Background Systems Biology Markup Language (SBML) is gaining broad usage as a standard for representing dynamical systems as data structures. The open source statistical programming environment R is widely used by biostatisticians involved in microarray analyses. An interface between SBML and R does not exist, though one might be useful to R users interested in SBML, and SBML users interested in R.
Background
Systems Biology Markup Language (SBML) is gaining broad usage as a standard for representing dynamical systems as data structures. The open source statistical programming environment R is widely used by biostatisticians involved in microarray analyses. An interface between SBML and R does not exist, though one might be useful to R users interested in SBML, and SBML users interested in R.Results A model structure that parallels SBML to a limited degree is defined in R. An interface between this structure and SBML is provided through two function definitions: write.SBML() which maps this R model structure to SBML level 2, and read.SBML() which maps a limited range of SBML level 2 files back to R. A published model of purine metabolism is provided in this SBML-like format and used to test the interface. The model reproduces published time course responses before and after its mapping through SBML.
Results
A model structure that parallels SBML to a limited degree is defined in R. An interface between this structure and SBML is provided through two function definitions: write.SBML() which maps this R model structure to SBML level 2, and read.SBML() which maps a limited range of SBML level 2 files back to R. A published model of purine metabolism is provided in this SBML-like format and used to test the interface. The model reproduces published time course responses before and after its mapping through SBML.Conclusions List infrastructure preexisting in R makes it well-suited for manipulating SBML models. Further developments of this SBML-R interface seem to be warranted.
Conclusions
List infrastructure preexisting in R makes it well-suited for manipulating SBML models. Further developments of this SBML-R interface seem to be warranted.

Background Systems biology markup language (SBML) is a standard for representing dynamical systems of biological interest [ 1 , 2 ]. Interfaces between SBML and high level computational environments are currently being developed for Mathematica [ 3 ] and Matlab [ 4 ], but to the author's knowledge, no such efforts are being carried forth for R/S-plus. This brief paper presents the author's initial developments toward a two-way SBML-R interface. The interface is currently limited in the range of SBML input files that it can handle. For example, it only handles SBML level 2 and does not handle "Events" and "FunctionDefinitions." The interface can nevertheless be used for some models, examples [ 5 , 6 ] of which are included under "demo" in the SBMLR package [ 7 ]. This paper provides an explicit example of one approach to an SBML-R interface. It is assumed throughout that the reader is already quite familiar with both SBML [ 8 ] and R [ 9 ].
Background
Systems biology markup language (SBML) is a standard for representing dynamical systems of biological interest [ 1 , 2 ]. Interfaces between SBML and high level computational environments are currently being developed for Mathematica [ 3 ] and Matlab [ 4 ], but to the author's knowledge, no such efforts are being carried forth for R/S-plus. This brief paper presents the author's initial developments toward a two-way SBML-R interface. The interface is currently limited in the range of SBML input files that it can handle. For example, it only handles SBML level 2 and does not handle "Events" and "FunctionDefinitions." The interface can nevertheless be used for some models, examples [ 5 , 6 ] of which are included under "demo" in the SBMLR package [ 7 ]. This paper provides an explicit example of one approach to an SBML-R interface. It is assumed throughout that the reader is already quite familiar with both SBML [ 8 ] and R [ 9 ].Implementation The software exists completely in R. It is comprised of four functions and is currently being distributed as a developmental package called "SBMLR" through Bioconductor [ 10 ]. The software was written subject to two constraints: 1) models expressed in SBML-like R must be exchangeable with a range of SBML models; and 2) models must be amenable to simulation in R. The first subsection that follows defines an SBML-like R model structure, the second illustrates how it can be used in simulations, and the third describes its conversions into and out of SBML. An SBML-Like Model Structure in R To facilitate mappings between SBML and R, an SBML-like list structure is defined in this subsection using the purine metabolism model of Curto et al. [ 6 ] as a specific example (Figure  1 ). In this figure and elsewhere, ellipses (...) indicate missing code not critical to current discussions; complete source codes are available through the SBMLR package [ 7 ]. The essential components of an SBML model, namely, its compartments, species and reactions, are all present in this R analog of an SBML model. In the model of Curto et al. [ 4 ], there is one compartment, be it the cell or the entire human body, and 18 species: 2 boundary conditions (bc = True) and 16 state variables (bc = False), each with an initial condition (ic) or value. Each reaction is a list that includes a reaction id, the names of species that are reactants (reacts), the names of species that are reaction rate modulators (mods), the names of species that are produced by the reaction (prods), parameter values (params), and the reaction rate law (law) function definition. In this framework, only state variables need be listed as products, boundary condition reactants can equivalently be listed as modulators, and missing terms (e.g. mods in reactions 1 and 37) are equivalent to a NULL assignment. The rate law function has as its input arguments two vectors, one carrying the concentrations of reactants and modulators (r), the other carrying reaction parameter values (p). If the body of the rate law function contains  n  statements, the first  n -1 trivially convert input vector components into variables with the same names. The  n th statement then contains the complete reaction rate law. It can occupy multiple lines, but it must be a single statement, i.e. it cannot depend on substitution variables temporarily defined in preceding statements. SBML-like Model Execution in R Model definition codes such as that given in Figure  1 , when placed in a separate file (e.g. Curto.r), can be sourced into a parent script to become globally available for simulations. For example, the purine metabolism model of Curto et al. [ 6 ] can be simulated using the execution code shown in Figure  2 . This code simulates the response to a 10-fold increase in phosphoribosylpyrophosphate (PRPP) at time  t  = 0 and plots the responses of inosine monophosphate (IMP) and hypoxanthine (HX) as shown in Figure  3 . Two functions called by this script are defined in the SBMLR package and shown in Figure  4 . They are, getIncidenceMatrix(), which computes the incidence/stoichiometry matrix used by the second function, fderiv(), which computes state derivatives for integration by the function lsoda() of the "odesolve" package. In getIncidenceMatrix(), the incidence matrix is generated automatically using an  i  loop over the rows (i.e. state variables) and a  j  loop over the columns (i.e. reactions). If a state is a product of a reaction, the corresponding matrix element becomes a positive integer equal to its stoichiometry [factor() converts string names to factors so that summary() can count them], and similarly for reactants, though with negative numbers entering the matrix in this case (or possibly zero, if a reactant of a reaction happens to also be a product of the same reaction). The function fderiv() creates the current species vector by overriding initial states with current states clipped to positive values, and by overriding any time varying boundary conditions defined by rules (SBML rules are not needed for the purine model, but are needed to implement other models [ 5 ]). The function fderiv() then computes the reaction rate flux vector (v) based on the current species vector (St) and multiplies it by the incidence matrix to produce the current state derivative vector (xp). The names of xp and v are reset at the end of each function call to override the problem of variables gaining new composite names from the names of their expression arguments. A Two-Way Interface between SBML and R Two functions comprise the SBML-R interface: write.SBML() converts SBML-like R models (e.g. Curto.r) into SBML models (e.g. Curto.xml), and read.SBML() converts SBML models (e.g. Curto.xml) into an SBML-like R model (e.g. CurtoX.r). A key component of these two interface functions is a locally defined recursive function named recurs(). This function converts arbitrary R expressions into arbitrary MathML expressions, and vice-versa; it is defined differently, locally, in each of the two functions. In write.SBML(), shown in Figure  5 , recurs() initially takes as its input argument the last component of the body of the kinetic rate law function definition, which is the entire rate law expression (as mentioned above, rate laws involving multiple R statements are not supported). In R, expressions are LISP like in that they contain a first element, the operator, and the remaining elements, the arguments, any of which can be an expression. If the operator is the parentheses operator, the action taken is that of a unary identity operator, and we simply skip it and move on to its argument since parentheses are not needed in MathML. Each nested call to the function recurs() sends "<apply>" and the converted operator to the output file on its way in, and a matching "</apply>" on its way out. Nested calling continues until all nodes of the expression tree are of class "name" or "numeric," i.e. when all found objects are leaves of the tree rather than "expressions" that require further parsing. Leaves are then sent to the output file bracketed by <ci> and </ci>. The second of the two SBML-R interface functions, read.SBML(), maps a limited range of SBML level 2 files (function definitions and events are not handled) into SBML-like R model files. Portions of read.SBML() are given in Figure  6 . The main difference between this function, read.SBML(), and the previous function, write.SBML(), is that here, rather than using parse() to decompose the list-of-lists structure of the model defined in R, the SBML model is instead decomposed as an XML object using xmlTreeParse() of the XML package available to R [ 11 ]. In read.SBML(), the locally defined recursive function recurs() uses an overkill of parentheses to avoid operator precedence issues. This recursive function is passed a MathML reaction rate law which it parses recursively until the leaves of the tree (the "ci") are all found. During the recursion a corresponding R expression is built as a vector of character strings which, upon exit from the last of the recursive calls, is collapsed into a single string and sent to the output file as the last line of the current rate law function definition.
