Clinic variation in glycaemic control for children with Type 1 diabetes in England and Wales: a population-based, multilevel analysis

Does size matter? Hospital volume and resource use in paediatric diabetes care

AIMS

Paediatric diabetes care has become increasingly specialised due to the multidisciplinary approach and technological developments. Guidelines recommend sufficient experience of treatment teams. This study evaluates associations between hospital volume and resource use and hospital expenditure in Dutch children with diabetes.

METHODS

Retrospective cohort study using hospital claims data of 5082 children treated across 44 Dutch hospitals (2019-2020). Hospitals were categorised into three categories; small (≥20-100 patients), medium (≥100-200 patients) and large (≥200 patients). All-cause hospitalisations, consultations, technology and hospital expenditure were analysed and adjusted for age, sex, socio-economic status (SES) and hospital of treatment.

RESULTS

Fewer hospitalisations were observed in large hospitals compared to small hospitals (OR 0.48; [95% CI 0.32-0.72]; p < 0.001). Median number of yearly paediatrician visits was 7 in large and 6 in small hospitals, the significance of which was attenuated in multilevel analysis (OR ≥7 consultations: 1.89; [95%CI 0.74-4.83]; p = 0.18). Technology use varies between individual hospitals, whereas pump usage and real-time continuous glucose monitoring showed no significant differences between hospital volumes. Mean overall expenditure was highest in medium-sized centres with €6434 per patient (IQR €2555-7955); the difference in diabetes care costs was not significant between hospital patient volumes.

CONCLUSIONS

Care provision patterns vary by hospital patient volume. Large hospitals had the lowest hospitalisation rates. The use of diabetes technology was not different between hospital patient volumes. Medium-sized hospitals showed the highest overall expenditure, but diabetes care costs were similar across hospital volumes.

Correlation between centre size, metabolic variation and mean HbA1c in major paediatric diabetes centres

AIM

To exploit a relatively homogeneous national health care context and a national diabetes database to address the questions: Is there an optimal clinic/centre size in determining outcomes?; and Can improvement in median centre outcomes be driven by reducing variability in outcome?

METHODS

Using the Australasian Diabetes Database Network, data from seven tertiary hospital paediatric diabetes clinics for patients with type one diabetes from Australia were recorded from 6-month uploads: September 2017, March 2018, September 2018 and March 2019. Data from 25 244 patient visits included demographic variables, HbA1C, number of patient visits and insulin regimens.

RESULTS

There was no association between centre size and median HbA1C. On the other hand, there was a significant association between or median absolute deviation of HbA1C outcomes and the median HbA1C result between centres. On average every two thirds of a median absolute deviation increase in clinic HbA1C was associated with a 1.0% (10.9 mmol/mol) increase in median clinic HbA1C.

CONCLUSIONS

Our data have shown that it is likely difficult for centres to have a low median HbA1C if there is high variance of HbA1C's within centres or within centre treatment groups. This appears to be true regardless of centre size. These findings need to be carefully considered by teams who wish to lower their clinic median HbA1C.

Between-hospital variation in indicators of quality of care: a systematic review

BACKGROUND

Efforts to mitigate unwarranted variation in the quality of care require insight into the 'level' (eg, patient, physician, ward, hospital) at which observed variation exists. This systematic literature review aims to synthesise the results of studies that quantify the extent to which hospitals contribute to variation in quality indicator scores.

METHODS

Embase, Medline, Web of Science, Cochrane and Google Scholar were systematically searched from 2010 to November 2023. We included studies that reported a measure of between-hospital variation in quality indicator scores relative to total variation, typically expressed as a variance partition coefficient (VPC). The results were analysed by disease category and quality indicator type.

RESULTS

In total, 8373 studies were reviewed, of which 44 met the inclusion criteria. Casemix adjusted variation was studied for multiple disease categories using 144 indicators, divided over 5 types: intermediate clinical outcomes (n=81), final clinical outcomes (n=35), processes (n=10), patient-reported experiences (n=15) and patient-reported outcomes (n=3). In addition to an analysis of between-hospital variation, eight studies also reported physician-level variation (n=54 estimates). In general, variation that could be attributed to hospitals was limited (median VPC=3%, IQR=1%-9%). Between-hospital variation was highest for process indicators (17.4%, 10.8%-33.5%) and lowest for final clinical outcomes (1.4%, 0.6%-4.2%) and patient-reported outcomes (1.0%, 0.9%-1.5%). No clear pattern could be identified in the degree of between-hospital variation by disease category. Furthermore, the studies exhibited limited attention to the reliability of observed differences in indicator scores.

