The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity

Chronic back pain prevalence at small area level in England - the design and validation of a 2-stage static spatial microsimulation model

Spatially disaggregated estimates provide valuable insights into the nature of a disease. They highlight inequalities, aid public health planning and identify avenues for further research. Spatial microsimulation is advantageous in that it can be used to create large microdata sets with intact microlevel relationships between variables, which allows analysis of relationships between variables locally. This methodological paper outlines the design and validation of a 2-stage static spatial microsimulation model for chronic back pain prevalence across England, suitable for policy modelling. Data used was obtained from the Health Survey for England and the 2011 Census. Microsimulation was performed using SimObesity, a previously validated static deterministic program, and the synthetic chronic back pain microdataset was internally validated. The paper also highlights modelling considerations for researchers embarking on similar work, as well as future directions for research in this area of microsimulation.

Small-area estimation and analysis of HIV/AIDS indicators for precise geographical targeting of health interventions in Nigeria. a spatial microsimulation approach

BACKGROUND

Precise geographical targeting is well recognised as an indispensable intervention strategy for achieving many Sustainable Development Goals (SDGs). This is more cogent for health-related goals such as the reduction of the HIV/AIDS pandemic, which exhibits substantial spatial heterogeneity at various spatial scales (including at microscale levels). Despite the dire data limitations in Low and Middle Income Countries (LMICs), it is essential to produce fine-scale estimates of health-related indicators such as HIV/AIDS. Existing small-area estimates (SAEs) incorporate limited synthesis of the spatial and socio-behavioural aspects of the HIV/AIDS pandemic and/or are not adequately grounded in international indicator frameworks for sustainable development initiatives. They are, therefore, of limited policy-relevance, not least because of their inability to provide necessary fine-scale socio-spatial disaggregation of relevant indicators.

METHODS

The current study attempts to overcome these challenges through innovative utilisation of gridded demographic datasets for SAEs as well as the mapping of standard HIV/AIDS indicators in LMICs using spatial microsimulation (SMS).

RESULTS

The result is a spatially enriched synthetic individual-level population of the study area as well as microscale estimates of four standard HIV/AIDS and sexual behaviour indicators. The analysis of these indicators follows similar studies with the added advantage of mapping fine-grained spatial patterns to facilitate precise geographical targeting of relevant interventions. In doing so, the need to explicate socio-spatial variations through proper socioeconomic disaggregation of data is reiterated.

CONCLUSIONS

In addition to creating SAEs of standard health-related indicators from disparate multivariate data, the outputs make it possible to establish more robust links (even at individual levels) with other mesoscale models, thereby enabling spatial analytics to be more responsive to evidence-based policymaking in LMICs. It is hoped that international organisations concerned with producing SDG-related indicators for LMICs move towards SAEs of such metrics using methods like SMS.

Estimating County Level Health Indicators Using Spatial Microsimulation

Given the importance of understanding health outcomes at fine spatial scales, iterative proportional fitting (IPF), a form of small area estimation, was applied to a fixed number of health-related variables (obesity, overweight, diabetes) taken from regionalized 2019 survey responses (n = 5474) from the Idaho Behavioral Risk Factor Surveillance System (BRFSS). Using associated county-level American Community Survey (ACS) census data, a set of constraints, which included age categorization, race, sex, and education level, were used to create county-level weighting matrices for each variable, for each of the seven (7) Idaho public health districts. Using an optimized modeling construction technique, we identified significant constraints and grouping splits for each variable/region, resulting in estimates that were internally and externally validated. Externally validated model results for the most populated counties showed correlations ranging from .79 to .85, with p values all below .05. Estimates indicated higher levels of obesity and overweight individuals for midsouth and southwestern Idaho counties, with a cluster of higher diabetes estimates in the center of the state (Gooding, Lincoln, Minidoka, and Jerome counties). Alternative external sources for health outcomes aligned extremely well with our estimates, with wider confidence intervals in more rural counties with sparse populations.