Implementation
The software exists completely in R. It is comprised of four functions and is currently being distributed as a developmental package called "SBMLR" through Bioconductor [ 10 ]. The software was written subject to two constraints: 1) models expressed in SBML-like R must be exchangeable with a range of SBML models; and 2) models must be amenable to simulation in R. The first subsection that follows defines an SBML-like R model structure, the second illustrates how it can be used in simulations, and the third describes its conversions into and out of SBML.An SBML-Like Model Structure in R To facilitate mappings between SBML and R, an SBML-like list structure is defined in this subsection using the purine metabolism model of Curto et al. [ 6 ] as a specific example (Figure  1 ). In this figure and elsewhere, ellipses (...) indicate missing code not critical to current discussions; complete source codes are available through the SBMLR package [ 7 ]. The essential components of an SBML model, namely, its compartments, species and reactions, are all present in this R analog of an SBML model. In the model of Curto et al. [ 4 ], there is one compartment, be it the cell or the entire human body, and 18 species: 2 boundary conditions (bc = True) and 16 state variables (bc = False), each with an initial condition (ic) or value. Each reaction is a list that includes a reaction id, the names of species that are reactants (reacts), the names of species that are reaction rate modulators (mods), the names of species that are produced by the reaction (prods), parameter values (params), and the reaction rate law (law) function definition. In this framework, only state variables need be listed as products, boundary condition reactants can equivalently be listed as modulators, and missing terms (e.g. mods in reactions 1 and 37) are equivalent to a NULL assignment. The rate law function has as its input arguments two vectors, one carrying the concentrations of reactants and modulators (r), the other carrying reaction parameter values (p). If the body of the rate law function contains  n  statements, the first  n -1 trivially convert input vector components into variables with the same names. The  n th statement then contains the complete reaction rate law. It can occupy multiple lines, but it must be a single statement, i.e. it cannot depend on substitution variables temporarily defined in preceding statements.
An SBML-Like Model Structure in R
To facilitate mappings between SBML and R, an SBML-like list structure is defined in this subsection using the purine metabolism model of Curto et al. [ 6 ] as a specific example (Figure  1 ). In this figure and elsewhere, ellipses (...) indicate missing code not critical to current discussions; complete source codes are available through the SBMLR package [ 7 ]. The essential components of an SBML model, namely, its compartments, species and reactions, are all present in this R analog of an SBML model. In the model of Curto et al. [ 4 ], there is one compartment, be it the cell or the entire human body, and 18 species: 2 boundary conditions (bc = True) and 16 state variables (bc = False), each with an initial condition (ic) or value. Each reaction is a list that includes a reaction id, the names of species that are reactants (reacts), the names of species that are reaction rate modulators (mods), the names of species that are produced by the reaction (prods), parameter values (params), and the reaction rate law (law) function definition. In this framework, only state variables need be listed as products, boundary condition reactants can equivalently be listed as modulators, and missing terms (e.g. mods in reactions 1 and 37) are equivalent to a NULL assignment. The rate law function has as its input arguments two vectors, one carrying the concentrations of reactants and modulators (r), the other carrying reaction parameter values (p). If the body of the rate law function contains  n  statements, the first  n -1 trivially convert input vector components into variables with the same names. The  n th statement then contains the complete reaction rate law. It can occupy multiple lines, but it must be a single statement, i.e. it cannot depend on substitution variables temporarily defined in preceding statements.nnnSBML-like Model Execution in R Model definition codes such as that given in Figure  1 , when placed in a separate file (e.g. Curto.r), can be sourced into a parent script to become globally available for simulations. For example, the purine metabolism model of Curto et al. [ 6 ] can be simulated using the execution code shown in Figure  2 . This code simulates the response to a 10-fold increase in phosphoribosylpyrophosphate (PRPP) at time  t  = 0 and plots the responses of inosine monophosphate (IMP) and hypoxanthine (HX) as shown in Figure  3 . Two functions called by this script are defined in the SBMLR package and shown in Figure  4 . They are, getIncidenceMatrix(), which computes the incidence/stoichiometry matrix used by the second function, fderiv(), which computes state derivatives for integration by the function lsoda() of the "odesolve" package. In getIncidenceMatrix(), the incidence matrix is generated automatically using an  i  loop over the rows (i.e. state variables) and a  j  loop over the columns (i.e. reactions). If a state is a product of a reaction, the corresponding matrix element becomes a positive integer equal to its stoichiometry [factor() converts string names to factors so that summary() can count them], and similarly for reactants, though with negative numbers entering the matrix in this case (or possibly zero, if a reactant of a reaction happens to also be a product of the same reaction). The function fderiv() creates the current species vector by overriding initial states with current states clipped to positive values, and by overriding any time varying boundary conditions defined by rules (SBML rules are not needed for the purine model, but are needed to implement other models [ 5 ]). The function fderiv() then computes the reaction rate flux vector (v) based on the current species vector (St) and multiplies it by the incidence matrix to produce the current state derivative vector (xp). The names of xp and v are reset at the end of each function call to override the problem of variables gaining new composite names from the names of their expression arguments.
SBML-like Model Execution in R
Model definition codes such as that given in Figure  1 , when placed in a separate file (e.g. Curto.r), can be sourced into a parent script to become globally available for simulations. For example, the purine metabolism model of Curto et al. [ 6 ] can be simulated using the execution code shown in Figure  2 .This code simulates the response to a 10-fold increase in phosphoribosylpyrophosphate (PRPP) at time  t  = 0 and plots the responses of inosine monophosphate (IMP) and hypoxanthine (HX) as shown in Figure  3 . Two functions called by this script are defined in the SBMLR package and shown in Figure  4 . They are, getIncidenceMatrix(), which computes the incidence/stoichiometry matrix used by the second function, fderiv(), which computes state derivatives for integration by the function lsoda() of the "odesolve" package. In getIncidenceMatrix(), the incidence matrix is generated automatically using an  i  loop over the rows (i.e. state variables) and a  j  loop over the columns (i.e. reactions). If a state is a product of a reaction, the corresponding matrix element becomes a positive integer equal to its stoichiometry [factor() converts string names to factors so that summary() can count them], and similarly for reactants, though with negative numbers entering the matrix in this case (or possibly zero, if a reactant of a reaction happens to also be a product of the same reaction).tijThe function fderiv() creates the current species vector by overriding initial states with current states clipped to positive values, and by overriding any time varying boundary conditions defined by rules (SBML rules are not needed for the purine model, but are needed to implement other models [ 5 ]). The function fderiv() then computes the reaction rate flux vector (v) based on the current species vector (St) and multiplies it by the incidence matrix to produce the current state derivative vector (xp). The names of xp and v are reset at the end of each function call to override the problem of variables gaining new composite names from the names of their expression arguments.A Two-Way Interface between SBML and R Two functions comprise the SBML-R interface: write.SBML() converts SBML-like R models (e.g. Curto.r) into SBML models (e.g. Curto.xml), and read.SBML() converts SBML models (e.g. Curto.xml) into an SBML-like R model (e.g. CurtoX.r). A key component of these two interface functions is a locally defined recursive function named recurs(). This function converts arbitrary R expressions into arbitrary MathML expressions, and vice-versa; it is defined differently, locally, in each of the two functions. In write.SBML(), shown in Figure  5 , recurs() initially takes as its input argument the last component of the body of the kinetic rate law function definition, which is the entire rate law expression (as mentioned above, rate laws involving multiple R statements are not supported). In R, expressions are LISP like in that they contain a first element, the operator, and the remaining elements, the arguments, any of which can be an expression. If the operator is the parentheses operator, the action taken is that of a unary identity operator, and we simply skip it and move on to its argument since parentheses are not needed in MathML. Each nested call to the function recurs() sends "<apply>" and the converted operator to the output file on its way in, and a matching "</apply>" on its way out. Nested calling continues until all nodes of the expression tree are of class "name" or "numeric," i.e. when all found objects are leaves of the tree rather than "expressions" that require further parsing. Leaves are then sent to the output file bracketed by <ci> and </ci>. The second of the two SBML-R interface functions, read.SBML(), maps a limited range of SBML level 2 files (function definitions and events are not handled) into SBML-like R model files. Portions of read.SBML() are given in Figure  6 . The main difference between this function, read.SBML(), and the previous function, write.SBML(), is that here, rather than using parse() to decompose the list-of-lists structure of the model defined in R, the SBML model is instead decomposed as an XML object using xmlTreeParse() of the XML package available to R [ 11 ]. In read.SBML(), the locally defined recursive function recurs() uses an overkill of parentheses to avoid operator precedence issues. This recursive function is passed a MathML reaction rate law which it parses recursively until the leaves of the tree (the "ci") are all found. During the recursion a corresponding R expression is built as a vector of character strings which, upon exit from the last of the recursive calls, is collapsed into a single string and sent to the output file as the last line of the current rate law function definition.
