Background The increasing prevalence of obesity is a major public health problem in many countries. Built environment factors are known to be associated with obesity, which is an important risk factor for type 2 diabetes. Online geocoding services could be used to identify regions with a high concentration of obesogenic factors. The aim of our study was to examine the feasibility of integrating information from online geocoding services for the assessment of obesogenic environments. Methods We identified environmental factors associated with obesity from the literature and translated these factors into variables from the online geocoding services Google Maps and OpenStreetMap (OSM). We tested whether spatial data points can be downloaded from these services and processed and visualized on maps. True- and false-positive values, false-negative values, sensitivities and positive predictive values of the processed data were determined using search engines and in-field inspections within four pilot areas in Bavaria, Germany. Results Several environmental factors could be identified from the literature that were either positively or negatively correlated with weight outcomes in previous studies. The diversity of query variables was higher in OSM compared with Google Maps. In each pilot area, query results from Google showed a higher absolute number of true-positive hits and of false-positive hits, but a lower number of false-negative hits during the validation process. The positive predictive value of database hits was higher in OSM and ranged between 81 and 100% compared with a range of 63–89% for Google Maps. In contrast, sensitivities were higher in Google Maps (between 59 and 98%) than in OSM (between 20 and 64%). Conclusions It was possible to operationalize obesogenic factors identified from the literature with data and variables available from geocoding services. The validity of Google Maps and OSM was reasonable. The assessment of environmental obesogenic factors via geocoding services could potentially be applied in diabetes surveillance. Electronic supplementary material The online version of this article (10.1186/s12942-019-0177-9) contains supplementary material, which is available to authorized users.

Background and aimsModelling return on investment (ROI) from smoking cessation interventions requires estimates of their costs and benefits. This paper describes a standardized method developed to source both economic costs of tobacco smoking and costs of implementing cessation interventions for a Europe‐wide ROI model [European study on Quantifying Utility of Investment in Protection from Tobacco model (EQUIPTMOD)].DesignFocused search of administrative and published data. A standardized checklist was developed in order to ensure consistency in methods of data collection.Setting and participantsAdult population (15+ years) in Hungary, Netherlands, Germany, Spain and England. For passive smoking‐related costs, child population (0–15 years) was also included.MeasurementsCosts of treating smoking‐attributable diseases; productivity losses due to smoking‐attributable absenteeism; and costs of implementing smoking cessation interventions.FindingsAnnual costs (per case) of treating smoking attributable lung cancer were between €5074 (Hungary) and €52 106 (Germany); coronary heart disease between €1521 (Spain) and €3955 (Netherlands); chronic obstructive pulmonary disease between €1280 (England) and €4199 (Spain); stroke between €1829 (Hungary) and €14 880 (Netherlands). Costs (per recipient) of smoking cessation medications were estimated to be: for standard duration of varenicline between €225 (England) and €465 (Hungary); for bupropion between €25 (Hungary) and €220 (Germany). Costs (per recipient) of providing behavioural support were also wide‐ranging: one‐to‐one behavioural support between €34 (Hungary) and €474 (Netherlands); and group‐based behavioural support between €12 (Hungary) and €257 (Germany). The costs (per recipient) of delivering brief physician advice were: €24 (England); €9 (Germany); €4 (Hungary); €33 (Netherlands); and €27 (Spain).ConclusionsCosts of treating smoking‐attributable diseases as well as the costs of implementing smoking cessation interventions vary substantially across Hungary, Netherlands, Germany, Spain and England. Estimates for the costs of these diseases and interventions can contribute to return on investment estimates in support of national or regional policy decisions.

Background and aimThe cost‐effectiveness of internet‐based smoking cessation interventions is difficult to determine when they are provided as a complement to current smoking cessation services. The aim of this study was to evaluate the cost‐effectiveness of such an alternate package compared with existing smoking cessation services alone (current package).MethodsA literature search was conducted to identify internet‐based smoking cessation interventions in the Netherlands. A meta‐analysis was then performed to determine the pooled effectiveness of a (web‐based) computer‐tailored intervention. The mean cost of implementing internet based interventions was calculated using available information, while intervention reach was sourced from an English study. We used EQUIPTMOD, a Markov‐based state‐transition model, to calculate the incremental cost‐effectiveness ratios [expressed as cost per quality‐adjusted life years (QALYs) gained] for different time horizons to assess the value of providing internet‐based interventions to complement the current package.). Deterministic sensitivity analyses tested the uncertainty around intervention costs per smoker, relative risks, and the intervention reach.ResultsInternet‐based interventions had an estimated pooled relative risk of 1.40; average costs per smoker of €2.71; and a reach of 0.41% of all smokers. The alternate package (i.e. provision of internet‐based intervention to the current package) was dominant (cost‐saving) compared with the current package alone (0.14 QALY gained per 1000 smokers; reduced health‐care costs of €602.91 per 1000 smokers for the life‐time horizon). The alternate package remained dominant in all sensitivity analyses.ConclusionProviding internet‐based smoking cessation interventions to complement the current provision of smoking cessation services could be a cost‐saving policy option in the Netherlands.

IntroductionSuccessful breast conserving cancer surgeries come along with tumor free resection margins and account for cosmetic outcome. Positive margins increase the likelihood of tumor recurrence. Intra-operative fluorescence molecular imaging (IFMI) aims to focus surgery on malignant tissue thus substantially lowering the presence of positive margins as compared with standard techniques of breast conservation (ST). A goal of this paper is to assess the incremental number of surgeries and costs of IFMI vs. ST.MethodsWe developed a decision analytical model and applied it for an early evaluation approach. Given uncertainty we considered that IFMI might reduce the proportion of positive margins found by ST from all to none and this proportion is assumed to be reduced to 10% for the base case. Inputs included data from the literature and a range of effect estimates. For the costs of IFMI, respective cost components were added to those of ST.ResultsThe base case reduction lowered number of surgeries (mean [95% confidence interval]) by 0.22 [0.15; 0.30] and changed costs (mean [95% confidence interval]) by €-663 [€-1,584; €50]. A tornado diagram identified the Diagnosis Related Group (DRG) costs, the proportion of positive margins of ST, the staff time saving factor and the duration of frozen section analysis (FSA) as important determinants of this cost.ConclusionsThese early results indicate that IFMI may be more effective than ST and through the reduction of positive margins it is possible to save follow-up surgeries–indicating further health risk–and to save costs through this margin reduction and the avoidance of FSA.