User guide & technical appendix for the MSTIC webtool

Transcription

1 User guide & technical appendix for the MSTIC webtool Maximising Sexually Transmitted Infection Control in local populations via evidence-based sexual health service planning Last revised: 18 July

2 Welcome to the MSTIC webtool. You can use the MSTIC webtool to help inform decisionmaking choices regarding the impact of different combinations of health services on the predicted number of sexually transmitted infections (STIs) in a local population. Here we describe: Page What or who is MSTIC? 3 Why is the MSTIC webtool needed? 3 What can the MSTIC webtool do? 3 What can t the MSTIC webtool do? 4 What do I need to do to use the MSTIC webtool? 4 Table of information required to use the MSTIC webtool, response options (if applicable), possible source(s) of information and comments 5 What does my output from the MSTIC webtool mean? 10 Frequently Asked Questions: 10 Glossary 13 Disclaimer 14 Technical appendix for the MSTIC webtool 15 The MSTIC mathematical model Model structure Parameters Presentation of the modelling results in the MSTIC webtool Assumptions and in-built features of the MSTIC webtool Notes on the user-defined parameters in the MSTIC webtool References

3 What or who is MSTIC? MSTIC is an abbreviation for a project called Maximising Sexually Transmitted Infection Control in local populations. The MSTIC project was funded by the UK Medical Research Council and undertaken by a multidisciplinary team of clinicians, epidemiologists, public health experts, statisticians, mathematical modellers, and webdesigners from NHS Trusts and academic institutions across England. The MSTIC team is lead by Dr Cath Mercer who is based in the Centre for Sexual Health and HIV Research at University College London (see: Cath can be ed on: c.mercer@ucl.ac.uk. The MSTIC project team has developed an evidence-based decision-making webtool to help inform decision-making choices regarding the impact of different combinations of health services on the transmission of sexually transmitted infections (STIs) in local populations. This webtool presents the results of a mathematical model of STI transmission as a web-based software tool, enabling service-planners to utilise the results of the mathematical model without needing to be experts in mathematical modelling, epidemiology or information technology. In terms of assessing the impact of different combinations of health services, the webtool only takes account of the most important demographic, behavioural, and service characteristics identified from the MSTIC mathematical model. However, neither the MSTIC mathematical model nor the MSTIC webtool are black boxes as detailed information is provided in the User Guide s Technical Appendix as to the characteristics that the MSTIC mathematical model does and does not take account of and why. The science underpinning the MSTIC webtool is also due to be published shortly in a peerreviewed scientific journal. Why is the MSTIC webtool needed? Recent national guidelines called to improve access to sexual health care, in part, by expanding the role of primary care in the diagnosis and treatment of STIs. 1,2 New commissioning models of care were introduced to develop more specialised STI services within primary care, including the highly variable Locally Enhanced Services (LES) 3,4. However, there is little evidence or guidance as to the impact of different combinations of sexual health services on public health outcomes such as the number of new STI cases. Given the challenge of limited resources, it is vital that service planners identify the most effective combination of services for their locality and the MSTIC webtool can help achieve this. What can the MSTIC webtool do? The MSTIC webtool is based on the results of a mathematical model of STI transmission for different types of population. Service planners can input data about their local population and health services offering care for STIs into the webtool to estimate the number of chlamydia and gonorrhoea infections in their type of population. The webtool can be used to estimate the local public health impact of existing service configurations as well as exploring the relative public health impacts of different service configurations. 3

