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Cadernos de Saúde Pública

versão impressa ISSN 0102-311Xversão On-line ISSN 1678-4464

Cad. Saúde Pública vol.35 no.5 Rio de Janeiro  2019  Epub 23-Maio-2019

http://dx.doi.org/10.1590/0102-311x00033417 

ARTICLE

Mining in Twitter for adverse events from malaria drugs: the case of doxycycline

Felipe Vieira Duval1 
http://orcid.org/0000-0003-4476-1277

Fabrício Alves Barbosa da Silva1 
http://orcid.org/0000-0002-8172-5796

1 Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil.

ABSTRACT

During the post-marketing period, when medicines are used by large population contingents and for longer periods, unexpected adverse events (AE) can occur, potentially altering the drug’s risk-benefit ratio enough to demand regulatory action. AE are health problems that can occur during treatment with a pharmaceutical product, which in the drug’s post-marketing period can require a significant increase in health care and result in unnecessary and often fatal harm to patients. Therefore, a key objective for the health system is to identify AE as soon as possible in the post-marketing period. Some countries have pharmacovigilance systems responsible for collecting voluntary reports of post-marketing AE, but studies have shown that social networks can be used to obtain more and faster reports. The current project’s main objective is to build a totally automated system using Twitter as a source to detect both new and previously known AE and conduct the statistical analysis of the resulting data. A system was thus built to collect, process, analyze, and assess tweets in search of AE, comparing them to U.S. Food and Drug Administration (FDA) data and the reference standard. The results allowed detecting new and existing AE related to the drug doxycycline, showing that Twitter can be useful in pharmacovigilance when employed jointly with other data sources.

Keywords: Drug and Narcotic Control; Biological Ontologie; Natural Language Processing; Social Media; Database

Introduction

During the post-marketing period, when medicines are used by large population contingents and for longer periods, adverse events (AE) can occur that can alter the drug’s risk-benefit ratio enough to require regulatory action. AE are defined as health problems that can emerge in a user or patient during treatment with a pharmaceutical product, potentially resulting from medication errors, deviation in the drugs’ quality, adverse drug reactions (ADR), drug-drug interactions, and intoxications 1.

According to the World Health Organization (WHO), pharmacovigilance is defined as “as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem2. Pharmacovigilance is responsible for identifying, assessing, and monitoring the occurrence of drug-related AE, with the aim of guaranteeing that the benefits outweigh the risks caused by them 1. To achieve this objective, the main instrument in pharmacovigilance is spontaneous reporting, informing government agencies on AE that have occurred with the drugs’ use.

In Brazil, pharmacovigilance activities are shared by the state and municipal health surveillance agencies and the Brazilian Health Regulatory Agency (Anvisa) 2,3. The rate of AE reports received by Anvisa is low 4, often far lower than the target proposed by the international literature, which suggests 300 reports per million inhabitants 5. It is thus necessary to use other sources to detect AE.

AE can be identified during the drug’s study phase prior to marketing, known as the clinical phase. Clinical tests occur in three distinct phases, known as phases I, II, and III, conducted with healthy volunteers and a limited number of patients. In addition, patient selection and treatment generally differ from actual clinical practice 6,7. AE detected later, in the post-marketing period (also known as phase IV), may require a significant increase in health care and result in unnecessary and often fatal harm to patients 8. Therefore, the discovery of AE as soon as possible in the post-marketing period is a key objective for health systems and especially for pharmacovigilance systems.

Computational methods commonly referred to as “signal detection” allow drug safety evaluators to analyze large data volumes to identify risk signals for potential AE, and also serve as an essential component of pharmacovigilance. For example, the U.S. Food and Drug Administration (FDA) routinely uses a signal tracking process to calculate statistics, reporting associations for all the millions of drug combinations and events in its system for communicating AE 8. These signals alone are not sufficient to establish a causal relationship, but they are considered early warnings that require in-depth assessment by specialists to establish causality.

