Monitoring the Health of Urban Populations: What Statistics Do We Need?

Thunhurst, Colin; Macfarlane, Alison

doi:10.2307/2982888

Cited by 12 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thunhurst and MacFarlane () pointed out the importance of identifying areas of outstanding health risk. As discussed in chapter 1 of Lawson et al .…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, a separate study must re-evaluate these constructs, independently of our study, to understand how the formal or informal rules of the society deal with these with regard to HIV and AIDS care. Thunhurst and MacFarlane (1992) pointed out the importance of identifying areas of outstanding health risk. As discussed in chapter 1 of Lawson et al (2003), adequate statistical analysis of disease mapping such as for HIV mortality can assist policy makers to decide how to allocate relevant resources to areas of outstanding health risk.…”

Section: Discussionmentioning

confidence: 99%

“…() found that poverty helps to predict mortality when controlled for other variables including age, CD4 cell counts, albumin and viral load. Furthermore, Thunhurst and MacFarlane () argued that the interaction between social, economic, environmental and behavioural factors impacting health is well recognized. They argued for the necessity of analyses to establish areas of outstanding health risk to help to solve resource allocation problems, as suggested in the first chapter of Lawson et al .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Income Inequality Measures on Human Immunodeficiency Virus Mortality: a Spatiotemporal Bayesian Perspective

Aktekin

Musal

2014

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

Summary Social, economic, environmental and behavioural factors impacting health are well recognized in the literature. We consider the use of various income inequality measures in addition to a poverty measure and investigate their effects on human immunodeficiency virus (HIV) mortality. In doing so, we make use of models that can capture zero inflation and spatiotemporal effects. The research is motivated by the lack of studies from an inference and modelling perspectives in explaining HIV mortality by using measures that take into account socio‐economic status as well as time and location. Such a study can help policy makers to identify cases of environmental injustice and areas of outstanding health risk to assist in resource allocation problems. In our numerical example, we make use of mortality data obtained for the state of New York, estimate model parameters from a Bayesian inference perspective and discuss the implications and interpretations of various income inequality measures. The methodological novelty of our study is the introduction of a zero‐inflated Poisson model that can account for both spatial and temporal effects across 5 years (2000–2004). The practical novelty of our study is its attempt to find inequality measures which can improve our understanding of HIV mortality risk. Our results indicate that, for the data at hand, if inequality is calculated on the basis of county‐specific income shares rather than the whole state, HIV mortality can be better explained. In addition, accounting for temporal and spatial effects was found to contribute to our understanding of HIV mortality risk.

show abstract

“…Thunhurst and MacFarlane () pointed out the importance of identifying areas of outstanding health risk. As discussed in chapter 1 of Lawson et al .…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Income Inequality Measures on Human Immunodeficiency Virus Mortality: a Spatiotemporal Bayesian Perspective

Aktekin

Musal

2014

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

show abstract

“…In estimating the distributions of the parameters for all three classes of models, we used MCMC methods and did not encounter any convergence issues. As argued by Thunhurst and MacFarlane [5], it is crucial to identify areas of outstanding health risk. Lawson et al [6] made the argument that proper statistical modeling and estimation will help policy makers solve resource allocation problems.…”

Section: Conclusion and Summary Of Findingsmentioning

confidence: 99%

Bayesian spatial modeling of HIV mortality via zero‐inflated Poisson models

Musal

Aktekin

2012

Statistics in Medicine

View full text Add to dashboard Cite

In this paper, we investigate the effects of poverty and inequality on the number of HIV-related deaths in 62 New York counties via Bayesian zero-inflated Poisson models that exhibit spatial dependence. We quantify inequality via the Theil index and poverty via the ratios of two Census 2000 variables, the number of people under the poverty line and the number of people for whom poverty status is determined, in each Zip Code Tabulation Area. The purpose of this study was to investigate the effects of inequality and poverty in addition to spatial dependence between neighboring regions on HIV mortality rate, which can lead to improved health resource allocation decisions. In modeling county-specific HIV counts, we propose Bayesian zero-inflated Poisson models whose rates are functions of both covariate and spatial/random effects. To show how the proposed models work, we used three different publicly available data sets: TIGER Shapefiles, Census 2000, and mortality index files. In addition, we introduce parameter estimation issues of Bayesian zero-inflated Poisson models and discuss MCMC method implications.

show abstract

“…(In the UK, command papers are reports presented to parliament, as a statement of government policy on a specific subject). At the time the HSE was set up, several sources reported a lack of morbidity data needed to measure and monitor the health of the UK population [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A review of the use of health examination data from the Health Survey for England in government policy development and implementation

Oyebode

Mindell

2014

Arch Public Health

View full text Add to dashboard Cite

BackgroundInformation is needed at all stages of the policy making process. The Health Survey for England (HSE) is an annual cross-sectional health examination survey of the non-institutionalised general population in England. It was originally set up to inform national policy making and monitoring by the Department of Health. This paper examines how the nurse collected physical and biological measurement data from the HSE have been essential or useful for identification of a health issue amenable to policy intervention; initiation, development or implementation of a strategy; choice and monitoring of targets; or assessment and evaluation of policies.MethodsSpecific examples of use of HSE data were identified through interviews with senior members of staff at the Department of Health and the Health and Social Care Information Centre. Policy documents mentioned by interviewees were retrieved for review, and reference lists of associated policy documents checked. Systematic searches of Chief Medical Officer Reports, Government ‘Command Papers’, and clinical guidance documents were also undertaken.ResultsHSE examination data have been used at all stages of the policy making process. Data have been used to identify an issue amenable to policy-intervention (e.g. quantifying prevalence of undiagnosed chronic kidney disease), in strategy development (in models to inform chronic respiratory disease policy), for target setting and monitoring (the 1992 blood pressure target) and in evaluation of health policy (the effect of the smoking ban on second hand smoke exposure).ConclusionsA health examination survey is a useful part of a national health information system.

show abstract

Monitoring the Health of Urban Populations: What Statistics Do We Need?

Cited by 12 publications

References 24 publications

Analysis of Income Inequality Measures on Human Immunodeficiency Virus Mortality: a Spatiotemporal Bayesian Perspective

Analysis of Income Inequality Measures on Human Immunodeficiency Virus Mortality: a Spatiotemporal Bayesian Perspective

Bayesian spatial modeling of HIV mortality via zero‐inflated Poisson models

A review of the use of health examination data from the Health Survey for England in government policy development and implementation

Contact Info

Product

Resources

About