4 What can t the MSTIC webtool do? The MSTIC webtool is designed to help service planners consider the impact of different combinations of health services on the number of new cases of chlamydia and gonorrhoea in their local population. As such, the webtool will not specify which configuration of services is correct as it will be important for service planners to also take account of other factors such as the cost of delivering these services in their locality. The MSTIC webtool does not aim to advise on service planning at the individual service level i.e. how an individual service achieves a certain volume of provision. The duration of an average slot, and the number and combination of staff and resources necessary to make a certain number of slots available, will vary by service. The MSTIC webtool also does not aim to advise on clinical practice which is already governed by clinical guidelines and best practice 5. Where guidance changes or new technologies are introduced, this can be taken into account in the inputs of the MSTIC model. For example, the introduction of a new technology may mean that a service can increase the number of appointments it can offer by a certain percentage for the same resource input. In addition, it is important to acknowledge the limitations of the MSTIC model underlying the MSTIC webtool: Predictions are provided for the next quarter (13 weeks) so changes in service provision influence presentation of new cases in this short-term time-frame rather than community-level prevalence of disease. Over time and beyond that provided by the webtool, uncovering more cases will lead to a decrease in community-level prevalence. The model does not include locally-representative data with respect to sexual behaviour as it uses data collected for a national probability survey; consequently there will be geographic heterogeneity in the burden of disease, some of which will be due to heterogeneity in sexual behaviour. What do I need to do to use the MSTIC webtool? The webtool requires users to input information specific to their area for eight questions. It is therefore helpful to gather together this information before starting to use the webtool. If the information is not available then users can choose to use default values. However, it is important to remember that the more user tailor their inputs to their local population, the better the fit of the outputs for their local population. This table lists the information required to use the MSTIC webtool, response options (where applicable), and possible sources of information. Further details are given in the Technical Appendix under Notes on using the MSTIC webtool. 4

5 Table of information required to use the MSTIC webtool, response options (if applicable), possible source(s) of information and comments. Question number in MSTIC webtool Information required MSTIC webtool response options (if applicable) Possible source(s) of information Comments Information specific to your area: Q1 Q2 An estimate of the number of women aged years living in your local area. An estimate of the number of men aged years living in your local area. Not applicable UK Census data: /get-data/index.html The model is validated for populations of between 10,000 and 1 million people aged years so the total of Q1 and Q2 should be between 10,000 and 1 million. If you try and enter a total outside of these numbers then you will get an error message and will have to amend your inputs to Q1 and Q2 in order for the webtool to work. Q3 The type of area/population you want to plan services for. Select one only from: Urban, Suburban Rural Not applicable If unsure, choose the closest area/population type. 5

6 Question number in MSTIC webtool Information required MSTIC webtool response options (if applicable) Possible source(s) of information Comments Information about the services you would like to consider for your area: Type A, e.g. genitourinary medicine (GUM) clinics Q4 The total number of appointments available (or that you would like to make available) in all GUM clinics in your local area each week. Not applicable Local GUM clinics: This can be estimated from the average weekly number of patients seen by all GUM clinics in your local area. If you do not wish the webtool to consider GUM clinics for your area then leave this blank. The maximum number of patients seeking care from all GUM clinics in your area each week. Not applicable Local GUM clinics: This can be estimated from the maximum weekly number of patients seen by all GUM clinics in your local area. If you do not wish the webtool to consider GUM clinics for your area then leave this blank. If you do not wish the webtool to consider GUM clinics for your area then leave this blank. The level of provider-led partner notification achieved by the GUM clinics in your area. Select one only from: None Some Majority Local GUM clinics None means that none of the partners are treated specifically as a result of providerled partner notification; in contrast some corresponds to around 15% of partners treated as a result of provider-led partner notification; while majority corresponds to around 35% of partners treated as a result of provider-led partner notification. 6

7 Question number in MSTIC webtool Information required MSTIC webtool response options (if applicable) Possible source(s) of information Comments Information about the services you would like to consider for your area: Type B, e.g. general practice (GP) Q5 The total weekly capacity of all general practices in your area excluding those offered through LES sessions. Not applicable This can be estimated as the total number of non-les GP appointments available (or that you would like to make available) in all general practices in your area each week. If you do not wish the webtool to consider GP for your area then leave this blank. The weekly demand for non-les GP appointments in your area. Not applicable This can be estimated as the maximum weekly number of patients seeking a non-les appointment from all general practices in your area, so not just for a suspected STI. If you do not wish the webtool to consider GP for your area then leave this blank. The level of provider-led partner notification achieved by general practices not offering LES for STIs in your area. Select one only from: None Some Majority General practices. If you do not wish the webtool to consider GP for your area then leave this blank. See Q4 for comments on the achieved levels of provider-led partner notification; 7

