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At least 19 recordsLinked to original sources
PubMed1996

Assessment tools: small area analysis.

Small area analysis, the examination of geographic variation in the medical care utilization of populations, provides a method for analyzing medical care resource use and can lead to improved medical care. Variations in rates of hospital admissions for most common causes of hospitalization are related to differences in the supply of medical care resources, such as hospital beds, and uncertainty in the outcomes of different diagnostic and therapeutic procedures. Introducing clinicians to practice variation can lead to process improvements. The article describes small area analysis and the limitations of this methodology.

Data Collection↗
PubMed1992

Can small-area analysis detect variation in surgery rates? The power of small-area variation analysis.

A variety of statistical methods can be used in small-area analysis to test whether there is more variation than would be expected by chance alone. However, the power of these methods to detect existing variation has never been studied. The authors used data regarding back surgery in Washington State to suggest several types of variation that might exist (alternative hypotheses), and then used computer simulation to determine the power, or the probability of detecting this variation. The chi-square test had the highest power of all methods considered against most alternative hypotheses. Power is higher if there are no multiple admissions, rates are higher, and counties have larger or similar population size. Problems of accounting for multiple admissions, adjustment for age and sex, choosing the optimum size of small areas, and detection of outliers also are discussed.

Age Factors↗
PubMedDate not supplied

Small area analysis: descriptive epidemiology in health services research.

Small area analysis is descriptive epidemiology applied to health care events. Several methodological issues complicate such studies. Nevertheless, it is possible to derive several conclusions about the determinants of regional variation in medical care use from the small area analysis literature. Most of these conclusions are specific to the procedures, times, and places studied. Changes in technology, economic incentives, and epidemiological factors may invalidate study findings. Nevertheless, small area analysis studies are useful management tools if performed periodically rather than as one-time events. Small area analysis can contribute to our understanding and control of costs, quality, and the accessibility of care, and thus have relevance for both management and policy.

Adult↗
PubMed1993

Small area analysis: abortion statistics.

Small area analysis has developed over the last two or three decades as a useful tool in health services research, as it allows the identification of areas within health or local authority districts with high rates of morbidity and mortality, and thus provides a useful base for planning the delivery of health services. A profile was compiled for Liverpool Family Health Services Authority on planned parenthood in the Liverpool District, with the aim of identifying where resources are needed most - which parts of the City, and which groups of women, are most in need. The profile included an analysis of various outcome measures, including abortion statistics, which can be used as a guide to the apparent effectiveness of services. Using a combination of statistics on NHS abortions for electoral wards, and private abortions by postal district, it became apparent that, on the whole, areas of high NHS induced abortion rates also have high private (British Pregnancy Advisory Service; BPAS) induced abortion rates, and vice versa. The maps for NHS and BPAS abortion rates suggest that total abortion rates are high in City centre wards, and low in areas south of the City. This would suggest that there are differences in social factors, family planning provision, and other factors which are influencing abortion rates. Although available indicators would suggest that City centre wards are in greatest need of improved family planning provision, these are the wards which are relatively well provided with health authority family planning clinics.(ABSTRACT TRUNCATED AT 250 WORDS)

Abortion, Induced↗
PubMed1990

What is too much variation? The null hypothesis in small-area analysis.

A small-area analysis (SAA) in health services research often calculates surgery rates for several small areas, compares the largest rate to the smallest, notes that the difference is large, and attempts to explain this discrepancy as a function of service availability, physician practice styles, or other factors. SAAs are often difficult to interpret because there is little theoretical basis for determining how much variation would be expected under the null hypothesis that all of the small areas have similar underlying surgery rates and that the observed variation is due to chance. We developed a computer program to simulate the distribution of several commonly used descriptive statistics under the null hypothesis, and used it to examine the variability in rates among the counties of the state of Washington. The expected variability when the null hypothesis is true is surprisingly large, and becomes worse for procedures with low incidence, for smaller populations, when there is variability among the populations of the counties, and when readmissions are possible. The characteristics of four descriptive statistics were studied and compared. None was uniformly good, but the chi-square statistic had better performance than the others. When we reanalyzed five journal articles that presented sufficient data, the results were usually statistically significant. Since SAA research today is tending to deal with low-incidence events, smaller populations, and measures where readmissions are possible, more research is needed on the distribution of small-area statistics under the null hypothesis. New standards are proposed for the presentation of SAA results.

