In my postdoc, I led a few projects that used administrative claims databases to characterize antibiotic use, specifically Medicare for a project about trends in antibiotic use in association with particular diagnoses, Truven’s MarketScan database for a project about seasonal macrolide prescribing and seasonal gonorrhea resistance, and MarketScan again for a big project relating the distribution of antibiotic use and with resistance.
I wrote a version of this post as a guide for colleagues in my lab and elsewhere for thinking about using this kind of information. I’ve posted it here too.
Why measure antibiotic use?
Reducing antibiotic use can lower healthcare costs, avoid antibiotic-associated adverse events like C. difficile infection, and decrease antibiotic resistance. Public health and hospital officials want to measure antibiotic use to, for example, measure the effect of a stewardship intervention on antibiotic use.
As scientists of antibiotic resistance, we have a more complex goal. We develop models of the relationship between predictors of resistance, including antibiotic use, and the resulting resistance rates. These models can then be used to predict the future of resistance and predict the effect that a stewardship intervention with some specified change in antibiotic use will have on antibiotic resistance.
Individual- and population-level use-resistance associations
There are two relatively simple extremes for thinking about this relationship. First, an individual’s antibiotic use correlates with their resistance. A great example is the data in the plot below, adapted from Carothers AIM 2003, which shows use of quinolones against quinolone resistance in H. pylori. This study is remarkable because H. pylori is an infrequently-transmitted pathogen —we think that someone gets H. pylori once and keeps it for decades— and the study subjects are Alaska Natives who have access to a single healthcare provider that kept track of their antibiotic use for the ~10 years before the study.
|No. quinolone doses||Patients with quinolone-resistant bacteria|
Based on this data like this, it would be easy to predict the effect of a change in antibiotic use within individuals. The weakness of this approach is that it is specific to the study population. In the typical epidemiology sense, you would say that different populations might have different intrinsic use-resistance relationships. For example, men and women could have different pharmacodynamics that lead to different selective pressures for the same drug use. In the more complicated infectious disease epidemiology sense, we can be confident of this use-resistance relationship only for the strains of H. pylori that are present in these individuals. H. pylori and quinolones is convenient combination because H. pylori is infrequently transmitted and quinolone resistance is probably acquired within each individual sequentially, like in tuberculosis. You might not expect an individual-level study like this to give you confidence about the population-level use-resistance relationship for, say, S. pneumoniae and β-lactams.
This brings me to the other extreme for thinking about use-resistance relationships: populations. Say you have some very large, completely separate populations. The fact that the populations are large means that you can count on each population to have access to the same set of circulating pathogens, so antibiotic use can select for resistance in the same way in each population. The complete separation between populations means that you don’t have to worry about how colonization pressure from a high-use, high-resistance population will push resistance into a low-use population.
This approach leads to large ecological studies, like the famous Goossens Lancet 2005, comparing antibiotic use and antibiotic use at the level of European countries.
There are a few problems with this approach, an important one being that correlation does not prove causation. There are many things that make France different from the Netherlands, so it may not be that antibiotic use alone should be expected to explain all the difference in resistance between those two places. A cross-sectional study like this also doesn’t account for the history of antibiotic use. It could be that antibiotic use was previously high in Spain and dropped just before the study, explaining why Spain is above the line.
The phylodynamic approach for use-resistance associations
Many of the issues raised by the individual- and population-level studies can be resolved with a phylodynamic approach. Roughly, the idea is this:
- Say you have some perfect sampling of pathogens. For each pathogen, you have its genome, its susceptibility profile to all relevant antibiotics, its collection date, and the identity of the patient it was sampled from.
- Given this information, infer the phylogeny and transmission history of all the sampled pathogens. In particular, identify the moments in which a pathogen acquired or lost resistance to an antibiotic. Pay attention also to the lineages that dead-ended because they were exterminated by antibiotic use, they were competed out by a more fit strain, or the host simply failed to transmit the pathogen before it was cleared by host immunity.
- Compare the history of resistance acquisition, resistance loss, susceptible lineage dead ends, or resistance lineage dead ends with the history of antibiotic use in the individuals that the pathogen was inhabiting when the event happened.
This approach unifies the individual- and population-level approaches. Antibiotic use is treated as having a direct effect only within the individual that used it, but an individual’s antibiotic use can still have an indirect effect on other individuals because they transmitted a resistance pathogen (or failed to transmit a sensitive one).