A Two-Way Interface between SBML and R
Two functions comprise the SBML-R interface: write.SBML() converts SBML-like R models (e.g. Curto.r) into SBML models (e.g. Curto.xml), and read.SBML() converts SBML models (e.g. Curto.xml) into an SBML-like R model (e.g. CurtoX.r). A key component of these two interface functions is a locally defined recursive function named recurs(). This function converts arbitrary R expressions into arbitrary MathML expressions, and vice-versa; it is defined differently, locally, in each of the two functions. In write.SBML(), shown in Figure  5 , recurs() initially takes as its input argument the last component of the body of the kinetic rate law function definition, which is the entire rate law expression (as mentioned above, rate laws involving multiple R statements are not supported). In R, expressions are LISP like in that they contain a first element, the operator, and the remaining elements, the arguments, any of which can be an expression. If the operator is the parentheses operator, the action taken is that of a unary identity operator, and we simply skip it and move on to its argument since parentheses are not needed in MathML. Each nested call to the function recurs() sends "<apply>" and the converted operator to the output file on its way in, and a matching "</apply>" on its way out. Nested calling continues until all nodes of the expression tree are of class "name" or "numeric," i.e. when all found objects are leaves of the tree rather than "expressions" that require further parsing. Leaves are then sent to the output file bracketed by <ci> and </ci>.The second of the two SBML-R interface functions, read.SBML(), maps a limited range of SBML level 2 files (function definitions and events are not handled) into SBML-like R model files. Portions of read.SBML() are given in Figure  6 . The main difference between this function, read.SBML(), and the previous function, write.SBML(), is that here, rather than using parse() to decompose the list-of-lists structure of the model defined in R, the SBML model is instead decomposed as an XML object using xmlTreeParse() of the XML package available to R [ 11 ]. In read.SBML(), the locally defined recursive function recurs() uses an overkill of parentheses to avoid operator precedence issues. This recursive function is passed a MathML reaction rate law which it parses recursively until the leaves of the tree (the "ci") are all found. During the recursion a corresponding R expression is built as a vector of character strings which, upon exit from the last of the recursive calls, is collapsed into a single string and sent to the output file as the last line of the current rate law function definition.Results The function write.SBML() was applied to Curto.r to generate Curto.xml and the function read.SBML() was then applied to Curto.xml to generate CurtoX.r. Execution of the script given in Figure  2  with line 4 of the execution code changed to act on CurtoX.r instead of Curto.r generated the same plots as before (Figure  3 ). This shows that the R model was successfully converted into an SBML file that can be reconverted back into a properly functioning R model. The intermediate file Curto.xml was successfully validated as an SBML level 2 file [ 12 ]. The SBML file could thus be imported into visualization packages such as JDesigner [ 13 ].
Results
The function write.SBML() was applied to Curto.r to generate Curto.xml and the function read.SBML() was then applied to Curto.xml to generate CurtoX.r. Execution of the script given in Figure  2  with line 4 of the execution code changed to act on CurtoX.r instead of Curto.r generated the same plots as before (Figure  3 ). This shows that the R model was successfully converted into an SBML file that can be reconverted back into a properly functioning R model. The intermediate file Curto.xml was successfully validated as an SBML level 2 file [ 12 ]. The SBML file could thus be imported into visualization packages such as JDesigner [ 13 ].Discussion If the model of Curto et al. [ 4 ] were implemented in R without any knowledge of SBML, a form that it might take is that given in Appendix B (Figure  7 ). Compared to its SBML-like counterparts, this code is more compact and easier to understand, e.g. the system's network connectivity is clearly visible. The disadvantage of such code is that it is not readily converted into SBML. Since the benefits of SBML are compelling, this disadvantage alone warrants the use of SBML-like model structures. As SBML evolves to handle a broader range of dynamical systems, it will become more and more challenging for simulation packages to handle all possible SBML models. It is envisioned here that the development of this SBML-R interface will be driven by its users, and not by the model representation capabilities of SBML, i.e. it is expected that the users of this interface will be programmers who are capable of modifying it as their needs require.
Discussion
If the model of Curto et al. [ 4 ] were implemented in R without any knowledge of SBML, a form that it might take is that given in Appendix B (Figure  7 ). Compared to its SBML-like counterparts, this code is more compact and easier to understand, e.g. the system's network connectivity is clearly visible. The disadvantage of such code is that it is not readily converted into SBML. Since the benefits of SBML are compelling, this disadvantage alone warrants the use of SBML-like model structures.As SBML evolves to handle a broader range of dynamical systems, it will become more and more challenging for simulation packages to handle all possible SBML models. It is envisioned here that the development of this SBML-R interface will be driven by its users, and not by the model representation capabilities of SBML, i.e. it is expected that the users of this interface will be programmers who are capable of modifying it as their needs require.Conclusions Compared to Matlab, which may be better equipped than R to simulate arbitrarily complex dynamical systems, R has the advantage of list handling infrastructure in parse() and xmlTreeParse(), and it also has the advantage of indexing by names instead of numbers. A further advantage, though not exploited here, is that R is object-oriented; in future versions of this interface, a print() method might be defined for objects of class SBMLR (i.e. models) to generate more readable renderings of models in R. Another advantage of R over Matlab is that it provides access to a much broader collection of microarray analysis tools, e.g. see Bioconductor [ 10 ]. This aspect is important for those individuals who are interested in biochemical systems analyses of microarray data [ 14 , 15 ]. For statisticians already familiar with R, there are also the obvious economies of maintaining system familiarity. Finally, perhaps the biggest advantage of R over Matlab is that it is freely available. On balance, there seems to be ample motivation for further developments of this interface between SBML and R.
Conclusions
Compared to Matlab, which may be better equipped than R to simulate arbitrarily complex dynamical systems, R has the advantage of list handling infrastructure in parse() and xmlTreeParse(), and it also has the advantage of indexing by names instead of numbers. A further advantage, though not exploited here, is that R is object-oriented; in future versions of this interface, a print() method might be defined for objects of class SBMLR (i.e. models) to generate more readable renderings of models in R. Another advantage of R over Matlab is that it provides access to a much broader collection of microarray analysis tools, e.g. see Bioconductor [ 10 ]. This aspect is important for those individuals who are interested in biochemical systems analyses of microarray data [ 14 , 15 ]. For statisticians already familiar with R, there are also the obvious economies of maintaining system familiarity. Finally, perhaps the biggest advantage of R over Matlab is that it is freely available. On balance, there seems to be ample motivation for further developments of this interface between SBML and R.Availability and requirements Project name: SBMLR Project home page:  Operating system(s): Windows XP Programming language: R 2.0 Other requirements: R packages: XML and ODESOLVE License: GNU GPL Any restrictions to use by non-academics: no restrictions
Availability and requirements
Project name: SBMLRProject home page:Operating system(s): Windows XPProgramming language: R 2.0Other requirements: R packages: XML and ODESOLVELicense: GNU GPLAny restrictions to use by non-academics: no restrictionsList of abbreviations SBML = Systems Biology Markup Language; XML = extensible markup language; MathML = Mathematical Markup Language; ODE = ordinary differential equation.
List of abbreviations
SBML = Systems Biology Markup Language; XML = extensible markup language; MathML = Mathematical Markup Language; ODE = ordinary differential equation.Authors' contributions TR is the sole contributor.
Authors' contributions
TR is the sole contributor.Appendix A The SBMLR package is available through Bioconductor as a developmental package [ 7 ]. It has been developed and tested only under Windows XP. To install, do NOT unzip the file SBMLR.zip after downloading to a local directory, rather, within the R GUI, click  packages  and  install from local zip . The XML package installs similarly [ 11 ]. Note that an error message from library(XML) can be resolved by copying the *.dll files of the XML package libs directory into the "C:\windows" directory. The ODESOLVE package must be installed before running simulations. This package is installed from the R GUI by clicking  packages  and  install from CRAN .
Appendix A
The SBMLR package is available through Bioconductor as a developmental package [ 7 ]. It has been developed and tested only under Windows XP. To install, do NOT unzip the file SBMLR.zip after downloading to a local directory, rather, within the R GUI, click  packages  and  install from local zip . The XML package installs similarly [ 11 ]. Note that an error message from library(XML) can be resolved by copying the *.dll files of the XML package libs directory into the "C:\windows" directory. The ODESOLVE package must be installed before running simulations. This package is installed from the R GUI by clicking  packages  and  install from CRAN .packagesinstall from local zippackagesinstall from CRANAppendix B The implementation of Curto et al.'s model shown in Figure  7  is independent of any knowledge of SBML. It is included here to illustrate what comes "naturally" when implementing a model in R, see Discussion.
Appendix B
The implementation of Curto et al.'s model shown in Figure  7  is independent of any knowledge of SBML. It is included here to illustrate what comes "naturally" when implementing a model in R, see Discussion.


Background Antibiotic overuse and misuse for upper respiratory tract infections in children is widespread and fuelled by public attitudes and expectations. This study assessed knowledge, beliefs, and practices regarding antibiotic use for these paediatric infections among children's caregivers' in Trinidad and Tobago in the English speaking Caribbean.
