Improving the Cost-efficiency of Preventive Chemotherapy: Impact of New Diagnostics on Stopping Decisions for Control of Schistosomiasis.

BACKGROUND: Control of schistosomiasis (SCH) relies on the regular distribution of preventive chemotherapy (PC) over many years. For the sake of sustainable SCH control, a decision must be made at some stage to scale down or stop PC. These "stopping decisions" are based on population surveys that assess whether infection levels are sufficiently low. However, the limited sensitivity of the currently used diagnostic (Kato-Katz [KK]) to detect low-intensity infections is a concern. Therefore, the use of new, more sensitive, molecular diagnostics has been proposed. METHODS: Through statistical analysis of Schistosoma mansoni egg counts collected from Burundi and a simulation study using an established transmission model for schistosomiasis, we investigated the extent to which more sensitive diagnostics can improve decision making regarding stopping or continuing PC for the control of S. mansoni. RESULTS: We found that KK-based strategies perform reasonably well for determining when to stop PC at a local scale. Use of more sensitive diagnostics leads to a marginally improved health impact (person-years lived with heavy infection) and comes at a cost of continuing PC for longer (up to around 3 years), unless the decision threshold for stopping PC is adapted upward. However, if this threshold is set too high, PC may be stopped prematurely, resulting in a rebound of infection levels and disease burden (+45% person-years of heavy infection). CONCLUSIONS: We conclude that the potential value of more sensitive diagnostics lies more in the reduction of survey-related costs than in the direct health impact of improved parasite control.

A Comparison of Markov and Mechanistic Models for Soil-Transmitted Helminth Prevalence Projections in the Context of Survey Design.

Globally, there are over 1 billion people infected with soil-transmitted helminths (STHs), mostly living in marginalized settings with inadequate sanitation in sub-Saharan Africa and Southeast Asia. The World Health Organization recommends an integrated approach to STH morbidity control through improved access to sanitation and hygiene education and the delivery of preventive chemotherapy (PC) to school-age children delivered through schools. Progress of STH control programs is currently estimated using a baseline (pre-PC) school-based prevalence survey and then monitored using periodical school-based prevalence surveys, known as Impact Assessment Surveys (IAS). We investigated whether integrating geostatistical methods with a Markov model or a mechanistic transmission model for projecting prevalence forward in time from baseline can improve IAS design strategies. To do this, we applied these 2 methods to prevalence data collected in Kenya, before evaluating and comparing their performance in accurately informing optimal survey design for a range of IAS sampling designs. We found that, although both approaches performed well, the mechanistic method more accurately projected prevalence over time and provided more accurate information for guiding survey design. Both methods performed less well in areas with persistent STH hotspots where prevalence did not decrease despite multiple rounds of PC. Our findings show that these methods can be useful tools for more efficient and accurate targeting of PC. The general framework built in this paper can also be used for projecting prevalence and informing survey design for other neglected tropical diseases.

Geostatistical modelling enables efficient safety assessment for mass drug administration with ivermectin in Loa loa endemic areas through a combined antibody and LoaScope testing strategy for elimination of onchocerciasis.

The elimination of onchocerciasis through community-based Mass Drug Administration (MDA) of ivermectin (Mectizan) is hampered by co-endemicity of Loa loa, as individuals who are highly co-infected with Loa loa parasites can suffer serious and occasionally fatal neurological reactions from the drug. The test-and-not-treat strategy of testing all individuals participating in MDA has some operational constraints including the cost and limited availability of LoaScope diagnostic tools. As a result, a Loa loa Antibody (Ab) Rapid Test was developed to offer a complementary way of determining the prevalence of loiasis. We develop a joint geostatistical modelling framework for the analysis of Ab and Loascope data to delineate whether an area is safe for MDA. Our results support the use of a two-stage strategy, in which Ab testing is used to identify areas that, with acceptably high probability, are safe or unsafe for MDA, followed by Loascope testing in areas whose safety status is uncertain. This work therefore contributes to the global effort towards the elimination of onchocerciasis as a public health problem by potentially reducing the time and cost required to establish whether an area is safe for MDA.