8 Question number in MSTIC webtool Information required MSTIC webtool response options (if applicable) Possible source(s) of information Comments Information about the services you would like to consider for your area: Type C, e.g. General practice lead Locally Enhanced Services (LES) Q6 The total weekly capacity of all GP-based LES in your area. Not applicable This can be estimated as the total number of LES appointments available (or that you would like to make available) in all general practices in your area each week. If you do not wish the webtool to consider GP-based LES for your area then leave this blank. The weekly demand for GP-based LES appointments in all GP practices in your area. Not applicable This can be estimated as the maximum number of patients seeking a GP-based LES appointment from all GP practices in your area each week. If you do not wish the webtool to consider GP-based LES for your area then leave this blank. The level of provider-led partner notification achieved by all GP-based LES in your area. Select one only from: None Some Majority General practices offering LES for STIs. If you do not wish the webtool to consider GP for your area then leave this blank. See Q4 for comments on the achieved levels of provider-led partner notification; 8

9 Question number in MSTIC webtool Information required MSTIC webtool response options (if applicable) Possible source(s) of information Comments Average delay between test, diagnosis & treatment for chlamydia/gonorrhoea (CT/GC) in your area Type C, e.g. General practice lead Locally Enhanced Services (LES) Q7 The average delay in days between patients having a test for chlamydia and/or gonorrhoea and receiving their diagnosis in your local area. A default average delay of 4 days can be selected. Local health services and/or laboratory data. A default delay can be selected. Q8 The average delay in days between patients receiving a chlamydia and/or gonorrhoea diagnosis and receiving their treatment in your local area. A default average delay of 0.5 days can be selected. Local health services and/or laboratory data. A default delay can be selected. 9

10 What does my output from the webtool mean? Once you have entered information into the MSTIC webtool for the questions above then the second screen of the MSTIC webtool summarises this information and gives you an estimate of the number of new cases of chlamydia and/or gonorrhoea presenting to health services each quarter per 100,000 people aged years in your population. As well as an incidence rate, the output is also presented as an estimate of the total number of new cases of chlamydia and/or gonorrhoea presenting to health services each quarter, given the population size specified at Q1 and Q2. You can print this output by using Ctrl P or click on the back button to take you to the previous screen so that you can vary your inputs to see how these changes impact on the output. In general, measures which increase the provision of type A services (i.e. services like GUM clinics) result in an increase in the number of individuals presenting to health services and therefore getting treated. You may want to consider absolute differences in the predicted number of cases between two or more scenarios, as well as relative differences. For example, if your first scenario generates 20 cases and your second scenario generates 25 cases, the second scenario would result in 25% more cases than the first. Frequently Asked Questions 1. Why does the MSTIC webtool only take account of STI health care available from 3 types of service? 2. Why does the MSTIC webtool not describe service types as in England s National Strategy for Sexual Health and HIV? 3. Why does the MSTIC mathematical model only predict the number of chlamydia and gonorrhoea cases and not other STIs? 4. The MSTIC webtool predicts the number of chlamydia and/or gonorrhoea cases presenting to health services, but specifically how many chlamydia cases is this? 5. Will the MSTIC webtool work regardless of the size of the population specified? 6. Why does the MSTIC webtool and model focus on the number of people aged years? 7. Why can I not specify the precise amount of provider-led partner notification undertaken by my health services? 8. Why does are the predicted number of cases expressed in the output per quarter rather than per week? 9. Why doesn t the MSTIC webtool and mathematical model take account of HIV testing? 10. The MSTIC webtool seems over-simplified as it does not require data to be entered for very many parameters is this the case? 10

