Carry out a Repository for Administrative and Clinical data Knotting

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1 Centro di Studio e Ricerca sulla Sanità Pubblica A scientific alliance for Carry out a Repository for Administrative and Clinical data Knotting Evaluating management and building evidence from real world health data: Lombardy Region, Italy Version: October 3, 2013 dei progetti in corso. Autore Giovanni Corrao - 1

2 dei progetti in corso. Autore Giovanni Corrao - 2

3 Table of contents 1. Preliminary remarks Page 9 2. Investigating real world clinical practice Insufficiency of clinical trials From clinical trials to observational investigations The baseline observational design The comparative effectiveness principle Potentiality of observational approach Using electronic archives Warranting good research practice Rationale Aims and articulation Strategic target General methodological purpose Clinical and public health purposes Health econometrics and health demand Articulation Common data framework and repository functioning (Working Package 1) dei progetti in corso. Autore Giovanni Corrao - 3

4 6. Clinical issues (Working Package 2) Cardiovascular disease Project CV1. Measuring burden of heart failure: prevalence, incidence and therapeutic approaches Project CV2. Measuring the economic burden of acute myocardial infarction. 44 Project CV3. Assessing the gap between trials and practice: the example of hypertensive and lipid lowering drug therapies Project CV4. Estimating cost-effectiveness of time-varying drug therapy using healthcare administrative databases. The case of statin in secondary prevention.. 54 Project CV5. Exploring the impact of prescribing and substituting generic drugs: the example of cardiovascular therapies. 58 Project CV6. Utilization, outcomes and costs of implantable cardioverter defibrillator (ICD).. 64 Project CV7. Electrocardiographic waveform analysis to predict the success of defibrillation in human victims of out-of hospital cardiac arrest and the association with in hospital outcomes and survival Diabetes Project DB1. Assessing adherence, long-term safety and costeffectiveness profiles of drug therapies for type 2 diabetes in clinical practice Project DB2. Measuring the healthcare burden of diabetes mellitus.. 81 Project DB3. Assessing the gap between guidelines and practice: the example of gestational diabetes Oncology Project k1. Clinical use, safety and effectiveness of novel high cost anticancer therapies after marketing approval: a record linkage study Respiratory diseases Project RD1. Measuring burden of chronic obstructive pulmonary disease: prevalence, incidence and therapeutic approaches dei progetti in corso. Autore Giovanni Corrao - 4

5 Project RD2. The clinical and economic burden of patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease. 98 Project RD3. Measuring the healthcare burden associated with individuals hospitalized for pneumonia Gastroenterointestinal diseases Project GID1. Measuring the burden of ulcerative colitis and Crohn s disease: incidence and costs Mental health Project MH1. Adherence, effectiveness and cost-effectiveness profiles of care journeys experienced from patients affected by severe mental disturbance Geriatrics Project GH1 Drug treatment of elderly patients affected by cardiovascular disease and other chronic comorbidities Paediatrics Project PD1 Hospitalization for pneumonia and empyema: incidence and association with pneumococcal conjugate vaccines and non-steroidal antiinflammatory drugs Environmental health Project EH1. Epidemiology of idiopathic pulmonary fibrosis (IPF) in Lombardy region and its relationship to air pollution 127 Project EH2. Cardioresopiratory diseases and environmental exposure: particulate matter toxicity and molecular risk markers (TOSCA). 132 Project EH3. Short-term effect of the exposure to particulate matter on hospitalizations and pharmaceutical prescriptions in Lombardy Project EH4. Exposure to particulate matter: short-term effect on stroke hospitalizations in Milan using ARPA monitoring stations Drug safety Project DS2. MEREaFAPS project: monitoring of events and adverse drug reactions in emergency department. Evaluation of preventable 150 dei progetti in corso. Autore Giovanni Corrao - 5

