General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare

BACKGROUND Good quality spatial data on Family Physicians or General Practitioners (GPs) are key to accurately measuring geographic access to primary health care. The validity of computed associations between health outcomes and measures of GP access such as GP density is contingent on geographical data quality. This is especially true in rural and remote areas, where GPs are often small in number and geographically dispersed. However, there has been limited effort in assessing the quality of nationally comprehensive, geographically explicit, GP datasets in Australia or elsewhere.Our objective is to assess the extent of association or agreement between different spatially explicit nationwide GP workforce datasets in Australia. This is important since disagreement would imply differential relationships with primary healthcare relevant outcomes with different datasets. We also seek to enumerate these associations across categories of rurality or remoteness. METHOD We compute correlations of GP headcounts and workload contributions between four different datasets at two different geographical scales, across varying levels of rurality and remoteness. RESULTS The datasets are in general agreement with each other at two different scales. Small numbers of absolute headcounts, with relatively larger fractions of locum GPs in rural areas cause unstable statistical estimates and divergences between datasets. CONCLUSION In the Australian context, many of the available geographic GP workforce datasets may be used for evaluating valid associations with health outcomes. However, caution must be exercised in interpreting associations between GP headcounts or workloads and outcomes in rural and remote areas. The methods used in these analyses may be replicated in other locales with multiple GP or physician datasets

Walkability and Greenness Do Not Walk Together: Investigating Associations between Greenness and Walkability in a Large Metropolitan City Context.

BACKGROUND: The existing environment literature separately emphasizes the importance of neighborhood walkability and greenness in enhancing health and wellbeing. Thus, a desirable neighborhood should ideally be green and walkable at the same time. Yet, limited research exists on the prevalence of such "sweet spot" neighborhoods. We sought to investigate this question in the context of a large metropolitan city (i.e., Sydney) in Australia. METHODS: Using suburb level normalized difference vegetative index (NDVI), percentage urban greenspace, Walk Score® (Walk Score, Seattle, WA, USA), and other data, we explored the global and local relationships of neighborhood-level greenness, urban green space (percent park area) with walkability applying both non-spatial and spatial modeling. RESULTS: We found an overall negative relationship between walkability and greenness (measured as NDVI). Most neighborhoods (represented by suburbs) in Sydney are either walkable or green, but not both. Sweet spot neighborhoods that did exist were green but only somewhat walkable. In addition, many neighborhoods were both less green and somewhat walkable. Moreover, we observed a significant positive relationship between percentage park area and walkability. These results indicate walkability and greenness have inverse and, at best, mixed associations in the Sydney metropolitan area. CONCLUSIONS: Our analysis indicates an overall negative relationship between greenness and walkability, with significant local variability. With ongoing efforts towards greening Sydney and improving walkability, more neighborhoods may eventually be transformed into becoming greener and more walkable

The disappearing seasonality of Autism conceptions in California

BACKGROUND Autism incidence and prevalence have increased dramatically in the last two decades. The autism caseload in California increased 600% between 1992 and 2006, yet there is little consensus as to the cause. Studying the seasonality of conceptions of children later diagnosed with autism may yield clues to potential etiological drivers. OBJECTIVE To assess if the conceptions of children later diagnosed with autism cluster temporally in a systematic manner and whether any pattern of temporal clustering persists over time. METHOD We searched for seasonality in conceptions of children later diagnosed with autism by applying a one-dimensional scan statistic with adaptive temporal windows on case and control population data from California for 1992 through 2000. We tested for potential confounding effects from known risk factors using logistic regression models. RESULTS There is a consistent but decreasing seasonal pattern in the risk of conceiving a child later diagnosed with autism in November for the first half of the study period. Temporal clustering of autism conceptions is not an artifact of composition with respect to known risk factors for autism such as socio-economic status. CONCLUSION There is some evidence of seasonality in the risk of conceiving a child later diagnosed with autism. Searches for environmental factors related to autism should allow for the possibility of risk factors or etiological drivers that are seasonally patterned and that appear and remain salient for a discrete number of years.Support for this article comes from the NIH Director’s Pioneer Award Program, part of the NIH Roadmap for Medical Research through grant number 1. DP1 OD003635-01

Modeling the probability distribution of positional errors incurred by residential address geocoding

