Regional practice variation in hysterectomy and the implementation of less invasive surgical procedures: A register-based study in the Netherlands.

INTRODUCTION: Many women experience bleeding disorders that may have an anatomical or unexplained origin. Although hysterectomy is the most definitive and common treatment, it is highly invasive and resource-intensive. Less invasive therapies are therefore advised before hysterectomy for women with fibroids or bleeding disorders. This study has two aims related to treating bleeding disorders and uterine fibroids in the Netherlands: (1) to evaluate the regional variations in prevalence and surgical approaches; and (2) to assess the associations between regional rates of hysterectomies and less invasive surgical techniques to analyze whether hysterectomy can be replaced in routine practice. MATERIAL AND METHODS: We completed a register-based study of claims data for bleeding disorders and fibroids in women between 2016 and 2020 using data from Statistics Netherlands for case-mix adjustment. Crude and case-mix adjusted regional hysterectomy rates were examined overall and by surgical approach. Coefficients of variation were used to measure regional variation and regression analyses were used to evaluate the association between hysterectomy and less invasive procedure rates across regions. RESULTS: Overall, 14 186 and 8821 hysterectomies were performed for bleeding disorders and fibroids, respectively. Laparoscopic approaches predominated (bleeding disorders 65%, fibroids 49%), followed by vaginal (bleeding disorders 24%, fibroids 5%) and abdominal (bleeding disorders 11%, fibroids 46%) approaches. Substantial regional differences were noted in both hysterectomy rates and the surgical approaches. For bleeding disorders, regional hysterectomy rates were positively associated with endometrial ablation rates (ß = 0.11; P = 0.21) and therapeutic hysteroscopy rates (ß = 0.14; P = 0.31). For fibroids, regional hysterectomy rates were positively associated with therapeutic hysteroscopy rates (ß = 0.10; P = 0.34) and negatively associated with both embolization rates (ß = -0.08; P = 0.08) and myomectomy rates (ß = -0.03; P = 0.82). CONCLUSIONS: Regional variation exists in the rates of hysterectomy and minimally invasive techniques. The absence of a significant substitution effect provides no clear evidence that minimally invasive techniques have replaced hysterectomy in clinical practice. However, although the result was not significant, embolization could be an exception based on its stronger negative association.

Identification of over- and undertreatment in the Dutch national cervical cancer screening program: A data linkage study at the hospital level.

Research into the quality of cancer screening programs often lacks the perspective of clinicians, missing insights into the performance of individual hospitals. This retrospective cohort study aimed to identify guideline deviation (specifically, overtreatment and undertreatment) related to the cervical cancer screening program in Dutch hospitals by deterministically linking nationwide insurance data with pathology data for cervical intraepithelial neoplasia (CIN). We then constructed quality indicators using the Dutch CIN guideline and National Health Care Institute recommendations to assess compliance with CIN management, treatment outcomes, and follow-up, using an empirical Bayes shrinkage model to correct for case-mix variation and hospitals with few observations. Data were linked for 115,899 of 125,751 (92%) eligible women. Overtreatment was observed in the see-and-treat approach (immediate treatment) for women with low-grade referral cytology (4%; hospital range, 0%-25%), CIN ≤ 1 treatment specimens (26%; hospital range, 10%-55%), and follow-up cervix cytology ≥2 months before the guideline recommendation after treatment for CIN 2 (2%; hospital range, 0%-9%) or CIN 3 (5%; hospital range, 0%-19%). By contrast, undertreatment was observed for treatment within 3 months after a CIN 3 biopsy result (90%; hospital range 59%-100%) and follow-up ≥2 months beyond the guideline recommendation after treatments for CIN 2 (21%, hospital range 7%-48%) and CIN 3 (20%, hospital range 7%-90%). In conclusion, we found evidence of CIN overtreatment and undertreatment in all measured domains at the hospital level. Guideline adherence could be improved by implementing the developed indicators in an audit and feedback instrument for use by healthcare professionals in routine practice.