Dedicated resources for subsequent assessment of each of the multiple signals normally generated by detection algorithms is not feasible. Resources deployed for false leads can undermine a pharmacovigilance system 9. Automated strategies are thus imperative to reduce the amounts of false-positives and set priorities in order to allow assessing only the most promising signals.

The article’s main contribution is thus the proposal for TweetAEMiner (Tweet Adverse Event Miner), an automated pharmacovigilance system capable of identifying new and existing drug-AE associations with the use of text mining.

Text mining consists of techniques to retrieve textual information, extract information, and process natural language with algorithms and methods for discovering knowledge, data mining, and machine learning 10.

Twitter was used in the current project as a text mining source. It is an unconventional database due to greater ease and speed in accessing its data. Examples of other unconventional databases that have been used recently in epidemiological surveillance are search logs 11,12,13 and social networks 14,15.

Most of the previous studies on text mining in pharmacovigilance have focused on electronic health records and medical case reports 16,17. Harpaz et al. 18 provide an in-depth study on the existing approaches to the post-marketing phase, exploring various resources such as electronic health records and spontaneous AE reporting systems. Social networks have also been used recently for this purpose. Leaman et al. 19 analyzed users’ comments in social networks and showed that they contain information on medicines that can be extracted for subsequent analysis. In a recent study, Yates & Goharian 20 analyzed the value of users’ commentary in revealing unknown AE, assessing ADR extracted from the SIDER database (http://sideeffects.embl.de/), which contains information on known AE.

Most studies that use Twitter as a data source and that focus on the medical field seek information other than AE. Some studies have used Twitter for this purpose 22,23,24 and have shown that the use of tweets can lead to real-time pharmacovigilance. Freifeld et al. 23 used Twitter to assess the level of agreement between tweets that mentioned AE (Proto-AE - posts with resemblance to AE) and spontaneous reports from the FDA Adverse Event Reporting System (FAERS). The study used 6.9 million tweets with the names of drugs, of which 4,401 were identified as Proto-AEs and showed that Twitter had almost three times more Proto-AE than the FDA reports 23.

Studies that search for AE in Twitter generally collect data from just a few months to find known ADR, use one or no ontology (a data model that represents a set of concepts and relationships within a domain) to do so, and have manual stages in the pipeline (a sequence of operations in which the exit from one stage/operation serves as the entry to the next operation in the sequence). This article uses an automatic pipeline for collecting, storing, and processing tweets that use a complete ontology totally focused on the search for ADR.

Due to limitations on the number of words that can be searched for in Twitter, this study focused on ADR from drugs for malaria, which was the neglected disease with the most tweets in 2014. Among these drugs, an analysis was done of AE related to doxycycline as found in tweets and compared to consolidated AE reports received by the FDA. However, the system described in this article can be adapted to monitor multiple diseases and drugs simultaneously.

Materials and methods

TweetAEMiner collects tweets continuously using Twitter’s API (application programming interface) with predetermined words (diseases or drugs). These tweets are stored in the database. The system periodically initiates the tweets’ processing and analysis. The system is currently configured for processing and analysis on Sundays, when a new week begins on the epidemiological calendar 25, but this periodicity can be altered easily if necessary. The tweets are processed with a natural language processor (NLP), and the data output from this processing is submitted to statistical analysis. Finally, the results are assessed against a reference standard.

The system generates a list of specific signals that are assessed against a reference standard. One signal corresponds to a “drug-AE” association identified by the pipeline.

Figure 1 shows the four stages in the pipeline: data extraction, processing, analysis, and assessment. Besides the stages, Figure 1 also shows the database used to store the tweets and the reference standard.

Note: system’s pipeline. Yellow shows the four stages in the process; green shows the databases used to store the tweets and as the reference standard; blue shows Twitter.

Figure 1 TweetAEMiner methodology. 

Extraction

Twitter has two API to collect tweets: REST API (http://dev.twitter.com/rest/public) and Streaming API (http://dev.twitter.com/docs/api/streaming). The two API only allow access to recent tweets, so those actually collected will be useful for future research. The material has been collected since early 2014 using the above-mentioned API.