11 1) Why does the MSTIC webtool only take account of care available from 3 types of service? The MSTIC mathematical model underlying the MSTIC webtool took account of genitourinary medicine (GUM) clinics, general practices that do not offer a locallyenhanced service (LES) for STIs, and general practices that do offer LES for STIs, 3,4 reflecting how recent national guidelines have called to improve access to sexual health care, in part, by expanding the role of primary care in the diagnosis and treatment of STIs. 1,2 In some areas, STI care is provided by other types of service, including young person s clinics and the National Chlamydia Screening Programme. 6 To take account of other such services, users may want to include the capacity of, and demand for, such services according to whether they consider them to be most like a Type A, B or C service. 2) Why does the MSTIC webtool not describe service types as in England s National Strategy for Sexual Health and HIV? The MSTIC webtool defines service type by the STI-related activity provided to patients. Doing so avoids use of health service labels, because labels such as sexual health Local Enhanced Service can mean different things in different areas. 3,4 Interpretation of the different levels (1, 2,3) of sexual health provision outlined in England s National Strategy for Sexual Health and HIV 1 also varies by area, and an added source of complexity here is that the level indicated by STI-related activity may not match that met by contraceptive or reproductive healthcare provision. The MSTIC webtool also avoids defining services by what they can theoretically provide but may do so only for a small minority of sexual health patients (or none at all). For instance, a general practitioner can order tests for chlamydia, gonorrhoea, syphilis and HIV for a patient, but there is evidence that few average sexual health patients currently receive all these tests when they visit their GP. 3) Why does the MSTIC mathematical model only predict the number of chlamydia and gonorrhoea cases and not other STIs? The MSTIC model concentrates on chlamydia and gonorrhoea since they are the most common acute STIs in the UK and so they form most of the public health burden of STIs in terms of numbers of cases, and are likely to be indicative of the success with which local health services are controlling STI rates in general. 4) The MSTIC webtool predicts the number of chlamydia and/or gonorrhoea cases presenting to health services, but specifically how many chlamydia cases is this? Note that for simplicity, the web model does not distinguish between number of cases of chlamydia and gonorrhoea but the two infections are assumed to occur at a ratio of 1.57 CT : 1 GC. 5) Will the MSTIC webtool work regardless of the size of the population specified? Yes, within reason. The model is validated for populations of between 10,000 and 1 million people aged years so the total of the numbers input at Q1 and Q2 should be between 10,000 and 1 million. If users try and enter a total greater than 1 million then they will get an error message and will have to amend their inputs to Q1 and Q2 in order for the webtool to work. 11

12 6) Why does the MSTIC webtool and model focus on the number of people aged years? In the UK, the majority of STIs occur to people in this age range. 7 7) Why can I not specify the precise amount of provider-led partner notification undertaken by my health services? It is difficult to know the precise level of provider-led partner notification that a health service achieves so for simplicity, the webtool asks users to select one of three levels of provider-led partner notification. 8) Why does are the predicted number of cases expressed in the output per quarter rather than per week? This corresponds to the time-frame used for collecting from GUM clinics for surveillance purposes. 9) Why doesn t the MSTIC webtool and mathematical model take account the level of HIV testing undertaken by services? In the UK, new HIV infections are too rare to be modelled at the local population level at which MSTIC operates. However, the potential public health impact of minimal testing, and of missing an HIV diagnosis is great as outcomes for individuals are worse when HIV is diagnosed late, as well as the implications for onward transmission. 10) The MSTIC webtool seems over- simplified as it does not require data to be entered for very many parameters is this the case? Users of the MSTIC webtool are only required to enter data on health service characteristics that have been identified from the MSTIC mathematical model as having a public health impact in terms of the number of chlamydia and gonorrhoea cases in a local population. However, the mathematical model underlying the MSTIC webtool takes account of a number of key behavioural variables such as the proportion of the male population who are men-who-have-sex-with-men (MSM), but, as such variables are likely to be fixed for a population (i.e. it is not possible for service planners to change the proportion of the male population who are MSM), MSTIC webtool users are not required to enter data for such hidden variables. A list of other hidden variables taken account in the MSTIC mathematical model is given in the Technical Appendix at the end of this document. 12

