Defining GP practice areas based on true service utilisation

Defining catchment boundaries and their populations for Aotearoa New Zealand's rural hospitals

Introduction There is considerable variation in the structure and resources of New Zealand (NZ) rural hospitals; however, these have not been recently quantified and their effects on healthcare outcomes are poorly understood. Importantly, there is no standardised description of each rural hospital's catchment boundary and the characteristics of the population living within this area. Aim To define and describe a catchment population for each of New Zealand's rural hospitals. Methods An exploratory approach to developing catchments was employed. Geographic Information Systems were used to develop drive-time-based geographic catchments, and administrative health data (National Minimum Data Set and Primary Health Organisation Data Set) informed service utilisation-based catchments. Catchments were defined at both the Statistical Area 2 (SA2) and domicile levels, and linked to census-based population data, the Geographic Classification for Health, and the area-level New Zealand Index of Socioeconomic Deprivation (NZDep2018). Results Our results highlight considerable heterogeneity in the size (max: 57 564, min: 5226) and characteristics of populations served by rural hospitals. Substantial differences in the age structure, ethnic composition, socio-economic profile, 'remoteness' and projected future populations, are noted. Discussion In providing a standardised description of each rural hospital's catchment boundary and its population characteristics, the considerable heterogeneity of the communities served by rural hospitals, both in size, rurality and socio-demographic characteristics, is highlighted. The findings provide a platform on which to build further research regarding NZ's rural hospitals and inform the delivery of high-quality, cost-effective and equitable health care for people living in rural NZ.

Urban or Rural GP? In the Czech Republic It Is not just Distances That Matter

This article proposes a combined mixed methods approach to categorising GP practices. It looks not only at location but also at differences in the nature of the work that rural GPs perform. A data analysis was conducted of the largest health insurance company in the Czech Republic (5.9 million patients, 60% of the population, 100% coverage within the Czech Republic). We performed two data analyses, one for 2014-2015 and one for 2016, and divided GP practices into urban, intermediate, and rural groups (taking into account the OECD methodology). We compared groups in terms of the total annual cost in CZK per adult registered insurance holders. The total volume of data indicated the financial costs of €1.52 billion and €2.57 billion respectively. Both analysis showed differences between all groups of practises which confirmed the assumption that the work of the GP is influenced by regionality. A multidisciplinary hospital is the main factor that fundamentally affects the way a GP's work in that area. The proposed principle of categorising general practices combines geographical and cost characteristics. This requires knowledge of the cost data of healthcare payer and on the basic demographic knowledge of the area. We suggest this principe may be transferrable and particularly suitable for categorising general practice.

Living environment, social support, and informal caregiving are associated with healthcare seeking behaviour and adherence to medication treatment: A cross-sectional population study

Despite the well-known associations between local environment and health, few studies have focused on environment and healthcare utilisation, for instance healthcare seeking behaviour or adherence. This study was aimed at analysing housing type, behaviour based on perceived local outdoor safety, social support, informal caregiving, demographics, socioeconomics, and long-term illness, and associations with health-seeking and adherence behaviours at a population level. This study used data from the Swedish National Public Health Survey 2004-2014, an annually repeated, large sample, cross-sectional, population-based survey study. In all, questionnaires from 100,433 individuals were returned by post, making the response rate 52.9% (100,433/190,000). Descriptive statistics and multiple logistic regressions were used to investigate associations between explanatory variables and the outcomes of refraining from seeking care and non-adherence behaviour. Living in rented apartment, lodger, a dorm or other was associated with reporting refraining from seeking care (adjusted OR 1.16, 95% CI 1.00-1.22), and non-adherence (adjusted OR 1.22; 95% CI 1.13-1.31). Refraining from going out due to a perceived unsafe neighbourhood was associated with refraining from seeking care (adjusted OR 1.59, 95% CI 1.51-1.67) and non-adherence (adjusted OR 1.26, 95% CI 1.17-1.36). Social support and status as an informal caregiver was associated with higher odds of refraining from seeking medical care and non-adherence. This study suggests that living in rental housing, refraining from going out due to neighbourhood safety concerns, lack of social support or informal caregiver status are associated with lower health-seeking behaviour and non-adherence to prescribed medication.

