Dataset Information

Exploring variation in low-value care: a multilevel modelling study.

ABSTRACT:

Background

Whether patients receive low-value hospital care (care that is not expected to provide a net benefit) may be influenced by unmeasured factors at the hospital they attend or the hospital's Local Health District (LHD), or the patients' areas of residence. Multilevel modelling presents a method to examine the effects of these different levels simultaneously and assess their relative importance to the outcome. Knowing which of these levels has the greatest contextual effects can help target further investigation or initiatives to reduce low-value care.

Methods

We conducted multilevel logistic regression modelling for nine low-value hospital procedures. We fit a series of six models for each procedure. The baseline model included only episode-level variables with no multilevel structure. We then added each level (hospital, LHD, Statistical Local Area [SLA] of residence) separately and used the change in the c statistic from the baseline model as a measure of the contribution of the level to the outcome. We then examined the variance partition coefficients (VPCs) and median odds ratios for a model including all three levels. Finally, we added level-specific covariates to examine if they were associated with the outcome.

Results

Analysis of the c statistics showed that hospital was more important than LHD or SLA in explaining whether patients receive low-value care. The greatest increases were 0.16 for endoscopy for dyspepsia, 0.13 for colonoscopy for constipation, and 0.14 for sentinel lymph node biopsy for early melanoma. SLA gave a small increase in c compared with the baseline model, but no increase over the model with hospital. The VPCs indicated that hospital accounted for most of the variation not explained by the episode-level variables, reaching 36.8% (95% CI, 31.9-39.0) for knee arthroscopy. ERCP (8.5%; 95% CI, 3.9-14.7) and EVAR (7.8%; 95% CI, 2.9-15.8) had the lowest residual variation at the hospital level. The variables at the hospital, LHD and SLA levels that were available for this study generally showed no significant effect.

Conclusions

Investigations into the causes of low-value care and initiatives to reduce low-value care might best be targeted at the hospital level, as the high variation at this level suggests the greatest potential to reduce low-value care.

SUBMITTER: Badgery-Parker T

PROVIDER: S-EPMC6543591 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Publications

Exploring variation in low-value care: a multilevel modelling study.

Badgery-Parker Tim T Feng Yingyu Y Pearson Sallie-Anne SA Levesque Jean-Frederic JF Dunn Susan S Elshaug Adam G AG

BMC health services research 20190530 1

<h4>Background</h4>Whether patients receive low-value hospital care (care that is not expected to provide a net benefit) may be influenced by unmeasured factors at the hospital they attend or the hospital's Local Health District (LHD), or the patients' areas of residence. Multilevel modelling presents a method to examine the effects of these different levels simultaneously and assess their relative importance to the outcome. Knowing which of these levels has the greatest contextual effects can h ...[more]