The central weakness of this approach is its complexity. In particular, sampling of pathogens will certainly not be perfect, so strong assumptions or confident assertions will need to be made about the pathogens’ phylogenies and transmission histories. It will also be relatively difficult to make projections about how a stewardship intervention will affect resistance, as the project will require making assumptions about how the intervention will change the use of all antibiotics, about the exact individuals in which the change will occur, and about the hosts’ contact network.
Nevertheless, this approach is certainly the future of the use-resistance modelling. Clearly it requires a very sophisticated data set, ideally one with perfect sampling of pathogens, genomic and phenotypic information about those pathogens, and antibiotic use histories from all the individuals that carried those pathogen lineages.
Data sources for antibiotic use
Here I focus on the antibiotic use part of the picture. Antibiotic use data comes in a few major forms.
Inpatient antibiotic use data is tracked by hospitals’ internal pharmacies. Hospitals keep this data for logistical and budgetary reasons, like ordering new stocks, as well as for policy reasons. In the US (at least), hospitals are not required to record this data in any kind of uniform way, so querying this data requires developing relationships with individual hospitals, getting them to export it for you, etc. (A hospital in Boston handed once handed one of my collaborators a bunch of data in the form of a 40,000 page pdf document.)
The US has at least two national surveys designed to measure healthcare behaviors across the whole country. NAMCS/NHAMCS are a sample of outpatient healthcare visits. The strength of that dataset is the linkage between diagnoses and prescriptions, so you have a good idea of what the antibiotic was intended to treat (Fleming-Dutra JAMA 2016). By contrast, the MEPS survey is a sample of households, allowing measurements of an individual’s whole antibiotic use (Olesen EID 2018). Neither survey has very many records, so it’s hard/impossible to make inferences about specific antibiotics, diagnoses, and patient populations.
Sales volumes databases, most notably the IQVIA (formerly known as IMS or Quintiles) Xponent database, can give information about both outpatient and inpatient antibiotic use by drug, location, and prescriber (Hicks CID 2015, Wang ICHE 2017). The strength of this data is its size and coverage: the Xponent database includes ~70% of the outpatient prescriptions made in the US. The disadvantage is these data are essentially a sample of prescribers and antibiotic dispensers. The volume of antibiotic use is not recorded by individual. IQVIA contacts a pharmacy, asks for its antibiotic sales data, and pays for that data. It does not ask about the identity of the individuals getting the antibiotics. The primary audience for sales volumes databases are pharmaceutical companies who want to predict the size of markets; they are not primarily intended for scientific research.
The final major form of data is administrative claims records. If a person is covered by insurance, then the healthcare provider and the insurance both keep records of the provided care so they know how much money to exchange. The obvious disadvantage of this data is that it only includes people with health insurance. The advantage is that it includes line-item information by person and by healthcare event, including things like picking up antibiotic prescriptions or visiting a doctor or hospital.
I have experience with three claims records systems, Medicare (Olesen BMJ 2018), MarketScan ([Olesen JID 2018]](https://www.ncbi.nlm.nih.gov/pubmed/30239814), Olesen eLife 2018), and the Massachusetts All Payers Claims Database (APCD). Medicare claims records come from a single provider, Medicare. The MarketScan database is a compilation of private insurance companies’ claims data. The APCD consists of data that Massachusetts insurance providers are legally required to provide to the state.
The rest of this document is about administrative claims records as a measure of antibiotic use, using my experience with these three data sets as a guide.
Claims records are for claims
It’s tempting to think of claims records as a perfect recording of antibiotic use. In fact, there are many caveats. It’s important to understand those caveats both to understand the potential limitations of the data and to be able to design sensitivity analyses that can quantify those limitations.
The first and most important thing to remember about claims records is that they are designed for billing purposes, not for scientific research. All the data in claims records is there only because it is useful to the insurer in keeping track of how much money it owes to whom. Government data, like from Medicare and Medicaid, may have some exceptions, because governments may like the idea of creating a dataset that can be used for research purposes. That being said, Medicare claims data exist mostly to make sure the government is not defrauded, not to be a nice tool for researchers. Similarly, although the APCD was set up explicitly as a way for industry and public-sector researchers to understand healthcare in Massachusetts, the data captured are only ones generated by the insurance providers for their own internal billing purposes.
As a corollary, the data that are in claims records are there because it is in the interest of those who collect and populate that data. For example, healthcare providers’ reimbursements can depend on the type of care provided to beneficiaries as well as the beneficiaries’ health status. This incentivizes providers to describe the care and the beneficiaries’ health in the way that provides them the greater reimbursement while not being guilty of fraud. Because the provided care and beneficiaries’ health are recorded as different kinds of codes, the practice is called “upcoding”. (The related term “coding shift” describes a situation where the care being provided remains the same but the codes used to describe the care change with time.)