Antibiotic prescribing patterns in general medical practices in England: Does area matter?

Antimicrobial resistance is an important public health concern. As most antibiotics are prescribed in primary care, understanding prescribing patterns in General Medical (GP) practices is vital. The aim of this study was a spatial pattern analysis of antibiotic prescribing rates in GP practices in England and to examine the association of potential clusters with area level socio-economic deprivation. The pattern analysis identified a number of hot and cold spots of antibiotic prescribing, with hot spots predominantly in the North of England. Spatial regression showed that patient catchments of hot spot practices were significantly more deprived than patient catchments of cold spot practices, especially in the domains of income, employment, education and health. This study suggests the presence of area level drivers resulting in clusters of high and low prescribing. Consequently, area level strategies may be needed for antimicrobial stewardship rather than national level strategies.

Use of age-specific hospital catchment populations to investigate geographical variation in inpatient admissions for children and young people in England: retrospective, cross-sectional study

OBJECTIVES

To develop a method for calculating age-specific hospital catchment populations (HCPs) for children and young people (CYP) in England. To show how these methods allow geographical variation in hospital activity to be investigated and addressed more effectively.

DESIGN

Retrospective, secondary analysis of existing national datasets.

SETTING

Inpatient care of CYP (0-18 years) in England.

PARTICIPANTS

Hospital Episode Statistics (HES) data were accessed for all inpatient admissions (elective and emergency) for CYP from birth to 18 years, 364 days, for 2011/2012-2014/2015. In 2014/2015, 857 112 admissions were analysed, from an eligible population of approximately 11.9 million CYP.

OUTCOME MEASURES

For each hospital Trust, the catchment population of CYP was calculated; Trust-level admission rates per thousand per year were then calculated for admissions due to (1) any diagnostic code, (2) primary diagnosis of epilepsy and (3) epilepsy listed as primary diagnosis or comorbidity.

RESULTS

Estimated 2014/2015 HCPs for CYP ranged from 268 558 for Barts Health NHS Trust to around 30 000 for the smallest acute general paediatric services and below 10 000 for many Trusts providing specialist services. As expected, the composition of HCPs was fairly consistent for age breakdown but levels of deprivation varied widely. After standardising for population characteristics, admission rates with a primary diagnosis of epilepsy ranged from 14.3 to 157.7 per 100 000 per year (11.0-fold variation) for Trusts providing acute general paediatric services. All-cause admission rates showed less variation, ranging from 4033 to 11 681 per 100 000 per year (2.9-fold variation).

CONCLUSIONS

Use of age-specific catchment populations allows variation in hospital activity to be linked to specific teams and care pathways. This provides an evidence base for initiatives to tackle unwarranted variation in healthcare activity and health outcomes.

Modeling Seasonal and Spatiotemporal Variation: The Example of Respiratory Prescribing

Many measures of chronic diseases, including respiratory disease, exhibit seasonal variation together with residual correlation between consecutive time periods and neighboring areas. We demonstrate a strategy for modeling data that exhibit both seasonal trend and spatiotemporal correlation, using an application to respiratory prescribing. We analyzed 55 months (2002-2006) of prescribing data from the northeast of England, in the United Kingdom. We estimated the seasonal pattern of prescribing by fitting a dynamic harmonic regression (DHR) model to salbutamol prescribing in relation to temperature. We compared the output of DHR models to static sinusoidal regression models. We used the DHR-fitted values as an offset in mixed-effects models that aimed to account for the remaining spatiotemporal variation in prescribing rates. As diagnostic checks, we assessed spatial and temporal correlation separately and jointly. Our application of a DHR model resulted in a better fit to the seasonal variation of prescribing than was obtained with a static model. After adjusting for the fitted values from the DHR model, we did not detect any remaining spatiotemporal correlation in the model's residuals. Using a DHR model and temperature data to account for the periodicity of prescribing proved to be an efficient way to capture its seasonal variation. The diagnostic procedures indicated that there was no need to model any remaining correlation explicitly.