Estimating quality of life: A spatial microsimulation model of well-being in Aotearoa New Zealand

Quality of life is a complex concept characterised by several dualities, it has many definitions depending on the field of research and an abundance of diverse objective and subjective measures. The latter often represents the extent of perceived (dis)satisfaction with various domains of life experienced by individuals or groups, and research is increasingly focusing on subjective measures of well-being to better understand personal drivers related to quality of life. A better understanding of these factors at a local level has potential to shed light on an often-overlooked aspect of the mental health landscape in Aotearoa New Zealand. Individual-level data on adults (15+ years) is sourced from the New Zealand Attitudes and Values Study 2018 (N = 47,949) and aggregate-level data from the Census 2018 (N = 3,775,854). Matching constraint variables include sex, age, ethnicity, highest qualification, and labour force status. Outcome variables include personal and national well-being scores from 0 to 10 (extremely dissatisfied-extremely satisfied). Spatial microsimulation is used to create a synthetic population based on the above data. Results show lower mean national well-being scores than personal well-being scores, with spatial variations that broadly reflect patterns of socioeconomic deprivation. Low mean values for both personal and national well-being scores are seen in rural areas of high socioeconomic deprivation, particularly those with large Māori populations. High mean values are associated with areas of low deprivation. Additionally, high national well-being scores are associated with areas of agricultural activity, particularly in the South Island. Consideration should be given to factors that influence responses in such topics however, including demographic profiles as well as economic and social conditions of individuals and their surrounding communities. This study demonstrates that spatial microsimulation can be used as a powerful tool to understand population well-being. It can help support future planning and resource allocation, aiding in achieving health equity.

Understanding the burden of chronic back pain: a spatial microsimulation of chronic back pain at small area level across England

PURPOSE

Chronic back pain (CBP) carries a significant burden. Understanding how and why CBP prevalence varies spatially, as well as the potential impact of policies to decrease CBP would prove valuable for public health planning. This study aims to simulate and map the prevalence of CBP at ward-level across England, identify associations which may explain spatial variation, and explore 'what-if' scenarios for the impact of policies to increase physical activity (PA) on CBP.

METHODS

A two-stage static spatial microsimulation approach was used to simulate CBP prevalence in England, combining national-level CBP and PA data from the Health Survey for England with spatially disaggregated demographic data from the 2011 Census. The output was validated, mapped, and spatially analysed using geographically weighted regression. 'What-if' analysis assumed changes to individuals' moderate-to-vigorous physical activity (MVPA) levels.

RESULTS

Large significant clusters of high CBP prevalence were found predominantly in coastal areas and low prevalence in cities. Univariate analysis found a strong positive correlation between physical inactivity and CBP prevalence at ward-level (R² = 0.735; Coefficient = 0.857). The local model showed the relationship to be stronger in/around cities (R² = 0.815; Coefficient: Mean = 0.833, SD = 0.234, Range = 0.073-2.623). Multivariate modelling showed this relationship was largely explained by confounders (R² = 0.924; Coefficient: Mean = 0.070, SD = 0.001, Range = 0.069-0.072). 'What-if' analysis showed a detectable reduction in CBP prevalence for increases in MVPA of 30 and 60 min (- 2.71%; 1, 164, 056 cases).

CONCLUSION

CBP prevalence varies at ward-level across England. At ward-level, physical inactivity is strongly positively correlated with CBP. This relationship is largely explained by geographic variation in confounders (the proportion of residents that are: over 60, in low-skilled jobs, female, pregnant, obese, smokers, white or black, disabled). Policies to increase PA by 30 min weekly MVPA will likely result in a significant reduction in CBP prevalence. To maximise their impact, policies could be tailored to areas of high prevalence, which are identified by this study.