PMID: 31146744

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Project description:Background Ethiopia has low malaria prevalence compared to most other malaria-endemic countries in Africa. However, malaria is still a major public health problem in the country. The binary logistic regression model has been widely used to analyse malaria indicator survey (MIS) data. However, most MIS have a hierarchical structure which may result in dependent data. Since this model assumes that conditional on the covariates the malaria statuses of individuals are independent, it ignores potential intra-cluster correlation among observations within a cluster and may generate biased analysis results and conclusions. Therefore, the aim of this study was to quantify the variation in the prevalence of malaria between sample enumeration areas (SEAs) or clusters, the effects of cluster characteristics on the prevalence of malaria using the intra-class correlation coefficient as well as to identify significant factors that affect the prevalence of malaria using the multilevel logistic regression modelling in three major regions of Ethiopia, namely Amhara, Oromia and Southern Nations, Nationalities and Peoples’ (SNNP). Methods Dataset for three regional states extracted from the 2011 Ethiopian National Malaria Indicator Surveys (EMIS) national representative samples was used in this study. It contains 9272 sample individuals selected from these regions. Various multilevel models with random sample SEA effects were applied taking into account the survey design weights. These weights are scaled to address unequal probabilities of selection within clusters. The spatial clustering of malaria prevalence was assessed applying Getis-Ord statistic to best linear unbiased prediction values of model random effects. Results About 53.82 and 28.72 per cents of the sampled households in the study regions had no mosquito net and sprayed at least once within the last 12 months, respectively. The results of this study indicate that age, gender, household had mosquito nets, the dwelling has windows, source of drinking water, the two SEA-level variables, i.e. region and median altitude, were significantly related to the prevalence of malaria. After adjusting for these seven variables, about 45% of the residual variation in the prevalence of malaria in the study regions was due to systematic differences between SEAs, while the remaining 55% was due to unmeasured differences between persons or households. The estimated MOR, i.e. the unexplained SEA heterogeneity, was 4.784. This result suggests that there is high variation between SEAs in the prevalence of malaria. In addition, the 80% interval odds ratios (IORs) related to SEA-level variables contain one suggesting that the SEA variability is large in comparison with the effect of each of the variable. Conclusions The multilevel logistic regression with random effects model used in this paper identified five individual / household and two SEA-level risk factors of malaria infection. Therefore, the public health policy makers should pay attentions to those significant factors, such as improving the availability of pure drinking water. Further, the findings of spatial clustering provide information to health policymakers to plan geographically targeted interventions to control malaria transmission.

Project description:Study questionHow much variation in oocyte yield after controlled ovarian stimulation (COS) can be accounted for by known patient and treatment characteristics?Summary answerThere is substantial variation in the COS responses of similar women and in repeated COS episodes undertaken by the same woman, which cannot be accounted for at present.What is already knownThe goal of individualized COS is to safely collect enough oocytes to maximize the chance of success in an ART cycle. Personalization of treatment rests on the ability to reduce variation in response through modifiable factors.Study design size durationMultilevel modelling of a routine ART database covering the period 1 October 2008-8 August 2012 was employed to estimate the amount of variation in COS response and the extent to which this could be explained by immutable patient characteristics and by manipulable treatment variables. A total of 1851 treatment cycles undertaken by 1430 patients were included. The study was not subject to attrition, as cancelled cycles were included in the analysis.Participants/materials setting methodsWomen aged 21-43 years undergoing ovarian stimulation for IVF (possibly with ICSI) using their own eggs at a tertiary care centre.Main results and the role of chanceSubstantial unexplained variation in COS response (oocyte yield): was observed (3.4-fold (95% CI: 3.12 to 3.61)). Only a relatively small amount of this variation (around 19%) can be explained by modifiable factors. A significant, previously undescribed predictor of response was the practitioner performing oocyte retrieval, with 1.5-fold variation between surgeons with the highest and lowest yields.Limitations reasons for cautionAlthough a large number of covariables were adjusted for in the analysis, including those that were used for dosing and determination of the stimulation regimen, this study is subject to confounding due to unmeasured variables and measurement error.Wider implications of the findingsThe present study suggests that there are limits to the extent that COS response can be predicted on the basis of known factors, or controlled by manipulation of treatment factors. Moreover, modifiable variation in response appears to be partially attributable to differences between surgeons performing oocyte retrieval. Consequently, consistent prevention of ineffective or unsafe responses to COS is not likely to be possible at present. Our results highlight the importance of blinding surgeons in RCTs. The data also suggest that there is likely to be limited scope for personalized treatment unless additional predictors of ovarian response can be identified.Study funding/competing interestsJ.W. is funded by a Doctoral Research Fellowship from the National Institute for Health Research (DRF-2014-07-050) supervised by S.A.R. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. J.W. is a statistical editor of the Cochrane Gynaecology and Fertility Group. S.A.R. is a statistical editor for Human Reproduction. J.W. also declares that publishing peer-reviewed articles benefits his career. A.L.M. has received consultation fees from MSD, Merck Serono, Ferring, TEVA, Roche, Beckman Coulter.

