Methods for confidence interval estimation of a ratio parameter with application to location quotients (original) (raw)
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Spatial statistical methods in health
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The study of the geographical distribution of disease incidence and its relationship to potential risk factors (referred to here as "geographical epidemiology") has provided, and continues to provide, rich ground for the application and development of statistical methods and models. In recent years increasingly powerful and versatile statistical tools have been developed in this application area. This paper discusses the general classes of problem in geographical epidemiology and reviews the key statistical methods now being employed in each of the application areas identified. The paper does not attempt to exhaustively cover all possible methods and models, but extensive references are provided to further details and to additional approaches. The overall aim is to provide a picture of the "current state of the art" in the use of spatial statistical methods in epidemiological and public health research. Following the review of methods, the main software environme...
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Social Science & Medicine, 2019
Small area health data are not always available on a consistent and robust routine basis across nations, necessitating the employment of small area estimation methods to generate local-scale data or the use of proxy measures. Geodemographic indicators are widely marketed as a potential proxy for many health indicators. This paper tests the extent to which the inclusion of geodemographic indicators in small area estimation methodology can enhance small area estimates of limiting long-term illness (LLTI). The paper contributes to international debates on small area estimation methodologies in health research and the relevance of geodemographic indicators to the identification of health care needs. We employ a multilevel methodology to estimate small area LLTI prevalence in England, Scotland and Wales. The estimates were created with a standard geographicallybased model and with a cross-classified model of individuals nested separately in both spatial groupings and nonspatial geodemographic clusters. LLTI prevalence was estimated as a function of age, sex and deprivation. Estimates from the cross-classified model additionally incorporated residuals relating to the geodemographic classification. Both sets of estimates were compared against direct estimates from the 2011 Census. Geodemographic clusters remain relevant to understanding LLTI even after controlling for age, sex and deprivation. Incorporating a geodemographic indicator significantly improves concordance between the small area estimates and the Census. Small area estimates are however consistently below the equivalent Census measures, with the LLTI prevalence in urban areas characterised as 'blue collar' and 'struggling families' being markedly lower. We conclude that the inclusion of a geodemographic indicator in small area estimation can improve estimate quality and enhance understanding of health inequalities. We recommend the inclusion of geodemographic indicators in public releases of survey data to facilitate better small area estimation but caution against assumptions that geodemographic indicators can, on their own, provide a proxy measure of health status.
Estimating spatial disease rates from health statistics without geographic identifiers
Acute respiratory infections (ARI) statistics in Cúcuta, Colombia are reported for each health service or health care providers rather than for residence area. Although official statistics are important sources of data to support evidence-based decisions for at-risk communities, these are of limited use if the geographical distribution of diseases cannot be identified. This study aims to calculate the rate of ARI in each of the Cúcuta’s urban sections using a spatial analysis of the distribution of the HCP.The spatial scope (geographical area of influence) of the health care providers was established from their spatial distribution and the population accessing their services. Three spatial aggregation levels were established considering the spatial scope of the primary, intermediate and tertiary health care providers. The ARI cases per urban section were calculated according to the spatial distribution of health care providers and the proportion of population, per urban section in e...
Preventive Medicine Reports
To quantify the HIV epidemic, the classical population-based prevalence and incidence rates (P rates) are the two most commonly used measures used for policy interventions. However, these P rates ignore the heterogeneity of the size of geographic region where the population resides. It is intuitive that with the same P rates, the likelihood for HIV can be much greater to spread in a population residing in a crowed small urban area than the same number of population residing in a large rural area. With this limitation, Chen and Wang proposed the geographic area-based rates (G rates) to complement the classical P rates. They analyzed the 2000-2012 US data on new HIV infections and persons living with HIV and found, as compared with other methods, using G rates enables researchers to more quickly detect increases in HIV rates. This capacity to reveal increasing rates in a more efficient and timely manner is a crucial methodological contribution to HIV research. To enhance this newly proposed concept of G rates, this article presents a discussion of 3 areas for further development of this important concept: (1) analysis of global HIV epidemic data using the newly proposed G rates to capture the changes globally; (2) development of the associated population density-based rates (D rates) to incorporate the heterogeneities from both geographical area and total population-at-risk; and (3) development of methods to calculate variances and confidence intervals for the P rates, G rates, and D rates to capture the variability of these indices.
Estimating Spatial Intensity and Variation in Risk from Locations Coarsened by Incomplete Geocoding
2000
The estimation of spatial intensity and relative risk are important inference problems in spatial epidemiologic studies. A standard component of data assimilation in these studies is the assignment of a geocode, i.e. point-level spatial coordinates, to the address of each subject in the study population. Unfortunately, when geocoding is performed by the pervasive method of street-segment matching to a