Description
This is your second Critical Review. Remember you should be reviewing a peer reviewed journal article that a) is about the same health topic you chose for Critical Review 1 AND b) uses a different study design (Chapter 6) from the article in Critical Review 1.
CRITICAL REVIEWS OF EPIDEMIOLOGICAL STUDIES
WHAT YOU WILL DO:
The ability for public health professionals to critically evaluate the scientific literature is an important skill. Developing and sharpening this skill will allow you to apply the best and most updated research to the application of your work. Research has influenced policy development, the dosage of various medications, the ability to manage the spread of disease, the safety of various medical programs, the impact of educational health programs, and many other public health functions.
After you have read Chapter 14 of the textbook and reviewed the examples provided, you will select a public health issue. You will research this public health issue all semester, so make sure it is something you are interested in. For example, you can research maternal health, obesity, mental health, environmental health, infectious diseases (tuberculosis, malaria, HIV/AIDS) or chronic conditions (cancer, diabetes, cardiovascular diseases). Once you have selected your semester topic, you will find THREE epidemiological studies about your topic. Each of the THREE articles must have a different study design (Chapter 6). Once you have your topic and articles you will write a critical review of the study using the outline discussed in Chapter 14 (Exhibit 14-1).
WHAT YOU WILL WRITE:
After reading Chapter 14 and your article you will write your review answering the questions found in Exhibit 14-1 of your textbook:
9 Questions regarding the “Collection of Data”
3 Questions regarding the “Analysis of Data”
6 Questions regarding the “Interpretation of Data”
WHAT YOU WILL SUBMIT:
You will submit two documents via the link provided in Canvas.
Your complete Critical Review of your article.
At the top of the page you will write a brief summary statement of the article you reviewed for Critical Review 1. This should include a statement about the health topic you are reviewing this semester and the study designs of articles 1 and 2. Remember this article should be about the SAME health topic as the article you reviewed for Critical Review 1. It should also use a DIFFERENT study design from Chapter 6 than the article you reviewed for Critical Review 1.
In addition, include the Title, Author, and Abstract of the paper you are reviewing. See Exhibit 14-2 in your textbook. You can copy and paste the abstract from the original article.
Then you will include and answer each of the 18 questions of the Outline/Guide provided (Exhibit 14-1). Be sure to write the question before answering it. This will make it easier to grade and ensure that you have fully completed the assignment. Each answer should be substantial in nature, meaning they should each be at least a few sentences long. Some questions will require more developed answers, meaning a few paragraphs in length. Do not copy and paste directly from your article. You must write each of your answers in your own words.
A PDF of the full article you reviewed.
This is why it is important that you are using the FSU Library to do your search not just Google Scholar. If you are not logged into the FSU Library, some articles will have a fee associated with them to download the copy. If you are using the FSU Library, then FSU has already paid for access to the article for you.
DUE DATES:
Identification of Topic & Articles – Due Friday, January 19 by 9am
Critical Review 1 – Due Monday, February 5 by 9am
Critical Review 2 – Due Monday, March 4 by 9am
Critical Review 3 – Due Monday, April 8 by 9am
Rubric
Critical Review 2
Critical Review 2
Criteria Ratings Pts
This criterion is linked to a Learning OutcomeIntroductionDid include a summary statement of the article you reviewed for Critical Review 1? This should include a statement about the health topic you are reviewing this semester and the study design of articles 1 and 2.
Is this article about the SAME health topic as the article you reviewed for Critical Review 1?
Does this article use a DIFFERENT study design from the article you reviewed for review 1?
Did you include the title, author and abstract of the paper you are reviewing?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat was the context of the study?
5 pts
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2.5 pts
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0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat were the objectives of the study?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat was the primary exposure of interest? Was this accurately measured?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat was the primary outcome of interest? Was this accurately measured?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat type of study was conducted?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeDescribe the study base, the process of subject selection, sample size, and ratio of propositi to comparison subjects.
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeCould there have been bias in the selection of study subjects? How likely was this bias?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeCould there have been bias in the collection of information? How likely was this bias?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat provisions were made to minimize influence of confounding factors prior to the analysis of data? Were these provisions sufficient, explain?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat methods were used to control confounding bias during data analysis? Were these methods sufficient, explain?
5 pts
Full Marks
2.5 pts
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0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat measure of association was reported in this study?
5 pts
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2.5 pts
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0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat measures of statistical stability were reported in this study?