Estimating BMI distributions by age and sex for local authorities in England: a small area estimation study

OBJECTIVES

Rates of overweight and obesity vary across England, but local rates have not been estimated for over 10 years. We aimed to produce new small area estimates of body mass index (BMI) by age and sex for each lower tier and unitary local authority in England, to provide up-to-date and more detailed estimates for the use of policy-makers and academics working in non-communicable disease risk and health inequalities.

DESIGN

We used generalised linear modelling to estimate the relationship between BMI with social/demographic markers in a cross-sectional survey, then used this model to impute a BMI for each adult in locally-representative populations. These groups were then disaggregated by 5-year age group, sex and local authority group.

SETTING

The Health Survey for England 2018 (cross-sectional BMI data for England) and Census microdata 2011 (locally representative).

PARTICIPANTS

A total of 6174 complete cases aged 16 and over were included.

OUTCOME MEASURES

Modelled group-level BMI as mean and SD of log-BMI. Extensive internal validation was performed, against the original data and external validation against the National Diet and Nutrition Survey and Active Lives Survey and previous small area estimates.

RESULTS

In 94% of age-sex are groups, mean BMI was in the overweight or obese ranges. Older and more deprived areas had the highest overweight and obesity rates, which were particularly in coastal areas, the West Midlands, Yorkshire and the Humber. Validation showed close concordance with previous estimates by local area and demographic groups.

CONCLUSION

This work updated previous estimates of the distribution of BMI in England and contributes considerable additional detail to our understanding of the local epidemiology of overweight and obesity. Raised BMI now affects the vast majority of demographic groups by age, sex and area in England, regardless of geography or deprivation.

Adult diet in England: Where is more support needed to achieve dietary recommendations?

Background

Small-area estimation models are regularly commissioned by public health bodies to identify areas of greater inequality and target areas for intervention in a range of behaviours and outcomes. Such local modelling has not been completed for diet consumption in England despite diet being an important predictor of health status. The study sets out whether aspects of adult diet can be modelled from previously collected data to define and evaluate area-level interventions to address obesity and ill-health.

Methods

Adults aged 16 years and over living in England. Consumption of fruit, vegetables, and sugar-sweetened beverages (SSB) are modelled using small-area estimation methods in English neighbourhoods (Middle Super Output Areas [MSOA]) to identify areas where reported portions are significantly different from recommended levels of consumption. The selected aspects of diet are modelled from respondents in the National Diet and Nutrition Survey using pooled data from 2008–2016.

Results

Estimates indicate that the average prevalence of adults consuming less than one portion of fruit, vegetables or 100% juice each day by MSOA is 6.9% (range of 4.3 to 14.7%, SE 0.06) and the average prevalence of drinking more than 330ml/day of SSB is 11.5% (range of 5.7 to 30.5%, SE 0.03). Credible intervals around the estimates are wider for SSB consumption. The results identify areas including regions in London, urban areas in the North of England and the South coast which may be prioritised for targeted interventions to support reduced consumption of SSB and/or an increase in portions of fruit and vegetables.

Conclusion

These estimates provide valuable information at a finer spatial scale than is presently feasible, allowing for within-country and locality prioritisation of resources to improve diet. Local, targeted interventions to improve fruit and vegetable consumption such as subsidies or voucher schemes should be considered where consumption of these foods is predicted to be low.

Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.

Dietary habits and metabolic response improve in obese children whose mothers received an intervention to promote healthy eating: randomized clinical trial

BACKGROUND

Lifestyles habits such as eating unhealthy foodscommence at home and are associated with the development of obesity and comorbidities such as insulin resistance, metabolic syndrome, and chronic degenerative diseases, which are the main causes of death in adults. The present study compared changes in dietary habits, behaviors and metabolic profiles of obese children whose mothers attended at the hospital to group sessions, with those who received the usual nutritional consultation.