13 Glossary Appointment Capacity Demand Genitourinary medicine clinic Local area Mathematical model Partner notification For brevity, this refers to consultations with healthcare providers that are either booked in advance or provided on a walk-in basis. The number of appointments available in a sexual health service. The number of people in the population with chlamydia and/or gonorrhoea who seek care for their infection(s). A highly-specialised STI service usually located at a hospital. Sometimes known as a GUM clinic or a sexual health clinic. Geographical boundaries frequently change and so instead of referring to, for example, Primary Care Trusts or electoral wards we use the generic local area and users can define this how they like, so long as the total population size is between 10,000 and 1 million see Frequently Asked Question number 5. A mathematical representation of a process, device or concept, using a number of variables to represent inputs and outputs and sets of equations to describe their interaction. Partner notification (PN), or contact tracing as it is sometimes referred, has long been acknowledged as an essential part of sexually transmitted infection (STI) care management. Its objectives are both patient-centred, that is, reducing the risk of reinfection for index cases by treating their sexual partner(s), and partner-centred, that is, prevention of onward transmission through testing and treating sexual partners. Provider-led partner notification Partner notification (PN) undertaken by a healthcare professional within the health service rather than by the patient. Service configuration The possible combinations of different types of sexual health services in a local area 13

14 Disclaimer The current version of the MSTIC webtool has been developed with reasonable endeavours to exercise reasonable care, skill and judgement and is provided in good faith. The MSTIC webtool aims to provide an estimate of the number of chlamydia and gonorrhoea cases for a local population given assumptions made about the local population and the health services provided for it. This estimate is based on the results of a mathematical model and as such there are limitations to this model that users need to be aware of and take into consideration. Neither the MSTIC project team as the developer of the webtool, any of their respective employees, contractors, agents or other persons acting on their behalf or under their control, nor the UK Medical Research Council as funders of the MSTIC project accept any responsibility in respect of the MSTIC webtool or any information derived from the use of the MSTIC webtool. Any reliance you place upon the MSTIC webtool or any information contained in or derived from the MSTIC webtool will be at your sole risk and it is solely your responsibility to ensure that the use of any information or the provision of any services or other commitments contracts or other legal obligations entered into by you meet your requirements. We reserve the right in our sole discretion, but without any obligation, to make amendments or improvements to or withdraw or correct any error or omission to the content of or any part of the webtool without notice. To the fullest extent permitted by applicable laws we hereby exclude liability for any claims, loss, demands or damages of any kind whatsoever with respect to the webtool or the information contained or derived from the webtool including, without limitation, direct, indirect, incidental or consequential loss or damages, whether arising from loss of profits, loss of revenue or otherwise and whether or not the possibility of such loss has been notified to us. the foregoing will apply whether such claims, loss or damages arise in tort, contract, negligence, under statute or otherwise. 14

15 Technical appendix for the MSTIC webtool The MSTIC mathematical model The mathematical model that underpins the MSTIC webtool is designed to simulate the transmission of chlamydia and gonorrhoea in a sexually-active population. The model takes account of population demography and service provision for sexual health, and can be used to investigate the effects of changing various factors involved in the management of sexually transmitted infections (STIs). The mathematical model has the following features: Individual-based: every simulated individual has a known history; Socially-explicit: all sexual contacts are known and recorded allowing a network of contacts to develop; Process-based: events results from specific processes such as infection, seeking care, and partner notification; Stochastic: events are modelled probabilistically, so random variation in individual behaviour is explicitly included. The model was written in the C programming language. A full technical description of the model is being prepared for publication. At its heart, the model relies on a set of individual profiles taken from the second British National Survey of Sexual Attitudes and Lifestyles (Natsal) 8,9, the most recent such study at the time the model was developed. Natsal-2 is a national probability survey of sexual behaviour conducted between 1999 and 2001 and as such, its data can be considered broadly representative of the British general population. Each profile in the model therefore represents an individual in the British general population and contains information regarding their sexual behaviour, including their gender, sexual orientation, whether they sexual partnerships were concurrent (i.e. overlapping), their tendency towards casual sex, and their use of condoms. These are explained in more detail in the following section. The profiles are then assembled into populations by randomly selecting from the array until known criteria of gender and sexual orientation are achieved. More information on the populations is given below. Individuals Natsal-2 reports, amongst other things, the sexual behaviour of 11,161 individuals, however, for the purposes of this project, the Natsal-2 dataset was restricted to 4,668 (2,331 men and 2,337 women) sexually active respondents at risk of acquiring sexually transmitted infection(s), defined as those reporting more than one sexual partner in the five years prior to their interview for Natsal-2. Other reasons for non-inclusion were missing data with respect to sexual experience, sexual partners, and condom usage. Sexual orientation Natsal-2 classifies individuals sexual experience (ever) on a six-point scale (Table A1), which this project uses as a proxy for sexual orientation. Only values of one to five were 15