MBGapp: A Shiny application for teaching model-based geostatistics to population health scientists.

User-friendly interfaces have been increasingly used to facilitate the learning of advanced statistical methodology, especially for students with only minimal statistical training. In this paper, we illustrate the use of MBGapp for teaching geostatistical analysis to population health scientists. Using a case-study on Loa loa infections, we show how MBGapp can be used to teach the different stages of a geostatistical analysis in a more interactive fashion. For wider accessibility and usability, MBGapp is available as an R package and as a Shiny web-application that can be freely accessed on any web browser. In addition to MBGapp, we also present an auxiliary Shiny app, called VariagramApp, that can be used to aid the teaching of Gaussian processes in one and two dimensions using simulations.

Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict.

This paper provides statistical guidance on the development and application of model-based geostatistical methods for disease prevalence mapping. We illustrate the different stages of the analysis, from exploratory analysis to spatial prediction of prevalence, through a case study on malaria mapping in Tanzania. Throughout the paper, we distinguish between predictive modelling, whose main focus is on maximizing the predictive accuracy of the model, and explanatory modelling, where greater emphasis is placed on understanding the relationships between the health outcome and risk factors. We demonstrate that these two paradigms can result in different modelling choices. We also propose a simple approach for detecting over-fitting based on inspection of the correlation matrix of the estimators of the regression coefficients. To enhance the interpretability of geostatistical models, we introduce the concept of domain effects in order to assist variable selection and model validation. The statistical ideas and principles illustrated here in the specific context of disease prevalence mapping are more widely applicable to any regression model for the analysis of epidemiological outcomes but are particularly relevant to geostatistical models, for which the separation between fixed and random effects can be ambiguous.

Explaining the Sex Effect on Survival in Cystic Fibrosis: a Joint Modeling Study of UK Registry Data.

BACKGROUND: Male sex is associated with better lung function and survival in people with cystic fibrosis but it is unclear whether the survival benefit is solely due to the sex-effect on lung function. METHODS: This study analyzes data between 1996 and 2015 from the longitudinal registry study of the UK Cystic Fibrosis Registry. We jointly analyze repeated measurements and time-to-event outcomes to assess how much of the sex effect on lung function also explains survival. These novel methods allow examination of association between percent of forced expiratory volume in 1 second (%FEV1) and covariates such as sex and genotype, and survival, in the same modeling framework. We estimate the probability of surviving one more year with a probit model. RESULTS: The dataset includes 81,129 lung function measurements of %FEV1 on 9,741 patients seen between 1996 and 2015 and captures 1,543 deaths. Males compared with females experienced a more gradual decline in %FEV1 (difference 0.11 per year 95% confidence interval [CI] = 0.08, 0.14). After adjusting for confounders, both overall level of %FEV1 and %FEV1 rate of change are associated with the concurrent hazard for death. There was evidence of a male survival advantage (probit coefficient 0.15; 95% CI = 0.10, 0.19) which changed little after adjustment for %FEV1 using conventional approaches but was attenuated by 37% on adjustment for %FEV1 level and slope in the joint model (0.09; 95% CI = 0.06, 0.12). CONCLUSIONS: We estimate that about 37% of the association of sex on survival in cystic fibrosis is mediated through lung function.

A geostatistical framework for combining spatially referenced disease prevalence data from multiple diagnostics.

Multiple diagnostic tests are often used due to limited resources or because they provide complementary information on the epidemiology of a disease under investigation. Existing statistical methods to combine prevalence data from multiple diagnostics ignore the potential overdispersion induced by the spatial correlations in the data. To address this issue, we develop a geostatistical framework that allows for joint modelling of data from multiple diagnostics by considering two main classes of inferential problems: (a) to predict prevalence for a gold-standard diagnostic using low-cost and potentially biased alternative tests; (b) to carry out joint prediction of prevalence from multiple tests. We apply the proposed framework to two case studies: mapping Loa loa prevalence in Central and West Africa, using miscroscopy, and a questionnaire-based test called RAPLOA; mapping Plasmodium falciparum malaria prevalence in the highlands of Western Kenya using polymerase chain reaction and a rapid diagnostic test. We also develop a Monte Carlo procedure based on the variogram in order to identify parsimonious geostatistical models that are compatible with the data. Our study highlights (a) the importance of accounting for diagnostic-specific residual spatial variation and (b) the benefits accrued from joint geostatistical modelling so as to deliver more reliable and precise inferences on disease prevalence.