Age Factors↗
PubMedDate not supplied

Small-area analysis: targeting high-risk areas for adolescent pregnancy prevention programs.

CONTEXT: Traditional methods of identifying areas in need of adolescent pregnancy prevention programs may miss small localities with high levels of adolescent childbearing. METHODS: Birthrates for 15-17-year-olds were computed for all California zip codes, and the zip codes with birthrates in the 75th percentile were identified. Panels of local experts in adolescent pregnancy reviewed these "hot spots" for accuracy and grouped them into potential project areas, based on their demographics, geography and political infrastructure. RESULTS: In all, 415 zip codes exceeded the 75th-percentile cut-off point of 62.8 births per 1,000, and 210 of them differed significantly from the state average of 44.5 per 1,000 for 15-17-year-olds. While all had high adolescent birthrates, they varied greatly in racial and ethnic mix, poverty and educational attainment, and certain perinatal measures such as inadequate prenatal care and repeat pregnancy. CONCLUSIONS: The use of zip code-level data holds promise for more effective program planning and intervention.

Adolescent↗
PubMed1998

Weighted ancestral area analysis and a solution of the redundant distribution problem.

A new cladistic method for the estimation of ancestral areas is based on reversible parsimony in combination with a weighting scheme that weights steps in positionally plesiomorphic branches more highly than steps in positionally apomorphic branches. By applying this method to cladograms of human mitochondrial DNA, the method is superior to previously proposed algorithms. The method is also an appropriate tool for the solution of the redundant distribution problem in area cladograms. Under the assumption of allopatric speciation, redundant distributions, i.e., sympatry of sister groups, show that dispersal has occurred; thus, the ancestral area of at least one sister group was smaller than the combined distribution of its descendants. With the weighted ancestral area analysis, the ancestral areas can be confined and at least some dispersal events can be distinguished from possible vicariance events. As applied to a cladogram of the Polypteridae, weighted ancestral area analysis is superior to Brooks parsimony analysis (assumption 0) and component analysis under assumptions 1 and 2 (Nelson and Platnick, 1981, Systematics and biogeography: Cladistics and vicariance. Columbia Univ. Press, New York.) in resolving redundancies. The results of the weighted ancestral area analysis may differ from the results of dispersal-vicariance analysis, because the rules of dispersal-vicariance analysis indirectly favor the questionable assumption that the ancestral species occupied only one unit area.

Algorithms↗
PubMed1984

Correlates of urban mortality: a social area analysis.

A method of social area analysis developed by Shevky and Bell is used to analyze mortality among the census tracts of Des Moines, Iowa. "Social rank, urbanization, and segregation indices as well as age-standardized death rates were calculated for each census tract. All variables were treated as continuous, and correlation and regression procedures were used to analyze the data. The findings were consistent with those of previous studies and all relationships were as expected. Regression analysis revealed that segregation contributed little to the explanation of variation in age-standardized death rates, suggesting that segregation is not an important determinant of life styles affecting mortality independent of social rank. The results were interpreted in terms of social class differences in accessibility to medical assistance and assumption of the sick role."

Americas↗
PubMed1992

Testing the null hypothesis in small area analysis.

The goal of small area analysis is often to demonstrate that hospital admission rates or procedure rates vary greatly among regions, suggesting the occurrence of unnecessary admissions or procedures in some regions. Recent articles have shown that such variation may be largely due to chance, even if no underlying differences exist among the small areas; thus, it is important to test if the observed variation is larger than expected by chance. In this article we discuss how the appropriate method for testing the null hypothesis depends on the distribution of the number of admissions at the person level. If it is not possible for an individual to have more than one admission for a given procedure, the appropriate test is a simple chi-square test. If multiple admissions are possible, a modified chi-square test can be used to account for the excess variability due to multiple admissions. Failure to make the correct modification to the chi-square test in this latter case can result in spurious results. This underscores the importance of collecting data on multiple admissions in order to estimate the distribution of the number of admissions at the individual-patient level.

Analysis of Variance↗
PubMed1987

Small area analysis: a review and analysis of the North American literature.