Thus, if there were a data field in a government claims database that was only of interest to researchers, the values populating that field will generally be of low quality. In general, the closer a data field is to the dollars and cents, the more reliable you can expect it to be.
Be careful about coverage
It’s crucial to have a very clear understanding of who has insurance coverage and for what kinds of services.
For example, Medicare has many “parts”, which are all quasi-separate insurance plans. Part A is hospital insurance, Part B is outpatient healthcare insurance, and Part D is outpatient prescription medicine insurance. Some people are on Parts A, B, and D; some people are only on some parts. Parts A, B, and D are called “fee-for-service”, meaning that Medicare pays healthcare providers itemwise for the services they provide. Importantly, Medicare only has information about antibiotic use from people who are on Part D.
Part C, usually referred to by its other name, Medicare Advantage, includes plans that are not fee-for-service. Instead, Medicare pays an HMO a fixed amount of money per beneficiary, depending on the beneficiary’s health. Because Medicare is not paying for individual procedures and drugs, Medicare’s claims records do not include information about those individual’s healthcare use.
As another example, the APCD includes data, roughly speaking, from all Massachusetts health insurance providers. Note, however, that this doesn’t necessarily mean that data from Massachusetts resident is included. If you’re happy to say that you’re studying a convenience sample of insurance plan holders, then this is fine. If it’s important that your study population be all Massachusetts residents, then you’ll need to think a little harder.
Finally, it’s important to note that insurance plans are not exclusive. People can generally have as much insurance from as many providers as they want. Medicare is particularly convenient because there’s good evidence that most people on Medicare don’t have other insurance. This is less true of private insurance plans like those in MarketScan or APCD. Claims records therefore are not guaranteed to tell you everything about a person’s antibiotic use.
Codes for drugs
The most important codes when working with antibiotics will be the one encoding the type of drug. These codes usually encode the actual product sold or the ingredient.
In the US, drug products are identified with NDCs (National Drug Code). Each NDC has three sub-codes: “labeler” (e.g., Pfizer), “product” (e.g., ciprofloxacin hydrochloride 500 mg), and “package” (e.g., 1 bottle with 100 tablets). (NDCs are written in different ways depending on where you put the hyphens between the subcodes and whether they are zero-padded, which is a headache but doesn’t introduce any ambiguity.) The FDA keeps a public list of the currently-active NDC codes and some historical lists, but different providers use different sets of codes. The Veterans Administration, for example, uses many NDC codes that are not used by the FDA. The lesson is that it’s important to know what universe of drug codes is being used in each data set.
There are many taxonomies of drug ingredients, although it seems like there is a slow coherence around the WHO’s Anatomical Therapeutic Chemical Classification (ATC). For example, the code for ciprofloxacin is J01MA02:
- J = antiinfectives for systemic use
- J01 = antibacterials for systemtic use
- J01M = quinolone antibacterials
- J01MA = fluoroquinolone antibacterials
- J01MA02 = ciprofloxacin
Claims data are concerned with cost and so usually record a product code like NDC, but as researchers we are interested in ingredients like those encoded in the WHO ATC. In some cases, the claims record administrators curate a product-to-ingredient mapping. For example, Medicare has a Formulary file and MarketScan has the Red Book. Unfortunately, those mappings may only take you part of the way. Medicare’s Formulary file links each outpatient prescription claim with a text-field ingredient like “ciprofloxacin HCl”, so that another mapping from “ciprofloxacin HCl” to “J01MA02” is required. In other cases, you will need to map the product to an ingredient entirely on your own, which may also require using the trade names. For example, ciprofloxacin was marketed under many trade names like “Ciloxan”.
The NLM’s Unified Medical Language System (UMLS) is the most complete mapping I’ve seen between product codes and ingredient names, but it is not easy to use.
Codes for visits and diagnoses
Although they don’t measure antibiotic use per se, it may be interesting to know about the visits and diagnoses associated with prescriptions. Most diagnoses are encoded using the International Classification of Diseases, originally motivated by Florence Nightingale’s experience as a nurse in the Crimean War, trying to communicate with healthcare providers who spoke different languages about patients’ conditions. For example, a ciprofloxacin prescription might be motivated by something as vague as pain during urination (code 7881, “dysuria”) or as specific as “intestinal infection due to enteropathogenic E. coli” (code 00801).