As an initial approach, tweets were collected that were related to neglected diseases such as malaria, dengue, Chagas disease, tuberculosis, and leishmaniasis 26. The queries were later expanded to other diseases, also including non-neglected diseases such as AIDS.

A preliminary analysis of the collected data indicated that malaria was the disease with the most tweets. Although some of these diseases still lack an associated drug, the messages referring to them may be useful in other projects, as for example in epidemiological studies.

Given the limited number of words that can be searched for in the respective social network, we only collected tweets on drugs used to treat malaria.

The website http://www.drugs.com was used to obtain the names of drugs related to malaria. The site allows finding names of both brand names and generic drugs. To facilitate the search for these data, a program was developed that relates the associated drugs to the name of each disease. Nineteen drugs were used for malaria, of which 10 were brand name drugs (Plaquenil, Malarone, Doryx, Lariam, Daraprim, Aralen, Fansidar, Morgidox, Ocudox, and Oraxyl) and 9 were generics (atovaquone, proguanil, doxycycline, mefloquine, pyrimethamine, sulfadoxine, hydroxychloroquine, chloroquine, and primaquine). Among these drugs, the one with the most tweets in 2014 was doxycycline, as shown in Table 1, and was thus chosen as the target for analysis.

TweetAEMiner was developed to allow the pipeline’s portability to other types of texts besides tweets, with a minimum of effort. Suffice it to adjust the extraction component to some text source other than Twitter.

Table 1 Numbers of tweets citing drugs used to treat malaria in 2014. 

Drugs n
Morgidox 0
Ocudox 0
Oraxyl 0
Daraprim 35
Sulfadoxine 61
Proguanil 98
Aralen 122
Doryx 173
Atovaquone 191
Fansidar 193
Pyrimethamine 216
Primaquine 359
Lariam 671
Hydroxychloroquine 819
Malarone 890
Plaquenil 982
Mefloquine 1,312
Chloroquine 2,912
Doxycycline 14,333

Reference standard

The reference standard was developed to be a widely accepted database with all the currently known AE. This meant mainly using Adverse Drug Reaction Classification System (ADReCS) 27, an ontology of terms for adverse reactions that uses medical sources. A linkage between diseases and their drugs was added to this ontology.

These sources were used to create a database with the target diseases, the drugs used in their treatment, and each one’s AE.

At present, only tweets in English are being processed, since all the sources used in the reference standard consist exclusively of words in English.

Processing

After extraction, the tweets are submitted to a NLP. Various NLP are used in medicine, such as Medlee 28, cTAKES 29, and MetaMap 30. cTAKES was chosen as an open code NLP used to extract information from free text, using different vocabularies from various medical sources.

cTAKES is used in a program that processes stored tweets, generating as output diseases, drugs, and the associated adverse reactions as well as other medical information found in the text.

Although TweetAEMiner uses tweets rather than spontaneous reports, the messages are filtered in order to have at least a drug and an AE, discarding those without them. The approach is similar to that of Proto-AE by Freifeld et al. 23.

This study uses a drug-based approach 31, chosen because we did not know the number of tweets with a given AE, as well as to determine the number of tweets with AEs and the drugs related to the target disease. With this approach, it is more appropriate to consider a tweet with the drug’s name than to collect any tweet that may not be related to drugs.

Analysis

After processing the tweets, a measure of disproportionality analysis is used for the data to be analyzed. Disproportionality analysis (DPA) in pharmacovigilance is the main class of analytical methods for spontaneous reporting systems (SRS) 18. SRS are reports that include one or more drugs, with one or more AE, and possibly some basic demographic data. These methods identify relevant associations in SRS databases, with a focus on projections of low data dimensionality, more specifically 2x2 contingency tables. Both the FDA and WHO use DPA methods to find these associations 18. This measure was used to classify drug-AE pairs identified in the previous processing stage. The analytical method can vary according to the data that are processed. SRS based on ADR most frequently perform signal detection using disproportionality measures.

The basic task for a DPA method is classification of the tables in order of “interest”. Different DPA methods focus on different statistical measures of association as their measure of “interest”. Table 2 presents the formulas for the most commonly used measures of association, together with their probabilistic interpretation, in which “¬drug” denotes the reports that do not include the target drug.

