Conclusions Taking PPIs is associated with a small excess of cause specific mortality including death due to cardiovascular disease, chronic kidney disease, and upper gastrointestinal cancer. The burden was also observed in patients without an indication for PPI use. Heightened vigilance in the use of PPI may be warranted.

Results There were 45.20 excess deaths (95% confidence interval 28.20 to 61.40) per 1000 patients taking PPIs. Circulatory system diseases (number of attributable deaths per 1000 patients taking PPIs 17.47, 95% confidence interval 5.47 to 28.80), neoplasms (12.94, 1.24 to 24.28), infectious and parasitic diseases (4.20, 1.57 to 7.02), and genitourinary system diseases (6.25, 3.22 to 9.24) were associated with taking PPIs. There was a graded relation between cumulative duration of PPI exposure and the risk of all cause mortality and death due to circulatory system diseases, neoplasms, and genitourinary system diseases. Analyses of subcauses of death suggested that taking PPIs was associated with an excess mortality due to cardiovascular disease (15.48, 5.02 to 25.19) and chronic kidney disease (4.19, 1.56 to 6.58). Among patients without documented indication for acid suppression drugs (n=116 377), taking PPIs was associated with an excess mortality due to cardiovascular disease (22.91, 11.89 to 33.57), chronic kidney disease (4.74, 1.53 to 8.05), and upper gastrointestinal cancer (3.12, 0.91 to 5.44). Formal interaction analyses suggested that the risk of death due to these subcauses was not modified by a history of cardiovascular disease, chronic kidney disease, or upper gastrointestinal cancer. Taking PPIs was not associated with an excess burden of transportation related mortality and death due to peptic ulcer disease (as negative outcome controls).

Proton pump inhibitors (PPIs) are widely used either as prescription or over-the-counter drugs. 1 2 Several studies suggest that taking PPIs is associated with a number of serious adverse events including cardiovascular disease, acute kidney injury, chronic kidney disease, dementia, pneumonia, gastric cancer, Clostridium difficile infections, and osteoporotic fractures. 3 Some of these adverse events are associated with an increased risk of death. Recent studies described an excess risk of all cause mortality among patients taking PPIs. 4 However, a detailed quantitative analysis of the cause specific mortality that is attributable to taking PPIs is not available. We hypothesized that taking PPIs is associated with an increased risk of cause specific mortality that are mapped to well characterized adverse events of PPIs. Identification of specific causes of death attributable to taking PPIs will inform the public about the risk of taking PPIs in the long term and could inform risk stratification, risk mitigation strategies, and help shape the development of deprescription interventions to reduce unnecessary or un-indicated PPI use. In this work, we built a longitudinal cohort of 214 467 United States veterans that were new users of acid suppression drugs— histamine H2 receptor antagonists (H2 blockers) or PPIs—and developed analytic strategies to estimate the all cause mortality and cause specific mortality associated with taking PPIs.

Methods

Overall study design and specification of a target trial We designed the cohort, exposure definitions, covariate choices, outcome definitions, and an analytic strategy based on the framework proposed by Hernán and Robins.5 We emulated a target randomized controlled trial of the comparative effect of new use of PPIs versus H2 blockers on the risk of all cause and cause specific mortality (details of the specified target trial protocol are presented in supplemental table 1). We then employed causal inference strategies to estimate the mortality attributable to PPI use (further described in the methods and in supplemental table 1).

Cohort design We selected new users of acid suppression drugs between 1 July 2002 and 30 June 2004 and followed them for up to 10 years to examine the associations between new use of PPIs and causes of death. New use was defined as having no record of an acid suppression drug prescription between 1 October 1999 and 30 June 2002. There were 405 490 new users of PPIs. To reduce the probability of misclassification, we further selected from this cohort 201 557 patients who were prescribed more than a 90 day supply of a PPI in the 180 day period after new PPI use. Additionally, 24 061 patients were excluded because they were taking H2 blockers during the 180 day period, resulting in 177 496 new users of PPI. There were 212 735 new users of H2 blockers and 69 731 of them were prescribed more than a 90 day supply in the 180 day period after new H2 blocker use. In this group of patients, 9528 were excluded because they were taking PPIs during the 180 day period, resulting in 60 203 new users of H2 blockers. In the two groups of new users of acid suppression drugs, 234 950 patients had known sex, race, and date of birth and were alive after 180 days of their first record of prescription. We selected new users whose prescribing physician had also prescribed PPIs or H2 blockers to other new users within the one year before the cohort patient’s first acid suppressant drug prescription, to facilitate building an instrumental variable. We further selected new users whose prescribing facility and clinic are known, yielding a final cohort of 214 467 patients (fig 1). Fig 1 Flowchart for cohort building