CONCLUSION

Hospital-level variation in quality indicator scores is generally small relative to residual variation. However, meaningful variation between hospitals does exist for multiple indicators, especially for care processes which can be directly influenced by hospital policy. Quality improvement strategies are likely to generate more impact if preceded by level-specific and indicator-specific analyses of variation, and when absolute variation is also considered.

PROSPERO REGISTRATION NUMBER

CRD42022315850.

Size matters: Influence of center size on quality of diabetes control in children and adolescents with type 1 diabetes-A longitudinal analysis of the DPV cohort

BACKGROUND

Treatment of patients with type 1 diabetes requires experience and a specific infrastructure. Therefore, center size might influence outcome in diabetes treatment.

OBJECTIVE

To analyze the influence of center size on the quality of diabetes treatment in children and adolescents in Germany and Austria.

PATIENTS AND METHODS

In 2009 and 2018, we analyzed metabolic control, acute complications, and rates of recommended screening tests in the DPV cohort. Diabetes centers were classified according to the number of patients from "XS" to "XL" (<20 [XS], ≥20 to <50 [S], ≥50 to <100 [M], ≥100 to <200 [L], ≥200 [XL]).

RESULTS

Over the 10-year period, metabolic control improved significantly in "M", "L" and "XL" diabetes centers. Treatment targets are best achieved in "M" centers, while "XS" centers have the highest mean hemoglobin A1c. The relation between hemoglobin A1c and center size follows a "v-shaped" curve. In 2009, conventional insulin therapy was most frequently used in "XS" centers, but in 2018, there was no difference in mode of insulin therapy according to center size. Use of CSII and sensor augmented CSII/hybrid closed loop increased with center size. Patients cared for in "XS" diabetes centers had the fewest follow-up visits per year. The rates of severe hypoglycemia and DKA were lowest in "XL" diabetes centers, and the rate of DKA was highest in "XS" centers.

CONCLUSION

Center size influences quality of care in pediatric patients with type 1 diabetes. Further investigations regarding contributing factors such as staffing and financial resources are required.

Clinic and patient variation in intermediate clinical outcomes for type 2 diabetes: a multilevel analysis

Background

Variation at different levels of diabetes care has not yet been quantified for low- and middle-income countries. Understanding this variation and its magnitude is important to guide policy makers in designing effective interventions. This study aims to quantify the variation in the control of glycated haemoglobin (HbA1c), systolic blood pressure (SBP) and low-density lipoprotein cholesterol (LDL-C) for type 2 diabetes (T2D) patients at the clinic and patient level and determine patient and clinic factors associated with control of these outcomes in T2D.

Methods

This is a cross-sectional study within the baseline data from the impact evaluation of the Enhanced Primary Health Care (EnPHC) intervention on 40 public clinics in Malaysia. Patients aged 30 and above, diagnosed with T2D, had a clinic visit for T2D between 01 Nov 2016 and 30 April 2017 and had at least one HbA1c, SBP and LDL-C measurement within 1 year from the date of visit were included for analysis. Multilevel linear regression adjusting for patient and clinic characteristics was used to quantify variation at the clinic and patient levels for each outcome.

Results

Variation in intermediate clinical outcomes in T2D lies predominantly (93% and above) at the patient level. The strongest predictors for poor disease control in T2D were the proxy measures for disease severity including duration of diabetes, presence of microvascular complications, being on insulin therapy and number of antihypertensives. Among the three outcomes, HbA1c and LDL-C results provide greatest opportunity for improvement.

Conclusion

Clinic variation in HbA1c, SBP and LDL-C accounts for a small percentage from total variation. Findings from this study suggest that standardised interventions need to be applied across all clinics, with a focus on customizing therapy based on individual patient characteristics.