A particular drug that causes a specific AE more than any other will normally have the highest measure of association. If an AE and a drug are stochastically independent, the measure of association receives a value of 1. Since each AE from an individual drug occurs in a small proportion of all the reports, we generally have a << b or a << c and c << d, and in practice these measures tend to have identical values and interpretations. For example, a value of 3 indicates that there are three times more reports involving a drug-AE pair than expected if there were no association between the two 32.

Table 2 Common measures of association in spontaneous reporting systems (SRS) analyses. 

Measure of association Formula Value Probabilistic interpretation
Relative reporting ratio (RRR)
t.am.n
35.57355
PrEAdrugPr(EA)
Proportional reporting ratio (PRR)
(at-n)c.n
37.36421
PrEAdrugPrEAdrug
Reporting odds ratio (ROR)
a.dc.b
37.57431
PrEAdrugPrEAdrugPrEAdrugPrEAdrug
Information component log2(RRR) 5.12573
log2PrEAdrugPrEA

AE: adverse events.

Note : the letters “a”, ”b”, ”c”, and ”d” are values from the 2x2 contingency table for a drug and an AE. The letters “m”, “n”, and “t” are sums, as exemplified in Duval et al. 26.

The associations are calculated using the frequentist approach proportional reporting ratio (PRR) for disproportionality analysis. Bayesian measures tend to produce extreme values that are less extreme than PRR when the number of cases is very small. However, when the sensitivity, specificity, and predictive power of these measures were compared using Dutch data in 2002 33, no important differences were found when at least three cases were reported. In addition, PRR has already been used in various studies to detect ADE in spontaneous reporting systems, 32,34,35 and it is one of the principal measures used in the European Union. Together with PRR, the 95% confidence interval (95%CI) was calculated and the χ2 test was performed to validate the signals generated, as is performed by the SRS used by the European Union, called EudraVigilance 34.

Assessment

TweetAEMiner verifies in the data analysis whether there was some signal (a “drug-AE” association) as in EudraVigilance, calculating the measure of disproportionality, PRR, together with its 95%CI and the use of the χ2 test.

Since PRR is a highly sensitive method, it can generate many false positives, especially if the number of reports is low. To reduce this, one of the criteria used is to calculate the 95%CI.

The 95%CI for the Napierian logarithm of PRR is estimated as , in which “se” is the standard error of the mean of the natural logarithm of PRR 33,36. If PRR is shown with the 95%CI, it will be considered a disproportionality signal when 34: lower limit of the interval ≥ 1; number of cases ≥ 3.

Another signal detection measure used together with PRR is the χ2 statistic, a test of independence of categorical variables used as an alternative measure of the contingency table’s heterogeneity with a drug D and an AE 34.

If PRR is shown with the χ2, it will be considered a disproportionality signal when: PRR ≥ 2; χ2 ≥ 4; number of cases ≥ 3.

Besides analysis of the tweets, the FDA data were also analyzed to compare the signals generated in the two. The signals detected in each of the analyses were grouped in three types:

(a) Type A: generated by the criterion of the confidence interval for PRR, that is, when the lower limit of the 95%CI for PRR is greater than or equal to 1 and the number of tweets/reports is greater than or equal to 3;

(b) Type B: generated by the χ2 criterion, that is, PRR ≥ 2; χ2 ≥ 4 and the number of tweets/reports is ≥ 3;

(c) Type C: when there are both type A and B signals.

Results

One of the article’s main results was the development of an automatic tool to collect and analyze AE in Twitter. Among the 19 malaria drugs that were used to filter the tweets, doxycycline yielded the most messages, as shown in Table 1, and was thus chosen for the analysis. Assessment of the results included a comparison of the analysis of data obtained by the TweetAEMiner and FDA data obtained by the htpps://open.fda.gov website.

Analysis of Twitter data

Calculation in the disproportionality analysis used the PRR measure, only considering the tweets that cited some AE. The synonyms for ADR in the ADReCS were also used in the count to build the contingency tables.