16 used as a value of six indicates no sexual experience and these individuals were excluded from the profiles: Table A1: Number of individuals by sexual experience (ever) and gender Sexual experience Males Females 1: Opposite sex only 2,081 2,045 2: Most often opposite sex but at least one same sex experience : About equally often with both sexes : Most often same sex but at least one opposite sex experience : Same sex only 31 0 Total: 2,331 2,337 Concurrency This was a binary variable to indicate concurrency, that is having two or more sexual partners concurrently in the last five years, or monogamy that is either one sexual partner in the last five years or multiple partners where the partnerships did not overlap. Table A2: Number of individuals by whether experienced concurrent sexual partnerships in the last five years and gender Concurrent sexual partnerships Males Females Yes No Total: Propensity for casual sex No direct information on this variable could be obtained from Natsal-2. However, the survey did contain questions on the number of new sexual partners in the four weeks prior to interview. This was used to calculate the average number of new sexual partners per week for each individual in the profile. Table A3: Average number of new partners in the last four weeks by gender Average number of new partners Males Females >2 3 0 Total: Condom usage Natsal-2 included a question on the number of sex partners in the last year where a condom was not used. By dividing this by the number of sex partners in the last year, an estimate of the probability of condom use with sexual partner(s) in the last year was obtained. 16

17 Table A4: Estimated probability of condom use in the last year by gender Probability of condom use Males Females Never % % % 11 8 Always Total: Population characterisation The three area/demography types considered by the mathematical model were characterised as urban, suburban, and rural populations. These differed from one another according to the sex ratio as recorded in the 2001 Census and the percentage of the male population aged years who are estimated to be men who have sex with men (MSM), according to Natsal-2 8. The model builds a random population of the desired demographic type by randomly selecting thousands of profiles until they match the sex ratio and proportion of MSM seen in the three area/demography types. Table A5: Population characterisation Population type Sex ratio ( : ) %(95% CI) of men who are MSM Urban ( ) Suburban ( ) Rural ( ) Model structure The model concentrates on estimating the number of individuals with new gonorrhoea and/or chlamydia infections who seek care. The model concentrates just on chlamydia and gonorrhoea infections because these constitute the majority of sexually transmitted infections in the UK. The model assumes that each week, each infected individual can acquire new sexual partners, infect any of their sexual partners, and/or initiate health care-seeking behaviour for their infections. Each disease is modelled separately to account for their differences in transmission. The model assumes that once individuals seek care, they may attend appointments and receive a diagnosis, then take prescribed treatment for their infection(s). At each stage of seeking care, the model simulates the behaviours of such as individuals failing to attend appointments, health services failing to correctly diagnose and treat an infection, and individuals failing to take their treatment. In most cases, individuals diagnosed with a STI will inform their partners and advise them to seek care themselves, termed patient-led partner notification (PN). However, in some instances an individual s sexual partners in the previous three months may be actively traced by healthcare professionals and invited to attend for care, termed provider-led PN. The model considers the extent of provider-led PN achieved by services and assumes that this immediately initiates care-seeking behaviour in the 17

18 individual s contacted partner; although they have the same chance as any patient of failing to be tested and treated due to non-attendance, false negative tests, and/or failing to take their treatment. Of course, it is important to acknowledge that not all partners are traceable, not all partners traced initiate care-seeking of their own, and that the healthcare provider the partner(s) choose is not necessarily the same one as the index patient. These states and processes are summarised in Figure 1. (Note that states are a combination of stage of infection and awareness of infection that determine sexual behaviour and initiation of processes; while processes are suites of simulated careseeking behaviours that lead to changes in states.) Figure 1: States and processes in the MSTIC mathematical model. 18