METHODS

Randomized clinical trial, 177 mother/obese child pairs participated, 90 in the intervention group and 87 in the control group. The intervention group attended six group education sessions to promote healthy eating, being this an alternative of change of habits in children with obesity. The control group received the usual nutritional consultation; both groups were followed up for 3 months. Frequency of food consumption, behaviors during feeding in the house and metabolic profile was evaluated. Mixed effect linear regression models were used to evaluate the effect of the intervention on the variables of interest, especially in HOMA-IR.

RESULTS

The intervention group reduced the filling of their dishes (p = 0.009), forcing the children to finish meals (p = 0.003) and food substitution (p <  0.001), moreover increased the consumption of roasted foods (p = 0.046), fruits (p = 0.002) and vegetables (p <  0.001). The children in the control group slightly increased HOMA-IR levels (0.51; 95% CI - 0.48 to 1.50), while the children in the intervention group significantly decreased (- 1.22; 95% CI - 2.28 to - 1.16). The difference in HOMA-IR between the control and intervention group at the end of the follow-up was - 1.67; 95% CI: - 3.11 to - 0.24.

CONCLUSIONS

The educational intervention improved some eating habits at home, as well as HOMA-IR levels; why we consider that it can be an extra resource in the management of childhood obesity.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT04374292 (Date assigned: May 5, 2020). Retrospectively registered.

Creating a long-term future for big data in obesity research

Big data are part of the future in obesity research. The ESRC funded Strategic Network for Obesity has together generated a series of papers, published in the International Journal for Obesity illustrating various aspects of their utility, in particular relating to the large social and environmental drivers of obesity. This article is the final part of the series and reflects upon progress to date and identifies four areas that require attention to promote the continued role of big data in research. We additionally include a 'getting started with big data' checklist to encourage more obesity researchers to engage with alternative data resources.

Microsimulation model of child and adolescent overweight: making use of what we already know

BACKGROUND

New Zealand has high rates of child overweight and obesity when compared with other countries. Despite an abundance of research documenting the problem, it is unclear what the most effective policy changes or interventions are, and how policy changes might unfold over time within complex systems.

METHODS

We use estimates derived from meta-analyses to create a dynamic microsimulation model of child overweight (including obesity). Using census records we created a synthetic birth cohort of 10,000 children. Information on parental education, ethnicity and father's socio-economic position at birth were taken from census records. We used the New Zealand Health Survey to estimate population base rates for the prevalence of overweight and obesity. Information on other modifiers (such as maternal smoking, breastfeeding, preterm birth, regular breakfast consumption and so forth) were taken from three birth cohorts: Christchurch Health and Development Study, The Dunedin Multidisciplinary Health and Development Study and the Pacific Islands Families Study. Published intervention studies were used to derive plausible estimates for changes to modifiers.

RESULTS

Reducing the proportion of mothers classified as overweight and obesity (-3.31(95% CI -3.55; -3.07) percentage points), reducing the proportion of children watching two or more hours of TV (-3.78(95% CI -4.01; -3.54)), increasing the proportion of children eating breakfast regularly (-1.71(95% CI -1.96; -1.46)), and reducing the proportion of children born with high birth weights (-1.36(95% CI -1.61; -1.11)), lead to sizable decreases in the estimated prevalence of child overweight (including obesity). Reducing the proportion of mothers giving birth by caesarean (-0.23(95% CI -0.49; -0.23)) and increasing parental education (-0.07(95% CI -0.31; 0.18)) did not impact upon child overweight rates.

CONCLUSIONS

We created a working simulation model of New Zealand children that can be accessed by policy makers and researchers to determine known relationships between predictors and child overweight, as well as potential gains from targeting specific pathways.