Climate, human behaviour or environment: individual-based modelling of Campylobacter seasonality and strategies to reduce disease burden.

BACKGROUND: With over 800 million cases globally, campylobacteriosis is a major cause of food borne disease. In temperate climates incidence is highly seasonal but the underlying mechanisms are poorly understood, making human disease control difficult. We hypothesised that observed disease patterns reflect complex interactions between weather, patterns of human risk behaviour, immune status and level of food contamination. Only by understanding these can we find effective interventions. METHODS: We analysed trends in human Campylobacter cases in NE England from 2004 to 2009, investigating the associations between different risk factors and disease using time-series models. We then developed an individual-based (IB) model of risk behaviour, human immunological responses to infection and environmental contamination driven by weather and land use. We parameterised the IB model for NE England and compared outputs to observed numbers of reported cases each month in the population in 2004-2009. Finally, we used it to investigate different community level disease reduction strategies. RESULTS: Risk behaviours like countryside visits (t = 3.665, P < 0.001 and t = - 2.187, P = 0.029 for temperature and rainfall respectively), and consumption of barbecued food were strongly associated with weather, (t = 3.219, P = 0.002 and t = 2.015, P = 0.045 for weekly average temperature and average maximum temperature respectively) and also rain (t = 2.254, P = 0.02527). This suggests that the effect of weather was indirect, acting through changes in risk behaviour. The seasonal pattern of cases predicted by the IB model was significantly related to observed patterns (r = 0.72, P < 0.001) indicating that simulating risk behaviour could produce the observed seasonal patterns of cases. A vaccination strategy providing short-term immunity was more effective than educational interventions to modify human risk behaviour. Extending immunity to 1 year from 20 days reduced disease burden by an order of magnitude (from 2412-2414 to 203-309 cases per 50,000 person-years). CONCLUSIONS: This is the first interdisciplinary study to integrate environment, risk behaviour, socio-demographics and immunology to model Campylobacter infection, including pathways to mitigation. We conclude that vaccination is likely to be the best route for intervening against campylobacteriosis despite the technical problems associated with understanding both the underlying human immunology and genetic variation in the pathogen, and the likely cost of vaccine development.

Reducing contamination risk in cluster-randomized infectious disease-intervention trials.

Background: Infectious disease interventions are increasingly tested using cluster-randomized trials (CRTs). These trial settings tend to involve a set of sampling units, such as villages, whose geographic arrangement may present a contamination risk in treatment exposure. The most widely used approach for reducing contamination in these settings is the so-called fried-egg design, which excludes the outer portion of all available clusters from the primary trial analysis. However, the fried-egg design ignores potential intra-cluster spatial heterogeneity and makes the outcome measure inherently less precise. Whereas the fried-egg design may be appropriate in specific settings, alternative methods to optimize the design of CRTs in other settings are lacking. Methods: We present a novel approach for CRT design that either fully includes or fully excludes available clusters in a defined study region, recognizing the potential for intra-cluster spatial heterogeneity. The approach includes an algorithm that allows investigators to identify the maximum number of clusters that could be included for a defined study region and maintain randomness in both the selection of included clusters and the allocation of clusters to either the treatment group or control group. The approach was applied to the design of a CRT testing the effectiveness of malaria vector-control interventions in southern Malawi. Conclusions: Those planning CRTs to evaluate interventions should consider the approach presented here during trial design. The approach provides a novel framework for reducing the risk of contamination among the CRT randomization units in settings where investigators determine the reduction of contamination risk as a high priority and where intra-cluster spatial heterogeneity is likely. By maintaining randomness in the allocation of clusters to either the treatment group or control group, the approach also permits a randomization-valid test of the primary trial hypothesis.