Background
Antibiotic overuse and misuse for upper respiratory tract infections in children is widespread and fuelled by public attitudes and expectations. This study assessed knowledge, beliefs, and practices regarding antibiotic use for these paediatric infections among children's caregivers' in Trinidad and Tobago in the English speaking Caribbean.Methods In a cross-sectional observational study, by random survey children's adult caregivers gave a telephone interview from November 1998 to January 1999. On a pilot-tested evaluation instrument, respondents provided information about their knowledge and beliefs of antibiotics, and their use of these agents to treat recent episodes (< previous 30 days) of upper respiratory tract infections in children under their care. Caregivers were scored on an antibiotic knowledge test and divided based on their score. Differences between those with high and low scores were compared using the chi-square test.
Methods
In a cross-sectional observational study, by random survey children's adult caregivers gave a telephone interview from November 1998 to January 1999. On a pilot-tested evaluation instrument, respondents provided information about their knowledge and beliefs of antibiotics, and their use of these agents to treat recent episodes (< previous 30 days) of upper respiratory tract infections in children under their care. Caregivers were scored on an antibiotic knowledge test and divided based on their score. Differences between those with high and low scores were compared using the chi-square test.Results Of the 417 caregivers, 70% were female and between 18–40 years, 77% were educated to high school and beyond and 43% lived in urban areas. Two hundred and forty nine (60%) respondents scored high (≥ 12) on antibiotic knowledge and 149 (34%) had used antibiotics in the preceding year. More caregivers with a high knowledge score had private health insurance (33%), (p < 0.02), high school education (57%) (p < 0.002), and had used antibiotics in the preceding year (p < 0.008) and within the last 30 days (p < 0.05). Caregivers with high scores were less likely to demand antibiotics (p < 0.05) or keep them at home (p < 0.001), but more likely to self-treat with antibiotics (p < 0.001). Caregivers administered antibiotics in 241/288 (84%) self-assessed severe episodes of infection (p < 0.001) and in 59/126 (43%) cough and cold episodes without visiting a health clinic or private physician (p < 0.05).
Results
Of the 417 caregivers, 70% were female and between 18–40 years, 77% were educated to high school and beyond and 43% lived in urban areas. Two hundred and forty nine (60%) respondents scored high (≥ 12) on antibiotic knowledge and 149 (34%) had used antibiotics in the preceding year. More caregivers with a high knowledge score had private health insurance (33%), (p < 0.02), high school education (57%) (p < 0.002), and had used antibiotics in the preceding year (p < 0.008) and within the last 30 days (p < 0.05). Caregivers with high scores were less likely to demand antibiotics (p < 0.05) or keep them at home (p < 0.001), but more likely to self-treat with antibiotics (p < 0.001). Caregivers administered antibiotics in 241/288 (84%) self-assessed severe episodes of infection (p < 0.001) and in 59/126 (43%) cough and cold episodes without visiting a health clinic or private physician (p < 0.05).Conclusions In Trinidad and Tobago, caregivers scoring low on antibiotic knowledge have erroneous beliefs and use antibiotics inappropriately. Children in their care receive antibiotics for upper respiratory tract infections without visiting a health clinic or a physician. Educational interventions in the community on the consequences of inappropriate antibiotic use in children are recommended. Our findings emphasise the need to address information, training, legislation and education at all levels of the drug delivery system towards discouraging self-medication with antibiotics in children.
Conclusions
In Trinidad and Tobago, caregivers scoring low on antibiotic knowledge have erroneous beliefs and use antibiotics inappropriately. Children in their care receive antibiotics for upper respiratory tract infections without visiting a health clinic or a physician. Educational interventions in the community on the consequences of inappropriate antibiotic use in children are recommended. Our findings emphasise the need to address information, training, legislation and education at all levels of the drug delivery system towards discouraging self-medication with antibiotics in children.

Background Acute respiratory infections and diarrhoeal diseases are the leading causes of childhood mortality, resulting in 25–33% of all deaths in children in developing countries. Worldwide, antibiotics are the most commonly prescribed and abused drugs for upper respiratory tract infections (URTIs) [ 1 , 2 ]. In surveys on antibiotic use, about 20 % of prescriptions were inappropriate [ 3 ] and a high proportion of patients received antibiotics during clinic visits [ 4 ]. A significant force driving the occurrence and the spread of antibiotic resistance is the inappropriate use of antibiotics in primary and ambulatory care settings.  Streptococcus pneumoniae ,  Hemophilus influenzae  and  Moraxella catarrhalis , the most common bacterial pathogens causing acute bacterial rhinosinusitis (ABRS) in children, in both the developed and developing countries, have already demonstrated resistance to the first line antibiotics [ 5 - 9 ]. The Sinus and Allergy Health Partnership (SAHP) guidelines for the treatment of ABRS observe antibiotics prescribed for ABRS are not only ineffective, but may contribute to the development of antibiotic-resistant bacterial infections [ 10 ] which is supported by the increasing resistance of  Streptococcus pneumoniae  and the increasing prevalence of strains resistant to the beta-lactams and co-trimoxazole. Unnecessary antibiotic use in viral respiratory illnesses in humans is a key factor influencing the emergence and spread of resistant pneumococci. Inappropriate antibiotic use may be consequent to misdiagnosis of the illness (viral and bacterial URTIs present with similar symptoms), patients' expectations, and their demands which induce physicians to prescribe antibiotics [ 11 ]. In the United States, a higher proportion of infections due to penicillin resistant pnuemococci among young children and whites have been attributed to an overuse of antibiotics [ 12 , 13 ]. Epidemiological studies demonstrate recent antibiotic use is strongly associated with carriage of resistant pneumococci in the community and the individual, and in patients with invasive pneumococcal disease, recent antibiotic use has been associated with increased risk of infection with a resistant strain [ 14 ]. Previous community-oriented studies suggest an irrational use of antibiotics, particularly in the developing and lesser-developed countries. Factors influencing antibiotic resistance are the higher incidence of infectious diseases in children, the lack of access to health care, costs and poor regulatory controls on the use of prescription drugs such as antibiotics, coupled with low antibiotic knowledge prompting increased self medication with these drugs [ 15 - 17 ]. As the first health-decision makers in deciding when to initiate antibiotic treatment and limit their unnecessary use, mothers and caregivers must have the appropriate knowledge to enable correct decisions. This first Caribbean study investigated the knowledge, beliefs and practices of children's caregivers in Trinidad and Tobago regarding antibiotic utilisation and explored these beliefs in self-administration of antibiotics in childhood URTIs. Gaining a better understanding of caregivers' management of childhood URTIs and factors that influence their use of antibiotics will allow appropriate educational interventions and reduce unnecessary antibiotic use in children.