5 pts
Full Marks
2.5 pts
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0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat were the major results of this study?
5 pts
Full Marks
2.5 pts
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0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeHow is the interpretation of these results affected by information bias, selection bias, and confounding? Discuss both the direction and magnitude of any bias.
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeHow is the interpretation of these results affected by any non-differential misclassification? Discuss both the magnitude and direction of this misclassification.
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeDid the discussion section adequately address the limitations of the study?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeWhat were the authors’ main conclusions? Were they justified by the findings?
5 pts
Full Marks
2.5 pts
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0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeTo what larger population may the results of this study be generalized?
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
This criterion is linked to a Learning OutcomeMechanicsDid you include a copy of the full article you are reviewing?
Clarity, Grammar, Spelling, Formatting (Did you include each question before answering it?)
5 pts
Full Marks
2.5 pts
Half
0 pts
No Marks
5 pts
Total Points: 100
Critical Review 1 is down below to refer from
Unformatted Attachment Preview
1
Critical Review 1: High Rates of Recurrent Tuberculosis Disease: A Populationlevel Cohort Study
Name
USF
2/4/24
2
Topic: Recurrent Tuberculosis Disease in Urban Populations
High Rates of Recurrent Tuberculosis Disease: A Population-level Cohort Study
Sabine M Hermans, Nesbert Zinyakatira, Judy Caldwell, Frank G J Cobelens, Andrew Boulle,
Robin Wood
https://doi.org/10.1093/cid/ciaa470
https://academic.oup.com/cid/article/72/11/1919/5825249
Abstract
Background
Retreatment tuberculosis (TB) disease is common in high-prevalence settings. The risk of
repeated episodes of recurrent TB is unknown. We calculated the rate of recurrent TB per
subsequent episode by matching individual treatment episodes over 13 years.
Methods
All recorded TB episodes in Cape Town between 2003 and 2016 were matched by
probabilistic linkage of personal identifiers. Among individuals with a first episode notified in
Cape Town and who completed their prior treatment successfully, we estimated the recurrence
rate stratified by subsequent episodes and HIV status. We adjusted person-time to background
mortality by age, sex, and HIV status.
Results
A total of 292 915 TB episodes among 263 848 individuals were included. The rate of
recurrent TB was 16.4 per 1000 person-years (95% CI, 16.2–16.6) and increased per subsequent
episode (8.4-fold increase, from 14.6 to 122.7 per 1000 from episode 2 to 6, respectively). These
increases were similarly stratified by HIV status. Rates among HIV positives were higher than
among HIV negatives for episodes 2 and 3 (2- and 1.5-fold higher, respectively) and the same
after that.
Conclusions
TB recurrence rates were high and increased per subsequent episode, independent of HIV
status. This suggests that HIV infection is insufficient to explain the high burden of recurrence; it
is more likely due to a high annual risk of infection combined with an increased risk of infection
or progression to disease associated with a previous TB episode. The very high recurrence rates
would justify increased TB surveillance of patients with >1 episode.
1. What was the context of the study?
The study examined TB retreatment in Cape Town, which had an estimated
population of 3.7 million in 2011. Free TB care was provided in 101 clinics with limited
3
private-sector involvement. The study looked at the treatment episodes from 2003 to
2016 based on information from the Electronic TB Register, which investigated
recurrence and the factors that influenced it. The population-based cohort study was
designed to investigate TB patterns in Cape Town with a focus on treatment outcomes,
HIV status, and demographic factors. The Human Research Ethics Committee of the
University of Cape Town and the Cape Town City Health Department approved the
study.
2. What were the objectives of the study?
The research focused on measuring the incidence of recurrent TB among the
population of Cape Town from 2003 to 2016. Treatment outcomes were analyzed,
considering such factors as HIV status and demographics. The main objectives included
establishing recurrence rates, looking for patterns according to prior therapies, and
assessing the role of HIV in recurrence. The study aimed to shed light on the functioning
of TB relapse in a highly populated city and guide the prevention and recovery measures.
3. What was the primary exposure of interest? Was this accurately measured?
The most significant exposure of concern was the manifestation of recurrent TB
episodes. The study used the ETR in Cape Town from 2003 to 2016 to measure TB
treatment episodes accurately. The factors that were analyzed in the analysis included
treatment outcomes, demographics, and HIV status to determine the TB recurrence
population.
