Utilization of ambulatory care studies: testing the assumptions

Health Services Research, 1980

For this study I gathered information on sources of ambulatory care and ambulatory care utilization, together with social, demographic, and health information. I applied a revision of Andersen's behavioral utilization model to all these data to try to explain patterns of ambulatory care utilization. Data are from a household survey of Rhode Island residents that was conducted in 1974. I have used multiple classification analysis (MCA), since the provider variable formed from the information on medical care sources is best conceptualized as being measured at a nominal level. It emphasizes both the number of different affiliations and the specialty and type of each affiliation.

PubMed, 1988

There are two conceptual problems with the study of the utilization of ambulatory care: 1) a problem of definition and 2) a problem of measurement. Large multivariate studies conceptualize utilization in a straightforward and static manner that is reflected in the way they measure utilization: as a sum of the number of visits to physicians over a period of time. However, utilization can be construed as a process occurring through time. The object of an analysis of utilization is to examine the causes of the changes in the levels of utilization patients undergo from one period to the next. In this paper, a Markovian model for studying utilization as a process is set up, using data on utilization from a sample of 2,149 patients from Montreal (1981). Results show that the effect of age and sex on utilization is not structured in the same way when using a Markovian model as when using a traditional model, where utilization is measured by the number of visits in one period. Thus, the results of large multivariate studies are most probably biased.

Annual Review of Public Health, 1999

Important questions about health care are often addressed by studying health care utilization. Utilization data have several characteristics that make them a challenge to analyze. In this paper we discuss sources of information, the statistical properties of utilization data, common analytic methods including the two-part model, and some newly available statistical methods including the generalized linear model. We also address issues of study design and new methods for dealing with censored data. Examples are presented.

BMC Health Services Research

Background: To improve care, planners require accurate information about nursing home (NH) residents and their healthcare use. We evaluated how accurately measures of resident user status and healthcare use were captured in the Minimum Data Set (MDS) versus administrative data. Methods: This retrospective observational cohort study was conducted on all NH residents (N = 8832) from Winnipeg, Manitoba, Canada, between April 1, 2011 and March 31, 2013. Six study measures exist. NH user status (newly admitted NH residents, those who transferred from one NH to another, and those who died) was measured using both MDS and administrative data. Rates of in-patient hospitalizations, emergency department (ED) visits without subsequent hospitalization, and physician examinations were also measured in each data source. We calculated the sensitivity, specificity, positive and negative predictive values (PPV, NPV), and overall agreement (kappa, κ) of each measure as captured by MDS using administrative data as the reference source. Also for each measure, logistic regression tested if the level of disagreement between data systems was associated with resident age and sex plus NH owner-operator status. Results: MDS accurately identified newly admitted residents (κ = 0.97), those who transferred between NHs (κ = 0. 90), and those who died (κ = 0.95). Measures of healthcare use were captured less accurately by MDS, with high levels of both under-reporting and false positives (e.g., for in-patient hospitalizations sensitivity = 0.58, PPV = 0.45), and moderate overall agreement levels (e.g., κ = 0.39 for ED visits). Disagreement was sometimes greater for younger males, and for residents living in for-profit NHs. Conclusions: MDS can be used as a stand-alone tool to accurately capture basic measures of NH use (admission, transfer, and death), and by proxy NH length of stay. As compared to administrative data, MDS does not accurately capture NH resident healthcare use. Research investigating these and other healthcare transitions by NH residents requires a combination of the MDS and administrative data systems.

Objective To identify factors that lead people to visit a doctor in Brazil and assess differences between socioeconomic groups. Methods A cross-sectional study comprising 1,260 subjects aged 15 or more was carried out in southern Brazil. Demographic, socioeconomic, health needs and regular source of care data were analyzed concerning visits to a doctor within two months from the interview. Adjusted prevalence ratios and 95% confidence intervals were calculated using Poisson regression. Results Adjusted PR showed that women having stressful life events, health insurance, and a regular doctor increased the outcome. A dose-related response was found with selfreported health, and the probability of visiting a doctor increased with health needs. Analysis in the chronic disease group revealed that uneducated lower income subjects had a 62% reduction in the chance of visiting a doctor compared to uneducated higher income ones. However, as it was seen a significant interaction between income and education, years of schooling increased utilization in this group. Conclusions Results suggest the existence of health inequity in the poorest group that could be overcome with education. Specific measures reinforcing the importance of having a regular doctor may also improve access in the underserved group. Resumo Objetivo Identificar os fatores que levam uma pessoa a consultar o médico no Brasil e avaliar as diferenças entre grupos socioeconômicos. Métodos Foi realizado um estudo transversal com 1.260 pessoas de 15 anos ou mais no sul do Brasil. Foram analisados dados demográficos, socioeconômicos, de necessidade em saúde e de fonte definida para consulta quanto a visita ao médico nos últimos dois meses. Foram calculadas as razões ajustadas de prevalência (RP) e os Intervalos de Confiança de 95% (IC 95%), utilizando a regressão de Poisson.