Assessment of the effect of larval source management and house improvement on malaria transmission when added to standard malaria control strategies in southern Malawi: study protocol for a cluster-randomised controlled trial.

BACKGROUND: Due to outdoor and residual transmission and insecticide resistance, long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) will be insufficient as stand-alone malaria vector control interventions in many settings as programmes shift toward malaria elimination. Combining additional vector control interventions as part of an integrated strategy would potentially overcome these challenges. Larval source management (LSM) and structural house improvements (HI) are appealing as additional components of an integrated vector management plan because of their long histories of use, evidence on effectiveness in appropriate settings, and unique modes of action compared to LLINs and IRS. Implementation of LSM and HI through a community-based approach could provide a path for rolling-out these interventions sustainably and on a large scale. METHODS/DESIGN: We will implement community-based LSM and HI, as additional interventions to the current national malaria control strategies, using a randomised block, 2 × 2 factorial, cluster-randomised design in rural, southern Malawi. These interventions will be continued for two years. The trial catchment area covers about 25,000 people living in 65 villages. Community participation is encouraged by training community volunteers as health animators, and supporting the organisation of village-level committees in collaboration with The Hunger Project, a non-governmental organisation. Household-level cross-sectional surveys, including parasitological and entomological sampling, will be conducted on a rolling, 2-monthly schedule to measure outcomes over two years (2016 to 2018). Coverage of LSM and HI will also be assessed throughout the trial area. DISCUSSION: Combining LSM and/or HI together with the interventions currently implemented by the Malawi National Malaria Control Programme is anticipated to reduce malaria transmission below the level reached by current interventions alone. Implementation of LSM and HI through a community-based approach provides an opportunity for optimum adaptation to the local ecological and social setting, and enhances the potential for sustainability. TRIAL REGISTRATION: Registered with The Pan African Clinical Trials Registry on 3 March 2016, trial number PACTR201604001501493.

Modeling Seasonal and Spatiotemporal Variation: The Example of Respiratory Prescribing.

Many measures of chronic diseases, including respiratory disease, exhibit seasonal variation together with residual correlation between consecutive time periods and neighboring areas. We demonstrate a strategy for modeling data that exhibit both seasonal trend and spatiotemporal correlation, using an application to respiratory prescribing. We analyzed 55 months (2002-2006) of prescribing data from the northeast of England, in the United Kingdom. We estimated the seasonal pattern of prescribing by fitting a dynamic harmonic regression (DHR) model to salbutamol prescribing in relation to temperature. We compared the output of DHR models to static sinusoidal regression models. We used the DHR-fitted values as an offset in mixed-effects models that aimed to account for the remaining spatiotemporal variation in prescribing rates. As diagnostic checks, we assessed spatial and temporal correlation separately and jointly. Our application of a DHR model resulted in a better fit to the seasonal variation of prescribing than was obtained with a static model. After adjusting for the fitted values from the DHR model, we did not detect any remaining spatiotemporal correlation in the model's residuals. Using a DHR model and temperature data to account for the periodicity of prescribing proved to be an efficient way to capture its seasonal variation. The diagnostic procedures indicated that there was no need to model any remaining correlation explicitly.

A Comparative Assessment of Track Plates to Quantify Fine Scale Variations in the Relative Abundance of Norway Rats in Urban Slums.

