A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation

BACKGROUND

Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people's weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE).

METHODS

Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley's K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic.

RESULTS

We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley's K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results.

DISCUSSION

The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications.

Using data from online geocoding services for the assessment of environmental obesogenic factors: a feasibility study

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.

Secondary data in diabetes surveillance - co-operation projects and definition of references on the documented prevalence of diabetes

In addition to the Robert Koch Institute's health surveys, analyses of secondary data are essential to successfully developing a regular and comprehensive description of the progression of diabetes as part of the Robert Koch Institute's diabetes surveillance. Mainly, this is due to the large sample size and the fact that secondary data are routinely collected, which allows for highly stratified analyses in short time intervals. The fragmented availability of data means that various sources of secondary data are required in order to provide data for the indicators in the four fields of action for diabetes surveillance. Thus, a milestone in the project was to check the suitability of different data sources for their usability and to carry out analyses. Against this backdrop, co-operation projects were specifically funded in the context of diabetes surveillance. This article presents the results that were achieved in co-operation projects between 2016 and 2018 that focused on a range of topics: from evaluating the usability of secondary data to statistically modelling the development of epidemiological indices. Moreover, based on the data of the around 70 million people covered by statutory health insurance, an initial estimate was calculated for the documented prevalence of type 2 diabetes for the years 2010 and 2011. To comparably integrate these prevalences over the years in diabetes surveillance, a reference definition was established with external expertise.

Estimates of costs for modelling return on investment from smoking cessation interventions

BACKGROUND AND AIMS

Modelling 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)].

DESIGN

Focused search of administrative and published data. A standardized checklist was developed in order to ensure consistency in methods of data collection.

SETTING AND PARTICIPANTS

Adult population (15+ years) in Hungary, Netherlands, Germany, Spain and England. For passive smoking-related costs, child population (0-15 years) was also included.

MEASUREMENTS

Costs of treating smoking-attributable diseases; productivity losses due to smoking-attributable absenteeism; and costs of implementing smoking cessation interventions.

FINDINGS

Annual 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).

CONCLUSIONS

Costs 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.

Cost-effectiveness of increasing the reach of smoking cessation interventions in Germany: results from the EQUIPTMOD

AIMS

To evaluate costs, effects and cost-effectiveness of increased reach of specific smoking cessation interventions in Germany.

DESIGN

A Markov-based state transition return on investment model (EQUIPTMOD) was used to evaluate current smoking cessation interventions as well as two prospective investment scenarios. A health-care perspective (extended to include out-of-pocket payments) with life-time horizon was considered. A probabilistic analysis was used to assess uncertainty concerning predicted estimates.

SETTING

Germany.

PARTICIPANTS

Cohort of current smoking population (18+ years) in Germany.

INTERVENTIONS

Interventions included group-based behavioural support, financial incentive programmes and varenicline. For prospective scenario 1 the reach of group-based behavioral support, financial incentive programme and varenicline was increased by 1% of yearly quit attempts (= 57 915 quit attempts), while prospective scenario 2 represented a higher reach, mirroring the levels observed in England.

MEASUREMENTS

EQUIPTMOD considered reach, intervention cost, number of quitters, quality-of-life years (QALYs) gained, cost-effectiveness and return on investment.

FINDINGS

The highest returns through reduction in smoking-related health-care costs were seen for the financial incentive programme (€2.71 per €1 invested), followed by that of group-based behavioural support (€1.63 per €1 invested), compared with no interventions. Varenicline had lower returns (€1.02 per €1 invested) than the other two interventions. At the population level, prospective scenario 1 led to 15 034 QALYs gained and €27 million cost-savings, compared with current investment. Intervention effects and reach contributed most to the uncertainty around the return-on-investment estimates. At a hypothetical willingness-to-pay threshold of only €5000, the probability of being cost-effective is approximately 75% for prospective scenario 1.

CONCLUSIONS

Increasing the reach of group-based behavioural support, financial incentives and varenicline for smoking cessation by just 1% of current annual quit attempts provides a strategy to German policymakers that improves the population's health outcomes and that may be considered cost-effective.

Costs and effects of intra-operative fluorescence molecular imaging - A model-based, early assessment

Introduction

Successful 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.

Methods

We 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.

Results

The 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.

Conclusions

These 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.

Is it cost-effective to provide internet-based interventions to complement the current provision of smoking cessation services in the Netherlands? An analysis based on the EQUIPTMOD

BACKGROUND AND AIM

The 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).

METHODS

A 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.

RESULTS

Internet-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.

CONCLUSION

Providing internet-based smoking cessation interventions to complement the current provision of smoking cessation services could be a cost-saving policy option in the Netherlands.