Creating and Managing Spatial-Weighting Matrices with the Spmat Command

Drukker, David M.; Peng, Hua; Prucha, Ingmar R.; Raciborski, Rafal

doi:10.1177/1536867x1301300202

Cited by 138 publications

(81 citation statements)

References 14 publications

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“…The LSOA centroid coordinates (longitude and latitude) were inputted to the spmat command in Stata and a 32 482×32 482 matrix of inverse distance (in miles) was obtained. 38 The matrix, which holds a detailed distance mapping of each LSOA with all other LSOAs, allowed us to quantify geographical connectivity and proximity, with closer areas having larger values (or weights). This was then used to generate the prevalence, quality of care, and morbidity load measures reported previously, for all LSOAs from 2006-07 to 2011-12.…”

Section: Spatial Weighted Estimates Datasetsmentioning

confidence: 99%

Investigating the relationship between quality of primary care and premature mortality in England: a spatial whole-population study

Kontopantelis

Springate

Ashworth

et al. 2015

BMJ

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Objectives To quantify the relationship between a national primary care pay-for-performance programme, the UK's Quality and Outcomes Framework (QOF), and all-cause and cause-specific premature mortality linked closely with conditions included in the framework.Design Longitudinal spatial study, at the level of the "lower layer super output area" (LSOA).Setting 32482 LSOAs (neighbourhoods of 1500 people on average), covering the whole population of England (approximately 53.5 million), from 2007 to 2012.Participants 8647 English general practices participating in the QOF for at least one year of the study period, including over 99% of patients registered with primary care.Intervention National pay-for-performance programme incentivising performance on over 100 quality-of-care indicators.Main outcome measures All-cause and cause-specific mortality rates for six chronic conditions: diabetes, heart failure, hypertension, ischaemic heart disease, stroke, and chronic kidney disease. We used multiple linear regressions to investigate the relationship between spatially estimated recorded quality of care and mortality.Results All-cause and cause-specific mortality rates declined over the study period. Higher mortality was associated with greater area deprivation, urban location, and higher proportion of a non-white population. In general, there was no significant relationship between practice performance on quality indicators included in the QOF and all-cause or cause-specific mortality rates in the practice locality. ConclusionsHigher reported achievement of activities incentivised under a major, nationwide pay-for-performance programme did not seem to result in reduced incidence of premature death in the population. IntroductionPrimary care has enormous potential to improve population health outcomes-including mortality from common chronic conditions-through early intervention in the disease process 1 2 and coordinated provision of care. Effective primary care is associated with reduced morbidity, increased longevity, and more equitable health outcomes, 3 4 but quality of primary care varies widely between providers.5 6 Traditional physician payment systems have facilitated this variation, with fee-for-service systems potentially incentivising over-investigation and over-treatment, and capitation systems potentially incentivising under-utilisation. Neither approach directly rewards high quality care or investment in quality improvement. [7][8][9] In order to improve patient outcomes, policymakers worldwide have attempted to link remuneration for providers to quality of care through pay-for-performance programmes. Multiple programmes have been implemented across a range of settings, but clear evidence for improved patient outcomes is yet to emerge. [10][11][12] In the United Kingdom a national primary care incentive scheme was introduced in 2004. The Quality and Outcomes Framework (QOF), one of largest pay-for-performance programmes in the world, links up to 25% of family practitioners' income to performance on o...

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Section: Spatial Weighted Estimates Datasetsmentioning

confidence: 99%

Investigating the relationship between quality of primary care and premature mortality in England: a spatial whole-population study

Kontopantelis

Springate

Ashworth

et al. 2015

BMJ

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“… We used the Stata command spmat (Drukker et al ) to compute the haversine distance matrix, and the spatdiag command provided by Pisati () for LM and robust LM tests on spatial dependence. For the estimations, we employed the R spdep package (Bivand, Hauke, and Kossowski ; Bivand and Piras ).…”

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confidence: 99%

A Spatial Analysis of the R&D-Productivity Nexus at Firm Level

Cardamone¹

2016

Growth and Change

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The aim of this paper is to evaluate the effect of research and development (R&D) on productivity by taking into account productivity spillovers. To this end, by using a sample of Italian manufacturing firms over the period 2004–2006 provided by the Xth UniCredit‐Capitalia survey (2008), we have analyzed the role of R&D in firm productivity by using a spatial autoregressive model. In so doing, we have allowed the productivity of each firm to be affected by the productivity of nearby firms. Results show that R&D significantly affects Italian firm productivity and that productivity spillovers across firms matter. Moreover, productivity is found to be positively affected by intrasectoral R&D spillovers, while intersectoral R&D spillovers do not have a significant effect.

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“…Our count models also included an exposure parameter based on the natural log of housing units in each neighborhood (constrained to 1). All analyses were conducted in Stata (Stata Corp., 2015), which includes routines for assessing spatial autocorrelation (Pisati, 2001) and the creation of spatially-lagged variables (Drukker, Peng, Prucha, & Raciborski, 2013). …”

Section: Methodsmentioning

confidence: 99%

Distribution and Neighborhood Correlates of Sober Living House Locations in Los Angeles

Mericle

Karriker‐Jaffe

Gupta

et al. 2016

American J of Comm Psychol

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Sober living houses (SLHs) are alcohol and drug-free living environments for individuals in recovery. The goal of this study was to map the distribution of SLHs in Los Angeles (LA) County, California (N=260) and examine neighborhood correlates of SLH density. Locations of SLHs were geocoded and linked to tract-level Census data as well as to publicly available information on alcohol outlets and recovery resources. Neighborhoods with SLHs differed from neighborhoods without them on measures of socioeconomic disadvantage and accessibility of recovery resources. In multivariate, spatially-lagged hurdle models stratified by monthly fees charged (less than $1400/month vs. $1400/month or greater), minority composition and accessibility of treatment were associated with the presence of affordable SLHs. Accessibility of treatment was also associated with the number of affordable SLHs in those neighborhoods. Higher median housing value and accessibility of treatment were associated with whether a neighborhood had high-cost SLHs, and lower population density was associated with the number of high-cost SLHs in those neighborhoods. Neighborhood factors are associated with the availability of SLHs, and research is needed to better understand how these factors affect resident outcomes, as well as how SLHs may affect neighborhoods over time.

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Creating and Managing Spatial-Weighting Matrices with the Spmat Command

Cited by 138 publications

References 14 publications

Investigating the relationship between quality of primary care and premature mortality in England: a spatial whole-population study

Investigating the relationship between quality of primary care and premature mortality in England: a spatial whole-population study

A Spatial Analysis of the R&D-Productivity Nexus at Firm Level

Distribution and Neighborhood Correlates of Sober Living House Locations in Los Angeles

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