Access, use, and challenges of claims data analyses in Germany The American Heart Association's recommendations for expanding the applications of existing and future clinical registries: a policy statement from the American Heart Association. False Dichotomies and Health Policy Research Designs: Randomized Trials Are Not Always the Answer National policy development for cotrimoxazole prophylaxis in Malawi, Uganda and Zambia: the relationship between Context, Evidence and Links Does health insurance improve health?: Evidence from a randomized community-based insurance rollout in rural Burkina Faso. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. The role for pragmatic randomized controlled trials (pRCTs) in comparative effectiveness research
Worldwide , information infrastructures have developed rapidly towards big data over the past few years . The evolution of powerful computers has made it possible to improve the data exchange process within the healthcare system [ 1 ] . This technological trend has also triggered new and powerful possibilities in health service-related research ( e.g. , cl aims data analyses ) , because it now allows the analysis of more complex and larger data sets . In this editorial , we focus on cl aims data analyses , because they are an important and powerful source of information to support the decision-making processes of healthcare stakeholders , research ers , and policy-makers regarding various aspects of the healthcare system . A few years ago , the statutory health insurance ( SHI ) companies in Germany made their data available for research purpose s , because they realized the comprehensive potential of these data for both rational allocation of re sources and for health services research to optimize healthcare provisions . The German government increasingly supports and funds cl aims data research , thus confirming the growing relevance of such research . Furthermore , recently , this research field developed a sophisticated method ological framework for comprehensively analyzing and interpreting the data in the context of various research questions . The significant increase in the number of cl aims data studies ( and frequency of policy-relevant research ) confirms this trend [ 2 , 3 ] ( see Fig. 1 ) . Cl aims data studies mainly use the information stored in data warehouses of the SHI . As a form of administrative data , primarily collected for billing and reimbursement purpose s , cl aims data belong to the category of secondary data . These data are transmitted directly from healthcare providers to SHI funds , which include information on ( all ) cross-sector contacts between the insured and the healthcare system in their data base . Compared with clinical trials , cl aims data analyses could reflect real-life healthcare provisions . Further advantages of this data source are the costefficient generation of data and a typically large study population compared with r and omized controlled trials , which research ers often view as the gold st and ard for providing information on treatment efficacy and safety in a clinical setting . These data also help include groups that are typically more difficult to observe through primary data collection ( e.g. , children , severely ill individuals , dementia patients , or residents of nursing homes ) . Over the last 15 years , almost one-third of the published cl aims data studies have focused on health economics [ 2 ] . Furthermore , due to its advantages , research ers have previously used this data source for health services research on treatment patterns , under and overtreatment of specific diseases , epidemiology studies , and as an input factor for modeling studies [ 4 ] . The aim of all these studies was to increase transparency and efficiency of the healthcare system by analyzing real-world evidence . Moreover , the latest national call for proposals confirms that there is both an increase in the importance of and positive development in cl aims data research . The German government and the Federal Joint Committee ( Gemeinsamer Bundesausschuss , G-BA ) established the ‘ Innovation Fund ’ , which will provide funding of 75 million euros per year for projects dealing with health service research over a period of 4 years . In such projects , it is & Sarah Neubauer Clinical registries play an important role in measuring healthcare delivery and supporting quality improvement for individuals with cardiovascular disease and stroke . Well- design ed clinical registry programs provide important mechanisms to monitor patterns of care , evaluate healthcare effectiveness and safety , and improve clinical outcomes . The use of clinical registries is likely to grow given the increasing focus on measuring and improving healthcare delivery and patient outcomes by stakeholders in both the private and public sectors . The American Heart Association ( AHA ) has a longst and ing commitment to promoting the innovative and effective use of clinical registries . The importance of clinical registries was highlighted recently in an AHA Scientific Statement on “ Essential Features of a Surveillance System to Support the Prevention and Management of Heart Disease and Stroke ” in the United States.1 This policy statement exp and s on the previous scientific statement by providing recommendations to policy makers and the healthcare community for expansion of the applications of existing and future clinical registries . The term “ clinical registry ” is defined here as an observational data base of a clinical condition , procedure , therapy , or population in which there are often no registry-m and ated approaches to therapy and relatively few inclusion or exclusion criteria . The focus of clinical registries is to capture data that reflect “ real-world ” clinical practice in large patient population s. The data from clinical registries do not replace the need for traditional r and omized controlled trials . Rather , registries and trials are complementary approaches , each with unique advantages and imperfections.2 Such clinical registries do not solely contain cl aims or administrative data yet may be linked to such data sources . There are at least 3 classifications of clinical registries based on the patient population , including procedure/therapy/encounter-based , disease-based , and population -based registries . Registries also can be classified from a functional perspective , such as whether the registry is used to conduct clinical research , Some medical scientists argue that only data from r and omized controlled trials ( RCTs ) are trustworthy . They cl aim data from natural experiments and administrative data sets are always spurious and can not be used to evaluate health policies and other population -wide phenomena in the real world . While many acknowledge biases caused by poor study design s , in this article we argue that several valid design s using administrative data can produce strong findings , particularly the interrupted time series ( ITS ) design . Many policy studies neither permit nor require an RCT for cause- and -effect inference . Framing our arguments using Campbell and Stanley ’s classic research design monograph , we show that several “ quasi-experimental ” design s , especially interrupted time series ( ITS ) , can estimate valid effects ( or non-effects ) of health interventions and policies as diverse as public insurance coverage , speed limits , hospital safety programs , drug abuse regulation and withdrawal of drugs from the market . We further note the recent rapid uptake of ITS and argue for exp and ed training in quasi-experimental design s in medical and graduate schools and in post-doctoral curricula Background Several frameworks have been constructed to analyse the factors which influence and shape the uptake of evidence into policy processes in re source poor setting s , yet empirical analyses of health policy making in these setting s are relatively rare . National policy making for cotrimoxazole ( trimethoprim-sulfamethoxazole ) preventive therapy in developing countries offers a pertinent case for the application of a policy analysis lens . The provision of cotrimoxazole as a prophylaxis is an inexpensive and highly efficacious preventative intervention in HIV infected individuals , reducing both morbidity and mortality among adults and children with HIV/AIDS , yet evidence suggests that it has not been quickly or evenly scaled-up in re source poor setting s. Methods Comparative analysis was conducted in Malawi , Ug and a and Zambia , using the case study approach . We applied the ‘ RAPID ’ framework developed by the Overseas Development Institute ( ODI ) , and conducted a total of 47 in-depth interviews  across the three countries to examine the influence of context ( including the influence of donor agencies ) , evidence ( both local and international ) , and the links between research er , policy makers and those seeking to influence the policy process . Results Each area of analysis was found to have an influence on the creation of national policy on cotrimoxazole preventive therapy ( CPT ) in all three countries . In relation to context , the following were found to be influential : government structures and their focus , donor interest and involvement , healthcare infrastructure and other uses of cotrimoxazole and related drugs in the country . In terms of the nature of the evidence , we found that how policy makers perceived the strength of evidence behind international recommendations was crucial ( if evidence was considered weak then the recommendations were rejected ) . Further , local operational research results seem to have been taken up more quickly , while r and omised controlled trials ( the gold st and ard of clinical research ) was not necessarily translated into policy so swiftly . Finally the links between different research and policy actors were of critical importance , with overlaps between research er and policy maker networks crucial to facilitate knowledge transfer . Within these networks , in each country the policy development process relied on a powerful policy entrepreneur who helped get cotrimoxazole preventive therapy onto the policy agenda . Conclusions This analysis underscores the importance of considering national level variables in the explanation of the uptake of evidence into national policy setting s , and recognising how local policy makers interpret international evidence . Local priorities , the ways in which evidence was interpreted , and the nature of the links between policy makers and research ers could either drive or stall the policy process . Developing the underst and ing of these processes enables the explanation of the use ( or non-use ) of evidence in policy making , and potentially may help to shape future strategies to bridge the research -policy gaps and ultimately improve the uptake of evidence in decision