Norway rats (Rattus norvegicus) living in urban environments are a critical public health and economic problem, particularly in urban slums where residents are at a higher risk for rat borne diseases, yet convenient methods to quantitatively assess population sizes are lacking. We evaluated track plates as a method to determine rat distribution and relative abundance in a complex urban slum environment by correlating the presence and intensity of rat-specific marks on track plates with findings from rat infestation surveys and trapping of rats to population exhaustion. To integrate the zero-inflated track plate data we developed a two-component mixture model with one binary and one censored continuous component. Track plate mark-intensity was highly correlated with signs of rodent infestation (all coefficients between 0.61 and 0.79 and all p-values < 0.05). Moreover, the mean level of pre-trapping rat-mark intensity on plates was significantly associated with the number of rats captured subsequently (Odds ratio1.38; 95% CI 1.19-1.61) and declined significantly following trapping (Odds ratio 0.86; 95% CI 0.78-0.95). Track plates provided robust proxy measurements of rat abundance and distribution and detected rat presence even when populations appeared 'trapped out'. Tracking plates are relatively easy and inexpensive methods that can be used to intensively sample settings such as urban slums, where traditional trapping or mark-recapture studies are impossible to implement, and therefore the results can inform and assess the impact of targeted urban rodent control campaigns.

Spatiotemporal Determinants of Urban Leptospirosis Transmission: Four-Year Prospective Cohort Study of Slum Residents in Brazil.

BACKGROUND: Rat-borne leptospirosis is an emerging zoonotic disease in urban slum settlements for which there are no adequate control measures. The challenge in elucidating risk factors and informing approaches for prevention is the complex and heterogeneous environment within slums, which vary at fine spatial scales and influence transmission of the bacterial agent. METHODOLOGY/PRINCIPAL FINDINGS: We performed a prospective study of 2,003 slum residents in the city of Salvador, Brazil during a four-year period (2003-2007) and used a spatiotemporal modelling approach to delineate the dynamics of leptospiral transmission. Household interviews and Geographical Information System surveys were performed annually to evaluate risk exposures and environmental transmission sources. We completed annual serosurveys to ascertain leptospiral infection based on serological evidence. Among the 1,730 (86%) individuals who completed at least one year of follow-up, the infection rate was 35.4 (95% CI, 30.7-40.6) per 1,000 annual follow-up events. Male gender, illiteracy, and age were independently associated with infection risk. Environmental risk factors included rat infestation (OR 1.46, 95% CI, 1.00-2.16), contact with mud (OR 1.57, 95% CI 1.17-2.17) and lower household elevation (OR 0.92 per 10m increase in elevation, 95% CI 0.82-1.04). The spatial distribution of infection risk was highly heterogeneous and varied across small scales. Fixed effects in the spatiotemporal model accounted for the majority of the spatial variation in risk, but there was a significant residual component that was best explained by the spatial random effect. Although infection risk varied between years, the spatial distribution of risk associated with fixed and random effects did not vary temporally. Specific "hot-spots" consistently had higher transmission risk during study years. CONCLUSIONS/SIGNIFICANCE: The risk for leptospiral infection in urban slums is determined in large part by structural features, both social and environmental. Our findings indicate that topographic factors such as household elevation and inadequate drainage increase risk by promoting contact with mud and suggest that the soil-water interface serves as the environmental reservoir for spillover transmission. The use of a spatiotemporal approach allowed the identification of geographic outliers with unexplained risk patterns. This approach, in addition to guiding targeted community-based interventions and identifying new hypotheses, may have general applicability towards addressing environmentally-transmitted diseases that have emerged in complex urban slum settings.

Future trends and inequalities in premature coronary deaths in England: Modelling study.