4. What was the primary outcome of interest? Was this accurately measured?
The primary outcome of interest was the rate of recurrent TB in the Cape Town
population. This result was correctly measured based on the ETR data from 2003 to 2016.
Episodes of recurrent TB were determined using the matching protocol, chaining
individual treatment episodes, and collecting a comprehensive cohort. The study offered
4
an in-depth analysis of TB recurrence ratings amidst several factors, such as treatment
responses, age groups, and HIV positivity. The validation data set was used to support the
accuracy of the outcome measurement. It compared the study’s matching algorithm with a
manually matched research database for the reliability of capturing recurrent TB cases.
5. What type of study was conducted?
The population-based cohort study was conducted in the Cape Town metropolitan
area.
6. Describe the study base, subject selection process, sample size, and the ratio of
proposition to comparison subjects.
The study population comprised all drug-sensitive TB treatment episodes reported
in Cape Town between 2003 and 2016. The exclusion criteria were patients with drugresistant TB. The researchers used a probabilistic matching protocol to assemble a cohort.
The virtual cohort included people who had their first TB treatment episode in Cape
Town during these years. The researchers excluded those who had been treated for TB at
the first episode. This cohort provided the basis for incidence analyses. Because there
were no identification numbers, it was impossible to integrate the data with the South
African vital registration system. Therefore, the researchers adjusted for mortality by
applying estimates of mortality rates by age, sex, HIV status, and calendar year. The
cohort included 292,915 episodes from 263,848 individuals. The adequacy of the sample
size made it possible to use robust methods of analysis of recurrent TB stratified by
gender and age, as well as HIV status. The recurrent-to-population ratio was beneficial in
understanding TB dynamics.
7. Could there have been bias in the selection of study subjects? How likely was this
bias?
5
There was a potential bias considering that patients with drug-resistant TB and
those who self-reported prior TB treatment were excluded. Bias was minimized using a
large cohort and strict matching criteria. The study population mirrors those with drugsensitive TB in Cape Town from 2003 to 2016. The exclusion criteria were designed to
ensure the analysis focused on reoccurring drug-sensitive TB cases. Although some bias
cannot be avoided in observational studies, efforts were made to reduce it as much as
possible. The reliability of subject selection is underpinned by the high sensitivity and
specificity of the matching algorithm in the validation dataset. In general, the design of
the study sought to minimize selection bias.
8. Could there have been bias in the collection of information? How likely was this
bias?
Information collection bias might exist, especially in terms of HIV status, as it
was unclear in 25% of the cases. This ambiguity also dwindled over the study period.
This bias is recognized, but measures are taken to overcome it. Sensitivity and specificity
calculations for the matching algorithm in the validation dataset allow confidence in the
legitimacy of the data collection. There were challenges, but the large dataset and
validation processes sought to reduce bias. The study also identifies limitations and calls
for a more conservative interpretation of findings, given the constraints regarding the
information available. While the study may be potentially biased, it provides valuable
information on recurrent TB.
9. What provisions were made to minimize the influence of confounding factors prior
to the analysis of data? Were these provisions sufficient? Explain.
Confounding factors were controlled by adjusting person-years for unmeasured
mortality. Additional checks of potential bias were the sensitivity analyses and validation
procedures. Despite the limitations, attempts were made to improve the robustness. The
6
study also noted limitations in fully capturing factors such as mobility or increased
mortality after TB treatment. Statistical adjustments and validation against a research
database were conducted to extend the internal validity. The generalized review took into
account several factors affecting the recurrence of TB. While some limitations continued,
the research worked on developing a comprehensive understanding of recurrent TB
processes.
10. What methods were used to control confounding bias during data analysis? Were
these methods sufficient? Explain.
The research deployed Stata and Microsoft Excel for data analysis, including
statistical techniques and controls for confounding factors. These approaches included
person-years, mortality adjustments, and sensitivity analyses. The researchers recognized
possible biases and verified the linkage and matching algorithm. Although efforts were
made to prevent confounding, the limitations in estimating mortality and migration were
recognized. The study’s statistical approach and sensitivity analyses were designed to
improve the validity of findings amidst necessary challenges in controlling confounding
biases. All in all, the methods chosen were considered adequate, given the significant
degree of confounding factors in TB.
11. What measure of association was reported in this study?
The study reported incidence rates of recurrent TB per 1000 person-years at risk
(PYAR) to assess the association.
12. What measures of statistical stability were reported in this study?
The study reported 95% confidence intervals (CIs) for incidence rates, providing a
measure of statistical stability.