making From 2004 to 2006 , a  community-based health insurance ( CBI ) scheme was rolled out in Nouna District , Burkina Faso , with the objective of improving access to health services and population health . We explore the r and om timing of the insurance rollout generated by the stepped wedge cluster-r and omized design to evaluate the welfare and health impact of the insurance program . Our results suggest that the insurance had limited effects on average out-of-pocket expenditures in the target areas , but substantially reduced the likelihood of catastrophic health expenditure . The introduction of the insurance scheme did not have any effect on health outcomes for children and young adults , but appears to have increased mortality among individuals aged 65 and older . The negative health effects of the program appear to be primarily driven by the adverse provider incentives generated by the scheme and the result ing decline in the quality of care received by patients OBJECTIVES Health decision-makers involved with coverage and payment policies are increasingly developing policies that seek information on " real-world " ( RW ) outcomes . Motivated by these initiatives , the International Society for Pharmacoeconomics and Outcomes Research ( ISPOR ) created a Task Force on Real-World Data to develop a framework to assist health-care decision-makers in dealing with RW data , especially related to coverage and payment decisions . METHODS Task Force cochairs were selected by the ISPOR Board of Directors . Cochairs selected chairs for four working groups on : clinical outcomes , economic outcomes , patient-reported outcomes , and evidence hierarchies . Task Force members included representatives from academia , the pharmaceutical industry , and health insurers . The Task Force met on several occasions , conducted frequent correspondence and exchanges of drafts , and solicited comments on three drafts from a core group of external review ers and from the ISPOR membership . RESULTS We defined RW data as data used for decision-making that are not collected in conventional r and omized controlled trials ( RCTs ) . We considered several characterizations : by type of outcome ( clinical , economic , and patient-reported ) , by hierarchies of evidence ( which rank evidence according to the strength of research design ) , and by type of data source ( supplementary data collection alongside RCTs , large simple trials , patient registries , administrative cl aims data base , surveys , and medical records ) . Our report discusses eight key issues : 1 ) the importance of RW data ; 2 ) limitations of RW data ; 3 ) the fact that the level of evidence required depends on the circumstance ; 4 ) the need for good research practice s for collecting and reporting RW data ; 5 ) the need for good process in using RW data in coverage and reimbursement decisions ; 6 ) the need to consider costs and benefits of data collection ; 7 ) the ongoing need for modeling ; and 8) the need for continued stakeholder dialogue on these topics . CONCLUSIONS Real-world data are essential for sound coverage and reimbursement decisions . The types and applications of such data are varied , and context matters greatly in determining the value of a particular type in any circumstance . It is critical that policymakers recognize the benefits , limitations , and method ological challenges in using RW data , and the need to consider carefully the costs and benefits of different forms of data collection in different situations There is a growing appreciation that our current approach to clinical research leaves important gaps in evidence from the perspective of patients , clinicians , and payers wishing to make evidence -based clinical and health policy decisions . This has been a major driver in the rapid increase in interest in comparative effectiveness research ( CER ) , which aims to compare the benefits , risks , and sometimes costs of alternative health-care interventions in ‘ the real world ’ . While a broad range of experimental and nonexperimental methods will be used in conducting CER studies , many important questions are likely to require experimental approaches – that is , r and omized controlled trials ( RCTs ) . Concerns about the generalizability , feasibility , and cost of RCTs have been frequently articulated in CER method discussion s. Pragmatic RCTs ( or ‘ p RCTs ’ ) are intended to maintain the internal validity of RCTs while being design ed and implemented in ways that would better address the dem and for evidence about real-world risks and benefits for informing clinical and health policy decisions . While the level of interest and activity in conducting p RCTs is increasing , many challenges remain for their routine use . This article discusses those challenges and offers some potential ways forward
INTERPRETATION Increased health insurance coverage generally appears to increase access to health care facilities , improve financial protection and improve health status , although findings are not totally consistent .
BACKGROUND Exp and ing public health insurance seeks to attain several desirable objectives , including increasing access to healthcare services , reducing the risk of catastrophic healthcare expenditures , and improving health outcomes .
The extent to which these objectives are met in a real-world policy context remains an empirical question of increasing research and policy interest in recent years .