BACKGROUND: Coronary heart disease (CHD) is a major cause of premature mortality, particularly in deprived groups. Might recent declines in overall mortality obscure different rates of decline among social strata, creating potentially misleading views on inequalities? METHODS: We used a Bayesian analysis of an age-period-cohort model for the English population. We projected age-specific premature CHD mortality (ages 35-74) by gender and area-based deprivation status for the period 2007-2035, using 1982-2006 as the input. Deprivation status was measured by Index of Multiple Deprivation quintiles, which aggregate seven types of deprivation, including health and income. We analysed inequality in premature CHD mortality. We investigated the annual changes in inequality and the contributions of changes in each IMDQ to the overall annual changes, using both absolute (probability) and relative (logit) scales. We quantified inequality using the statistical variance in the probability of premature death among deprivation quintiles. RESULTS: The overall premature CHD mortality trends conceal marked heterogeneities. Our models predict more rapid declines in premature CHD mortality for the most affluent quintiles than for the most deprived (annualized rate of decline 2006-2025, 7.5% [95% Credible Interval 4.3-10.5%] versus 5.4% [2.2-8.7%] for men, and 6.3% [3.0-9.9%] versus 5.9% [1.5-10.8%] for women). For men, the posterior probability that the rate of decline is greater for the most affluent was 82%. Variance in premature CHD mortality across deprivation quintiles was projected to decrease by approximately 81% [28-95%] among men and by 89% [30-99%] among women. This decrease was particularly driven by the most deprived groups due to their higher premature death rates. However, relative inequality was projected to rise by 93% among men [81-125%] and rise by 13% [-25-58%] among women. These increases are also mostly influenced by the most deprived, reflecting their slower declines in premature deaths. CONCLUSIONS: Overall, premature coronary death rates in England continue to decline steeply. Absolute inequalities are decreasing, reflecting declines in the high premature mortality in deprived groups. However, relative inequalities are projected to widen further, reflecting slower mortality declines in the most deprived groups. More aggressive and progressive prevention policies are urgently needed.

The Health Equity and Effectiveness of Policy Options to Reduce Dietary Salt Intake in England: Policy Forecast.

BACKGROUND: Public health action to reduce dietary salt intake has driven substantial reductions in coronary heart disease (CHD) over the past decade, but avoidable socio-economic differentials remain. We therefore forecast how further intervention to reduce dietary salt intake might affect the overall level and inequality of CHD mortality. METHODS: We considered English adults, with socio-economic circumstances (SEC) stratified by quintiles of the Index of Multiple Deprivation. We used IMPACTSEC, a validated CHD policy model, to link policy implementation to salt intake, systolic blood pressure and CHD mortality. We forecast the effects of mandatory and voluntary product reformulation, nutrition labelling and social marketing (e.g., health promotion, education). To inform our forecasts, we elicited experts' predictions on further policy implementation up to 2020. We then modelled the effects on CHD mortality up to 2025 and simultaneously assessed the socio-economic differentials of effect. RESULTS: Mandatory reformulation might prevent or postpone 4,500 (2,900-6,100) CHD deaths in total, with the effect greater by 500 (300-700) deaths or 85% in the most deprived than in the most affluent. Further voluntary reformulation was predicted to be less effective and inequality-reducing, preventing or postponing 1,500 (200-5,000) CHD deaths in total, with the effect greater by 100 (-100-600) deaths or 49% in the most deprived than in the most affluent. Further social marketing and improvements to labelling might each prevent or postpone 400-500 CHD deaths, but minimally affect inequality. CONCLUSIONS: Mandatory engagement with industry to limit salt in processed-foods appears a promising and inequality-reducing option. For other policy options, our expert-driven forecast warns that future policy implementation might reach more deprived individuals less well, limiting inequality reduction. We therefore encourage planners to prioritise equity.

A three-dimensional point process model for the spatial distribution of disease occurrence in relation to an exposure source.

We study methods for how to include the spatial distribution of tumours when investigating the relation between brain tumours and the exposure from radio frequency electromagnetic fields caused by mobile phone use. Our suggested point process model is adapted from studies investigating spatial aggregation of a disease around a source of potential hazard in environmental epidemiology, where now the source is the preferred ear of each phone user. In this context, the spatial distribution is a distribution over a sample of patients rather than over multiple disease cases within one geographical area. We show how the distance relation between tumour and phone can be modelled nonparametrically and, with various parametric functions, how covariates can be included in the model and how to test for the effect of distance. To illustrate the models, we apply them to a subset of the data from the Interphone Study, a large multinational case-control study on the association between brain tumours and mobile phone use.

The evaluation of a tailored intervention to improve the management of suspected viral encephalitis: protocol for a cluster randomised controlled trial.