13. What were the significant results of this study?
Expanding the series of TB cases, we included 292 915 TB episodes among
individuals, numbering 263 848. The rate of recurrent TB amounted to 16.4/1000 person-
7
years (95% CI, 16.2–16.6) and was dose-dependent; the risk increased 8.4-fold per
subsequent episode from 14.6/1000 during episode 2 to 122. The changes in the inputs
are parallel stratified according to the HIV status. The rates among HI+ were higher than
HI- for episodes 2 and 3 in the following proportion: 2- and 1.5-fold, respectively, and
after that, the same. The recurrence of tuberculosis was discovered to be high, going up
with every additive incidence. Among those completing treatment, the morbidity
increased twice after the first episode’s onset and twenty times for the fifth one. The rates
rose in the first two years posttreatment and averaged afterward. In case of recurrence,
HIV infections represented initial recurrence, but the difference was not significant after
the third episode. Factors including past TB treatment along with high transmission rates
of TB discussed by the research may lead to the repetition of the illness.
14. How is the interpretation of these results affected by information bias, selection bias,
and confounding? Discuss both the direction and magnitude of any bias.
The large sample size and follow-up duration of the study lend to robustness.
Information bias stems from the HIV unknown status of the previous years, which may
have resulted in the alteration of results. Selection bias is also a limiting factor because
leaving aside recurrences in Cape Town might result in a lack of underestimation. Failure
to account for unmeasured mortality and migration may also bias person-years
adjustment, introducing confounding. This study could bias a bias direction that could be
directed towards under-estimation data might be biased due to information bias, mainly
in the early years with unknown HIV status. This loss to selection, leading to throwing
away the recurrences beyond Cape Town, may correspond to underestimating the actual
recurrence rate. Failure to control for unmeasured mortality would result in survival bias
and is highly likely to reduce recurrence rates. Besides, not adjusting for migration risks
8
an overestimation of the person-years. Although recognized and avoided as far as that
could be done, the bias level was the problem. The study recognizes the limitations; the
authors try to minimize the dependencies through specific matching criteria. The high TB
recurrence observations clearly show the problem’s value, even if potential biases are
acknowledged. In this regard, the study also points to the requirement of increasing
further research on the mechanism that drives recurrent TB, which gives revealing means
to determine new courses of interventions and preventive measures.
15. How is the interpretation of these results affected by any non-differential
misclassification? Discuss both the magnitude and direction of this misclassification.
Non-differential misclassification, especially with unknown HIV status,
underestimates the impact of HIV on the recurrence rates. The direction is towards
underestimation. Despite attempts to reduce misclassification, there are limitations due to
the unclear HIV status in some instances. Such magnitude is tolerable because of the
decrease in unknown HIV status over time. However, its influence on recurrence rates
among those with multiple episodes, more so for the younger ones, impacts the overall
interpretation. Despite the challenges outlined above, the study opens new insights into
the patterns of recurrent TB.
16. Did the discussion section adequately address the limitations of the study?
The discussion section went well to address the study’s limitations. It
acknowledged problems like the high proportion of episodes with unknown HIV status,
particularly in the early years. The likely misclassification and the selection bias were
disclosed. The measurement of person-years at risk was off, but the study worked to
overcome this aspect by discovering the survival bias. There was also a limit to the large
demographic of episodes with unknown HIV status, accumulated predominantly in the
first decades of the cohort. The number of participants in the sample size that was
9
analyzed to detect the effect of HIV, especially among multiple recurrences, was reduced.
The limitation of drug-sensitive TB notifications resulted in an underestimation of total
relapse. However, their incorporation would have blurred the findings as their recurrence
could be treated as a non-response to therapy. They also acknowledged the potential to
have overestimated follow-up time due to survival bias: It is known that mortality rates
after an episode of TB are elevated, but this was not used in person-years correction as no
reliable estimates were available from a similar population or setting [39–41]. This would
have resulted in an even higher misrepresentation of the actual recurrence rate. The extra
shortcomings were the accessibility of no-immigration information to readjust the PYAR
and the fact that the mortality rates stratified by age and HIV position were sourced from
records for the Western Cape Province instead of Cape Town.