Local level estimates of food, drink and tobacco expenditure for Great Britain

We present expenditure estimates for 106 product categories across Great Britain for the years 2008-2016. Estimates are at the Local Authority District level (n = 380) and the categories cover all food, drink and tobacco commodities. Reliable, local level expenditure estimates are crucial for understanding broader market trends, assessing economic stability and for projections. This is especially important for commodities such as alcohol, tobacco and unhealthy foods due to their role in the prevalence of non-communicable diseases. There has been relatively little research into local area spatial patterns of expenditure, with existing estimates often of insufficient resolution for informing planning decisions. We use spatial microsimulation to create an archive of expenditure datasets. This was achieved by linking socio-demographic foundations with detailed datasets on individual expenditure. Whilst initially developed to aid investigations into sociodemographic trends in the meat industry, the data have reuse potential in a number of disciplines, including public health, economics, retail geography and environmental management. The framework could be applied to other regions with appropriate data.

Food Changes and Geography: Dietary Transition in Colombia

Background:

The food transition can no longer be studied in developed countries because the so-called Western diet now predominates in these areas. However, in developing countries, it is still possible to study the food transition. It is a novel concept that complements other transitions such as the demographic, economic, nutritional and epidemiological transitions.

Objectives:

The objectives of this study were to a) estimate the average departmental adherence to the three pre-established food patterns, b) assess adherence patterns based on the Global Spatial Analysis, c) evaluate whether the Local Spatial Variations in the adherence to food patterns are random or follow defined patterns (cluster) and d) generate 2D maps to graphically locate the food patterns that compose the phenomenon of the food transition occurring in Colombia.

Methods:

The National Survey of the Nutritional Situation in Colombia, 2010 was analyzed. Based on factor analysis, three consumption patterns were established; Protein/Fiber, Snack and Snack and Traditional/Starch and the average departmental adhesion was estimated. The global and local spatial variation was calculated with the Moran indexes.

Findings:

the average adherence to the traditional consumption/starch pattern was –0.00 (95% CI: –0.12 to 0.12). The mean adherence to the protein/fiber intake pattern was –0.07 (95% CI: –0.16 to 0.03). The average adherence to the pattern of snack consumption was –0.03 (95% CI: –0.11 to 0.05). The three patterns of food consumption values for the Global Total Moran Index, for men and women were positive and statistically significant.

Conclusions:

The food transition experienced by Colombia is not homogeneous and there are well defined clusters for adherence in the three predefined food patterns. Within the clusters there are differences by sex. In regions where the traditional pattern/starch predominates, the presence of the snack pattern is very weak.

SimAlba: A Spatial Microsimulation Approach to the Analysis of Health Inequalities

This paper presents applied geographical research based on a spatial microsimulation model, SimAlba, aimed at estimating geographically sensitive health variables in Scotland. SimAlba has been developed in order to answer a variety of "what-if" policy questions pertaining to health policy in Scotland. Using the SimAlba model, it is possible to simulate the distributions of previously unknown variables at the small area level such as smoking, alcohol consumption, mental well-being, and obesity. The SimAlba microdataset has been created by combining Scottish Health Survey and Census data using a deterministic reweighting spatial microsimulation algorithm developed for this purpose. The paper presents SimAlba outputs for Scotland's largest city, Glasgow, and examines the spatial distribution of the simulated variables for small geographical areas in Glasgow as well as the effects on individuals of different policy scenario outcomes. In simulating previously unknown spatial data, a wealth of new perspectives can be examined and explored. This paper explores a small set of those potential avenues of research and shows the power of spatial microsimulation modeling in an urban context.

Geographic disparities in Healthy Eating Index scores (HEI-2005 and 2010) by residential property values: Findings from Seattle Obesity Study (SOS)

BACKGROUND

Higher socioeconomic status (SES) has been linked with higher-quality diets. New GIS methods allow for geographic mapping of diet quality at a very granular level.

OBJECTIVE

To examine the geographic distribution of two measures of diet quality: Healthy Eating Index (HEI 2005 and HEI 2010) in relation to residential property values in Seattle-King County.