BACKGROUND: The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated geocoding and E911 geocoding. RESULTS: Positional errors were determined for 1423 rural addresses in Carroll County, Iowa as the vector difference between each 100%-matched automated geocode and its true location as determined by orthophoto and parcel information. Errors were also determined for 1449 60%-matched geocodes and 2354 E911 geocodes. Huge (> 15 km) outliers occurred among the 60%-matched geocoding errors; outliers occurred for the other two types of geocoding errors also but were much smaller. E911 geocoding was more accurate (median error length = 44 m) than 100%-matched automated geocoding (median error length = 168 m). The empirical distributions of positional errors associated with 100%-matched automated geocoding and E911 geocoding exhibited a distinctive Greek-cross shape and had many other interesting features that were not capable of being fitted adequately by a single bivariate normal or t distribution. However, mixtures of t distributions with two or three components fit the errors very well. CONCLUSION: Mixtures of bivariate t distributions with few components appear to be flexible enough to fit many positional error datasets associated with geocoding, yet parsimonious enough to be feasible for nascent applications of measurement-error methodology to spatial epidemiology

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

BACKGROUND To develop a method to use survey data to establish catchment areas of primary care or Primary Care Service Areas. Primary Care Service Areas are small areas, the majority of patients resident in which obtain their primary care services from within the geography. METHODS The data are from a large health survey (n =267,153, year 2006-2009) linked to General Practitioner service use data (year 2002-2010) from New South Wales, Australia. Our methods broadly follow those used previously by researchers in the United States of America and Switzerland, with significant modifications to improve robustness. This algorithm allocates post code areas to Primary Care Service Areas that receive the plurality of patient visits from the post code area. RESULTS Consistent with international findings the median Localization Index or the median percentage of patients that obtain their primary care from within a Primary Care Service Area is 55% with localization increasing with rurality. CONCLUSIONS With the additional methodological refinements in this study, Australian Primary Care Service Areas have great potential to be of value to policymakers and researchers

Spatial clusters of autism births and diagnoses point to contextual drivers of increased prevalence

Autism prevalence has risen dramatically over the past two decades in California. Although often suggested to have been crucial to the rise of autism, environmental and social contextual drivers of diagnosis have not been extensively examined. Identifying the spatial patterning of autism cases at birth and at diagnosis can help clarify which contextual drivers are affecting autism's rising prevalence. Children with autism not co-morbid with mental retardation served by the California Department of Developmental Services during the period 1992–2005 were matched to California's Birth Master Files. We search for spatial clusters of autism at time of birth and at time of diagnosis using a spatial scan approach that controls for key individual-level risk factors. We then test whether indicators of neighborhood-level diagnostic resources are associated with the diagnostic clusters and assess the extent of clustering by autism symptom severity through a multivariate scan. Finally, we test whether children who move into neighborhoods with higher levels of resources are more likely to receive an autism diagnosis relative to those who do not move with regard to resources. Significant birth and diagnostic clusters of autism are observed independent of key individual-level risk factors. While the clusters overlap, there is a strong positive association between the diagnostic clusters and neighborhood-level diagnostic resources. In addition, children with autism who are higher functioning are more likely to be diagnosed within a cluster than children with autism who are lower functioning. Most importantly, children who move into a neighborhood with more diagnostic resources than their previous residence are more likely to subsequently receive an autism diagnosis than children whose neighborhood resources do not change. We identify birth and diagnostic clusters of autism in California that are independent of individual-level autism risk factors. Our findings implicate a causal relationship between neighborhood-level diagnostic resources and spatial patterns of autism incidence but do not rule out the possibility that environmental toxicants have also contributed to autism risk

The Spatial Structure of Autism in California, 1993-2001

This article identifies significant high-risk clusters of autism based on residence at birth in California for children born from 1993 to 2001. These clusters are geographically stable. Children born in a primary cluster are at four times greater risk for autism than children living in other parts of the state. This is comparable to the difference between males and females and twice the risk estimated for maternal age over 40. In every year roughly 3% of the new caseload of autism in California arises from the primary cluster we identify-a small zone 20 km by 50 km. We identify a set of secondary clusters that support the existence of the primary clusters. The identification of robust spatial clusters indicates that autism does not arise from a global treatment and indicates that important drivers of increased autism prevalence are located at the local level