17. What were the authors’ main conclusions? Did the findings justify them?
The authors concluded that Cape Town experiences a high rate of recurrent TB,
increasing with subsequent episodes. This conclusion is supported by the
large‐population-based cohort, enabling accurate incidence rate estimations. The research
highlighted an 18% relapse percentage, dispelling the belief that HIV, which was
contracted during the receiving of detention, accentuates the high resistance burden. The
results imply that approaches other than high ART coverage need to be implemented to
curtail TB reprise. It is correct that the authors underlined the notion of studying
mechanisms, making their relationship, and relying on active TB surveillance for those
with multiple episodes. This strengthens the perception of TB dynamics in the populace.
18. To what larger population may the results of this study be generalized?
The study’s findings may be generalized to urban populations with a high TB
burden, similar to Cape Town, especially those with a significant prevalence of HIV.
10
However, caution is needed when applying results to areas with different epidemiological
profiles or lower TB prevalence.
Reference
Hermans, S. M., Zinyakatira, N., Caldwell, J., Cobelens, F. G., Boulle, A., & Wood, R.
(2021). High rates of recurrent tuberculosis disease: a population-level cohort
study. Clinical infectious diseases, 72(11), 1919-1926.
https://doi.org/10.1093/cid/ciaa470
Clinical Infectious Diseases
MAJOR ARTICLE
High Rates of Recurrent Tuberculosis Disease:
A Population-level Cohort Study
Sabine M. Hermans,1,2, Nesbert Zinyakatira,3,4 Judy Caldwell,5 Frank G. J. Cobelens,1 Andrew Boulle,3,4 and Robin Wood2,6
1
Amsterdam UMC, University of Amsterdam, Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam Public Health Research Institute, Amsterdam, The
Netherlands, 2Desmond Tutu HIV Centre, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa, 3School of Public Health and Family Medicine,
University of Cape Town, Cape Town, South Africa, 4Western Cape Government Health, Cape Town, South Africa, 5City Health, Department of Health, Cape Town, South Africa, and 6Department of
Medicine, University of Cape Town, Cape Town, South Africa
Background. Retreatment tuberculosis (TB) disease is common in high-prevalence settings. The risk of repeated episodes of
recurrent TB is unknown. We calculated the rate of recurrent TB per subsequent episode by matching individual treatment episodes
over a period of 13 years.
Methods. All recorded TB episodes in Cape Town between 2003 and 2016 were matched by probabilistic linkage of personal
identifiers. Among individuals with a first episode notified in Cape Town and who completed their prior treatment successfully we
estimated the recurrence rate stratified by subsequent episode and HIV status. We adjusted person-time to background mortality by
age, sex, and HIV status.
Results. A total of 292 915 TB episodes among 263 848 individuals were included. The rate of recurrent TB was 16.4 per 1000
person-years (95% CI, 16.2–16.6), and increased per subsequent episode (8.4-fold increase, from 14.6 to 122.7 per 1000 from episode
2 to 6, respectively). These increases were similar stratified by HIV status. Rates among HIV positives were higher than among HIV
negatives for episodes 2 and 3 (2- and 1.5-fold higher, respectively), and the same thereafter.
Conclusions. TB recurrence rates were high and increased per subsequent episode, independent of HIV status. This suggests
that HIV infection is insufficient to explain the high burden of recurrence; it is more likely due to a high annual risk of infection combined with an increased risk of infection or progression to disease associated with a previous TB episode. The very high recurrence
rates would justify increased TB surveillance of patients with >1 episode.
Keywords. antitubercular agents/therapeutic use; incidence; recurrence; South Africa; epidemiology.
Tuberculosis (TB) has seen a worldwide resurgence since the
advent of the human immunodeficiency virus (HIV) epidemic
in the 1990s. The World Health Organization launched its End
TB Strategy in 2014 with the aim to cut TB incidence by 90%
by 2035 [1]. Globally, the burden of disease is still very high;
TB notification rates are decreasing but much more slowly than
needed to reach this aim. There is an urgent need for improved
understanding of propagating factors of the epidemic.
Tuberculosis disease among previously treated individuals (recurrent TB) constitutes 5–30% of the TB burden, with higher proportions found in high-prevalence settings [2–4]. Recurrence may
be due to endogenous relapse or exogenous reinfection. In highprevalence settings reinfection is thought to drive the higher proportion of retreatment due to higher transmission rates [4, 5], and
Received 12 December 2019; editorial decision 7 April 2020; accepted 22 April 2020; published
online April 25, 2020.
Correspondence: S. M. Hermans, Amsterdam Institute for Global Health and Development,
AHTC Tower C4, Paasheuvelweg 25, 1105 BP Amsterdam ([email protected]).