A sequential model for the structure of health care utilization

Traditional measurement models of health care utilization are not able to represent the complex structure of health care utilization. In this qualitative study, we, therefore, developed a new model to represent the health care utilization structure. In Norway and Germany, we conducted episodic interviews, participant observation and a concurrent context analysis. Data was analyzed by thematic coding in the framework of grounded theory. Consultations do very often not only have one single reason for encounter. They are usually not independent events but form part of consultation sequences. We could find structural differences between Norway and Germany regarding the flow of information between consultations and which providers are involved in health care in what way. This leads to a sequential model, in which health care utilization is seen as sequences of consultations. Such health care utilization sequences consist of nodes which are connected by edges. Nodes represent patient-provider contacts and edges depict the flow of information. Time and the level of health care providers are dimensions in the model. These sequences can be described by different measures and aggregated on population level. Thus, the sequential model can be further used in analyzing health care utilization quantitatively, e.g., by using routine data.

Catchments of general practice in different countries--a literature review

The purpose of this paper is to review the current research on catchment areas of private general practices in different developed countries because healthcare reform, including primary health care, has featured prominently as an important political issue in a number of developed countries. The debates around health reform have had a significant health geographic focus.

Conceptually, GP catchments describe the distribution, composition and profile of patients who access a general practitioner or a general practice (i.e. a site or facility comprising one or more general practitioners). Therefore, GP catchments provide important information into the geographic variation of access rates, utilisation of services and health outcomes by all of the population or different population groups in a defined area or aggregated area.

This review highlights a wide range of diversity in the literature as to how GP catchments can be described, the indicators and measures used to frame the scale of catchments. Patient access to general practice health care services should be considered from a range of locational concepts, and not necessarily constrained by their place of residence. An analysis of catchment patterns of general practitioners should be considered as dynamic and multi-perspective. Geographic information systems provide opportunities to contribute valuable methodologies to study these relationships. However, researchers acknowledge that a conceptual framework for the analysis of GP catchments requires access to real world data. Recent studies have shown promising developments in the use of real world data, especially from studies in the UK.

Understanding the catchment profiles of individual GP surgeries is important if governments are serious about patient choice being a key part of proposed primary health reforms. Future health planning should incorporate models of GP catchments as planning tools, at the micro level as well as the macro level, to assist policies on the allocation of resources so that opportunities for good health outcomes for all groups within society, especially those who have been systematically denied equitable access, are maximised.

Association between respiratory prescribing, air pollution and deprivation, in primary health care

BACKGROUND

We investigated the association between respiratory prescribing, air quality and deprivation in primary health care. Most previous studies have used data from secondary and tertiary care to quantify air pollution effects on exacerbations of asthma and chronic obstructive pulmonary disease (COPD). However, these outcomes capture patients who suffer from relatively severe symptoms.

METHODS

This is a population-based ecological study. We analysed respiratory medication (salbutamol) prescribed monthly by 63 primary care practices, UK. Firstly, we captured the area-wide seasonal variation in prescribing. Then, using the area-wide variation in prescribing as an offset, we built a mixed-effects model to assess the remaining variation in relation to air quality and demographic variables.

RESULTS

An increase of 10 μg/m(3) in ambient PM10 was associated with an increase of 1% (95% CI: 0.1-2%) in salbutamol prescribing. An increase of 1 SD in income and employment deprivation was associated with an increase of 20.5% (95% CI: 8.8-33.4%) and 14.7% (95% CI: 4.3-26.2%) in salbutamol prescribing rate, respectively.

CONCLUSIONS

The study provides evidence that monthly respiratory prescribing in primary care is a useful indicator of the extent to which air pollution exacerbates asthma and COPD symptoms. Respiratory prescribing was higher on deprived populations.

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.