Background
Acute respiratory infections and diarrhoeal diseases are the leading causes of childhood mortality, resulting in 25–33% of all deaths in children in developing countries. Worldwide, antibiotics are the most commonly prescribed and abused drugs for upper respiratory tract infections (URTIs) [ 1 , 2 ]. In surveys on antibiotic use, about 20 % of prescriptions were inappropriate [ 3 ] and a high proportion of patients received antibiotics during clinic visits [ 4 ].A significant force driving the occurrence and the spread of antibiotic resistance is the inappropriate use of antibiotics in primary and ambulatory care settings.  Streptococcus pneumoniae ,  Hemophilus influenzae  and  Moraxella catarrhalis , the most common bacterial pathogens causing acute bacterial rhinosinusitis (ABRS) in children, in both the developed and developing countries, have already demonstrated resistance to the first line antibiotics [ 5 - 9 ]. The Sinus and Allergy Health Partnership (SAHP) guidelines for the treatment of ABRS observe antibiotics prescribed for ABRS are not only ineffective, but may contribute to the development of antibiotic-resistant bacterial infections [ 10 ] which is supported by the increasing resistance of  Streptococcus pneumoniae  and the increasing prevalence of strains resistant to the beta-lactams and co-trimoxazole. Unnecessary antibiotic use in viral respiratory illnesses in humans is a key factor influencing the emergence and spread of resistant pneumococci. Inappropriate antibiotic use may be consequent to misdiagnosis of the illness (viral and bacterial URTIs present with similar symptoms), patients' expectations, and their demands which induce physicians to prescribe antibiotics [ 11 ].Streptococcus pneumoniaeHemophilus influenzaeMoraxella catarrhalisStreptococcus pneumoniaeIn the United States, a higher proportion of infections due to penicillin resistant pnuemococci among young children and whites have been attributed to an overuse of antibiotics [ 12 , 13 ]. Epidemiological studies demonstrate recent antibiotic use is strongly associated with carriage of resistant pneumococci in the community and the individual, and in patients with invasive pneumococcal disease, recent antibiotic use has been associated with increased risk of infection with a resistant strain [ 14 ].Previous community-oriented studies suggest an irrational use of antibiotics, particularly in the developing and lesser-developed countries. Factors influencing antibiotic resistance are the higher incidence of infectious diseases in children, the lack of access to health care, costs and poor regulatory controls on the use of prescription drugs such as antibiotics, coupled with low antibiotic knowledge prompting increased self medication with these drugs [ 15 - 17 ]. As the first health-decision makers in deciding when to initiate antibiotic treatment and limit their unnecessary use, mothers and caregivers must have the appropriate knowledge to enable correct decisions. This first Caribbean study investigated the knowledge, beliefs and practices of children's caregivers in Trinidad and Tobago regarding antibiotic utilisation and explored these beliefs in self-administration of antibiotics in childhood URTIs. Gaining a better understanding of caregivers' management of childhood URTIs and factors that influence their use of antibiotics will allow appropriate educational interventions and reduce unnecessary antibiotic use in children.Methods Setting Trinidad and Tobago, a twin island republic in the Caribbean located off the Venezuelan coast with a population of 1.3 million people is the second largest country in the English speaking Caribbean. About 74% of the country's population live in the urban areas and 60% of the population is aged between 15–64 years. The literacy rate in Trinidad and Tobago is 98% and at least 70% of the country's adult population has completed secondary (high) school to the pre-university level. Medical care in Trinidad and Tobago is publicly financed through three (3) regional health authorities (North West, South West and Eastern regions) in Trinidad, and one in Tobago. Secondary and tertiary care are provided at one general hospital in Port-of-Spain, one in San Fernando (1,245 beds), and at two county hospitals in Trinidad (111 beds), and at one hospital in Tobago (96 beds), besides institutions for specialised services. Primary health care is provided at 82 health centers in Trinidad and 19 in Tobago. The ratio of the population to a health center ranges from < 3000 per center in Tobago to > 21,000 per center in the north-west region. There are 33 private hospitals and approximately 45% of the population preferentially uses the private sector services. Private general practitioners are concentrated in the cities and larger towns and the estimated ratio of physician per population is 7.5 per 10, 000 inhabitants [ 18 ]. Design This prospective cross sectional observational study was conducted in Trinidad and Tobago from November 1998 to January 1999 in randomly selected subjects interviewed over the telephone. The study design and methods have been described previously [ 19 ]. Briefly, a sample size of 800 participants with a working telephone was calculated based on 80% power to detect a difference of at least 3% use of antibiotics giving an error of 0.05. This being the first such telephone survey in the country, with no experience of the rejection rate, 1,600 telephone numbers were randomly obtained from a sampling frame of 167,272 telephone subscribers of The Telecommunication Services of Trinidad and Tobago, the only telephone service provider in the country. Of 824 respondents, 753 agreed to participate with a response rate of 91.4%. At the outset of the study participants were questioned if they were caring for a child ≤ 12 years and the term 'antibiotic' was explained in a simple sentence :  "Antibiotics are drugs that are prescribed for the treatment of diseases caused by germs" . The pilot-tested questionnaire consisted of 42 items in three parts, designed to investigate knowledge, beliefs and practices of antibiotics [ 19 ]. The first part on the caregiver's demographic data included the employment status, health insurance and educational background. The second part inquired about caregivers' knowledge and beliefs. To determine antibiotic knowledge participants were asked to identify 4 antibiotics from a list of 8 commonly used drugs, and could attain a maximum score of 16. Caregivers' beliefs about antibiotics were determined using the following three questions: 1. Do you think antibiotics can cure all infections? 2. Do you believe antibiotics are free from side-effects? 3. Do you think antibiotics are generally safe? From our earlier report and the pilot project of the current study respondents differentiated their understanding of 'side-effects' and 'safety'. The former was associated with unwanted disturbances from drug therapy on the quality of life and the latter was associated with life-threatening issues like organ toxicity and death. The third part of the questionnaire ascertained caregivers' practices of antibiotic use. They were asked about symptoms which children in their care had in the past 30 days, their assessment of the symptom severity, whether they sought medical assistance and if they administered any antibiotic to the child. Respondents were not asked to name the antibiotic. Information on suspected side-effects or allergic reactions was excluded from the final questionnaire as these responses in the pilot study were uncertain and subject to memory recall. Analysis Four hundred and seventeen of the 753 respondents were adult caregivers with children in the family and their responses were analysed. An antibiotic knowledge score was created based on the caregivers' responses to eight common drugs, which included four antibiotics. A high Antibiotic Knowledge Score (AKS) was defined as that at or above the median score. Data were analysed using SPSS version 11.0 (Chicago), and associations were determined by the Chi square test for antibiotic knowledge and caregiver's education, beliefs and practices and recent and past antibiotic use for URTIs.
Methods
Setting Trinidad and Tobago, a twin island republic in the Caribbean located off the Venezuelan coast with a population of 1.3 million people is the second largest country in the English speaking Caribbean. About 74% of the country's population live in the urban areas and 60% of the population is aged between 15–64 years. The literacy rate in Trinidad and Tobago is 98% and at least 70% of the country's adult population has completed secondary (high) school to the pre-university level. Medical care in Trinidad and Tobago is publicly financed through three (3) regional health authorities (North West, South West and Eastern regions) in Trinidad, and one in Tobago. Secondary and tertiary care are provided at one general hospital in Port-of-Spain, one in San Fernando (1,245 beds), and at two county hospitals in Trinidad (111 beds), and at one hospital in Tobago (96 beds), besides institutions for specialised services. Primary health care is provided at 82 health centers in Trinidad and 19 in Tobago. The ratio of the population to a health center ranges from < 3000 per center in Tobago to > 21,000 per center in the north-west region. There are 33 private hospitals and approximately 45% of the population preferentially uses the private sector services. Private general practitioners are concentrated in the cities and larger towns and the estimated ratio of physician per population is 7.5 per 10, 000 inhabitants [ 18 ].
Setting
Trinidad and Tobago, a twin island republic in the Caribbean located off the Venezuelan coast with a population of 1.3 million people is the second largest country in the English speaking Caribbean. About 74% of the country's population live in the urban areas and 60% of the population is aged between 15–64 years. The literacy rate in Trinidad and Tobago is 98% and at least 70% of the country's adult population has completed secondary (high) school to the pre-university level. Medical care in Trinidad and Tobago is publicly financed through three (3) regional health authorities (North West, South West and Eastern regions) in Trinidad, and one in Tobago. Secondary and tertiary care are provided at one general hospital in Port-of-Spain, one in San Fernando (1,245 beds), and at two county hospitals in Trinidad (111 beds), and at one hospital in Tobago (96 beds), besides institutions for specialised services. Primary health care is provided at 82 health centers in Trinidad and 19 in Tobago. The ratio of the population to a health center ranges from < 3000 per center in Tobago to > 21,000 per center in the north-west region. There are 33 private hospitals and approximately 45% of the population preferentially uses the private sector services. Private general practitioners are concentrated in the cities and larger towns and the estimated ratio of physician per population is 7.5 per 10, 000 inhabitants [ 18 ].Design This prospective cross sectional observational study was conducted in Trinidad and Tobago from November 1998 to January 1999 in randomly selected subjects interviewed over the telephone. The study design and methods have been described previously [ 19 ]. Briefly, a sample size of 800 participants with a working telephone was calculated based on 80% power to detect a difference of at least 3% use of antibiotics giving an error of 0.05. This being the first such telephone survey in the country, with no experience of the rejection rate, 1,600 telephone numbers were randomly obtained from a sampling frame of 167,272 telephone subscribers of The Telecommunication Services of Trinidad and Tobago, the only telephone service provider in the country. Of 824 respondents, 753 agreed to participate with a response rate of 91.4%. At the outset of the study participants were questioned if they were caring for a child ≤ 12 years and the term 'antibiotic' was explained in a simple sentence :  "Antibiotics are drugs that are prescribed for the treatment of diseases caused by germs" . The pilot-tested questionnaire consisted of 42 items in three parts, designed to investigate knowledge, beliefs and practices of antibiotics [ 19 ]. The first part on the caregiver's demographic data included the employment status, health insurance and educational background. The second part inquired about caregivers' knowledge and beliefs. To determine antibiotic knowledge participants were asked to identify 4 antibiotics from a list of 8 commonly used drugs, and could attain a maximum score of 16. Caregivers' beliefs about antibiotics were determined using the following three questions: 1. Do you think antibiotics can cure all infections? 2. Do you believe antibiotics are free from side-effects? 3. Do you think antibiotics are generally safe? From our earlier report and the pilot project of the current study respondents differentiated their understanding of 'side-effects' and 'safety'. The former was associated with unwanted disturbances from drug therapy on the quality of life and the latter was associated with life-threatening issues like organ toxicity and death. The third part of the questionnaire ascertained caregivers' practices of antibiotic use. They were asked about symptoms which children in their care had in the past 30 days, their assessment of the symptom severity, whether they sought medical assistance and if they administered any antibiotic to the child. Respondents were not asked to name the antibiotic. Information on suspected side-effects or allergic reactions was excluded from the final questionnaire as these responses in the pilot study were uncertain and subject to memory recall.