Table 3 shows the PRR report for the drug doxycycline with the drug’s known AE in the reference standard and which had at least one tweet.

In some situations, when the number of tweets with the target drug and AE in question is greater than zero and the number of tweets with the AE but without the target drug is equal to zero, the PRR cannot be calculated. It is thus arbitrarily assigned “99.9” in the “PRR” column in Table 3 to reflect the presence of a possible signal. In these cases, the limits of the confidence interval are not calculated, as can be seen in the columns “PRR(-)” and “PRR(+)”.

Signals were detected for two possible new AEs: alopecia and rosacea. Both also appear in the FDA data in the same period, as shown in Table 4. In the FDA, more than 200 AE are reported.

Table 3 Proportional reporting ratio (PRR) report for adverse events (AE) with the drug doxycycline (Twitter). 

AE PRR(-) * PRR ** PRR(+) *** χ² Tweets FDA #
Abdominal discomfort Not calculated 99.9 Not calculated 2.356 11
Abdominal distension Not calculated 99.9 Not calculated 1.071 5
Abdominal pain upper Not calculated 99.9 Not calculated 6.434 30
Abscess Not calculated 99.9 Not calculated 0.428 2
Anaemia 0.197 1.634 13.568 0.166 6
Anaphylactic reaction 0.038 0.272 1.933 1.529 2 YES
Angioedema 0.108 0.233 0.504 12.807 12
Anorexia 0.017 0.272 4.353 0.764 1
Anxiety 1.812 4.466 11.007 10.022 82 YES
Aphthous stomatitis 1.079 4.493 18.716 4.035 33
Arthralgia 0.314 0.953 2.894 0.006 14
Back pain 0.427 0.657 1.012 2.897 70
Blood pressure increased Not calculated 99.9 Not calculated 2.356 11
Bronchitis 0.113 0.272 0.654 7.651 10
Candidiasis Not calculated 99.9 Not calculated 2.999 14
Cough 0.567 1.634 4.706 0.664 24
Decreased appetite Not calculated 99.9 Not calculated 0.428 2
Dermatitis Not calculated 99.9 Not calculated 0.214 1 YES
Diarrhoea 0.214 0.681 2.169 0.336 10
Discomfort 0.017 0.272 4.353 0.764 1 YES
Dyspepsia Not calculated 99.9 Not calculated 0.642 3
Dysphagia 0.055 0.272 1.349 2.293 3
Ear infection 0.113 0.272 0.654 7.651 10
Emotional distress Not calculated 99.9 Not calculated 0.214 1 YES
Fungal infection 1.417 4.539 14.543 6.159 50
Gingivitis Not calculated 99.9 Not calculated 0.214 1
Haemolytic anaemia 0.017 0.272 4.353 0.764 1
Headache 0.165 0.327 0.648 8.937 18
Hypersensitivity 0.482 0.754 1.179 1.211 72
Hypertension Not calculated 99.9 Not calculated 1.713 8
Infection 2.664 4.341 7.076 32.958 271
Inflammation Not calculated 99.9 Not calculated 1.499 7
Influenza 0.229 0.256 0.285 528.852 557
Injury 0.172 0.363 0.767 6.032 16 YES
Insomnia 0.088 0.182 0.377 20.92 12
Intracranial pressure increase Not calculated 99.9 Not calculated 0.214 1
Leukopenia Not calculated 99.9 Not calculated 0.428 2
Malaise Not calculated 99.9 Not calculated 0.856 4 YES
Muscle spasms Not calculated 99.9 Not calculated 2.356 11 YES
Myalgia 0.085 0.817 7.852 0.024 3
Nasal congestion Not calculated 99.9 Not calculated 0.214 1
Nasopharyngitis 0.009 0.091 0.872 5.37 1
Nausea 0.943 3.949 16.54 3.245 29
Oedema 0.009 0.091 0.872 5.37 1
Oesophageal ulcer Not calculated 99.9 Not calculated 0.642 3 YES
Oesophagitis Not calculated 99.9 Not calculated 0.642 3
Oropharyngeal pain 0.039 0.163 0.683 6.316 3
Pain 1.556 2.465 3.905 12.485 181
Photosensitivity reaction Not calculated 99.9 Not calculated 1.928 9 YES
Pigmentation disorder 0.049 0.545 6.005 0.199 2
Rash 0.974 2.451 6.17 3.048 45
Rhinorrhoea Not calculated 99.9 Not calculated 0.214 1
Sinusitis 0.172 0.272 0.432 27.638 36
Stevens-Johnson syndrome 0.036 0.091 0.229 32.26 6
Stomatitis Not calculated 99.9 Not calculated 0.428 2
Swelling 1.383 10.076 73.414 6.272 37
Tension Not calculated 99.9 Not calculated 4.716 22
Thrombocytopenia Not calculated 99.9 Not calculated 0.428 2
Tooth abscess 0.038 0.272 1.933 1.529 2
Toothache Not calculated 99.9 Not calculated 1.499 7
Ulcer Not calculated 99.9 Not calculated 3.643 17
Urticaria 0.064 0.117 0.213 55.349 15
Vomiting Not calculated 99.9 Not calculated 3.428 16 YES