6 reactions and costs of Adverse Drug Reactions. Project DS2. Gastrointestinal and cardiovascular safety profiles of non-steroidal anti-inflammatory drugs (NSAIDs) Other issues (miscellaneous) Project OI1. Estimating prevalence and incidence rates of rheumatoid arthritis from regional healthcare databases Project OI2. Risk/benefit profile of bisphosphonates therapy in primary/secondary prevention of osteoporotic fractures Health econometrics and health demand (Working Package 3) Project HE1 CRISP Project HEP2. Administrative databases as a tool for identifying healthcare demand and costs in an over-one million population Educational issues (Working Package 4) Scientific board and accredited laboratories Scientific board Project managers Laboratories 185 APPENDIX 1. Main characteristics of available healthcare utilization database covering the entire population of Lombardy Region. 187 Health register Hospital discharge database. 189 Emergency room database 190 Outpatient drug prescriptions database 191 dei progetti in corso. Autore Giovanni Corrao - 6

7 File F. 192 Exemptions database 193 Outpatients specialistic services database 194 Data warehouse DENALI Mental health services (PSICHE) database Delivery assistance forms database Vaccination register Pharmacovigilance Database 201 APPENDIX 2. Methods for controlling misclassification and confounding 203 A2.1. Measurement errors and misclassification. 204 A Outcome misclassification 204 A Exposure misclassification A Strategies of accounting for misclassification A Algebraic methods A Sensitivity analyses. 207 A External adjustment 207 A2.2. Confounding and beyond A Sources of confounding 210 A Strategies of accounting for confounding: a general guide A Accounting for confounders through study design A Restricting the study cohort 211 A Matching. 212 A Case-only designs A Accounting for confounding through data analysis A Stratification and regression modelling A Using proxy measures. 215 dei progetti in corso. Autore Giovanni Corrao - 7

8 A Sensitivity analysis. 216 A Instrumental variable estimation. 219 A Beyond confounding 220 A Yet on the confounding definition A Intermediate variables and overadjustment 221 A Collider variables 222 A2.References APPENDIX 3. Curricula vitae and recent pubblications concerning the issues of scientific board members Giovanni Corrao Alberico Catapano Giancarlo Cesana Carlo La Vecchia Giorgio Vittadini 241 Giuseppe Mancia APPENDIX 4. Staff members, potentiality and main characteristics of accredited LABORATORIES affering to the PHARMACOEPIDEMIOLOGY AND HEALTHCARE RESEARCH EPIDEMIOLOGY AND PUBLIC HEALTH REASERCH 247 EPIDEMIOLOGY 249 HEALTH ECONOMETRICS AND HEALTH DEMAND. 250 PHARMACOVIGILANCE AND DRUG UTILISATION RESEARCH 252 dei progetti in corso. Autore Giovanni Corrao - 8

9 1. Preliminary remarks The objective of the is of implementing a repository through the integration of different data sources for addressing questions still open in the setting of diseases relevant for clinics and public health. We would like to emphasize that the repository does not imply the transferring the data from the place where they are stored from their owners (e.g. Lombardy Region or Italian Society of General Medicine), to a unique warehouse. Rather, the is a proposal for data drawing from several sources according to a specific protocol approved from the regional authority and the scientific board. The questions we would like to answer through the use of the repository are related to diseases frequency and prognosis (prevalence, incidence, survival), healthcare utilization patterns and safety, effectiveness and cost-effectiveness profiles in real world clinical practice. Both, general methodological issues and specific clinical and public health questions will be investigated for increasing the value of the regional health data by exploiting them from the decision maker point of view. In other words, the main strategic aim of the dei progetti in corso. Autore Giovanni Corrao - 9

10 is of realising a flywheel which allows to the regional authority of obtaining solid data and strong evidence for addressing health policies. The use of the term flywheel is justified by the fact that both regional authority and regional research teams would benefit from the. The regional authority because a no-cost instrument for addressing health policies should be obtained. The regional research teams because of availability of good-quality health data for scientific purposes. Finally, it is expected that the boosts methodological and applicative research in the field of observational research (OR). Accordingly, training courses to will be carried out within. dei progetti in corso. Autore Giovanni Corrao - 10