Project description:ObjectiveTo explore whether responses to questions in surveys of patients that purport to assess the performance of general practices or doctors reflect differences between practices, doctors, or the patients themselves.DesignSecondary analysis of data from a study of access to general practice, combining data from a survey of patients with information about practice organisation and doctors consulted, and using multilevel modelling at practice, doctor, and patient level.SettingNine primary care trusts in England.Participants4573 patients who consulted 150 different doctors in 27 practices.Main outcome measuresOverall satisfaction; experience of wait for an appointment; reported access to care; satisfaction with communication skills.ResultsThe experience based measure of wait for an appointment was more discriminating between practices (practice level accounted for 20.2% (95% confidence interval 9.1% to 31.3%) of variance) than was the overall satisfaction measure (practice level accounted for 4.6% (1.6% to 7.6%) of variance). Only 6.3% (3.8% to 8.9%) of the variance in the doctors' communication skills measure was due to differences between doctors; 92.4% (88.5% to 96.4%) of the variance occurred at the level of the patient (including differences between patients' perceptions and random variation). At least 79% of the variance on all measures occurred at the level of the patient, and patients' age, sex, ethnicity, and housing and employment status explained some of this variation. However, adjustment for patients' characteristics made very little difference to practices' scores or the ranking of individual practices.ConclusionsAnalyses of surveys of patients should take account of the hierarchical nature of the data by using multilevel models. Measures related to patients' experience discriminate more effectively between practices than do measures of general satisfaction. Surveys of patients' satisfaction fail to distinguish effectively between individual doctors because most of the variation in doctors' reported performance is due to differences between patients and random error rather than differences between doctors. Although patients' reports of satisfaction and experience are systematically related to patients' characteristics such as age and sex, the effect of adjusting practices' scores for the characteristics of their patients is small.

Project description:Despite the importance of identifying and reducing wasteful health care use, few direct measures of overuse have been developed. Direct measures are appealing because they identify specific services to limit and can characterize low-value care even among the most efficient providers.To develop claims-based measures of low-value services, examine service use (and associated spending) detected by these measures in Medicare, and determine whether patterns of use are related across different types of low-value services.Drawing from evidence-based lists of services that provide minimal clinical benefit, we developed 26 claims-based measures of low-value services. Using 2009 claims for 1,360,908 Medicare beneficiaries, we assessed the proportion of beneficiaries receiving these services, mean per-beneficiary service use, and the proportion of total spending devoted to these services. We compared the amount of use and spending detected by versions of these measures with different sensitivity and specificity. We also estimated correlations between use of different services within geographic areas, adjusting for beneficiaries' sociodemographic and clinical characteristics.Use and spending detected by 26 measures of low-value services in 6 categories: low-value cancer screening, low-value diagnostic and preventive testing, low-value preoperative testing, low-value imaging, low-value cardiovascular testing and procedures, and other low-value surgical procedures.Services detected by more sensitive versions of measures affected 42% of beneficiaries and constituted 2.7% of overall annual spending. Services detected by more specific versions of measures affected 25% of beneficiaries and constituted 0.6% of overall spending. In adjusted analyses, low-value spending detected in geographic regions at the 5th percentile of the regional distribution of low-value spending ($227 per beneficiary) exceeded the difference in detected low-value spending between regions at the 5th and 95th percentiles ($189 per beneficiary). Adjusted regional use was positively correlated among 5 of 6 categories of low-value services (mean r for pairwise, between-category correlations, 0.33; range, 0.14-0.54; P???.01).Services detected by a limited number of measures of low-value care constituted modest proportions of overall spending but affected substantial proportions of beneficiaries and may be reflective of overuse more broadly. Performance of claims-based measures in supporting targeted payment or coverage policies to reduce overuse may depend heavily on how the measures are defined.

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Dataset Information

Exploring variation in low-value care: a multilevel modelling study.

Background

Methods

Results

Conclusions

Publications

Exploring variation in low-value care: a multilevel modelling study.

Similar Datasets

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