METHODS

The Seattle Obesity Study (SOS) collected data from a population-based sample of King County adults in 2008-09. Socio-demographic data were obtained by 20-min telephone survey. Dietary data were obtained from food frequency questionnaires (FFQs). Home addresses were geocoded to the tax parcel and residential property values were obtained from the King County tax assessor. Multivariable regression analyses using 1116 adults tested associations between SES variables and diet quality measured (HEI scores).

RESULTS

Residential property values, education, and incomes were associated with higher HEI scores in bivariate analyses. Property values were not collinear with either education or income. In adjusted multivariable models, education and residential property were better associated with HEI, compared to than income. Mapping of HEI-2005 and HEI-2010 at the census block level illustrated the geographic distribution of diet quality across Seattle-King County.

CONCLUSION

The use of residential property values, an objective measure of SES, allowed for the first visual exploration of diet quality at high spatial resolution: the census block level.

Sub regional estimates of morbidities in the English elderly population

This study focuses on identifying the future trends and spatial concentrations of morbidities in the English elderly population. The morbidities to be estimated are: coronary heart disease; strokes; diabetes; cancer; respiratory illnesses and arthritis in the 60 year and older household residential population. The technique used is a spatial microsimulation of the elderly population of local authorities in England using data from the 2001 Census and the English Longitudinal Study of Ageing. The longitudinal nature of the microsimulated population is then used to estimate the morbidity prevalences for local authorities in 2010/2011. With this knowledge, planners will be able to focus the available health and care resources in those areas with greatest need. For most of these morbidities, there is evidence of a strong correlation between the type of authority and the estimated prevalence rates.

The construction of a decision tool to analyse local demand and local supply for GP care using a synthetic estimation model

BACKGROUND

This study addresses the growing academic and policy interest in the appropriate provision of local healthcare services to the healthcare needs of local populations to increase health status and decrease healthcare costs. However, for most local areas information on the demand for primary care and supply is missing. The research goal is to examine the construction of a decision tool which enables healthcare planners to analyse local supply and demand in order to arrive at a better match.

METHODS

National sample-based medical record data of general practitioners (GPs) were used to predict the local demand for GP care based on local populations using a synthetic estimation technique. Next, the surplus or deficit in local GP supply were calculated using the national GP registry. Subsequently, a dynamic internet tool was built to present demand, supply and the confrontation between supply and demand regarding GP care for local areas and their surroundings in the Netherlands.

RESULTS

Regression analysis showed a significant relationship between sociodemographic predictors of postcode areas and GP consultation time (F [14, 269,467] = 2,852.24; P <0.001). The statistical model could estimate GP consultation time for every postcode area with >1,000 inhabitants in the Netherlands covering 97% of the total population. Confronting these estimated demand figures with the actual GP supply resulted in the average GP workload and the number of full-time equivalent (FTE) GP too much/too few for local areas to cover the demand for GP care. An estimated shortage of one FTE GP or more was prevalent in about 19% of the postcode areas with >1,000 inhabitants if the surrounding postcode areas were taken into consideration. Underserved areas were mainly found in rural regions.

CONCLUSIONS

The constructed decision tool is freely accessible on the Internet and can be used as a starting point in the discussion on primary care service provision in local communities and it can make a considerable contribution to a primary care system which provides care when and where people need it.

Systems science and childhood obesity: a systematic review and new directions

As a public health problem, childhood obesity operates at multiple levels, ranging from individual health behaviors to school and community characteristics to public policies. Examining obesity, particularly childhood obesity, from any single perspective is likely to fail, and systems science methods offer a possible solution. We systematically reviewed studies that examined the causes and/or consequences of obesity from a systems science perspective. The 21 included studies addressed four general areas of systems science in obesity: (1) translating interventions to a large scale, (2) the effect of obesity on other health or economic outcomes, (3) the effect of geography on obesity, and (4) the effect of social networks on obesity. In general, little research addresses obesity from a true, integrated systems science perspective, and the available research infrequently focuses on children. This shortcoming limits the ability of that research to inform public policy. However, we believe that the largely incremental approaches used in current systems science lay a foundation for future work and present a model demonstrating the system of childhood obesity. Systems science perspective and related methods are particularly promising in understanding the link between childhood obesity and adult outcomes. Systems models emphasize the evolution of agents and their interactions; such evolution is particularly salient in the context of a developing child.