FDA: U.S. Food and Drug Administration.

Note: When a signal is detected by χ2, the cell is filled in red; when a signal is detected by the 95% confidence interval (95%CI) for PRR, the cell is filled in orange. The FDA column is filled in green if the signal appeared in both Twitter and FDA.

* Lower limit of the 95%CI for PRR;

** PRR value for the AE;

*** Upper limit of the 95%CI for PRR;

# Shows that there was a signal for this AE in FDA in the same period of 2014.

Table 4 Comparison of numbers of adverse events (AE) found in tweets and in the U.S. Food and Drug Administration (FDA) reports for the malaria drug doxycycline in the year 2014. 

AE Tweets FDA reports
Abdominal discomfort 11 21
Abdominal distension 5 10
Abdominal pain upper 30 32
Abscess 2 -
Alopecia 155 18
Anaemia 6 33
Anaphylactic reaction 2 12
Angioedema 12 -
Anorexia 1 -
Anxiety 82 86
Aphthous stomatitis 33 -
Arthralgia 14 48
Back pain 70 29
Blood pressure increased 11 16
Bronchitis 10 33
Candidiasis 14 -
Cough 24 48
Decreased appetite 2 36
Dermatitis 1 11
Diarrhoea 10 96
Discomfort 1 17
Dyspepsia 3 11
Dysphagia 3 17
Ear infection 10 -
Emotional distress 1 47
Fungal infection 50 -
Gingivitis 1 -
Haemolytic anaemia 1 -
Headache 18 119
Hypersensitivity 72 29
Hypertension 8 22
Infection 271 19
Inflammation 7 14
Influenza 557 16
Injury 16 54
Insomnia 12 29
Intracranial pressure increase 1 -
Leukopenia 2 -
Malaise 4 91
Muscle spasms 11 42
Myalgia 3 32
Nasal congestion 1 -
Nasopharyngitis 1 20
Nausea 29 200
Oedema 1 12
Oesophageal ulcer 3 18
Oesophagitis 3 -
Oropharyngeal pain 3 23
Pain 181 122
Photosensitivity reaction 9 18
Pigmentation disorder 2 -
Rash 45 90
Rhinorrhoea 1 15
Rosace 27 9
Sinusitis 36 18
Stevens-Johnson syndrome 6 -
Stomatitis 2 -
Swelling 37 9
Tension 22 -
Thrombocytopenia 2 12
Tooth abscess 2 -
Toothache 7 -
Ulcer 17 -
Urticaria 15 47
Vomiting 16 137

Analysis of FDA data

Analysis of the FDA data is done in the same way as with Twitter, but using the FDA reports during the same period with the 19 drugs.

Unlike Twitter, the drug with the most reports in FDA was hydroxychloroquine. Oraxyl was the only drug with no reports in 2014 (Table 5). Since the reports focus specifically on the detection of AE, it is normal for their analysis to produce a large number of signals. Doxycycline, for example, was reported with more than 200 different AE, 138 of which generated signals.