Clinical Infectious Diseases® 2021;72(11):1919–26
© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases
Society of America. This is an Open Access article distributed under the terms of the Creative
Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/
by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any
medium, provided the original work is not altered or transformed in any way, and that the
work is properly cited. For commercial re-use, please contact [email protected]
DOI: 10.1093/cid/ciaa470
limited data have suggested the risk of recurrence due to reinfection
is increased after a previous episode of TB disease [6]. Repeated
recurrences in the same individual add to the TB burden, but the
extent has not been quantified due to difficulties in identifying recurrence in routinely collected data because of a lack of longitudinal
patient registration systems.
Cape Town has one of the highest burdens of TB disease
worldwide. Annually, almost one-third of TB represents
retreatment [7], but the risk of repeated episodes of recurrent
TB and the impact of HIV on this burden are unknown.
We developed a stepwise probabilistic linkage protocol to identify
TB treatment episodes in the same individual within Cape Town over
a period of 13 years and validated it through comparison with a manually matched subset of the database. We calculated the incidence of
recurrent TB disease stratified by the number of previous episodes
and by previous treatment outcome. Among those who had completed their prior treatment, we calculated incidence stratified by year
of follow-up, age, gender, and HIV status.
METHODS
Study Setting
The Cape Town metropolitan area encompassed an estimated
population of 3.7 million people in 2011 [8]. Diagnosis and
Recurrent TB • cid 2021:72 (1 June) • 1919
treatment of tuberculosis are provided free of charge by 101 primary care clinics. Only a small number of TB cases are treated
in the private sector as drugs are difficult to access outside of the
national TB-control program [9].
Diagnosis and treatment of TB follow national guidelines
[10]. Diagnostic tests available were sputum smear microscopy
and, after 2013, Xpert MTB/RIF. Empirical treatment (after
negative or no tests performed) declined since the introduction
of Xpert (32% to 19%) [11]. Drug-sensitive TB is treated with a
2-month intensive phase of isoniazid, rifampicin, ethambutol,
and pyrazinamide, followed by 4 months of isoniazid and rifampicin. Patients with rifampicin-resistant TB are referred to
multidrug-resistant TB (MDR-TB) units; these patients are recorded in a separate database.
Study Design and Population
In this population-based cohort study we included all recorded
drug-sensitive TB treatment episodes in Cape Town from 1
January 2003 to 31 March 2016. Patients who failed treatment
due to drug-resistant TB were excluded. We defined a TB treatment episode as a TB notification in the Electronic TB Register
(ETR) [12]. We matched these individual episodes using a
probabilistic matching protocol (see below) to create a cohort
of persons with 1 or more episodes of TB.
We then generated a virtual cohort consisting of all persons
whose first TB treatment episode was notified in Cape Town
during these years. Persons who, at that episode, reported to
have had TB treatment previously were excluded. This cohort
formed the basis of the incidence analyses. Linkage to the South
African vital registration system was not possible due to lack of
identification numbers; we therefore adjusted for unmeasured
mortality by applying estimates of mortality rates by age, sex,
HIV status, and calendar year.
Data Sources
Tuberculosis notification data were abstracted from the ETR for
Cape Town [12]. We sourced estimates of annual HIV prevalence, HIV incidence, and mortality rates by 5-year age group,
gender, and HIV status from the Western Cape version of the
Thembisa model, a mathematical model of the South African
HIV epidemic, and a demographic projection model [13].
Data Linkage
The Provincial Health Data Centre (PHDC) of the Western
Cape linked and collated all unique patient identifiers (PIDs)
used across provincial health services [14]. The ETR database
was linked and collated in the same process using the PID for
the treatment episodes that had this recorded. For episodes
without PID, probabilistic linkage using the recorded demographic information was performed to link to an existing PID.
Among those that could not be linked, repeated episodes within
individuals and with those with a PID were identified using
1920 • cid 2021:72 (1 June) • Hermans et al
stepwise probabilistic linkage (Supplementary Figure 1). These
matches were designated “probable,” “possible,” and “potential”
depending on the certainty of the match. After manual review
of all potential matches and a sample of probable and possible matches, all categories were included in the final matched
dataset. (See the online data supplement for detailed information regarding the data linkage procedures [Supplementary
Table 1].) Linkage was performed using Microsoft SQL Server
Management Studio (SSMS) 2014 (Microsoft Corporation,
Redmond, WA).