11 2. Investigating real world clinical practice 2.1. Insufficiency of clinical trials Most of the efficacy and safety data on drugs, and more in general on healthcare services, is generated from randomized, clinical trials (RCTs). However, high quality scientific evidence from RCTs may not be generalisable to everyone likely to take the drug, or use the service [1]. RCTs, in fact, usually have a small sample size that often under-represents vulnerable patients and they focus on short-term efficacy and safety in a controlled environment that is often far from routine clinical practice. Moreover, the RCT outcome sufficient to win marketing approval often fails to answer the more relevant questions are faced by patients, doctors and public health officiers [2]. Finally, patients enrolled in RCTs achieve very high and almost optimal compliance, while in the clinical practice compliance is really impaired [3-10]. Such limitations make it inevitable that epidemiologic observational research is performed post marketing to define these issues [11] From clinical trials to observational investigations Health insurers, physicians, and patients worldwide need information on the comparative effectiveness and safety of prescription drugs in routine care [12]. Although randomized clinical trials (RCTs) are the gold standard to determine a drug s efficacy against placebo, dei progetti in corso. Autore Giovanni Corrao - 11

12 it is well recognized that results of such studies may not accurately reflect effectiveness of therapies delivered in typical practice [13 15]. In addition, clinical decisions usually involve choices among therapies yet sponsors of drug trials have limited motivation to test new drugs against existing therapies [16]. This implies that large nonexperimental observational studies supply data, information and evidence completing our knowledge based on RCTs. Nevertheless, controversy sometimes surrounds observational studies [17]. This implies that methodologic issues concerning study s design, conduction and interpretation need of great attention in the epidemiologic framework The baseline observational design A preliminary description of the baseline design in observational epidemiologic framework, the so called population-based cohort design, will be made in this paragraph. A cohort is defined by subjects meeting a set of eligibility criteria and by entry and exit time points. Consider, as hypothetical example, a cohort investigation for studying issues concerning antidiabetic therapies. Entry into the cohort may be defined by calendar time (spanned by the study, e.g., any time after January 1, 2004), by age (any age before 40th birthday), by events (the first use of oral hypoglycaemic medication), or by disease status (the date of diagnosis of type 2 diabetes). Exit from the cohort may be defined by the first occurrence of specific calendar time (e.g., December 31, 2010), age (exit at 80th birthday), events (death; exit from the study; the first switching from oral hypoglycaemic therapy to insulin), or disease status (first occurrence of coronary heart disease). The cohort of incident users of oral hypoglycaemic drugs may be illustrated graphically as in the figure. This figure, based on 6 subjects, is plotted in terms of calendar time, with subjects ranked according to their date of entry into the cohort, which corresponds to the first prescription of the considered drug class (e.g. incident users of oral hypoglycaemic dei progetti in corso. Autore Giovanni Corrao - 12

13 drugs). The restriction to new initiators of the study drugs (inception cohort) will mitigate those issues and will also ensure that patient characteristics are assessed before the start of the study drug and can therefore not be the consequence of the drug, similar to the principle of RCTs. The advantage of the so-called new user design has been summarized [18]. It is important to stress that the included six incident users potentially are all the individuals belonging to the target population who started therapy during the observational period. This is a first peculiarity of observational studies with respect to RCTs. These last, in fact, often select patients from clinical centres of excellence, exclude patients who are more vulnerable to adverse effects of therapy and those affected by comorbidities or submitted to co -treatments in the absence, however, of a target population from which incident users arise. This means that population-based cohort studies are virtually free from external selection bias (lack of generalizability) and, hence, adequately describe real data generated from unselected target populations. Cohort members of incident drug users are followed for recording two families of data. The first one concerns drug exposure. The figure depicts a strong heterogeneity of drug exposure for both type (e.g. two classes of oral hypoglycaemic agents are represented in the figure) and duration (e.g. sporadic, intermittent and continuous exposures are also represented in the figure). This is the second substantial peculiarity of observational studies dei progetti in corso. Autore Giovanni Corrao - 13