BACKGROUND: Viral encephalitis is a devastating condition for which delayed treatment is associated with increased morbidity and mortality. Clinical audits indicate substantial scope for improved detection and treatment. Improvement strategies should ideally be tailored according to identified needs and barriers to change. The aim of the study is to evaluate the effectiveness and cost-effectiveness of a tailored intervention to improve the secondary care management of suspected encephalitis. METHODS/DESIGN: The study is a two-arm cluster randomised controlled trial with allocation by postgraduate deanery. Participants were identified from 24 hospitals nested within 12 postgraduate deaneries in the United Kingdom (UK). We developed a multifaceted intervention package including core and flexible components with embedded behaviour change techniques selected on the basis of identified needs and barriers to change. The primary outcome will be a composite of the proportion of patients with suspected encephalitis receiving timely and appropriate diagnostic lumbar puncture within 12 h of hospital admission and aciclovir treatment within 6 h. We will gather outcome data pre-intervention and up to 12 months post-intervention from patient records. Statistical analysis at the cluster level will be blind to allocation. An economic evaluation will estimate intervention cost-effectiveness from the health service perspective. TRIAL REGISTRATION: Controlled Trials: ISRCTN06886935.

Association between respiratory prescribing, air pollution and deprivation, in primary health care.

BACKGROUND: We investigated the association between respiratory prescribing, air quality and deprivation in primary health care. Most previous studies have used data from secondary and tertiary care to quantify air pollution effects on exacerbations of asthma and chronic obstructive pulmonary disease (COPD). However, these outcomes capture patients who suffer from relatively severe symptoms. METHODS: This is a population-based ecological study. We analysed respiratory medication (salbutamol) prescribed monthly by 63 primary care practices, UK. Firstly, we captured the area-wide seasonal variation in prescribing. Then, using the area-wide variation in prescribing as an offset, we built a mixed-effects model to assess the remaining variation in relation to air quality and demographic variables. RESULTS: An increase of 10 µg/m(3) in ambient PM10 was associated with an increase of 1% (95% CI: 0.1-2%) in salbutamol prescribing. An increase of 1 SD in income and employment deprivation was associated with an increase of 20.5% (95% CI: 8.8-33.4%) and 14.7% (95% CI: 4.3-26.2%) in salbutamol prescribing rate, respectively. CONCLUSIONS: The study provides evidence that monthly respiratory prescribing in primary care is a useful indicator of the extent to which air pollution exacerbates asthma and COPD symptoms. Respiratory prescribing was higher on deprived populations.

Mapping English GP prescribing data: a tool for monitoring health-service inequalities.

OBJECTIVE: The aim of this paper was to show that easily interpretable maps of local and national prescribing data, available from open sources, can be used to demonstrate meaningful variations in prescribing performance. DESIGN: The prescription dispensing data from the National Health Service (NHS) Information Centre for the medications metformin hydrochloride and methylphenidate were compared with reported incidence data for the conditions, diabetes and attention deficit hyperactivity disorder, respectively. The incidence data were obtained from the open source general practitioner (GP) Quality and Outcomes Framework. These data were mapped using the Ordnance Survey CodePoint Open data and the data tables stored in a PostGIS spatial database. Continuous maps of spending per person in England were then computed by using a smoothing algorithm and areas whose local spending is substantially (at least fourfold) and significantly (p<0.05) higher than the national average are then highlighted on the maps. SETTING: NHS data with analysis of primary care prescribing. POPULATION: England, UK. RESULTS: The spatial mapping demonstrates that several areas in England have substantially and significantly higher spending per person on metformin and methyphenidate. North Kent and the Wirral have substantially and significantly higher spending per child on methyphenidate. CONCLUSIONS: It is possible, using open source data, to use statistical methods to distinguish chance fluctuations in prescribing from genuine differences in prescribing rates. The results can be interactively mapped at a fine spatial resolution down to individual GP practices in England. This process could be automated and reported in real time. This can inform decision-making and could enable earlier detection of emergent phenomena.