Data Validation
We validated our linkage and matching algorithm by comparing
with a manually matched research database of a peri-urban
community in Cape Town and a subset of the final matched
dataset for the same period [15]. Taking the research database
as the gold standard, we calculated the sensitivity and specificity
of the matching algorithm.
Statistical Methods
Within this cohort we calculated person-years at risk (PYAR) of
a second episode of TB from the end of treatment until either
a second episode or the end of study follow-up (March 2016),
whichever came first. In those who developed a second episode,
the PYAR of a third episode were calculated from the end of
treatment of the second episode until the third episode or the
end of follow-up. This was repeated for all subsequent TB treatment episodes.
We adjusted the person-years of participants after their last
episode of TB (ie, until the end of study follow-up) to estimates of mortality in the general Cape Town population [13].
Mortality rates in strata of 5-year age groups, gender, HIV
status, and calendar year (thereby taking into account the effect of antiretroviral therapy [ART]) were applied to the personyears per stratum to calculate the number of person-years to be
subtracted. We did not adjust for increased mortality after TB
treatment or mobility of participants outside of Cape Town as
no reliable estimates were available.
Using these person-years we calculated the incidence rate
and timing of recurrent TB per 1000 PYAR (with 95% confidence intervals [CIs]) by previous treatment outcome (treatment completion or cure vs default) and by the number of
previous treatment episodes.
To avoid including recurrences that were continuations of
the prior episode, all additional analyses were restricted to recurrences after treatment completion or cure. Among those,
we further stratified rates by year after the end of treatment,
sex, age, and HIV status. In case of HIV seroconversion between episodes, the accrued person-time was halved between
the HIV-negative and HIV-positive person-time. We also performed a sensitivity analysis restricting to bacteriologically
confirmed episodes and censoring on other episodes as participants received treatment for them.
All analyses were performed using Stata 16.0 SE (StataCorp,
College Station, TX) and Microsoft Excel 2013 (Microsoft
Corporation).
Regulatory Approval
This study was approved by the Human Research Ethics
Committee at the University of Cape Town and by the Cape
Town City Health Department.
RESULTS
There were a total of 363 559 treatment episodes recorded in
the Cape Town ETR between January 2003 and March 2016
(Supplementary Figure 2). Of these, 24% were linked to another
episode (Figure 1). The proportion of recurrent cases increased
per calendar year, reflecting accrual of person-time at risk over
the study period (Supplementary Figure 3).
In the validation dataset, 834 out of 3243 (26%) notified
TB episodes were linked to another TB episode. Of these, the
Figure 1.
matching algorithm identified 586 (sensitivity, 70%). Nine
matches were false positive (specificity, 99%).
Our algorithm identified 324 416 individuals (Figure 1). At
their first episode in the ETR, 60 568 (19%) were recorded as
having been treated previously. These and any subsequent episodes in the same individual were excluded, to leave 292 915
episodes. These represented 263 848 individuals and formed the
cohort for the remainder of the analysis.
Among these individuals, 23 422 (9%) experienced 2 TB
treatment episodes, 4303 (2%) experienced 3, 978 (0.4%) experienced 4, and 366 (0.1%) experienced 5 or more episodes
during follow-up. Table 1 shows their baseline characteristics
by treatment episode. More men than women had TB, and this
difference increased per subsequent episode. The average age
at first episode was 30 years and increased to 34 years for subsequent episodes. Bacteriological confirmation increased from
68% to 75% and was much lower in children (8% increasing to
20%). The proportion of extrapulmonary TB decreased per episode (from 17% to 5%). Human immunodeficiency virus status
was unknown in 25% of treatment episodes; this declined rapidly from 100% in 2003 to less than 10% from 2009 onwards
Flow diagram of TB treatment episodes included in the analysis. Abbreviations: ETR, Electronic TB Register; TB, tuberculosis.
Recurrent TB • cid 2021:72 (1 June) • 1921
Table 1.