Data sources We used Department of Veterans Affairs databases in the study.6 The Department of Veterans Affairs operates the largest integrated healthcare system in the US—a veteran specific national health service—to discharged veterans of the US armed forces.7 The Department of Veterans Affairs provides a broad range of healthcare at 1400 healthcare facilities, including 143 Department of Veterans Affairs hospitals and 1241 outpatient sites of care of varying complexity to over 9 million veterans (estimated in 2018) enrolled in the Department of Veterans Affairs healthcare program.789 All enrolled veterans have access to the Department of Veterans Affairs’s comprehensive medical benefits package including inpatient hospital care; outpatient services; preventive, primary, and specialty care; prescriptions; mental healthcare; home healthcare; geriatric and extended care; medical equipment; and prosthetics.89 We used medical SAS datasets from the Department of Veterans Affairs corporate data warehouse, which provided data on inpatient and outpatient encounters, to obtain information about demographic characteristics, healthcare system and clinic encounters, comorbidities, procedures, and surgeries.1011121314151617 We also collected demographic information from the Department of Veterans Affairs Vital Status databases.6 The Department of Veterans Affairs Managerial Cost Accounting System Laboratory Results from Department of Veterans Affairs corporate data warehouse provided laboratory results of cohort patients.101112131417181920 The Department of Veterans Affairs corporate data warehouse Outpatient Pharmacy domain provided outpatient prescription records and an identifier for the hospital and Veterans Integrated Service Network in which the prescription was provided.4212223 Additionally, we used National Death Index data to collect information about date of death and the underlying cause of death.24

Outcomes Study outcomes were specific causes of death based on national death index underlying cause of death coded based on ICD-10 (international classification of diseases, 10th revision).2425 Causes of death were categorized into the following categories: circulatory system diseases; neoplasms; respiratory system diseases; external causes; endocrine, nutritional, and metabolism diseases; nervous system diseases; digestive system diseases; mental and behavioral disorders; genitourinary system diseases; infectious and parasitic diseases; and other causes. Based on results from causes of death, we further defined subcauses of death within those categories which exhibited statistical significance and for which there existed well characterized evidence supporting a relation between taking PPIs and adverse events which could be associated with cause specific mortality.3 These subcauses included death due to cardiovascular diseases, upper gastrointestinal cancer, Clostridium difficile infections, and chronic kidney disease.3

Exposure We applied an intention to treat design for new use of acid suppressant drugs. Intention to treat with PPIs was defined as a prescription of more than a 90 day supply of a PPI in the 180 day period since first prescription, and with no H2 blocker prescriptions within this period. Intention to treat with H2 blockers, which served as an active comparator control, was defined as a prescription of more than a 90 day supply of an H2 blocker in the 180 day period since first prescription, and with no PPI prescriptions within this period. Use of an active comparator, compared with a non-user control, allows for comparison to those who were prescribed another drug with similar indications; this approach might increase the overlap of characteristics between groups, and might reduce the potential for confounding by indication.26 In all analyses, we used days of supply as an indication of number days with a prescription.

Covariates We collected covariates within one year before the first acid suppressant prescription. We selected basic demographics, health service utilization characteristics, and indications for prescription of acid suppressant drugs based on previous knowledge including age, sex, race, year of first prescription, number of outpatient visits, total length of stay in hospital, level of complexity of the hospital in which the prescription was provided, type of clinic in which the prescription was provided, location of the hospital where the prescription was provided, gastresophageal reflux disease, upper gastrointestinal tract bleeding, ulcer disease, H pylori infection, Barrett’s esophagus, achalasia, stricture, and esophageal adenocarcinoma.416172728 Age, number of outpatient visits and total length of stay hospital were modeled as cubic spline functions. Level of hospital complexity was defined as outpatient clinic, medical center, and healthcare system. Clinic type was defined as gastroenterology, primary care, and other. Location of hospital was defined by the Veterans Integrated Service Network.2930 To more closely emulate our target trial, which would have random assignment of acid suppressant drug, and to reduce bias from non-random assignment by reducing imbalances in patient characteristics between PPIs and H2 blockers, we built a high dimensional propensity score using pre-exposure information in domains including outpatient ICD-9 (international classification of diseases, ninth revision) diagnoses, outpatient Current Procedural Terminology codes, inpatient ICD-9 diagnoses, inpatient procedures, inpatient surgeries, and outpatient pharmacy and laboratory records.31 We used physicians’ prescribing preference as an instrumental variable to reduce the probability that an observed association (between PPIs and causes of death) is contributed by unmeasured confounders.3233