The Ninth Revision (ICD9) was introduced in the 1970s. The US healthcare system switched to ICD10 in October 2015; other countries changed at other times. ICD9 and ICD10 do not have a one-to-one mapping, making it particularly challenging to do analyses across a switch between systems.
Codes are also used to record the procedures that took place in a healthcare event and the circumstances of the visit. For example, Medicare claims records include a Healthcare Common Procedure Coding System (HCPCS, pronounced “hick-picks”) code that can mark an in-person doctor’s office visit. Alternatively, one might look for a Current Procedural Terminology (CPT) code for an outpatient service.
Antibiotic might be administered during a visit but not explicitly recorded in a claim. Two examples include inpatient stays, which generally don’t include show line-item claims for each antibiotic used, and in-office antibiotic administrations, like the single-dose treatments for gonorrhea. In these cases, the claims records might include a procedure code showing that drugs were administered, but which drugs won’t be specified.
Thinking through the chain of events
Many of the caveats about interpreting antibiotic claims data can be organized by thinking through the data generation and capture during the chain of events that leads to someone taking antibiotics.
First, someone has to have health insurance. Insurance providers keep track of their plan members in a “membership” or “denominator” file. This has enough weird caveats that I’ll break that into a separate section below.
Second, someone has to develop symptoms that make them interested in getting antibiotics. At this point, all sort of short-circuits could happen. Someone might not go to the doctor because they don’t have access to a doctor, or can’t afford the copay, or their symptoms aren’t severe enough. Or, they might not bother going to a doctor and instead buy antibiotic over the counter. (This is illegal and probably not very common in the US.) Or, they might use some antibiotics left over from a friend’s or family member’s prescription. These short-circuits are difficult to account for.
Third, someone has to have an encounter with a prescriber. As discussed above in the section about procedure and diagnosis codes, this encounter may generate some claims records. In other cases, the encounter, if it did happen, generates no claims (Riedle ICHE 2017). This may because the person went to a prescriber in a healthcare system that produced a claim not captured by the dataset being studied. Or, it may be because the prescriber didn’t file a claim, because the visit was not eligible for insurance reimbursement, perhaps because the patient paid out of pocket.
Fourth, the prescriber has to write a prescription. There’s a whole literature about what things beyond diagnoses modulate prescribers’ behavior.
Fifth, the patient has to fill the prescription they’ve been given. This is the event that leads to the antibiotic prescription fill claim. Again, there are all sorts of reasons that someone might have gotten a prescription but they fail to fill it (e.g., they can’t afford it, or their symptoms aren’t bad enough that they, or they just deal with the pain for some reason). It’s also possible that the fill occurred but wasn’t recorded for some reason, like if the patient pays out of pocket so that insurance is never billed.
Finally, someone has to actually take the antibiotics they’ve picked up. Compliance with prescribers’ instructions is certainly lower than 100%, and it almost certainly varies in important ways with patient characteristics. There are a few studies that track patients’ pill-taking behavior by giving them medicine bottles with timers measuring when the cap is opened and little springs measuring the weight of the pills remaining in the bottle. It’s unclear how those results relate to compliance for antibiotics. The only case where there is a clear method for testing for compliance internal to the data is with prophylactic antibiotic use. If someone has ~12 claims for nitrofurantoin in a year, you can guess they are on prophylaxis for UTIs. You can then check how many days of drug were present in each fill and compare that to the spacing between fills. For example, someone might get 30 days of drug but wait 45 days between fills.
In some cases, like Medicare, each record in the denominator file is a single person. (Actually, the records in Medicare’s data are indexed by Medicare IDs, but the organization that provides Medicare data to researchers cross-references those IDs to make a single, unique-to-a-person beneficiary ID.) In other cases, like MarketScan and APCD, each record is a person-in-a-plan. A single person might have multiple records because they changed insurance plans. The database provider might try to link those records with a common ID using some inference based on other information about the plan member (e.g., their name, address, etc.), but it’s not always clear how well that works.
One way to work around these difficulties is to only include certain people in the analysis. For example, when working with the MarketScan and Medicare data, I only included individuals who were on insurance for all 12 months in a year. This has the advantage that the rate of antibiotic use —say, claims per 1,000 people per year— is easy to calculate: claims in year X divided by number of members in year X. The disadvantage is that it introduces bias by systematically excluding people with certain kinds of healthcare behavior, which is probably linked with their health status. A more complete approach would be to consider antibiotic use and membership by month, but this requires more somewhat more complex bookkeeping.