Variations in health service use rates by geographic area have long interested researchers and policymakers. Typically, investigators comparing population-based health care utilization rates among geographic areas have demonstrated substantial variations in use among seemingly similar communities. One method of investigation is "small area analysis." Numerous areas in North America have been studied extensively using this technique. This research has attempted to document the amount of variation found in health care use rates among areas; determine whether or not there is a pattern to such use in high- versus low-use areas; and identify the variables that are associated with the variation and explain a portion of the variation. Beyond this, many researchers have attempted to ascertain whether such variables are associated with characteristics of the population, whether they reflect differences in access and need, or whether a substantial portion of the variation is associated with differences in the medical care system itself. This review discusses the methods used to define the areas, the dependent variables that have been studied and the patterns found within them, the independent variables that have been tested, the statistical methods and analysis procedures used, the results of each study, and the policy recommendations emanating from the research. More importantly, based on what has been learned, the paper provides researchers in small area analysis with a set of recommendations for both analyzing and reporting results. These recommendations are designed to facilitate the development of a common research methodology, increase the comparability across studies, and enhance the use of this technique in the health policy formulation process.

Bibliographies as Topic↗
PubMed1987

Small area analysis: methodology and application.

The data derived from small area analysis type studies carried out in Iowa, Maine and other states is the focus of wide attention from many of the health care delivery system constituencies. These studies look at local patterns of practice. Results of such studies dramatically highlight the wide variances in rates of hysterectomies, prostatectomies or admissions for pulmonary disease between one population area and the next. These variances lead to some provocative discussions of their causes and relationships. Much has already been debated in the health care forum about the meanings of the data, and much more commentary can be expected as consumer groups, UR/QA professionals, those paying health care costs and the federal government join the discussions. In hopes of offering insight into small area analysis, JAMRA presents the following article.

Catchment Area, Health↗
PubMed2006

Ascertaining variation in hospitalization risk among immigrants using small area analysis.

INTRODUCTION: Data on hospitalization of immigrants to the United States are sparse, but small area analysis may improve the utility of existing data. METHODS: Applying logistic regression analysis to 2000 census and hospitalization data for New York City, we examined the odds of hospital admission by major diagnostic category and global region of birth after controlling for covariates. We used individual-level covariates to control for age, race, and gender. By matching the patient's zip code of residence to census data, we then added median household income, the proportion of persons born in a particular global region, and the proportion of foreign-born persons living in the same zip code as independent variables. RESULTS: The total proportion of foreign-born persons in a zip code predicts a lower hospitalization rate for most major diagnostic categories and most foreign-born groups. However, Africa-born persons have a higher odds of hospitalization for most major diagnostic categories - up to 1.79 (95% confidence interval 1.73, 1.86) for blood and blood forming disorders - relative to native-born persons. The odds of hospitalization among Africa-born persons for most conditions are over 3 times higher than other foreign-born groups. Hospitalization odds for Latin American-born persons were also higher than native-born persons across major diagnostic categories. CONCLUSION: Small area analysis generally predicts hospitalization rates that coincide with mortality studies and may serve as a useful tool for hypothesis testing in immigrant health.

Adolescent↗
PubMed2004

Diversity and disparity: GIS and small-area analysis in six Chicago neighborhoods.

Small-area analysis in health is essential in uncovering local-level disparities often masked by health estimates for large areas (e.g., cities, counties, states). In this context, 14 health status indicators (HSIs) were examined for six Chicago community areas that reflect the substantial diversity of the city. HSIs were compared over time (from 1989-90 to 1999-2000) and across community areas. Important disparities among these community areas in mortality rates, birth outcomes, and infectious diseases were found. In many cases the disparities were in the expected direction with the richest and predominantly White community area experiencing the lowest rates. However, some surprises did manifest themselves. For example, only the poorest community area experienced a statistically significant decline in the infant mortality rate. Since so much of attention is now being paid to reducing and eliminating these disparities, it is important to examine their existence to better understand how to minimize them.

Chicago↗
PubMed1989

An area analysis of major causes of death among under 65 year olds in Auckland--1977-85.