Design
This prospective cross sectional observational study was conducted in Trinidad and Tobago from November 1998 to January 1999 in randomly selected subjects interviewed over the telephone. The study design and methods have been described previously [ 19 ]. Briefly, a sample size of 800 participants with a working telephone was calculated based on 80% power to detect a difference of at least 3% use of antibiotics giving an error of 0.05. This being the first such telephone survey in the country, with no experience of the rejection rate, 1,600 telephone numbers were randomly obtained from a sampling frame of 167,272 telephone subscribers of The Telecommunication Services of Trinidad and Tobago, the only telephone service provider in the country. Of 824 respondents, 753 agreed to participate with a response rate of 91.4%. At the outset of the study participants were questioned if they were caring for a child ≤ 12 years and the term 'antibiotic' was explained in a simple sentence :  "Antibiotics are drugs that are prescribed for the treatment of diseases caused by germs" ."Antibiotics are drugs that are prescribed for the treatment of diseases caused by germs"The pilot-tested questionnaire consisted of 42 items in three parts, designed to investigate knowledge, beliefs and practices of antibiotics [ 19 ]. The first part on the caregiver's demographic data included the employment status, health insurance and educational background. The second part inquired about caregivers' knowledge and beliefs. To determine antibiotic knowledge participants were asked to identify 4 antibiotics from a list of 8 commonly used drugs, and could attain a maximum score of 16. Caregivers' beliefs about antibiotics were determined using the following three questions: 1. Do you think antibiotics can cure all infections? 2. Do you believe antibiotics are free from side-effects? 3. Do you think antibiotics are generally safe? From our earlier report and the pilot project of the current study respondents differentiated their understanding of 'side-effects' and 'safety'. The former was associated with unwanted disturbances from drug therapy on the quality of life and the latter was associated with life-threatening issues like organ toxicity and death. The third part of the questionnaire ascertained caregivers' practices of antibiotic use. They were asked about symptoms which children in their care had in the past 30 days, their assessment of the symptom severity, whether they sought medical assistance and if they administered any antibiotic to the child. Respondents were not asked to name the antibiotic. Information on suspected side-effects or allergic reactions was excluded from the final questionnaire as these responses in the pilot study were uncertain and subject to memory recall.Analysis Four hundred and seventeen of the 753 respondents were adult caregivers with children in the family and their responses were analysed. An antibiotic knowledge score was created based on the caregivers' responses to eight common drugs, which included four antibiotics. A high Antibiotic Knowledge Score (AKS) was defined as that at or above the median score. Data were analysed using SPSS version 11.0 (Chicago), and associations were determined by the Chi square test for antibiotic knowledge and caregiver's education, beliefs and practices and recent and past antibiotic use for URTIs.
Analysis
Four hundred and seventeen of the 753 respondents were adult caregivers with children in the family and their responses were analysed. An antibiotic knowledge score was created based on the caregivers' responses to eight common drugs, which included four antibiotics. A high Antibiotic Knowledge Score (AKS) was defined as that at or above the median score. Data were analysed using SPSS version 11.0 (Chicago), and associations were determined by the Chi square test for antibiotic knowledge and caregiver's education, beliefs and practices and recent and past antibiotic use for URTIs.Results The majority of respondents was female (70%), ≥ 31 years, (72%) and had completed high school (pre-university) education (77%). There was a marginally high (57%) representation from the rural area (Table  1 ), but residence showed no relation to the determinants of the study. The ratio of respondents with African and Indian heritage bore close similarity to the ethnic profile of the population in Trinidad and Tobago. Seventy one percent of caregivers with children did not have private health insurance. Comparable proportions of caregivers, 35% and 34% respectively reported using antibiotics recently (< 30 days) and in the past (within the last one year). The median antibiotic knowledge score was 12 and was determined by the correct identification of penicillin, tetracycline, 'Augmentin', and 'Bactrim' as antibiotics from a list of 8 common drugs. Two hundred and forty-nine (60%) caregivers scored at or above the median score. The significant predictors of high antibiotic knowledge in caregivers were those who were employed, had private health insurance and, high school education. More caregivers with a high AKS had used antibiotics recently (p < 0.037) and in the past (p < 0.008) (Table  2 ). Table 1 Characteristics of children's caregivers in Trinidad and Tobago. Characteristics Caregivers with children (n = 417) Number of people in the house (mean +/- SD) 4.89 +/- 1.9 Number of children (mean +/- SD) 2.26 +/- 1.38 Gender: Males 126 (30)% Females 291 (70%)* Age group 18–30 yrs 112 (28%) 31–40 yrs 165 (42%) > 41 yrs 129 (30%) Residence Rural 235 (57%) Urban 176 (43%) Ethnicity African 142 (35%) Indian 158 (39%) Others 110 (29%) Education Primary school 96 (23%) High school 228 (55%)* College 90 (22%) Health Insurance With Private Health insurance 114 (29%) Without Private Health insurance 281 (71%)* Antibiotics used in previous year 143 (34%) Antibiotics used in last 30 days 149 (35%) * Significant at p < 0.05 Table 2 Caregivers' Antibiotic Knowledge Score (AKS) and associated factors Factors AKS <12(n = 168) (%) AKS ≥ 12(n = 249) (%) p VALUE Gender Males 51 (30) 51 (30) 0.96 Females 117 (70) 117 (70) Residence Rural 75 (46) 101 (41) 0.29 Urban 88 (54) 147 (59) Health Insurance Has insurance 35 (22) 79 (33) 0.02* Does not have 122 (78) 159 (67) Age Groups 18–30 yrs 55 (34) 57 (23) 0.07 31–40 yrs 60 (37) 105 (43) >41 yrs 47 (29) 82 (34) Ethnicity African 57 (35) 85 (35) 0.70 Asian 67 (41) 91(37) Others 41 (24) 69 (28) Education Primary 53 (37) 43 (17) 0.002* High School 87 (52) 141 (57) Tertiary Education 28 (18) 62 (26) Employment Employed 69 (43) 130 (54) 0.09 Self Employed 28 (18) 40 (17) Housewife/retired/unemployed 62 (39) 71 (29) Recent and past antibiotic use Used in the last 12 months 45 (27) 98 (40) 0.008* Not used in the last 12 months 118 (70) 150 (60) Used in the last 30 days 50 (30) 99 (40) 0.037* Not used in the last 30 days 122 (73) 150 (60) Significant at (p < 0.05) A majority of respondents had correct beliefs regarding whether 'antibiotics cure all infections' (54% [227/417]) and 'antibiotics are free from side effects' (61% [253/417]). Few (11% [49/417]) respondents believed antibiotics are generally safe. and some caregivers (18%–24%) remained non-responsive to all questions. The AKS of caregivers did not influence their beliefs (Table  3 ). Eighty six caregivers (22%) admitted to demanding antibiotics from a doctor. More caregivers (28%) with a low AKS demanded antibiotic prescriptions (p < 0.05) and kept these drugs at home (33%) (p < 0.001), to treat illnesses. Self-initiation of treatment for URTIs with antibiotics was more frequent (p < 0.05) among caregivers who had a high AKS. Caregiver's knowledge scores were not associated with the use of antibiotics given by relatives and / or friends or with compliance with the course whether recommended by the pharmacist or the doctor. (Table  3 ). Table 3 Antibiotic beliefs and practices of Caregivers in Trinidad FACTORS AKS <12 (%) AKS ≥ 12 (%) p value 1. Antibiotic Beliefs: a. Cure all infections Agree 48 (37) 63 (30) 0.17 Disagree 81 (63) 146 (70) b. Free from side -effects Agree 21 (18) 44 (21) 0.30 Disagree 99 (82) 153 (79) c. Generally safe Agree 21 (17) 28 (13) 0.40 Disagree 105 (83) 183 (87) 2. Antibiotic Practices Demands from doctor for URTI in children Yes 41 (28) 45 (19) 0.05* No 106 (72) 188 (81) Keeps at home Yes 53 (33) 45 (19) 0.001** No 107 (67) 195 (91) Self treatment Yes 33 (12) 69 (32) No 154 (88) 150 (68) 0.001** Given by friends and relatives Yes 16 (10) 25 (11) No 142 (90) 213 (89) 0.9 Compliance Yes 89 (63) 152 (71) No 52 (37) 63 (29) 0.13 *Significant at p < 0.05, ** Significant at p < 0.001 Caregivers reported as many as 450 episodes of URTIs in children within the previous 30 days (1.07 episodes per family) and 149 (35%) caregivers self-administered antibiotics in 64% (288/450) of these episodes. Cough and cold was the most frequently reported URTI symptom (48%, 214/450) followed by fever (28%,128/450) and sore throat (24%,108 / 450). Caregivers were more likely to give children antibiotics when they perceived URTIs to be severe [241/288 (84%)] (p < 0.001), and administered these drugs for the common cold [112/136, (82%)] fever [75/87 (83%)] and sore throat [54/65 (86%)] (Table  4 ). In 16% of episodes which caregivers deemed to be of mild severity, they self-administered antibiotics. Children received antibiotics without visiting a health clinic or a private physician for 126 (44%) URTI episodes, and more frequently for the common cold 59 (43.7%) compared with fever and sore throat (p < 0.05). Table 4 Antibiotic administration by caregivers for severe URTIs (n = 288) and visits to health provider URTI Episodes Assessed severe by caregiver (%) Visited a health clinic/private physician (%) Yes No Total Yes No Total Cough and Cold 112(82) 24(18) 136 77 (57) 59(43)* 136 Fever 75 (83) 12(17) 87 55 (63) 32(37) 87 Sore throat 54 (86) 11(14) 65 30 (46) 35(54) 65 Total 241(84)** 47 (16) 288 162(56) 126(44) 288 * Significant at p < 0.05 ** Significant at p < 0.001
Results