Another work-around is to be more careful (or creative) with the denominator. When working with the APCD, we considered three denominators: the straightforward one using the Member Eligibility file, a more complex one where we filtered the Member Eligibility file according to member’s primary insurance, and finally the census. We considered the second approach because it seemed like most of the antibiotic use was coming through members’ primary insurance, so including secondary insurance members was somehow inflating the denominator, shrinking the antibiotic use rate. We considered the third denominator, just counting the number of people in Massachusetts, because the APCD theoretically covers all (or most of) people in Massachusetts. We basically picked the denominator that gave the closest results to other estimates of antibiotic use rates.
Units of antibiotic use
Antibiotic use can be measured with different units. US antibiotic use is often measured in units of prescriptions, probably because of the influence of the Xponent database and NAMCS/NHAMCS surveys. The typical unit for population-level antibiotic use is prescriptions per 1,000 people per year.
Claims records provide somewhat different information. As described in the “chain of events” section, a claim is not equivalent to a prescription. Someone can get a prescription and not fill it, leading to no claim. Or, someone can get one prescription that leads to many fills. These other fills, called “refills”, are often marked by a data field like “fill number” in claims records. Excluding refills is a better approximation for prescriptions, but including refills gives a better sense of antibiotic use. The two tend to be very different only for long-term prophylaxis, like in the nitrofurantoin for UTI example.
Claims records sometimes also include a “days’ supply” field, meaning that the pharmacist parsed the prescription for how many days of drug were included in the thing handed to the patient. This metric for use is further along the vector from prescriptions to fills, giving an even more fine-grained idea of how much drug is being used. In practice, most prescriptions for an antibiotic include the same number of days’ supply, so the results are not that much different.
One might go even further to dosage. European data are typically measured in defined daily doses (DDD) per 1,000 people per day (DID). The DDD is defined in the WHO ATC. For example, 1 gram of ciprofloxacin (J01MA02) administered orally is 1 DDD. Claims records presumably contain enough information to get the dosage. For example, an NDC code might tell you that the thing handed to the patient was a bottle with ten 500 mg tablets of ciprofloxacin hydrochloride, i.e., 5 DDD. I haven’t seen this kind of conversion attempted.
Getting the data
In general, to get data, you’ll need to either find someone who already has it (and broker a collaboration with them) or make a request to get it on your own.
Making your own request for the data will involve a few parts. You’ll prepare an application for the dataset, explaining what your research project is, why you need each variable, etc. Different datasets and variables require more justification. For example, Medicaid (not Medicare) data is very hard to get because CMS is very protective of Medicaid recipients. My guess is that Medicare and CHIA were more challenging to get than MarketScan, but the applications are all fairly onerous.
In some cases, requesting more specific data will require special justification. When I wrote an application for the APCD data, there was a special section where you had to justify why you would need 5-digit zipcode information rather than 3-digit. More broadly speaking, there might be different types of datasets that you can request. For example, Medicare has three levels of data sets:
- “Research Identifiable Files (RIFs) contain beneficiary level protected health information (PHI). Requests for RIF data require a Data Use Agreement (DUA) and are reviewed by CMS’s Privacy Board to ensure that the beneficiary’s privacy is protected and only the minimum data necessary are requested and justified.”
- “Limited Data Set (LDS) files also contain beneficiary level protected health information [… but] selected variables within the LDS files are blanked or ranged. LDS requests require a DUA, but do not go through a Privacy Board review.”
- “Public Use File (PUFs) […] have been edited and stripped of all information that could be used to identify individuals.” PUFs can be downloaded right off Medicare’s website.
An important thing to remember is that, because claims data are mostly about money, most people are interested in them for money reasons. A lot of claims data research is about the cost of procedures, quality of healthcare, etc. Reviewers might be surprised that you’re interested in antibiotic use because it affects bacteria, because most research using claims data is about how healthcare affects the humans.
The folks overseeing your data request will probably ask questions about your project. Their goal is to ensure that you have the data you need to be able to answer the question you want to. They also want to be sure that you actually need the data you request to answer the question at hand.
This process will also include questions about how the data will be stored. Claims data are protected health information (PHI). Even though the identifiers for the people in the dataset will almost certainly be masked, information that could potentially be used to match an individual (e.g., the fact that so-and-so picked up a prescription for drug D on day Y) is enough to make it PHI. This means that you’ll need a place to keep the data that is compliant with HIPAA standards.
Medicare and CHIA have a streamlined process for updating your data query for each year. So if you made a request for a certain kind of data, you can just request to re-up your dataset for that new year, rather than go through the whole data request process again.
All of these requests also cost money, order of $1,000 per file (e.g., prescription data, diagnosis data, etc.).