A brief report on Primary Care Service Area catchment geographies in New South Wales Australia

Mazumdar, Soumya; Feng, Xiaoqi; Konings, Paul; McRae, Ian; Girosi, Federico

doi:10.1186/1476-072x-13-38

Cited by 14 publications

(23 citation statements)

References 15 publications

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“…Catchment areas can also delineate locations that are ostensibly served by a service facility. This type of information can help determine whether at-risk populations are getting access to health care [31], if patients with mental health issues can get adequate access to specialized doctors, or determine whether there is a differential stress on some services due to higher catchment flows in some others compared to others [32]. Upon inspection, the demand/supply ratios for the catchment areas provide insight into where resources need to be reallocated or where personnel need to be re-assign to better meet the demands of the population in the service area [33].…”

Section: Catchment Areasmentioning

confidence: 99%

Information fusion to estimate resilience of dense urban neighborhoods

Palladino

Bienenstock

West

et al. 2019

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

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Diverse sociocultural influences in rapidly growing dense urban areas may induce strain on civil services and reduce the resilience of those areas to exogenous and endogenous shocks. We present a novel approach with foundations in computer and social sciences, to estimate the resilience of dense urban areas at finer spatiotemporal scales compared to the state-ofthe-art. We fuse multi-modal data sources to estimate resilience indicators from social science theory and leverage a structured ontology for factor combinations to enhance explainability. Estimates of destabilizing areas can improve the decision-making capabilities of civil governments by identifying critical areas needing increased social services.Keywords: Dense urban areas, behavior modeling, social/cultural modeling, social capital, situation/threat assessment INTRODUCTIONTwo-thirds of the world's population is expected to live in urban areas by the year 2050. This rapid growth of dense neighborhoods with diverse sociocultural influences may strain civil services, e.g., law enforcement, health care, and waste management, reducing the resilience of these areas to exogenous and endogenous shocks. Aggregate statistics for large, sprawling, urban areas are likely to miss critical indicators.To address these issues, we developed a novel approach that advances the state-of-the-art by (i) ascertaining the stability and resilience of densely populated areas at much finer geospatial and temporal scales, and (ii) basing that estimate on a solid social science foundation. We fuse multi-modal data sources to estimate the prevalence of indicators from social theories (e.g., social capital). Aggregate statistics from structured city-level data (censuses, surveys) provide a baseline for the entire city, while social media and news reports provide features at shorter time scales, and smaller geographic areas (i.e., neighborhoods, city blocks) introducing deviations from the baseline. This social science foundation, combined with our structured ontology, leads to enhanced explainability of the stability assessment, increasing confidence in downstream decisions. Knowledge of destabilizing areas informs the decisionmaking process in civil governments by identifying neighborhoods in need of social services and civil control. Our computational framework estimates the capacity of a dense urban area to cope with stress and volatility, allowing governments, soldiers, humanitarian organizations, and private companies to better understand and manage local disturbances. THEORETICAL FRAME: SOCIAL SCIENCE FOUNDATIONThere is a need to bridge the gap between the computational and social sciences. Modern decision-makers require support from computational tools with near real-time feedback, however social science theories are still validated/quantified after lengthy surveys and structured interviews. We performed comprehensive research and built methods using open-source data and text analytics to allow data science to quantify social science theories at a rapid pace. As a first step...