19 Parameters The mathematical model operates through a set of parameters relating to sexual behaviour, the epidemiology and pathology of the STIs modelled, and disease treatment and management. These are listed by type in Table A6: Table A6Table A6: Parameters used in the MSTIC mathematical model Behavioural parameters Probability of casual partner being traceable Probability of establishing new regular partnership Duration of regular partnership Probability of abandoning care-seeking Probability of abandoning treatment Pathological parameters (disease specific) Probability of asymptomatic disease Incubation duration Infectious duration, asymptomatic Infectious duration, symptomatic Epidemiological parameters (disease specific) Transmission probabilities (with and without condom, from male or female) Probability of seeking care with asymptomatic infection Probability of seeking care with symptomatic infection Management parameters (service specific) Total number of appointments available Maximum demand for appointments Time between diagnostic test and result Time between result and treatment Efficacy of treatment Proportion of individuals traced through provider-led partner notification Maximum number of provider-led partner notifications possible per week Maximum number of re-tries for a given provider-led partner notification Presentation of the modelling results in the MSTIC webtool The MSTIC webtool is built from the output of the mathematical model. Rather than running the entire mathematical model each time someone uses the MSTIC webtool, the results of several thousand runs of the MSTIC mathematical model with different input parameters have been summarised using Generalised Linear Models (GLMs). GLMs summarise the average behaviour or 'dominant trends' in the output of a mathematical model and report the variation about the mean; they also provide a measure of the goodness of fit of their predictions. This allows the key parameters of the mathematical model to be input through the MSTIC webtool, and the effects that these would have on the output of the mathematical model can be provided through the GLMs. This process allows a minimal set of input parameters to be required to be input into the MSTIC webtool by the user; discounting those parameters which are difficult to obtain and/or 19

20 have little effect on the overall output. The GLM analysis had a goodness-of-fit for this output of This means that 44.5% of the variation in this parameter was due to the influence of the parameters, and the rest was due to the dynamic random nature of the model. Assumptions and in-built features of the MSTIC webtool Since the focus of the MSTIC webtool is to permit health service planners to compare the effect of different combinations of health services on the number of new cases of chlamydia and/or gonorrhoea estimated in a population aged years, only the parameters related to service delivery shown in Table A6 (under Management parameters ) are available to be changed in the MSTIC webtool. Values for the other parameters have been researched in the scientific and medical literature, or derived from data collected by the survey tool specially designed for the MSTIC project. Notes on the user-defined parameters in the MSTIC webtool Specifying population area/demography in the MSTIC webtool This section of the MSTIC webtool specifies information about the size and type of population in which to simulate infection. As described earlier (Table A5), choosing between urban, suburban, or rural demographic types determines the proportion of MSM in the population modelled. Specifying & characterising health services in the MSTIC webtool The MSTIC webtool considers three broad types of health services: Service type A includes specialist GUM clinics; Service type B includes non-specialist GPs; and Service type C includes GP-based Locally Enhanced Services for STIs. Capacity and demand The MSTIC webtool asks for a number of values about the capacity and demand of each service that is selected to be modelled reflecting how services differ in their capacity to test and treat positive cases of chlamydia and/or gonorrhoea. As such, here the user can make assumptions about whether capacity is meeting demand for care or demand is exceeding capacity. The numbers the user enters should be appropriate for the whole population aged years designated in the first step. The model will estimate the number of infected individuals in the population who might be seeking care in any given week, so the questions about capacity and demand are for all potential patients, not just those seeking care for chlamydia and/or gonorrhoea. The total capacity and demand have been chosen as inputs for two reasons. Firstly, it is easier to estimate numbers for a local area as they directly relate to the amount of service provision and the numbers of patients seen, and which are available from datasets such as the Genito-Urinary Medicine Access Monthly Monitoring (GUMAMM) reports collated by the Department of Health 10. Secondly, the model has been calibrated to provide data as a proportion of the total demand. The total number of appointments available for patients should be an estimate for the population to be modelled. These appointments may be all at a single clinic or practice 20