Using deaths that were registered between 1977 and 1985, an area analysis of Auckland is presented of the major causes of death among under 65 year olds. The analysis is of all cause mortality and of deaths from specific causes (heart disease, stroke, cancer, respiratory disease, motor vehicle accidents and suicide). The basic spatial entity for the analysis is the census area unit. It is found that there are consistent spatial variations. Three areas where mortality is generally higher than elsewhere in the region are apparent: south Auckland; to the north west of the central urban sector centered on Grey Lynn; and in the eastern parts of the central sector from Glenn Innes to Onehunga. The mortality in these areas is above average for all causes and for heart disease, stroke, cancer and respiratory disease.

Accidents, Traffic↗
PubMed2002

Small area analysis on a large scale--the California experience in mapping teenage birth "hot spots" for resource allocation.

Small-area analysis has become an important tool in the effective targeting of limited public health resources. In California, new funding for teenage pregnancy prevention programs required more and better information to justify the allocation of these funds to areas with the greatest need. Consequently, these funds were allocated using maps with census tract analyses of teenage birth rates and an overlay of geographic frequencies. State and local agencies' programs have responded with positive feedback to the maps, and public health management subsequently has augmented funding for mapping equipment and training. The lessons learned and future directions are discussed.

Adolescent↗
PubMed2002

Role of individual and contextual effects in injury mortality: new evidence from small area analysis.

OBJECTIVE: To analyse the role of individual and contextual variables in injury mortality inequalities from a small area analysis perspective, looking at the data for the city of Barcelona (Spain) for 1992-98. SETTING: Barcelona (Spain). METHODS: All injury deaths in residents older than 19, which occurred in the period 1992-98 were included (n=4393). Age and sex specific mortality rates were calculated for each educational level and each cause of death (traffic injuries, falls, drug overdose, suicide, other injuries). The contextual variables included were the proportion of men unemployed, and the proportion of men in jail, in each neighbourhood. Multilevel Poisson regression models were fitted using data grouped by age, educational level, and neighbourhood for each sex. RESULTS: Death rates were higher in males, at the extremes of the age distribution (under 44 and over 74 years), and for lower educational levels. The results of the Poisson multilevel models indicate that inequalities by educational level follow a gradient, with higher risks for the population with no schooling, after having adjusted for the contextual variables of the neighbourhood. Such inequalities were more important in the youngest age group (20-34 years), as relative risk of 5.41 (95% confidence interval (CI) 3.9 to 7.4) for all injury causes in males and 4.38 (95% CI 2.3 to 8.4) in females. The highest relative risks were found for drug overdose. There was a contextual neighbourhood effect (the higher the deprivation, the higher the mortality) after having taken into account individual variables. CONCLUSION: The findings underscore the need to implement injury prevention strategies not only at the individual level taking into account socioeconomic position, but also at the neighbourhood level.

Adult↗
PubMed2006

Analysis of patient flows for orthopedic procedures using small area analysis in Switzerland.

BACKGROUND: In general cantons regulate and control the Swiss health service system; patient flows within and between cantons are thereby partially disregarded. This paper develops an alternative spatial model, based upon the construction of orthopedic hospital service areas (HSAOs), and introduces indices for the analysis of patient streams in order to identify areas, irrespective of canton, with diverse characteristics, importance, needs, or demands. METHODS: HSAOs were constructed using orthopedic discharge data. Patient streams between the HSAOs were analysed by calculating three indices: the localization index (% local residents discharged locally), the netindex (the ratio of discharges of nonlocal incoming residents to outgoing local residents), and the market share index (% of local resident discharges of all discharges in local hospitals). RESULTS: The 85 orthopedic HSAOs show a median localization index of 60.8%, a market share index of 75.1%, and 30% of HSAOs have a positive netindex. Insurance class of bed, admission type, and patient age are partially but significantly associated with those indicators. A trend to more centrally provided health services can be observed not only in large urban HSAOs such as Geneva, Bern, Basel, and Zurich, but also in HSAOs in mountain sport areas such as Sion, Davos, or St.Moritz. Furthermore, elderly and emergency patients are more frequently treated locally than younger people or those having elective procedures. CONCLUSION: The division of Switzerland into HSAOs provides an alternative spatial model for analysing and describing patient streams for health service utilization. Because this small area model allows more in-depth analysis of patient streams both within and between cantons, it may improve support and planning of resource allocation of in-patient care in the Swiss healthcare system.

Adolescent↗