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Section: Catchment Areasmentioning

confidence: 99%

Information fusion to estimate resilience of dense urban neighborhoods

Palladino

Bienenstock

West

et al. 2019

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

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“…Walk Scores for ACT suburbs/SA2s were obtained from the Walk Score website 38. A map of Walk Scores at ACT suburbs is provided in figure 2. Access to general practitioners (GPs) : access to primary care is an important predictor of admittance to tertiary facilities 3940 Access to GPs is related to better health management and lesser use of hospital services 39…”

Section: Methodsmentioning

confidence: 99%

“…40 Access to GPs is related to better health management and lesser use of hospital services 3941 We created an access measure by drawing a circular buffer around the Mesh Blocks of the patients in the hospitalisation data.…”

Section: Methodsmentioning

confidence: 99%

Is Walk Score associated with hospital admissions from chronic diseases? Evidence from a cross-sectional study in a high socioeconomic status Australian city-state

et al. 2016

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ObjectivesTo explore patterns of non-communicable diseases (NCDs) in the Australian Capital Territory (ACT).To ascertain the effect of the neighbourhood built environmental features and especially walkability on health outcomes, specifically for hospital admissions from NCDs.DesignA cross-sectional analysis of public hospital episode data (2007–2013).SettingHospitalisations from the ACT, Australia at very small geographic areas.ParticipantsSecondary data on 75 290 unique hospital episodes representing 39 851 patients who were admitted to ACT hospitals from 2007 to 2013. No restrictions on age, sex or ethnicity.Main exposure measuresGeographic Information System derived or compatible measures of general practitioner access, neighbourhood socioeconomic status, alcohol access, exposure to traffic and Walk Score walkability.Main outcome measuresHospitalisations of circulatory diseases, specific endocrine, nutritional and metabolic diseases, respiratory diseases and specific cancers.ResultsGeographic clusters with significant high and low risks of NCDs were found that displayed an overall geographic pattern of high risk in the outlying suburbs of the territory. Significant relationships between neighbourhood walkability as measured by Walk Score and the likelihood of hospitalisation with a primary diagnosis of myocardial infarction (heart attack) were found. A possible relationship was also found with the likelihood of being hospitalised with 4 major lifestyle-related cancers.ConclusionsOur research augments the growing literature underscoring the relationships between the built environment and health outcomes. In addition, it supports the importance of walkable neighbourhoods, as measured by Walk Score, for improved health.

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“…They are thus “self‐sufficient” markets of primary care. The Localization Index (LI) or the “Preference Index” is a quantitative metric of this self‐sufficiency (Busato & Künzi, ; Mazumdar, Feng, Konings, McRae, & Girosi, ). The LI is the percentage of people living in the PCSA that obtain their Primary Care Practitioner/Physician (PCP) services from within the PCSA.…”

Section: Introductionmentioning

confidence: 99%

A new generation of Primary Care Service Areas or general practice catchment areas

Mazumdar

Pinzari

Bagheri

et al. 2017

Transactions in GIS

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Primary Care is fundamental to a well-functioning health system. Various geographical small areas including specialized geographies such as Primary Care Service Areas (PCSAs) are used to measure primary care relevant outcomes and services, or to target interventions. PCSAs are small areas, the majority of patients resident in which obtain their primary care services from within the geography. The extent of this selfsufficiency of use is measured by the Localization Index (LI). PCSAs have been built in the US, Australia and Switzerland using an allocation algorithm, which, while simple and easy to implement, may require the use of various ad-hoc parameters. In this article we propose an optimization based approach to creating PCSAs, -an approach which has previously been used to generate labour flow regions in Ireland. The approach is data driven, thus requiring a minimal number of ad-hoc parameters. We compared the resulting PCSAs (or`rPCSAs') with PCSAs generated using the traditional allocation algorithm. We found that rPCSAs were generally larger, offered greater LIs and reflected patient travel patterns better than traditional PCSAs. Accounting for the larger size of rPCSAs showed that rPCSAs offered better LIs than similar sized traditional PCSAs.

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A brief report on Primary Care Service Area catchment geographies in New South Wales Australia

Cited by 14 publications

References 15 publications

Information fusion to estimate resilience of dense urban neighborhoods

Information fusion to estimate resilience of dense urban neighborhoods

Is Walk Score associated with hospital admissions from chronic diseases? Evidence from a cross-sectional study in a high socioeconomic status Australian city-state

A new generation of Primary Care Service Areas or general practice catchment areas

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