patient Hospital hygiene barriers, that can avoid or facilitate transmission of resistant elements between patients Environmental bacterial flora that can vary itself as a consequence of spontaneous or induced mutations, and that can, also, vary depending of ecological competition between different strains. This bacterial diversity can actuate as a changing reservoir that can modify the probability for a specific strain to be or not present in an exact moment and setting. Each of this factors can inter-actuate on the other ones modifying them. The static approach implies that the effect of each factor on current patients is contemporaneous, and independent of precedent and followings time periods
Some physical phenomena are inevitable
Some physical phenomena are inevitable
Similarly, It looks like evident that we have to take time into account if we are trying to study the genesis of the bacterial resistance to antibiotics. A resistant bacteria needs time to transmit to other people or for contaminating inanimate objects that will used as reservoirs for subsequent infections. However ecological literature have not paid enough attention to this matter that looks like obvious
A dynamic approach consider that the size of each factor varies while time pass
On the other hand each factor can modify other factors. Antimicrobial use can modify bacterial flora biodiversity. The dynamic approach implies that this modification needs time to take place, in an ecological scale. Consequently, if antibiotic impact of resistance is mediated by diversity flora modifications, this effect could not be contemporaneous but retarded. On the other hand, bacterial flora diversity is a changing phenomenon that varies depending of external influences but that tends toward an equilibrium. Consequently when influence of a factor disappears, i.e. antimicrobial use, flora needs some time to recover its initial status. This theoretical construct implies, also, that impact of antimicrobial on resistance is a reversible phenomenon, at a ecological level.
As a consequence of this hypothesis, if we represent each one of this phenomena across time it is possible to find a retarded relationship between them.
There is few evidences of this hypothesis: Mc Gowan (1) cites several examples of retarded, at a monthly scale, of resistance after changes in antimicrobial use, for example: A rapid development of ciprofloxacin resistance in methicillin- susceptible and -resistant Staphylococcus aureus three month after the introduction of fluoroquinolones. But there is two graphics examples: Peña and Corbella (1) McGowan, J. E. Jr. 1991. Abrupt changes in antibiotic resistance. J Hosp Infect 18 Suppl A:202-10.
A lagged reduction on KP producing extended spectrum beta-lactamase incidence after a restriction on the use of 3 generation cephalosporines (1). (1)Pena, C., M. Pujol, C. Ardanuy, A. Ricart, R. Pallares, J. Linares, J. Ariza, and F. Gudiol . 1998. Epidemiology and successful control of a large outbreak due to Klebsiella pneumoniae producing extended-spectrum beta-lactamases. Antimicrob Agents Chemother 42 :53-8.
The same team report a reduction of Acinetobacter baumannii incidence after a reduction of carbapenems use in a Spanish hospital(1), this reduction is lagged (4 or 6 months after use decrease). But design of both studies don’t allows to measure ant quantify this relationship. (1) Corbella, X., A. Montero, M. Pujol, M. A. Dominguez, J. Ayats, M. J. Argerich, F. Garrigosa, J. Ariza, and F. Gudiol . 2000. Emergence and rapid spread of carbapenem resistance during a large and sustained hospital outbreak of multiresistant Acinetobacter baumannii. J Clin Microbiol 38 :4086-95.
Concomitants variations, that is to say, changes in antimicrobial use followed by changes in resistance in the same direction, are probably the most convincing since they take into account the time sequence between the suspected cause and the observed effect. They have been reported from various single hospitals or from single countries. However, pooled data, covering one or several years, were used to analyze temporal associations observed between two time periods, which means that these studies could not measure the delay necessary to observe an effect of antimicrobial use on resistance. Because data were only available on a yearly basis, attempts to take this delay into account empirically considered a 1-year delay. For example, as shown in the figure, Goossens et al found a correlation between the percentage of gentamicin-resistant Gram-negative bacilli and gentamicin use during the previous year. Interestingly, no correlation was observed when using gentamicin use from the same year.
Our experience at Hospital Vega Baja shows that there is often a 1-year delay, but not always. For example, as shown in this graphic, we generally observed such variations of the percentage of ceftazidime-resistant Gram-negative bacilli following variations in ceftazidime use, except in 1996 when ceftazidime resistance increased following a decrease in ceftazidime use in 1995! . On the other hand, If we estimate a correlation coefficient between both series we don’t find any significant relation between them, maybe because its small sample size.