Table 5 Number of adverse events (AE) in reports on malaria drugs in 2014. 

Drugs n
Oraxyl 0
Primaquine 24
Fansidar 34
Sulfadoxine 36
Aralen 48
Lariam 110
Daraprim 128
Pyrimethamine 198
Mefloquine 319
Malarone 385
Proguanil 429
Morgidox 533
Ocudox 533
Chloroquine 621
Doryx 640
Atovaquone 1,040
Doxycycline 6,079
Plaquenil 7,664
Hydroxychloroquine 10,564

Generation of type A, B, and C signals

No type A signals were generated by Twitter. The FDA generated a total of 51 type A signals, 40 of which are not in the reference standard. The 11 AE of signals that were in the reference standard are abdominal pain, discomfort, hypersensitivity, malaise, muscle spasms, myalgia, nausea, rash, erythematous rash, urticaria, and vomiting.

Two type B signals were generated by Twitter for the AE upper abdominal pain and tension, both present in the reference standard. Two other type B signals were also generated that are not in the reference standard for the AEs: alopecia and rosacea. Of these signals, only rosacea also occurred in the FDA data, which had a total of 24 type B signals, of which only menorrhagia is found in the reference standard.

Twitter generated a total of six type C signals for AEs: anxiety, aphthous stomatitis, fungal infection, infection, pain, and swelling. All are present in the reference standard of AE for doxycycline. Of these signals, only anxiety occurred in the FDA data, which had a total of 63 signals, eight of which were present in the reference standard: anaphylactic reaction, anxiety, dermatitis, emotional distress, injury, esophageal ulcer, photosensitivity reaction, and maculopapular rash, plus another 55 signals that are not found in the reference standard.

Discussion

In order to build a system capable of collecting, storing, and processing tweets related to drugs, a collector was first implemented using the API from Twitter itself. Since this API does not allow the acquisition of old messages, TweetAEMiner is already collecting tweets citing various drugs and diseases that were not the target of this article, but can be useful in future studies.

The disease with the most tweets was dengue, but since there are no drugs to treat it, the test study for the tool focused on drugs for malaria, the disease with the second most messages.

Tweets were collected throughout the year 2014 citing drugs related to malaria. Some of these drugs did not present any tweets, like Morgidox, Ocudox, and Oraxyl. Doxycycline was the drug that yielded the most tweets (14,333, without including similar drugs), as shown in Table 4. Other drugs either did not present a significant enough number of messages for any analysis or did not have any AE associated with them.

There is no consensus on the best approach for disproportionality analysis: frequentist or Bayesian 37. Both approaches are used in international research. The FDA uses Multi-Item Gamma-Poisson Shrinker (MGPS) 18, a Bayesian method. The frequentist method PRR was used in the European Union at the time our analysis was done, and the ROR method (reporting odd ratios) is now used. Meanwhile, the WHO uses Bayesian Confidence Propagation Neural Network (BCPNN) 18, which is a Bayesian version of information component. Based on these observations, we opted to conduct the first analysis with PRR, since it was simpler than the other methods.

The analysis in Twitter detected signals for eight known AE for doxycycline: abdominal pain upper, anxiety, aphthous stomatitis, fungal infection, infection, pain, swelling, and tension. Two other AE were detected that had not been related previously to doxycycline: alopecia and rosacea. Of the known AE for doxycycline detected by analysis of the tweets, only anxiety was also found in the analysis of the FDA data. It would be interesting to make this comparison for a longer period of time to verify whether the signals generated by Twitter for these eight AE tend to increase, remain constant, or decrease. If these signals continue to appear only in tweets, it would potentially indicate that people are using this social network more than formal reports of AE.

A comparison of Tables 3 and 4 shows the existence of three AEs present in the reference standard and that only generated signals in Twitter, since there were no associated FDA reports. They are: aphthous stomatitis, fungal infection, and tension. This shows that AEs that do not appear in the reports could also be detected in Twitter, since they are also AE for doxycycline.