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

Objectives: To 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. Design: A cross-sectional analysis of public hospital episode data (2007–2013). Setting: Hospitalisations from the ACT, Australia at very small geographic areas. Participants: Secondary 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 measures: Geographic Information System derived or compatible measures of general practitioner access, neighbourhood socioeconomic status, alcohol access, exposure to traffic and Walk Score walkability. Main outcome measures: Hospitalisations of circulatory diseases, specific endocrine, nutritional and metabolic diseases, respiratory diseases and specific cancers. Results: Geographic 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 lifestylerelated cancers. Conclusions: Our 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.Full Tex

Geocoding accuracy and the recovery of relationships between environmental exposures and health

<p>Abstract</p> <p>Background</p> <p>This research develops methods for determining the effect of geocoding quality on relationships between environmental exposures and health. The likelihood of detecting an existing relationship – statistical power – between measures of environmental exposures and health depends not only on the strength of the relationship but also on the level of positional accuracy and completeness of the geocodes from which the measures of environmental exposure are made. This paper summarizes the results of simulation studies conducted to examine the impact of inaccuracies of geocoded addresses generated by three types of geocoding processes: a) addresses located on orthophoto maps, b) addresses matched to TIGER files (U.S Census or their derivative street files); and, c) addresses from E-911 geocodes (developed by local authorities for emergency dispatch purposes).</p> <p>Results</p> <p>The simulated odds of disease using exposures modelled from the highest quality geocodes could be sufficiently recovered using other, more commonly used, geocoding processes such as TIGER and E-911; however, the strength of the odds relationship between disease exposures modelled at geocodes generally declined with decreasing geocoding accuracy.</p> <p>Conclusion</p> <p>Although these specific results cannot be generalized to new situations, the methods used to determine the sensitivity of results can be used in new situations. Estimated measures of positional accuracy must be used in the interpretation of results of analyses that investigate relationships between health outcomes and exposures measured at residential locations. Analyses similar to those employed in this paper can be used to validate interpretation of results from empirical analyses that use geocoded locations with estimated measures of positional accuracy.</p

Exploring the Use of Hospital and Community Mental Health Services among Newly Resettled Refugees

Importance: Resettled refugees in high-income countries represent a vulnerable population. It is known that refugees have high rates of trauma-related mental health issues; however, ad hoc research has generally revealed low rates of health services use among refugees. Such research usually samples a population at a single point in time and is based on targeted surveys. Because refugee populations change over time, such research becomes expensive and time-consuming for agencies interested in routinely publishing statistics of mental health services use among refugees. The linking of large administrative data sets to establish rates of use of mental health services among resettled refugees is a flexible and relatively inexpensive approach. Objective: To use data linkage to establish rates of mental health services use among resettled refugees relative to the general population. Design, Setting, and Participants: This cross-sectional study implemented data linkage from the Refugee Health Nurse Program for 10050 refugees who resettled in Sydney, Australia, from October 23, 2012, to June 8, 2017, with data concerning use of community mental health services and mental health hospitalization from New South Wales Health databases. Data were analyzed between June 1, 2019, and December 31, 2021. Main Outcomes and Measures: Rates of service contacts with community mental health services among the resettled refugees were compared with those of the general population by age, sex, and the most common International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, diagnosis codes. Length of community mental health service sessions and rates of mental health hospitalizations were also compared. Results: Among the 255 resettled refugees who had contacts with community mental health care services and were not missing data (median age, 35 [range, 4-80] years; 117 [64%] male and 138 [54%] female), 153 (60%) were born in Iraq and 156 (61%) were Arabic speaking. This population was less likely to use mental health services than the general population and had shorter community mental health consultations. The rate of contacts with community mental health services for depressive disorders among the resettled refugee population was 40% (95% CI, 33%-46%) lower than that among the general population. Rates of same-day hospitalization per 10000 person-years were not significantly different between the refugee population (4 [95% CI, 2-8]) and the general Australian population (7 [95% CI, 7-7]). However, the refugee population was 17% (95% CI, 6%-29%) more likely than the general Australian population to interact with the community mental health system for severe stress- and adjustment disorder-related diagnoses. Conclusions and Relevance: These findings suggest that refugees who have resettled in Australia tend to use fewer mental health services than the general population except for services devoted to stress- and adjustment disorder-related diagnoses. These findings also suggest that it is possible to successfully leverage data linkage to study patterns of mental health services use among resettled refugees.</p