However if we represent graphically the monthly percentage of ceftazidime-resistant GNB and the monthly antimicrobial use, we can oberve that, in general, they are phenomena with variations in a period time shorter than a year. As we will see further on, in this way we will find a significant relation
Simply smoothing the monthly series using a 5-month moving average transformation we are able to see a better empiric relationship between use and resistance, and besides we see that use precedes resistance
To explain the usefulness of Time Series Analysis for studying resistance and its relationship with antimicrobial use we must to introduce the concept of dynamic regression, owing to Pankratz In the graphic we can observe the monthly evolution since 1991to 2001 of hospital ciprofloxacin use (DDD/100 patients-days) and the monthly percentage of ciprofloxacin-resistant/intermediate Ps aeruginosa isolated in our hospital.
To improve the relationship visualization we plot both series smoothing them by means of a 5 months moving average transformation, i.e. the value plotted for a specific month is the average of the value observed that month, the two previous months and the two next months. Although these smoothed series has not statistical value, they give a better visual representation of the behavior of the two series. We can observe no relation between both series: when use increases, resistance increases, decreases or doesn’t change
If we calculate a contemporaneous correlation coefficient (i.e. Pearson) we don't find significance, pointing out that there is not relationship between antimicrobial use a certain month and the observed resistance that same month
But imagine that now we are going to calculate the correlation coefficient between resistance and the use four month before. To do this, we delay the antimicrobial use series: the correlation coefficient is different, but it remains without signification.
If we repeat this type of analysis delaying the antimicrobial use series several month, we could find a significative correlation coefficient. In this example this happens with a delay of 7 months
If we represent in a bar graphic the magnitude of the different correlation coefficients (during 24 months) and we represent then with their confidence interval, we can find other delays equally significant . In this example we can see an exponential decay of the magnitude of coefficients. We can interpret this saying that increases and decreases of antimicrobial use in our hospital is followed 7 month after by increases or decreases (always of the same sense, because coefficients are positive) of Ps aeruginosa resistance, but besides we can find a residual effect for several months. This graph is a representation of the so called CROSS-CORRELATION FUNCTION. But this analysis is not sufficient to assess the relationship because, maybe, there is also an influence of past values of resistance…..
What we have done is to delay the antimicrobial use series looking for the moment where the relationship is maximum
If in the same way we analyze the relationship between one series values and previous values of this same series, we could find a relationship among moments in the same series. This is what it is called AUTOCORRELATION
Our project is based on a methodology called Time Series Analysis (TSA): a group of statistical techniques whose origin is in the econometric sciences. The purpose is the statistical analysis of time series defined as SERIES OF DATA COLLECTED OVER TIME AT REGULAR AND SHORT INTERVALS. Unlike usual statistical methods, TSA take into account the possible relationship existing between consecutive observations. Increasing access to personal computers and availability of specific software applications enable routine use of these methods Among TSA we will use specific procedures for the study of stochastic process
In concret we will use ARIMA and Transfer Function Techniques
ARIMA models (also called Box-Jenkins models because these authors described a practical approach method to identify and estimate them) permit to capture the different components of a time series. AR (Autoregressive): influence of previous values I (Integrated): this term makes reference to the need or not of stationarisation of the series when their mean or their variance is not constant along the time. MA (Moving Average): abrupt changes in the near past e.g., for ceftazidime-resistant gram-negative bacilli: we can estimate that the expected percentage of resistant GNB in a certain month is equal to a constant value (3.3%) plus 0.34 by observed level of resistance three months before plus 0.26 26 by observed level of resistance five month before plus a residual term (that captures the part of the model not represented by the autoregressive components). The represented equation is an algebraic expression that doesn’t correspond exactly with the usual type of formulation in ARIMA modelization, but we use it here with an explaining aim.
We will speak, shortly, about these three methods for the construction of Transfer Function model
Haugh method is more comprehensible because it omit the filtering steep, one concept sometime difficult to understand
Prhaps this is the simplest and easiest method. It consists of estimating a regression equation including, in an empirical way, several lags of the explaining series, and progressively eliminating not significant lags. The disturbance concept makes reference to the difference between the original series and the adjusted one with only the significant explaining terms This is very useful to identify the stochastic part of the model: AR and MA terms).
Usually we use SCA and Eviews, though in the examples we will employ SPSS, because of its popularity and its simplicity
The first steep in every TSA is to visualize the series we will analyze. In our example, we can see a certain coincidence in the evolution of antimicrobial use and resistance values. On the other hand we can see that both series show many spikes, but varying around a certain trend, a certain evolution pattern. This evolution pattern is the representation of the underlying stochastic process of resistance and antimicrobial use seen in a ecological scale and measured through time.