Baseline Characteristics of Cohort Patients (at End of Treatment Episode), Stratified by Tuberculosis Episode (Up to Episode 5)
Episode
1
Total (n)
263 848
2
3
4
5
23 422
4303
978
260
Gender
Female
123 399 (46.8)
9600 (41.0)
1663 (38.6)
363 (37.1)
93 (35.8)
Male
140 366 (53.2)
13 402 (57.2)
2528 (58.7)
585 (59.8)
156 (60)
0–14
43 470 (16.5)
1625 (6.9)
114 (2.6)
18 (1.8)
2 (0.8)
15–24
49 347 (18.7)
3419 (14.6)
599 (13.9)
136 (13.9)
44 (16.9)
Age (years)
25–34
75 803 (28.7)
7819 (33.4)
1542 (35.8)
367 (37.5)
93 (35.8)
35–44
51 416 (19.5)
6282 (26.8)
1334 (31)
319 (32.6)
88 (33.8)
45–54
26 778 (10.1)
3126 (13.3)
548 (12.7)
111 (11.3)
31 (11.9)
55–64
11 547 (4.4)
915 (3.9)
142 (3.3)
26 (2.7)
2 (0.8)
≥65
5487 (2.1)
236 (1.0)
24 (0.6)
1 (0.1)
0 (0.0)
30 (16.0)
33 (13.0)
34 (11.0)
34 (10.0)
33 (9.0)
Age, mean (SD), years
Type of disease
Extrapulmonary
43 832 (16.6)
2527 (10.8)
363 (8.4)
68 (7.0)
13 (5.0)
Pulmonary
220 014 (83.4)
20 895 (89.2)
3940 (91.6)
910 (93.0)
247 (95.0)
Test(s) positive
149 221 (56.6)
15 651 (66.8)
3017 (70.1)
685 (70.0)
196 (75.4)
Test(s) negative
50 814 (19.3)
4669 (19.9)
911 (21.2)
212 (21.7)
48 (18.5)
No tests done
63 813 (24.2)
3102 (13.2)
375 (8.7)
81 (8.3)
16 (6.2)
Type of diagnosis
HIV status
Negative
106 907 (40.5)
8893 (38.0)
1601 (37.2)
381 (39.0)
117 (45.0)
Positive
86 401 (32.7)
11 789 (50.3)
2436 (56.6)
562 (57.5)
134 (51.5)
Unknown
70 540 (26.7)
2740 (11.7)
266 (6.2)
35 (3.6)
9 (3.5)
Outcome of previous episode
Completion or cure
N/A
17 808 (76.0)
2753 (64.0)
560 (57.3)
132 (50.8)
Default
N/A
4478 (19.1)
1353 (31.4)
370 (37.8)
110 (42.3)
Failure
N/A
388 (1.7)
73 (1.7)
20 (2.0)
9 (3.5)
Move or transfer out
N/A
445 (1.9)
75 (1.7)
17 (1.7)
4 (1.5)
Not evaluated
N/A
298 (1.3)
48 (1.1)
11 (1.1)
4 (1.6)
MDR or RIF resistance
N/A
5 (0.0)
1 (0.0)
0 (0.0)
1 (0.4)
Outcome of present episode
Completion or cure
220 017 (83.4)
17 481 (74.6)
2961 (68.8)
589 (60.2)
169 (65.0)
Default
21 261 (8.1)
3590 (15.3)
869 (20.2)
265 (27.1)
63 (24.2)
Death
9719 (3.7)
1343 (5.7)
277 (6.4)
78 (8.0)
16 (6.2)
Failed
1578 (0.6)
262 (1.1)
67 (1.6)
19 (1.9)
6 (2.3)
Moved or transferred
7660 (2.9)
500 (2.1)
73 (1.7)
15 (1.5)
4 (1.5)
Not evaluated
3449 (1.3)
214 (0.9)
37 (0.9)
8 (0.8)
2 (0.8)
MDR or RIF resistance
164 (0.1)
32 (0.1)
19 (0.4)
4 (0.4)
0 (0.0)
Matching certainty
Probable
N/A
21 892 (93.5)
4192 (97.4)
965 (98.7)
257 (98.8)
Possible
N/A
1242 (5.3)
93 (2.2)
12 (1.2)
3 (1.2)
Potential
N/A
288 (1.2)
18 (0.4)
1 (0.1)
0 (0.0)
Values are n (%) unless otherwise specified.
Abbreviations: HIV, human immunodeficiency virus; MDR, multidrug resistance; N/A, not applicable; RIF, rifampicin.
(Supplementary Figure 4A). We were able to assess HIV seroconversion between episodes in 66% of recurrences; 3% of
recurrences were in individuals who seroconverted between
episodes (Supplementary Figure 4C).
Stratified by previous treatment outcome, 17 808 out of
220 017 (8%) individuals who completed treatment and 4478
out of 21 261 (21%) individuals who did not comple