14 with respect to RCTs. These last, in fact, are based on the minimization of exposure heterogeneity. Conversely, one main characteristic of observational studies is that they are aimed to describe heterogeneity of drug exposure observed in real world clinical practice, including heterogeneity in the compliance to treatment and deviations of guideline-based clinical recomandations, and identifying components of heterogeneity affecting the outcome. The second family of data recorded during follow-up is the outcome onset. Outcome may be the disease that would be avoided or postponed by the therapy (e.g. switching to insulin as proxy of disease worsening or macrovascular events avoided for effect a given treatment regimen) as well as adverse events potentially linked with brief- or long-term drug therapy (e.g. cancer). This is the third substantial peculiarity of observational studies with respect to RCTs. These last, in fact, are often characterized by sample size and study duration that do not allow of investigating rare outcomes and long-term effects of exposure. Conversely, large population followed for several years from exposure starting are usually submitted to observational investigation in case of existing electronic databases covering the target population (cfr. par. 2.3). Besides this reference design, other ways for observing a given population has been widely used for epidemiologic porpuses. Among these, the nested case-control design, a direct derivation of the cohort one, has received great attention owing its higher computational efficiency with respect to the cohort design [19]. A complete review of observational designs proposed by the methodological and applicative literature, however, lies outside the objective of this report. The very clear review by Suissa [20] is a sutable introductive reader on this issue. dei progetti in corso. Autore Giovanni Corrao - 14

15 The comparative effectiveness principle There is a last substantial difference between observational studies and RCTs which we are choosing to emphatize in a separate paragraph owing its general implications. As pointed out by Cochran about 40 years ago [21], RCTs on the efficacy of drugs for their regulatory approval study the extent to which an intervention does more good than harm under ideal circumstances ( Can it work? ). For most conditions, however, physicians have a choice of two or more medications that can prevent, cure, avoid progression of, and reduce suffering from diseases. For physicians, it is therefore not a question of whether to prescribe a drug [22] but which drug of several alternatives [23]. In such situations, physicians need to understand their comparative effectiveness and safety. In fact, effectiveness assesses whether an intervention does more good than harm when provided under usual circumstances of health-care practice ( Does it work in practice? ). Hence, in the absence of enough head-to-head effectiveness trials, Comparative Effectiveness Research (CER) tries to solve the issue of limited generalizability to routine care and the lack of an active comparison group by studying post-marketing drug use data, often from large health-care utilization databases, and associate such use with relevant health outcomes [16]. It is important to be explicit about the definition of comparative effectiveness as it is applied in this report. With respect to the term comparative, this report will focus on the majority of circumstances when comparison can be made between two or more active dei progetti in corso. Autore Giovanni Corrao - 15

16 treatments rather than comparisons made between an active treatment and no treatment [12]. Consistently, it should be observed that the reference observational design above described is conceived for head-to-head comparison (rather than for active comparison) since it aims of evaluating whether difference in healthcare exposure (e.g. heterogeneity in oral hypoglycaemic agent prescribing or in compliance with the prescribed therapy), affects the outcome. With respect to the term effectiveness, this report will focus on the benefits of therapies, other than harms (as extensively examined in the field of pharmacoepidemiology) and costs (as extensively examined in pharmacoeconomics and health services research). Although large pharmacoepidemiologic studies have the advantage of being representative of routine care, they suffer from several methodological issues discussed below (cfr. par 3) Potentiality of observational approach Observational studies are suitable for studying several aspects of the impact of healthcare in routine clinical practice. Two items have been considered while the reference observational design was described (cfr. par ): exposure course (to drugs, or more in general to healthcare service) and outcome onset (related to hypothesized effectiveness or safety of therapy). Because of both exposure course and outcome onset generate costs for the National Health System (NHS), at least three objectives may be identified by this process: (i) profile of pharmacoutilization, or more in general of healthcare utilization, including the number of current (prevalent) users, new (incident) users, duration of use, persistence and adherence, to name a few [24]; (ii) risk-effectiveness profile of a given therapy, for investigating on both safety and effectiveness (that is efficacy in routine clinical practice) measuring they on comparable scale [1]; dei progetti in corso. Autore Giovanni Corrao - 16