There are two auto-correlation function: Simple: each lag is correlated individually (ACF) Partial: each lag is correlated adjusting for the other lag correlation coefficients (PACF) The objective of this examination is to detect an isolated coefficients in one of these functions. Depending of the function that show isolated significant coefficients we will decide to include AR terms in the model (if they are in the PACF) or MA terms (if isolated ‘spikes’ are in the ACF). Autocorrelation exploration inform us, also, about other time series characteristics: necessity or not to differencing, etc.. There is several rules to identify ARIMA models, but it is not suitable to talk about them in this lecture There is no need for differentiation or logarithmic transformation Two possibilities: AR(1,2,3), AR(1,3): Significant spike in the 3 first lags in PACF Coefficients decay in a slower and more progressive fashion in ACF (so it’s not an MA) This is necessary to fit both models and to compare
We estimate the AR(1,2,3) model
And the AR(1,3) model
The ARIMA model diagnostics correspond to a group of test whose interest is to assure the completion of a series of mathematical requirements in relation to the model identification stage.
An essential element is the analysis of residual series. They must not have any significant term (they must be ‘white noise). In the AR(1,3) we observe a significant term in lag 5, in both functions. We need to add an AR(5) term or a MA(5) term and to compare both models. After doing this we observe model fit better with the AR(5) term (not shown).
Now residuals looks like satisfactory
When there are more than a satisfactory model we have to compare them. For that we use residual variance and the Akaike Information Criterium. The best model must to have lowest values.
In the next steep we can forecast resistance value using the adjusted model
Once we have done the prediction, we compare it with real value following the estimation period: because we have left a short period that we have not included in order to do the comparison or because , as in this example, because we have waited a certain time and we compare reality with forecasting. We can observe that our forecast (its confidence interval) doesn't include the real series
Because our interest is to study the relationship between ceftazidime use and resistance we have to identify a model for it
Following the haugh method and once we have the two residual series we construct a CCF
We formulate and estimate the TF model using SPSS ARIMA procedure
Transfer Function has to be put to the same validation rules that univariant ARIMA
We see that the TF model is better than univariant model. Adding antimicrobial use improves the knowledge of the resistance series
Forecasting with TF model we see that now real resistance is included in the confidence interval, and besides forecasts look at a similar pattern than reality
Comparing predictions from both models we understand perfectly why including antimicrobial use improves fitting. We are analyzing an stochastic process where we have explained a part of it by means a know factor. If we knew more factors (level of compliance to hygiene protocols, another antimicrobial use, etc…), we perhaps would be able to increase the square regression coefficient ant to reduce the forecast confidence interval
Three years afterwards we continue to see that resistance evolution is parallel to antimicrobial use
Three years afterwards we continue to see that resistance evolution is parallel to antimicrobial use
In this graphic we can see the smoothed resistance and use series
We even fit a model where one period lagged use series (DDDLAG1) term remains on it, and besuides the stochastic part is simpler (term AR5 disappears), the model is more parsimonious
5 Este es otro ejemplo de relación entre el uso de un antibióticos hospitalario y resistencia de un microorganismo hospitalario al mismo
Objective: To study the relationship between MRSA outbreaks in a large teaching hospital (Aberdeen Royal Infirm., ARI) and a long stay, geriatric hospital (Woodend Hosp., WH) in the same Scottish region by means of time series analysis (TSA). Methods: Data on monthly non-duplicate MRSA and antibiotic use were collected in both hospitals for 01/1996-01/2002. Dynamic regression models were adjusted to measure relationships between data sets. Results: ARI %MRSA rose from <1% in 1996 to 38% in 02/2000, before it started to decline. WH had an independent MRSA outbreak mid-1997 to mid-1998 which then followed the ARI outbreak (Figure). TSA showed that: 1) pre-1999, ARI %MRSA followed WH %MRSA (lag=1 mth), whereas post-1999, ARI %MRSA lead WH %MRSA (lag=1 mth); 2) ARI %MRSA was closely related to previous ARI macrolide, 3rd-gen. cephalosporin and fluoroquinolone use, whereas WH %MRSA was related to previous WH macrolide use only. Conclusion: We found a dynamic relationship between MRSA outbreaks in two hospitals in the same region. The original WH outbreak initiated the ARI outbreak, then ARI lead the WH outbreak. Patient flux between hospitals can explain this relationship. Additionally, antibiotic use in both WH and ARI independently maintained the outbreaks.