Do patients' preferences prevail in hospital selection?: a comparison between discrete choice experiments and revealed hospital choice.

BACKGROUND: In patient choice, patients are expected to select the provider that best fits their preferences. In this study, we assess to what extent the hospital choice of patients in practice corresponds with their preferred choice. METHODS: Dutch patients with breast cancer (n = 631) and cataract (n = 1109) were recruited. We employed a discrete choice experiment (DCE) per condition to measure stated preferences and predict the distribution of patients across four hospitals. Each DCE included five attributes: patient experiences, a clinical outcome indicator, waiting time, travel distance and whether the hospital had been recommended (e.g., by the General Practitioner (GP)). Revealed choices were derived from claims data. RESULTS: Hospital quality was valued as most important in the DCE; the largest marginal rates of substitution (willingness to wait) were observed for the clinical outcome indicator (breast cancer: 38.6 days (95% confidence interval (95%CI): 32.9-44.2); cataract: 210.5 days (95%CI: 140.8-280.2)). In practice, it was of lesser importance. In revealed choices, travel distance became the most important attribute; it accounted for 85.5% (breast cancer) and 95.5% (cataract) of the log-likelihood. The predicted distribution of patients differed from that observed in practice in terms of absolute value and, for breast cancer, also in relative order. Similar results were observed in population weighted analyses. DISCUSSION: Study findings show that patients highly valued quality information in the choice for a hospital. However, in practice these preferences did not prevail. Our findings suggest that GPs played a major role and that patients mostly ended up selecting the nearest hospital.

On the correlation between outcome indicators and the structure and process indicators used to proxy them in public health care reporting.

Hospital quality indicators provide valuable insights for quality improvement, empower patients to choose providers, and have become a cornerstone of value-based payment. As outcome indicators are cumbersome and expensive to measure, many health systems have relied on proxy indicators, such as structure and process indicators. In this paper, we assess the extent to which publicly reported structure and process indicators are correlated with outcome indicators, to determine if these provide useful signals to inform the public about the outcomes. Quality indicators for three conditions (breast and colorectal cancer, and hip replacement surgery) for Dutch hospitals (2011-2018) were collected. Structure and process indicators were compared to condition-specific outcome indicators and in-hospital mortality ratios in a between-hospital comparison (cross-sectional and between-effects models) and in within-hospital comparison (fixed-effects models). Systematic association could not be observed for any of the models. Both positive and negative signs were observed where negative associations were to be expected. Despite sufficient statistical power, the share of significant correlations was small [mean share: 13.2% (cross-sectional); 26.3% (between-effects); 13.2% (fixed-effects)]. These findings persisted in stratified analyses by type of hospital and in models using a multivariate approach. We conclude that, in the context of compulsory public reporting, structure and process indicators are not correlated with outcome indicators, neither in between-hospital comparisons nor in within-hospital comparisons. While structure and process indicators remain valuable for internal quality improvement, they are unsuitable as signals for informing the public about hospital differences in health outcomes.

On selecting quality indicators: preferences of patients with breast and colon cancers regarding hospital quality indicators.