Socio-Economic Planning Sciences, 1975

This study examines the determinants of health services utifization among 2168 households in five New York and Pennsylvania counties. The purpose is to identify sub-population groups with relatively homogeneous patterns of health service use behavior and to determine for each the relative importance of various predictors, categorized into three broad dimensions-the need for care, predisposing factors and enabling factors. A two stage approach using multivariate analysis techniques is employed. Overall, the proportion of expenses paid by health insurance, Medicare, social class and the physician-population ratio in the county where health services are received are found to be important predictors of health services use. The significance of these and other predictors varies, however, from one subgroup to the next. The analytical strategy employed proves to be helpful both in understanding the differential patterns of health services utilization in subpopulations and in identifying impediments to health care. Moreover, the predictive models Of physician utilization are formulated.

Medical Care, 2011

Background: Little is known about how morbidity levels progress over time and the implications of these morbidity trajectories for healthcare utilization. Objectives: To identify and compare characteristics of people in different morbidity trajectories and to evaluate how morbidity trajectories impact the performance of diagnostic risk-adjustment models. Research Design: Morbidity trajectories were derived from 3-year (2002 to 2004) of claims from a national insurance system. These trajectories, with or without 2004 claims-based risk adjusters developed from the Adjusted Clinical Group case-mix system, were used to explain medical utilization in 2005. Subjects: A random sample of Taiwanese National Health Insurance beneficiaries continuously enrolled from 2002 to 2005 (n = 147,892). Measures: Adjusted R 2 of 5 types of healthcare expenditures. Results: On the basis of naturally occurring patterns, we identified 6 morbidity trajectory groups. People assigned to different trajectory groups have distinct demographics and medical utilization. The effect of adding morbidity trajectory indicators differed substantially by the comprehensiveness of baseline risk-adjustment models: the increase in adjusted R 2 ranged from 0.3% in the most comprehensive model to 5.7% in the demographics model. Conclusions: A simple morbidity trajectory classification over a 3-year period is almost as powerful a predictor of prospective medical utilization as more comprehensive baseline risk adjusters. It may be unnecessary to construct longitudinal morbidity trajectories if a comprehensive baseline model was adopted, especially for healthcare systems without the stability of continuous enrollment.

Health Services Research, 2005

Objective. To conduct an empirical test of the relationship between physician supply and hospitalization for ambulatory care sensitive conditions (ACSH). Data Sources/Study Setting. A data set of county ACSH rates compiled by the Safety Net Monitoring Initiative of the Agency for Healthcare Research and Quality (AHRQ). The analytical data set consists of 642 urban counties and 306 rural counties. We supplemented the AHRQ data with data from the Area Resource File and the Environmental Protection Agency. Study Design. Ordinary least squares regression estimated ACSH predictors. Physician supply, the independent variable of interest in this analysis, was measured as a continuous variable (MDs/100,000). Urban and rural areas were modeled separately. Separate models were estimated for ages 0-17, 18-39, and 40-64. Data Extraction Methods. Data were limited to 20 states having more than 50 percent of counties with nonmissing data. Principal Findings. In the urban models for ages 0-17, standardized estimates indicate that, among the measured covariates in our model, physician supply has the largest negative adjusted relationship with ACSH (po.0001). For ages 18-39 and 40-64, physician supply has the second largest negative adjusted relationship with ACSH (po.0001, both age groups). Physician supply was not associated with ACSH in rural areas. Conclusions. Physician supply is positively associated with the overall performance of the primary health care system in a large sample of urban counties of the United States.

2013

BMC Health Services Research

BackgroundFrequent healthcare users place a significant burden on health systems. Factors such as multimorbidity and low socioeconomic status have been associated with high use of ambulatory care services (emergency rooms, general practitioners and specialist physicians). However, the combined effect of these two factors remains poorly understood. Our goal was to determine whether the risk of being a frequent user of ambulatory care is influenced by an interaction between multimorbidity and socioeconomic status, in an entire population covered by a universal health system.MethodsUsing a linkage of administrative databases, we conducted a population-based cohort study of all adults in Quebec, Canada. Multimorbidity (defined as the number of different diseases) was assessed over a two-year period from April 1st 2012 to March 31st 2014 and socioeconomic status was estimated using a validated material deprivation index. Frequents users for a particular category of ambulatory services ha...