21 or else spread out over many clinics and practices in the local area. The exact number is not important, but it must be within the right order of magnitude for the population. The default values are estimated from the average total attendance of patients seeking care per week in the three demographies studied for the MSTIC project. The maximum number of patients seeking care is required because the MSTIC mathematical model proved most sensitive to those times when demand for services is at a peak, because it is at these times that delays tend to form if the capacity is insufficient. For the default values, we have assumed that the maximum total attendance of patients seeking care in any given week is a surrogate for peak demand. For GPs, it is assumed that supply meets demand, and that the maximum number of patients seeking care is equal to the total number of available appointments. Provider-led partner notification: In the mathematical model, provider-led partner notification is controlled by a number of different parameters. For the purposes of the MSTIC webtool, these parameters have been rolled-up into a single assessment of the level of provider-led PN achieved within a service. There are three options: none, some and majority. If 'none' is chosen, then it is assumed that the service provides no PN and relies entirely on patient-led PN. The 'some' option assumes a minimal amount of provider-led PN (in addition to patient-led PN) with limited success at tracing recent sexual partners, that is approximately 15% of sexual partners are notified by the provider. The 'majority' option assumes diligent provider-led PN efforts to trace all recent contactable sexual partners of the individual, and a reasonable success at doing this, that is, approximately 35% of sexual partners are notified by the provider. It should be noted that all services are assumed to have a minimum level of patient-led PN, where the current sexual partner(s) of the individual are notified of the potential risk of infection by the index patient. Variables common to all services The delay between performing a diagnostic test and receiving the results, and the delay between diagnosis of an STI and the start of treatment are both assumed to be independent of the service type. Hence these questions are not service-specific. Default values As part of the MSTIC project, data for the input parameters have been collated for the three types of population that the model considers: an urban population (Tower Hamlets in central London), a suburban population (Brent in North West London) and a rural population (Cornwall). These data are shown in Table A7. This means that the webtool user can choose to use some of these default data if they wish. 21

22 Table A7: Default values by variable, service type and population type Service Default value by population type Variable type Rural Suburban Urban Population size Number of males aged years n/a 91,700 62,100 56,300 Number of females aged years n/a 91,000 73,500 68,400 Capacity Total appointments possible for all patients (number per week) Total appointments possible for all patients *(number per week) Total appointments possible for all patients (number per week) Demand Maximum patients seeking care (number per week) Maximum patients seeking care * (number per week) Maximum patients seeking care (number per week) A B C A B C ,683 26,146 21,879 17** 17** 17** ,683 26,146 21,879 24** 24** 24** Partner notification Degree of Provider-led PN achieved A Majority Majority Majority Degree of Provider-led PN achieved B None None None Degree of Provider-led PN achieved C Some Some Some Notes for Table A7: * all patients, not just for STIs. ** per practice. 22

23 References 1. The National Strategy for Sexual Health and HIV. Department of Health, London, Medical Foundation for AIDS and Sexual Health. Standards for the management of sexually transmitted infections (STIs). London: Published by MedFASH on behalf of BASHH; Bailey AC, Johnson SA, Cassell JA. Are primary care-based sexually transmitted infection services in the UK delivering public health benefit? Int J STD AIDS 2010 January;21(1): Yung M, Denholm R, Peake J, Hughes G. Distribution and characteristics of sexual health service provision in primary and community care in England. Int J STD AIDS 2010 Sep;21(9): (last accessed 7 June 2011) (last accessed 7 June 2011) (last accessed 7 June 2011). 8. Johnson AM, Mercer CH, Erens B, Copas AJ, McManus S, Wellings K, Fenton KA, Korovessis C, Macdowall W, Nanchahal K, Purdon S & Field H. (2001) Sexual behaviour in Britain: partnerships, practices, and HIV risk behaviours. Lancet, 358, Erens B, McManus S, Field J, Korovessis C, Johnson AM, Fenton KA, Wellings, K. National Survey of Sexual Attitudes and Lifestyles II: Technical Report. NatCen, dstatistics/sexualhealth/index.htm (last accessed 7 June 2011). 23