17 (iii) cost-effectiveness profile of a given therapeutic course, for investigating the additional cost that would be accrued to avoid one outcome event as a consequence of implementing a given therapeutic intervention in the target population [25] Using electronic archives Routinely collected and electronically stored information on healthcare utilization in everyday clinical practice has proliferated over the past several decades. Large computerized databases with millions of observations of the use of drugs, biologics, devices, and procedures along with health outcomes may be useful in assessing which treatments are most effective and safe in routine care without long delays and the prohibitive costs of most RCTs [26]. Databases collecting health information can be classified into two broad categories: those that collect information for administrative purposes, such as filling claims for payment (denoted as administrative or healthcare utilization (HCU) databases), and those that serve as the patient s medical record and are therefore a primary mean by which physicians track health information on their patients (denoted as medical record (MR) databases). A major advantage of HCU data is that they reflect real world clinical practice for large and unselected populations [27]. Nevertheless, studies based on HCU data have been criticized for the incompleteness of the patients information such as markers of clinical disease severity, lifestyle habits, and socio-economic status, among others. In contrast, although MR data are richer of clinical and lifestyle information, they often suffer from the fact that any given practitioner provides only a piece of the care which a patient receives, and specialist and hospital cares are unlikely to be recorded in a common MR database [28]. dei progetti in corso. Autore Giovanni Corrao - 17

18 Data quality issues, as well as the selection of general practitioners who carefully take care to their patients, are other potential limitations of studies based on MR data Warranting good research practices There is controversy on how to best design and analyze nonrandomized studies on comparative treatment effects using databases, including HCU and MR databases, patient registries, and other routinely collected health-care data. Challenges of conducting epidemiologic and health services research studies from secondary data sources include concerns about the adequacy of study design, the relevance of the population and timeframe available for study, approaches to minimize confounding in the absence of randomization, and the specificity of clinical outcome assessment. Such threats to validity limit the usefulness of these studies and adoption of findings into policy and practice. With proper research design and application of an array of traditional and newer analytic approaches, such concerns can be addressed to improve our understanding of treatment effects. This report is based upon the assumption that, for optimizing the validity of findings from observational studies designed to inform health-care policy decisions, researchers employ a priori hypotheses in written protocol and data analysis plans before study implementation, that they follow reporting standards that make transparent to readers if, why, and how their analytic plans evolved, as well as provide a justification of the suitability of the database to test their hypotheses [12]. Although we recognize that exploratory analyses and data mining of large datasets are often used to generate hypotheses regarding the effectiveness and comparative dei progetti in corso. Autore Giovanni Corrao - 18

19 effectiveness of treatments, stricter criteria for the design and execution of studies as well as transparency in their reporting are required to justify the conclusion that such findings are robust enough to warrant changes in clinical practice or to influence policy decisions. Thus, the objective of this report is to lay out good research practices for comparative therapeutic effectiveness studies using secondary databases. We do not seek to be complete in our discussion of analytic options, nor will we fully explain all methods, but rather focus on the issues surrounding the most relevant designs and analytic techniques for secondary databases, and describe several applications in the public health issue. References [1] Dieppe P, Bartlett C, Davey P, et al. Balancing benefits and harms: the example of non-steroidal antiinflammatory drugs. Br Med J 2004;329:31 4 [2] Schneeweiss S, Avorn J. A review of uses of HEALTHCARE utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 2005;58: [3] Fitz-Simon N, Bennett K, Feely J. A review of studies of adherence with antihypertensive drugs using prescription databases. Therapeutics and Clinical Risk Management 2005;1: [4] Mazzaglia G, Mantovani L, Sturkenboom MC, et al. Patterns of persistence with antihypertensive medications in newly diagnosed hypertensive patients in Italy: a retrospective cohort study in primary care. J Hypertens 2005;23: [5] Van Wijk BLG, Klungel OH, Heerdink ER, et al. Rate and determinants of 10-year persistence with antihypertensive drugs. J Hypertens 2005;23: [6] Burke TA, Sturkenboom MC, Lu SE, et al. Discontinuation of hypertensive drugs among newly diagnosed hypertensive patients in UK general practice. J Hypertens 2006;24: [7] Elliott WJ, Plauschinat CA, Skrepnek GH, et al. Persistence, adherence, and risk of discontinuation associated with commonly prescribed antihypertensive drug monotherapies. J Am Board Fam Med 2007;20:72-80 [8] Corrao G, Zambon A, Parodi A, et al. Discontinuation of and changes in drug therapy for hypertension among newly treated patients: a population-based study in Italy. J Hypertens 2008;26: [9] Vrijens B, Vincze G, Kristanto P, et al. Adherence to prescribed antihypertensive drug treatments: dei progetti in corso. Autore Giovanni Corrao - 19