When investigating the two AE that were not in the reference standard (rosacea and alopecia) and that were detected by Twitter, we found that they also appeared in the FDA reports for the same period. There are reports not only that doxycycline can cause baldness, but also that it might be used to prevent it. On the AE rosacea, the vast majority of the tweets and reports indicated that the drug was used for its treatment, and that it was implicated as the cause 38.

Both alopecia and rosacea appear in the FDA reports, but only rosacea generated a signal in the data analysis. This is further evidence that the use of multiple data sources lends greater sensitivity to the automatic signal detection system, because if one considers only rare events, the analysis of multiple data sources is necessary to achieve the necessary statistical power and population heterogeneity to detect differences in the effectiveness of drugs in subpopulations, taking genetic, ethnic, and clinical differences into account 39.

The fact that alopecia is not in the reference standard means that it may be a potential new AE. This signal was not detected by FDA, only by Twitter, suggesting that this social network was able to detect signals that escape other sources.

Importantly, all the results of the analyses are signals, and not claims of a cause-and-effect relationship between the drug and the AE. In no way can such claims be made automatically, and subsequent studies led by specialists are needed to use these signals as initial warnings to justify more in-depth assessment.

Importantly, PRR and χ2 are measures of association, not of causality. Thus, some events may not have generated signals, even though they are related to the target drugs, and this occurs in the analyses of both Twitter and FDA. Neither of the two analyses generated signals for all the AE in the reference standard, as shown in Table 3.

Although the FDA reports focus precisely on identifying ADEs, the vast majority of the 138 signals were generated for AE not in the reference standard (40 type A, 23 type B, and 55 type C). In other words, only 20 AE were already associated with doxycycline in the reference standard.

The study’s results corroborate the idea that Twitter is useful for pharmacovigilance, but not as a stand-alone data source, rather as a complementary source. The social network proved capable of generating both new signals and those already in the reference standard, besides signals that were not obtained by analysis of the FDA data.

An emerging belief in pharmacovigilance research is that the combination of information from multiple data sources can lead to more effective and precise discovery of AE 8. Depending on the data sources used and the ways they are combined, it is believed that the resulting system can lead to increased statistical significance in the results or facilitate new discoveries that are not possible based on single data sources. This hypothesis recently received preliminary confirmation 8, but further research is necessary. The use of multiple pipelines with the data processing, assessment, and analysis stages, each with different data sources, would be a way of corroborating the hypothesis and serve as an important future direction for research.

Besides being corroborated as additional source, another important factor is the availability of Twitter data, which allows real-time access for the data analysis, while pharmacovigilance networks usually take time to make their data available. The FDA, for example, publishes data by quarter, but these data are not necessarily made public after three months. The data for the months of January, February, and March are only made public halfway into the next quarter. The analysis of Twitter data proved useful for building a more complete pharmacovigilance system. Through analysis of these data, AE were detected that were not in the reference standard (alopecia and rosacea), and of these, alopecia was not in the signals generated by the FDA. Still, further analyses are needed to corroborate these results in order to include other drugs and other surveillance periods. It would also be interesting to conduct an analysis based on another method, such as MGPS, which is used by the FDA.

Acknowledgments

The authors wish to thank Brazilian Graduate Studies Coordinating Board (Capes) for the financial support.

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Received: February 24, 2017; Revised: September 22, 2018; Accepted: October 18, 2018

Correspondence F. V. Duval Rua 35, Qd 73, Condomínio Colinazul nº 7, Niterói, RJ 24342-086, Brasil. felipeduval@gmail.com

Contributors

F. V. Duval participated in the analysis and interpretation of the data, writing the article and is responsible for all aspects of the work in ensuring the accuracy and integrity of any part of the work. F. A. B. Silva collaborated in the conception and design of the article, critical review relevant of the intellectual content and final approval of the version to be published.

Additional informations

ORCID: Felipe Vieira Duval (0000-0003-4476-1277); Fabrício Alves Barbosa da Silva (0000-0002-8172-5796).

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