The majority of respondents was female (70%), ≥ 31 years, (72%) and had completed high school (pre-university) education (77%). There was a marginally high (57%) representation from the rural area (Table  1 ), but residence showed no relation to the determinants of the study. The ratio of respondents with African and Indian heritage bore close similarity to the ethnic profile of the population in Trinidad and Tobago. Seventy one percent of caregivers with children did not have private health insurance. Comparable proportions of caregivers, 35% and 34% respectively reported using antibiotics recently (< 30 days) and in the past (within the last one year). The median antibiotic knowledge score was 12 and was determined by the correct identification of penicillin, tetracycline, 'Augmentin', and 'Bactrim' as antibiotics from a list of 8 common drugs. Two hundred and forty-nine (60%) caregivers scored at or above the median score. The significant predictors of high antibiotic knowledge in caregivers were those who were employed, had private health insurance and, high school education. More caregivers with a high AKS had used antibiotics recently (p < 0.037) and in the past (p < 0.008) (Table  2 ).Characteristics of children's caregivers in Trinidad and Tobago.CharacteristicsCaregivers with children (n = 417)Gender:Age groupResidenceEthnicityEducationHealth Insurance* Significant at p < 0.05Caregivers' Antibiotic Knowledge Score (AKS) and associated factorsFactorsAKS <12(n = 168) (%)AKS ≥ 12(n = 249) (%)p VALUEGenderResidenceHealth InsuranceAge GroupsEthnicityEducationEmploymentRecent and past antibiotic useSignificant at (p < 0.05)A majority of respondents had correct beliefs regarding whether 'antibiotics cure all infections' (54% [227/417]) and 'antibiotics are free from side effects' (61% [253/417]). Few (11% [49/417]) respondents believed antibiotics are generally safe. and some caregivers (18%–24%) remained non-responsive to all questions. The AKS of caregivers did not influence their beliefs (Table  3 ). Eighty six caregivers (22%) admitted to demanding antibiotics from a doctor. More caregivers (28%) with a low AKS demanded antibiotic prescriptions (p < 0.05) and kept these drugs at home (33%) (p < 0.001), to treat illnesses. Self-initiation of treatment for URTIs with antibiotics was more frequent (p < 0.05) among caregivers who had a high AKS. Caregiver's knowledge scores were not associated with the use of antibiotics given by relatives and / or friends or with compliance with the course whether recommended by the pharmacist or the doctor. (Table  3 ).Antibiotic beliefs and practices of Caregivers in TrinidadFACTORSAKS <12 (%)AKS ≥ 12 (%)1. Antibiotic Beliefs:2. Antibiotic Practices*Significant at p < 0.05, ** Significant at p < 0.001Caregivers reported as many as 450 episodes of URTIs in children within the previous 30 days (1.07 episodes per family) and 149 (35%) caregivers self-administered antibiotics in 64% (288/450) of these episodes. Cough and cold was the most frequently reported URTI symptom (48%, 214/450) followed by fever (28%,128/450) and sore throat (24%,108 / 450). Caregivers were more likely to give children antibiotics when they perceived URTIs to be severe [241/288 (84%)] (p < 0.001), and administered these drugs for the common cold [112/136, (82%)] fever [75/87 (83%)] and sore throat [54/65 (86%)] (Table  4 ). In 16% of episodes which caregivers deemed to be of mild severity, they self-administered antibiotics. Children received antibiotics without visiting a health clinic or a private physician for 126 (44%) URTI episodes, and more frequently for the common cold 59 (43.7%) compared with fever and sore throat (p < 0.05).Antibiotic administration by caregivers for severe URTIs (n = 288) and visits to health providerURTI EpisodesAssessed severe by caregiver (%)Visited a health clinic/private physician (%)* Significant at p < 0.05** Significant at p < 0.001Discussion This cross sectional study in Trinidad and Tobago determined the antibiotic knowledge of children's caregivers and the influence of this knowledge on their beliefs and use of these agents for URTIs in children under their care. We found high school education and higher socio-economic status (income permitted private health insurance) was significantly associated with higher knowledge scores. Similar associations between knowledge and antibiotic use were reported in a study in the Indian state of Kerala [ 20 ]. In the present study more caregivers who scored high on antibiotic knowledge, had used antibiotics (recently and in the past) compared with those who attained a low knowledge score. A significant proportion of caregivers in the present study had misconceptions that could contribute to the inappropriate use of antibiotics. Equal proportions of caregivers with high and low knowledge scores believed that antibiotics cure all infections and are free from side-effects. Even though URTIs are generally of viral aetiology [ 21 , 22 ], these mistaken beliefs may have steered antibiotic abuse from self treatment or over the counter demands at the pharmacy which are fostered from easy availability of these drugs at community pharmacies in Trinidad and Tobago [ 19 ]. In a survey from the United States, 48% of paediatricians reported parents do pressure them to prescribe antibiotics [ 23 ], and 78% of the sample believed educating parents on appropriate indications for antibiotic use was the single most important factor to promote suitable prescribing, suggesting effective communication between physicians and parents may reduce inappropriate antibiotic prescribing. Practices such as demanding a prescription for antibiotics from a physician, and keeping antibiotics at home (hoarding) were higher in caregivers with a low AKS. In Israel Shlomo et al [ 24 ] found lower education was a predictor of parents' expectations to receive antibiotics for URTIs and in Trinidad, Mohan et al reported that general practitioners attributed antibiotic over-prescribing in general practice to parents' demands [ 25 ]. A proclivity to demand antibiotics was associated with decreased knowledge and in children from insured families higher rates of antibiotic use were associated with low antibiotic knowledge and a tendency to demand antibiotics [ 26 ]. In Hong Kong educated respondents and working guardians had higher knowledge scores, and those who knew the viral aetiology of URTIs were less likely to demand antibiotics [ 27 ]. In the present study, the rate of self-treatment with antibiotics by caregivers, was higher in those who had a high AKS (32%vs12%). This may be consequent to caregivers needing to report for work following quicker recovery of children whom they care for. Braun and Fowles found a correlation between the expectation to get antibiotic treatment and parents' occupation and parents who worked full time had higher expectations to get antibiotic treatment, assuming perhaps that antibiotics shorten disease duration and allow an earlier return to work [ 28 ]. In Trinidad and Tobago at least 25% of the population demand a prescription for antibiotics from a doctor and 21% keep antibiotics in the house for emergency purposes [ 19 ]. Educational campaigns for the public can correct the widespread misconceptions on antibiotic use and storage. Earlier in describing the prescribing practices of Caribbean physicians we reported that respiratory tract infection was the most frequent reason for antibiotic prescriptions by physicians in the English and Dutch speaking Caribbean [ 29 ]. The influence of caregivers' (parents and relatives) knowledge on antibiotic use in children with URTIs has not been studied in any Caribbean region. Proportionately more children received antibiotics from caregivers for severe episodes of cold and cough, than for sore-throat and fever. Even for what they considered mild episodes (16%) of URTIs, caregivers administered antibiotics which as is current practice, probably obtained from community pharmacies on request [ 19 ]. In Malta parents gave antibiotics to their children without a prescription particularly for sore throat and the community pharmacy made the drugs available [ 30 ]. Caregivers in our study treated 44% of URTIs in children with antibiotics without consulting a physician or attending a health facility. We did not ascertain if the child suffered any unwanted effects of the drug, which information may have been important to discuss the consequences of freely giving antibiotics to children. We believe caregivers with health insurance, education beyond the primary level and a high AKS had obtained antibiotics informally at community pharmacies and those with a low knowledge score initiated medication with antibiotics from the home storage or given them by relatives and friends. A call for strict vigilance and enforced controls regarding 'over-the-counter' availability of antibiotics without a physician's prescription, despite being controlled drugs in Trinidad and Tobago, has been made in an earlier report [ 19 ]. Inappropriate antibiotic use is a common practice in the out patient setting in a clinic, or a physician's office [ 31 , 32 ], and has been attributed to the combination of time pressures of outpatient practice, diagnostic uncertainty, and physicians' misconceptions of patient expectations [ 33 ]. The cost and time spent for a visit to the health center or a physician's office and a genuine concern about the children's health could have pushed caregivers in Trinidad and Tobago to purchase antibiotics without a prescription which is a widespread practice here, and initiate treatment for common childhood respiratory tract illnesses. In Africa, Asia and Latin America antibiotics are obtained from pharmacies, hospitals and even from untrained vendors at the market place [ 21 , 34 - 36 ]. In Bavi, Vietnamese children were treated with antibiotics frequently by caregivers without physician consultation, resulting in a high prevalence of multi drug-resistant strains (MDR) among respiratory pathogens [ 37 ]. Contributing to the existence of the reservoir of MDR genes among bacterial pathogens undermines the effectiveness and success of antibiotic therapy in childhood respiratory tract infections in low-income countries.