This study was performed to study the relationship between MRSA prevalence at our hospital, Aberdeen Royal Infirmary, and MRSA prevalence in the surrounding community, the Grampian region (500,000 inh.). Methods: We calculated the monthly %MRSA at our hospital and the community (01/1996-02/2002). A dynamic regression model was adjusted to measure any relationship between both series. The monthly %MRSA in the community as well in the hospital begin from 0 (1996) and increase until its maximum level (38.2% at ARI and 17.2% at the community) at the first semester of 2000, then they showed a declining behaviour (Figure 1). We found two distinct periods in the model: a) from January 1996 until December 1999 where a dynamic relationship is detected between community resistance and its past values lagged three months (MRSACOMM) and b) from January 2000 to February 2002 when we didn’t found any relationship of community resistance and its own past values. In order to capture these two periods we introduced in the model a dummy variable (D00) which values was 0 for the first period and 1 for the rest. The product of this dummy variable and the term corresponding to 3 month lagged community resistance (MRSACOMM*D00) give us a coefficient of -0.32, this means that for this period the estimated effect of past resistance is not different from zero (0.27 – 0.32 = 0.05, p = ns). (Table) For both periods we found a strong relationship with the MRSA hospital resistance series lagged by one month (MRSACOMM). (Table) We tested several community antimicrobial use series but we didn’t find any relationship with community resistance.
¿Qué es el programa ViResiST? La palabra ViResiST es un acrónimo que hace referencia al tipo de metodología estadística que se utiliza en nuestra red de vigilancia de la resistencia, el uso de antibióticos y la relación existente entre ellos. ¿Qué objetivos persigue el proyecto? Comprender mejor y analizar la relación entre uso de antibióticos y resistencia, dado que: - Los datos de resistencia y uso de antibióticos están autocorrelacionados - Se tiene en cuenta el intervalo entre uso y resistencia - Se puede medir la magnitud del efecto de uno sobre la otra - Se pueden incorporar varios antibióticos en el modelo
The project is based on the collaboration between several centers that send its data to the Coordinating Center where some Statistical analysis are made using SCA software. Results are returned to Participating Centers
The first usefulness of the computer application is a look up table with the results of the ARIMA models fitted on each series of resistance. This look up table gather the expected resistance for the current quarter as percentages. This information can be used by the clinician at the time of the empiric therapy, when he suspect that the patient has an infection caused by a certain microorganism and he must decided among various antibiotics. Once the microorganism is known, the look up table also give the possibility of choosing the antibiotic with the least risk for resistance selection.
This application computes the expected probability to each microorganism in each specimen isolated in a concrete service. The interest of this information is on the possibility to guide the empirical therapy in our local setting. In our example, 57% of the blood specimen in surgical services could be SCN (Coagulase negative staplilococcus), 10%, S.aureus, 6% E.coli, and so on. If we know the probability for each microorganism we can choose the antibiotic presenting less resistance for this bacteria in OUR hospital, improving our success probabilities. We expect to include the risk for selection of resistance for each antibiotic as a output of the Transfer Function analysis
The program is an interactive application that allows to select different variables of interest. Selection is done by means of several menus. The program allows, also, to save our data and graphics. In the example we compare ciproflaxin resistance in E.coli isolated in adults people with the same microorganisms isolated in children. Because quinolones are rarely used in children, the observed resistance can be explained by the transmission from adults (at home) or from contaminated food.
Whe can see the evolution in the use of individual antibiotics or therapeutic groups, for the overall hospital or for diferent services. Example in the use of cephalosporin in hospital A. Whe can see the increase of use from 1997. The use is greater in winter. The left table shows dates of use that was used to make the graphic like DDD / 1000 patients-day
As in the hospital case, the program allow also to explore the antimicrobial community use, in this case as DDD/1000 inhabitant-days. The example show the strong seasonality pattern of amoxicillin-clavulanic acid in a Spanish Health Area
In the same screen we can compare resistance percentage evolution observed at several number of primary health care areas. The figure at the bottom shows comparative evolution of penicillin resistant or intermediate Streptococcus pneumoniae hospital and community strains in four Health Areas. It can be observed the great existing difference among C and E hospitals (Health Areas) on the one hand, and A and R hospitals on the other hand. You can see also a certain seasonal pattern in C Hospital resistance performance with a resistance increase during Autumn months. These differences in resistance level and in seasonal variation lead us to statistically compare it with antibiotic use in Primary Health Care.
In the example we can see the fluorquinolones use in several hospitals
The same comparison as in hospitals can be made for community (different Health Areas)
It is possible to match in the same graphic monthly percentage of resistance and monthly use of a certain antibiotic in a certain hospital or Primary Health Care area. This look allow us to appreciate the concomitant development of both parameters before proceeding to a statistical analysis.
