Pointless spatial modeling

Wilson, Katie; Wakefield, Jon

doi:10.1093/biostatistics/kxy041

Cited by 27 publications

(34 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the modelling side, we integrated point and areal data into a continuous model by constructing pseudo-points from areal data. Modelling approaches that integrate point and areal data as part of a joint model likelihood function are in development 66 but are currently computationally infeasible at the large geographical scales at which we currently model. Furthermore, we divided our models into 11 regional fits (see Supplementary Fig.…”

Section: Article Research Methodsmentioning

confidence: 99%

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Burstein¹,

Henry²,

Collison³

et al. 2019

Nature

Self Cite

188

180

View full text Add to dashboard Cite

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017 Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low-and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations. Gains in child survival have long served as an important proxy measure for improvements in overall population health and development 1,2. Global progress in reducing child deaths has been heralded as one of the greatest success stories of global health 3. The annual global number of deaths of children under 5 years of age (under 5) 4 has declined from 19.6 million in 1950 to 5.4 million in 2017. Nevertheless, these advances in child survival have been far from universally achieved, particularly in low-and middle-income countries (LMICs) 4. Previous subnational child mortality assessments at the first (that is, states or provinces) or second (that is, districts or counties) administrative level indicate that extensive geographical inequalities persist 5-7. Progress in child survival also diverges across age groups 4. Global reductions in mortality rates of children under 5-that is, the under-5 mortality rate (U5MR)-among post-neonatal age groups are greater than those for mortality of neonates (0-28 days) 4,8. It is relatively unclear how these age patterns are shifting at a more local scale, posing challenges to ensuring child survival. To pursue the ambitious Sustainable Development Goal (SDG) of the United Nations 9 to "end preventable deaths of newborns and children under 5" by 2030, it is vital for decision-makers at all levels to better understand where, and at what ages, child survival remains most tenuous.

show abstract

Section: Article Research Methodsmentioning

confidence: 99%

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Burstein¹,

Henry²,

Collison³

et al. 2019

Nature

Self Cite

188

180

View full text Add to dashboard Cite

show abstract

“…The limitations of allocating areal data to unknown locations have been illustrated. 55 Also, the same space-time model is used for many contiguous countries, and the reasonableness of this assumption has not been addressed. Finally, the stacking procedure that is used for covariate modeling is not statistically legitimate.…”

Section: Discussionmentioning

confidence: 99%

Space‐time modeling of child mortality at the Admin‐2 level in a low and middle income countries context

Godwin

Wakefield

2021

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

The Sustainable Development Goals call for a total reduction of preventable child mortality before 2030. Further, the goals state the desirability to have subnational mortality estimates. Estimates at this level are required for health interventions at the subnational level. In a low and middle income countries context, the data on mortality typically consist of household surveys, which are carried out with a stratified, cluster design, and census microsamples. Most household surveys collect full birth history (FBH) data on birth and death dates of a mother's children, but censuses collect summary birth history (SBH) data which consist only of the number of children born and the number that died. In previous work, direct (survey-weighted) estimates with associated variances were derived from FBH data and smoothed in space and time. Unfortunately, the FBH data from household surveys are usually not sufficiently abundant to obtain yearly estimates at the Admin-2 level (at which interventions are often made). In this paper we describe four extensions to previous work: (i) combining SBH data with FBH data, (ii) modeling on a yearly scale, to combine data on a yearly scale with data at coarser time scales, (iii) adjusting direct estimates in Admin-2 areas where we do not observe any deaths due to small sample sizes, (iv) acknowledge differences in data sources by modeling potential bias arising from the various data sources. The methods are illustrated using household survey and census data from Kenya and Malawi, to produce mortality estimates from 1980 to the time of the most recent survey, and predictions to 2020.

show abstract

“…While methods to evaluate out-of-sample performance of downscaling approaches have been developed (e.g. [20,52] ), their relevance has been essentially drawn from the results of studies based on simulated data, which may not necessarily apply to our case study. Also, since province-level is the finer spatial resolution of our reference data (JHU data [15] ), a crossvalidation approach based on spatial blocks [53] would not be feasible.…”

Section: Supporting Information (Si)mentioning

confidence: 99%

A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales

Python

Bender

Blangiardo³

et al. 2020

Preprint

View full text Add to dashboard Cite

As the COVID-19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID-19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in China, we compared counts of COVID-19 from near-real time spatially disaggregated data (city-level) with fine-spatial scale predictions from a Bayesian downscaling regression model applied to a reference province-level dataset. The results highlight discrepancies in the counts of coronavirus-infected cases at district level and identify districts that may require further investigation.

show abstract

Pointless spatial modeling

Cited by 27 publications

References 56 publications

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Space‐time modeling of child mortality at the Admin‐2 level in a low and middle income countries context

A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales

Contact Info

Product

Resources

About