20 longitudinal study of electronically compiled dosing histories. Br Med J 2008;336: [10] Perreault S, Dragomir A, Blais L, et al. Impact of adherence to statins on chronic heart failure in primary prevention. Br J Clin Pharmacol 2008;66: [11] Black N. Why we need observational studies to evaluate the effectiveness of HEALTHCARE. Br Med J 1996;312: [12] Berger ML, Mamdani M, Atkins D, et al. Good research practices for comparative effectiveness research: defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report Part I. Value in Health 2002;12: [13] Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 2000;342: [14] Concato J. Observational versus experimental studies: what s the evidence for a hierarchy? NeuroRx 2004;1:341 7 [15] Avorn J. In defense of pharmacoepidemiologic studies: embracing the yin and yang of drug research. N Engl J Med 2007;357: [16] Schneeweiss S. Developments in post-marketing comparative effectiveness research. Clin Pharmacol Ther 2007;82: [17] Sørensen HT, Lash TL, Rothman KJ. Beyond randomized controlled trials: a critical comparison of trials with nonrandomized studies. Hepatology 2006;44: [18] Ray WA. Evaluating Medication Effects Outside of Clinical Trials: New-User Designs. Am J Epidemiol 2003;158: [19] Essebag V, Platt RW, Abrahamowicz M, et al. Comparison of nested case control and survival analysis methodologies for analysis of timedependent exposure. BMC Med Res Methodol 2005;5:5 [20] Suissa S. Novel Approaches to Pharmacoepidemiology Study Design and Statistical Analysis. In: Pharmacoepidemiology (4 th edn). Strom BL (ed). Wiley, New York, 2005; [21] Cochrane, A. Effectiveness and Efficiency: Random Reflection on Health Services. Nuffiled Provincial Trust; London: 1972 [22] van Luijn JCF, Gribnau FWJ, Leufkens HGM. Availability of comparative trials for the assessment of new medicines in the European Union at the moment of market authorization. Br J Clin Pharm 2007;63: [23] Pisano, DJ.; Mantus, D. FDA Regulatory Affairs: A Guide for Prescription Drugs, Medical Devices, and Biologics. CRC Press; Boca Rotan, FL: 2004 dei progetti in corso. Autore Giovanni Corrao - 20

21 [24] Hallas J, Gaist D, Bjerrum L. The waiting time distribution as a graphical approach to epidemiologic measures of drug utilization. Epidemiology 1997;8: [25] Berger M, Teutsch S. Cost-effectiveness analysis: from science to application. Med Care 2005;43(Suppl.):S49 53 [26] Strom BL. Overview of automated databases in pharmacoepidemiology. In: Pharmacoepidemiology (4 th edn). Strom BL (ed). Wiley, New York, 2005; [27] Muhajarine N, Mustard C, Roos LL, et al. Comparison of survey and physician claims data for detecting hypertension. J Clin Epidemiol 1997;50:711 8 [28] Schneeweiss S, Wang P. Association between SSRI use and hip fractures and the effect of residual confounding bias in claims database studies. J Clin Psychopharmacol 2004;24:632-8 dei progetti in corso. Autore Giovanni Corrao - 21

22 dei progetti in corso. Autore Giovanni Corrao - 22

23 3. Rationale Our approach is based on the following points. Several important research questions can be answered by means of HCU data covering very large and well defined populations. For example, in our setting we can easily recognize patients who start with drug therapy (e.g. for treatment of heart failure or type 2 diabetes) from HCU databases covering residents in (or more properly National Health Service beneficiaries of) the Lombardy region (including about 10 million inhabitants). The primary advantages of using these data are that they are comprehensive, cost efficient, and free of the usual biases associated with survey methods such as recall bias, non-response, and subject attrition [1]. As an exercise for evaluating appropriateness of HCU databases in answering specific study questions, imagine now an ideal cohort study with prospectively collected studyspecific data items. This exercise will reveal that in HCU database many factors are not assessed at all [2], measured factors may be misclassified or missing [3], and some patients are less likely to show up in the databases than expected. This implies that coded information needs to be understood and analyzed as a set of proxies that indirectly describe dei progetti in corso. Autore Giovanni Corrao - 23

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