BACKGROUND: There is an increasing number of quality indicators being reported publicly with aim to improve the transparency on hospital care quality. However, they are little used by patients. Knowledge on patients' preferences regarding quality may help to optimise the information presented to them. OBJECTIVE: To measure the preferences of patients with breast and colon cancers regarding publicly reported quality indicators of Dutch hospital care. METHODS: From the existing set of clinical quality indicators, participants of patient group discussions first assessed an indicator's suitability as choice information and then identified the most relevant ones. We used the final selection as attributes in two discrete choice experiments (DCEs). Questionnaires included choice vignettes as well as a direct ranking exercise, and were distributed among patient communities. Data were analysed using mixed logit models. RESULTS: Based on the patient group discussions, 6 of 52 indicators (breast cancer) and 5 of 21 indicators (colon cancer) were selected as attributes. The questionnaire was completed by 84 (breast cancer) and 145 respondents (colon cancer). In the patient group discussions and in the DCEs, respondents valued outcome indicators as most important: those reflecting tumour residual (breast cancer) and failure to rescue (colon cancer). Probability analyses revealed a larger range in percentage change of choice probabilities for breast cancer (10.9%-69.9%) relative to colon cancer (7.9%-20.9%). Subgroup analyses showed few differences in preferences across ages and educational levels. DCE findings partly matched with those of direct ranking. CONCLUSION: Study findings show that patients focused on a subset of indicators when making their choice of hospital and that they valued outcome indicators the most. In addition, patients with breast cancer were more responsive to quality information than patients with colon cancer.

The use of quality information by general practitioners: does it alter choices? A randomized clustered study.

BACKGROUND: Following the introduction of elements of managed competition in the Netherlands in 2006, General Practitioners (GPs) and patients were given the role to select treatment hospital using public quality information. In this study we investigate to what extent hospital preferences of GP's are affected by performance indicators on medical effectiveness and patient experiences. We selected three conditions: breast cancer, cataract surgery, and hip and knee replacement. METHODS: After an inquiry 26 out of 226 GPs in the region signed up to participate in our study. After a 2:1 randomization, we analyzed the referral patterns in the region using three groups of GPs: GPs (n=17) who used the report cards and received personal clarification, GPs that signed up for the study but were assigned to the control group (n=9), and the GPs outside the study (n=200).We conducted a difference in differences analysis where the choice for a particular hospital was the dependent variable and time (2009 or 2010), the sum score of the CQI, the sum score of the PI's and dummy variables for the individual hospitals were used as independent variables. RESULTS: The analysis of the conditions together and cataract surgery and hip and knee replacement separately, showed no significant relationships between the scores on the report cards and the referral patterns of the GPs. For breast cancer our analysis revealed that GPs in the intervention group refer 1.0% (p=0.01) more to hospitals that score one percent point better on the indicators for medical effectiveness. CONCLUSION: Our study provides empirical evidence that GP referral patterns were unaffected by the available quality information, except for the outcome indicators for breast cancer care that were presented. This finding was surprising since our study was designed to identify changes in hospital preference (1) amongst the most motivated GP's, (2) that received personal clarification of the performance indicators, and (3) selected indicators/conditions from a large set of indicators that they believed were most important. This finding may differ when quality information is based on outcome indicators with a clinically relevant difference, as shown by our indicators for breast cancer treatment. We believe that the current set of (largely process) hospital quality indicators do not serve the GP's information needs and consequently quality plays little role in the selection of hospitals for treatment.

Explaining the differences in income-related health inequalities across European countries.

This paper provides new evidence on the sources of differences in the degree of income-related inequalities in self-assessed health in 13 European Union member states. It goes beyond earlier work by measuring health using an interval regression approach to compute concentration indices and by decomposing inequality into its determining factors. New and more comparable data were used, taken from the 1996 wave of the European Community Household Panel. Significant inequalities in health (utility) favouring the higher income groups emerge in all countries, but are particularly high in Portugal and - to a lesser extent - in the UK and in Denmark. By contrast, relatively low health inequality is observed in the Netherlands and Germany, and also in Italy, Belgium, Spain Austria and Ireland. There is a positive correlation with income inequality per se but the relationship is weaker than in previous research. Health inequality is not merely a reflection of income inequality. A decomposition analysis shows that the (partial) income elasticities of the explanatory variables are generally more important than their unequal distribution by income in explaining the cross-country differences in income-related health inequality. Especially the relative health and income position of non-working Europeans like the retired and disabled explains a great deal of 'excess inequality'. We also find a substantial contribution of regional health disparities to socio-economic inequalities, primarily in the Southern European countries.