Optimising the location of antenatal classes

OBJECTIVES

To combine microsimulation and location-allocation techniques to determine antenatal class locations which minimise the distance travelled from home by potential users.

DESIGN

Microsimulation modeling and location-allocation modeling.

SETTING

City of Leeds, UK.

PARTICIPANTS

Potential users of antenatal classes.

METHODS

An individual-level microsimulation model was built to estimate the number of births for small areas by combining data from the UK Census 2001 and the Health Survey for England 2006. Using this model as a proxy for service demand, we then used a location-allocation model to optimize locations.

FINDINGS

Different scenarios show the advantage of combining these methods to optimize (re)locating antenatal classes and therefore reduce inequalities in accessing services for pregnant women.

KEY CONCLUSIONS

Use of these techniques should lead to better use of resources by allowing planners to identify optimal locations of antenatal classes which minimise women's travel.

IMPLICATIONS FOR PRACTICE

These results are especially important for health-care planners tasked with the difficult issue of targeting scarce resources in a cost-efficient, but also effective or accessible, manner. (169 words).

Determinants and spatial patterns of adult overweight and hypertension in a high HIV prevalence rural South African population

We conducted a large population-based survey among adults measuring weight, height, and blood pressure nested within an HIV survey in rural KwaZulu-Natal, South Africa, to identify and characterize clusters of overweight and hypertension in a typical rural African population and to explore whether geographic clusters can be accounted for by established individual-level risk factors. 58.4% of the participants were overweight and 22.6% were hypertensive. One cluster of high prevalence of overweight (RR=1.50, p<0.001) was identified using Kulldorff spatial scan statistic as the most likely cluster, whereas a low-risk cluster was identified in the nearby high-density settlement area (RR=0.62, p<0.05). No geographic clusters of hypertension were identified. After controlling for age, sex, educational attainment, household wealth, marital status, place of residence, and HIV status, no spatial clustering of overweight remained. The results provided clear evidence for the localized clustering of overweight. Identification of clustering of chronic disease could provide additional insights into the prevention and control for the rural South African population.

Simulation models of obesity: a review of the literature and implications for research and policy

Simulation models (SMs) combine information from a variety of sources to provide a useful tool for examining how the effects of obesity unfold over time and impact population health. SMs can aid in the understanding of the complex interaction of the drivers of diet and activity and their relation to health outcomes. As emphasized in a recently released report of the Institute or Medicine, SMs can be especially useful for considering the potential impact of an array of policies that will be required to tackle the obesity problem. The purpose of this paper is to present an overview of existing SMs for obesity. First, a background section introduces the different types of models, explains how models are constructed, shows the utility of SMs and discusses their strengths and weaknesses. Using these typologies, we then briefly review extant obesity SMs. We categorize these models according to their focus: health and economic outcomes, trends in obesity as a function of past trends, physiologically based behavioural models, environmental contributors to obesity and policy interventions. Finally, we suggest directions for future research.

The geography of Fast Food outlets: a review

The availability of food high in fat, salt and sugar through Fast Food (FF) or takeaway outlets, is implicated in the causal pathway for the obesity epidemic. This review aims to summarise this body of research and highlight areas for future work. Thirty three studies were found that had assessed the geography of these outlets. Fourteen studies showed a positive association between availability of FF outlets and increasing deprivation. Another 13 studies also included overweight or obesity data and showed conflicting results between obesity/overweight and FF outlet availability. There is some evidence that FF availability is associated with lower fruit and vegetable intake. There is potential for land use policies to have an influence on the location of new FF outlets. Further research should incorporate good quality data on FF consumption, weight and physical activity.