Statistical analyses Characteristics and outcome events of cohort patients for the PPI and H2 blocker groups are reported as number and percentage, mean and standard deviation, or median and interquartile range, as appropriate. Kaplan-Meier curves of all cause mortality in new users of PPIs and H2 blockers are presented. To more closely mimic a target trial where new use of PPIs and H2 blockers is randomly assigned, we used the approach developed by Schneeweiss and colleagues to generate high dimensional propensity scores. This approach selects potential confounders among those included in our data domains based on their relative association for new use of PPIs compared with new use of H2 blockers.3134 We then used predefined covariables and algorithmically selected covariates together to generate propensity scores.3536 We applied inverse treatment probability weights based on the propensity scores to the cohort, which results in a weighted pseudo cohort where treatment assignment is independent of measured confounders.37 For the PPIs and H2 blockers groups, plots of the distributions of the propensity scores and standardized differences before and after weighting are provided in supplemental figures 1a-c. To reduce bias from unmeasured confounding, we applied instrumental variable analyses using the two-stage residual inclusion method to the weighted pseudo cohort.323338 We used physician-specific prescribing preference as the instrumental variable to account for unmeasured confounders that might not be accounted for in the high dimensional propensity score, which could include severity of diseases and other treatment indications.39 In the first stage, the residual between the observed and predicted probability of receiving the assigned treatment given instrumental variable was computed from logistic regression weighted by inverse treatment probability weights based on high dimensional propensity scores. In the second stage, we used the residual as an independent variable indicating unmeasured confounders in the inverse treatment probability weighted cause specific Cox survival analyses and Fine and Gray survival analyses. Physician prescription preferences in past patients has been used as an instrumental variable in the conduction of pharmacoepidemiologic studies.394041 To assess the strength of our instrumental variable, we conducted a logistic regression of the odds of being prescribed PPIs versus H2 blockers. Results suggested that a 10% increase in a physician prescribing preference toward prescribing PPIs in past patients was associated with a 35% (95% confidence interval 35% to 35%) increase in odds of the current patient being prescribed PPIs compared with H2 blockers after adjustment for patient characteristics at the time of prescription. These results suggest that we do not have a weak instrumental variable. Further discussion on instrumental variable assumptions can be found in the supplemental methods. We also applied negative and positive controls to examine if potential biases could have modified the relation between PPI use and cause specific mortality. We examined acute kidney injury as a positive outcome control, where previous studies have shown a relation with PPIs.22 We examined transportation related death as a negative outcome control following the approach described by Lipsitch and colleagues, where—based on current knowledge—we assumed that there should be no causal relation between PPI use and transportation related mortality.42 The relation of this exposure-outcome pair could share the same potential biases with PPIs and other cause specific deaths including unmeasured confounders, modeling algorithms, exposure measurement, outcome ascertainments, and other biases.42 We also applied death due to peptic ulcer disease as an additional negative outcome control, where, based on previous knowledge, we expect that PPI users should not have a higher risk of death due to peptic ulcer disease if treatment by indication has been accounted for; the choice of this outcome control was motivated by the fact that peptic ulcer disease is an underlying indication for PPI use and that the relation between this exposure-outcome pair could have the same potential bias as PPIs and other outcomes in the field of treatment by indication.43 In addition to the intention to treat design, since a proportion of new users of H2 blockers used PPIs later during follow-up, we conducted a sensitivity analyses that examined PPI ever-use as a time varying exposure. We also conducted a duration analysis in new users of PPIs where cumulative exposure was defined as the total number of days of PPI supply up to the last day of prescription. To address immortal time bias, the T 0 in this analysis was set to be the end of the last prescription.4 To further evaluate cause specific mortality, we analyzed detailed subcauses of death (as detailed in the outcomes section). In addition, to remove potential confounding by gastrointestinal disease severity, we repeated the analyses in a subcohort where patients had no documented gastrointestinal indications for acid suppression drugs before receipt of the first prescription. Moreover, we conducted formal interaction analyses to examine whether the presence of a baseline comorbid condition modified the relation between new PPI use and its related cause specific mortality. Main results are reported as the event rate per 100 people in the PPIs and H2 blockers groups, and as estimated excess burden associated with new use PPI per 1000 people based on estimated cumulative incidence rate probability at 10 years. To account for the impact on variance that results from inverse probability of treatment weighting and two stage residual inclusion methods,3344 we analyzed 1000 bootstrapped samples, and report the 2.5 and 97.5 centiles as 95% confidence intervals. A 95% confidence interval that does not cross 0 for absolute risk and does not cross 1 for ratio was considered statistically significant. Figure 2 and the supplemental methods show a detailed description of the analytic approach. All analyses were done using SAS Enterprise Guide version 7.1 (SAS Institute, Cary, NC). The study was approved by the Institutional Review Board of the Department of Veterans Affairs St Louis Health Care System, St Louis, MO. Fig 2 Flowchart for analytic approach