Discussion
This cross sectional study in Trinidad and Tobago determined the antibiotic knowledge of children's caregivers and the influence of this knowledge on their beliefs and use of these agents for URTIs in children under their care. We found high school education and higher socio-economic status (income permitted private health insurance) was significantly associated with higher knowledge scores. Similar associations between knowledge and antibiotic use were reported in a study in the Indian state of Kerala [ 20 ]. In the present study more caregivers who scored high on antibiotic knowledge, had used antibiotics (recently and in the past) compared with those who attained a low knowledge score.A significant proportion of caregivers in the present study had misconceptions that could contribute to the inappropriate use of antibiotics. Equal proportions of caregivers with high and low knowledge scores believed that antibiotics cure all infections and are free from side-effects. Even though URTIs are generally of viral aetiology [ 21 , 22 ], these mistaken beliefs may have steered antibiotic abuse from self treatment or over the counter demands at the pharmacy which are fostered from easy availability of these drugs at community pharmacies in Trinidad and Tobago [ 19 ]. In a survey from the United States, 48% of paediatricians reported parents do pressure them to prescribe antibiotics [ 23 ], and 78% of the sample believed educating parents on appropriate indications for antibiotic use was the single most important factor to promote suitable prescribing, suggesting effective communication between physicians and parents may reduce inappropriate antibiotic prescribing.Practices such as demanding a prescription for antibiotics from a physician, and keeping antibiotics at home (hoarding) were higher in caregivers with a low AKS. In Israel Shlomo et al [ 24 ] found lower education was a predictor of parents' expectations to receive antibiotics for URTIs and in Trinidad, Mohan et al reported that general practitioners attributed antibiotic over-prescribing in general practice to parents' demands [ 25 ]. A proclivity to demand antibiotics was associated with decreased knowledge and in children from insured families higher rates of antibiotic use were associated with low antibiotic knowledge and a tendency to demand antibiotics [ 26 ]. In Hong Kong educated respondents and working guardians had higher knowledge scores, and those who knew the viral aetiology of URTIs were less likely to demand antibiotics [ 27 ]. In the present study, the rate of self-treatment with antibiotics by caregivers, was higher in those who had a high AKS (32%vs12%). This may be consequent to caregivers needing to report for work following quicker recovery of children whom they care for. Braun and Fowles found a correlation between the expectation to get antibiotic treatment and parents' occupation and parents who worked full time had higher expectations to get antibiotic treatment, assuming perhaps that antibiotics shorten disease duration and allow an earlier return to work [ 28 ]. In Trinidad and Tobago at least 25% of the population demand a prescription for antibiotics from a doctor and 21% keep antibiotics in the house for emergency purposes [ 19 ]. Educational campaigns for the public can correct the widespread misconceptions on antibiotic use and storage.Earlier in describing the prescribing practices of Caribbean physicians we reported that respiratory tract infection was the most frequent reason for antibiotic prescriptions by physicians in the English and Dutch speaking Caribbean [ 29 ]. The influence of caregivers' (parents and relatives) knowledge on antibiotic use in children with URTIs has not been studied in any Caribbean region. Proportionately more children received antibiotics from caregivers for severe episodes of cold and cough, than for sore-throat and fever. Even for what they considered mild episodes (16%) of URTIs, caregivers administered antibiotics which as is current practice, probably obtained from community pharmacies on request [ 19 ]. In Malta parents gave antibiotics to their children without a prescription particularly for sore throat and the community pharmacy made the drugs available [ 30 ]. Caregivers in our study treated 44% of URTIs in children with antibiotics without consulting a physician or attending a health facility. We did not ascertain if the child suffered any unwanted effects of the drug, which information may have been important to discuss the consequences of freely giving antibiotics to children. We believe caregivers with health insurance, education beyond the primary level and a high AKS had obtained antibiotics informally at community pharmacies and those with a low knowledge score initiated medication with antibiotics from the home storage or given them by relatives and friends. A call for strict vigilance and enforced controls regarding 'over-the-counter' availability of antibiotics without a physician's prescription, despite being controlled drugs in Trinidad and Tobago, has been made in an earlier report [ 19 ].Inappropriate antibiotic use is a common practice in the out patient setting in a clinic, or a physician's office [ 31 , 32 ], and has been attributed to the combination of time pressures of outpatient practice, diagnostic uncertainty, and physicians' misconceptions of patient expectations [ 33 ]. The cost and time spent for a visit to the health center or a physician's office and a genuine concern about the children's health could have pushed caregivers in Trinidad and Tobago to purchase antibiotics without a prescription which is a widespread practice here, and initiate treatment for common childhood respiratory tract illnesses. In Africa, Asia and Latin America antibiotics are obtained from pharmacies, hospitals and even from untrained vendors at the market place [ 21 , 34 - 36 ]. In Bavi, Vietnamese children were treated with antibiotics frequently by caregivers without physician consultation, resulting in a high prevalence of multi drug-resistant strains (MDR) among respiratory pathogens [ 37 ]. Contributing to the existence of the reservoir of MDR genes among bacterial pathogens undermines the effectiveness and success of antibiotic therapy in childhood respiratory tract infections in low-income countries.Conclusions Inappropriate antibiotic use for paediatric URTIs in Trinidad and Tobago may have been facilitated by low knowledge, erroneous beliefs and easy availability of these drugs without the required prescription, at a retail pharmacy. A combination of education and communication to combat patients' expectations for treatment, and the physicians' appropriate prescription for antibiotics can halt inappropriate antibiotic use in children. Using narrow-spectrum antibiotics, promoting dialogue with caregivers to discuss symptom relief and antibiotic resistance, and encouraging active management of the child's illness with follow-up calls is recommended. Pharmacists have a serious responsibility not to dispense these agents without prescriptions and to discourage patients from obtaining these drugs for self-treatment. Widespread educational campaigns targeting the general public in Trinidad and Tobago, particularly parents and caregivers of young children should focus on the difference in bacterial and viral infections and the futility of treating viral infections with antibiotics. A multidisciplinary approach to rational antibiotic use, dispensing these drugs as 'prescription only medicine' and educating the public can halt inappropriate use and contain resistance.
Conclusions
Inappropriate antibiotic use for paediatric URTIs in Trinidad and Tobago may have been facilitated by low knowledge, erroneous beliefs and easy availability of these drugs without the required prescription, at a retail pharmacy. A combination of education and communication to combat patients' expectations for treatment, and the physicians' appropriate prescription for antibiotics can halt inappropriate antibiotic use in children. Using narrow-spectrum antibiotics, promoting dialogue with caregivers to discuss symptom relief and antibiotic resistance, and encouraging active management of the child's illness with follow-up calls is recommended. Pharmacists have a serious responsibility not to dispense these agents without prescriptions and to discourage patients from obtaining these drugs for self-treatment. Widespread educational campaigns targeting the general public in Trinidad and Tobago, particularly parents and caregivers of young children should focus on the difference in bacterial and viral infections and the futility of treating viral infections with antibiotics. A multidisciplinary approach to rational antibiotic use, dispensing these drugs as 'prescription only medicine' and educating the public can halt inappropriate use and contain resistance.List of abbreviations ABRS = Acute Bacterial Rhinosinusitis AKS = Antibiotic Knowledge Score SAHR = Sinus and Allergy Health Partnership URTI = Upper Respiratory Tract Infection
List of abbreviations
ABRS = Acute Bacterial RhinosinusitisAKS = Antibiotic Knowledge ScoreSAHR = Sinus and Allergy Health PartnershipURTI = Upper Respiratory Tract InfectionCompeting interests The author(s) declare that they have no competing interests.
Competing interests
The author(s) declare that they have no competing interests.Authors'contributions PP conceptualised the study and drafted the protocol, NP did data collection, and contributed to the draft manuscript, LMPP prepared the final manuscript. All authors contributed to the statistical analysis and the literature search.
Authors'contributions
PP conceptualised the study and drafted the protocol, NP did data collection, and contributed to the draft manuscript, LMPP prepared the final manuscript. All authors contributed to the statistical analysis and the literature search.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here: