Estimates of the incidence of tuberculosis (TB) and TB-related mortality are key indicators in the evaluation of B-control activities. But how are these estimates derived? The presentation highlights the key methods, and focuses on the uncertainties around the estimates.
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Measuring the burden of tuberculosis
1. The burden of tuberculosis
:
Measuring uncertainty
F R AN K VAN L E T H
A S S O C I A T E P R O F E S S O R O F G L O B A L H E A L T H
A M S T E R D A M U N I V E R S I T Y M E D I C A L C E N T E R S , U N I V E R S I T Y O F A M S T E R D A M
A M S T E R D A M I N S T I T U T E F O R G L O B A L H E A L T H A N D D E V E L O P M E N T
C H A I R T B n e t
5. www.aighd.org
Burden of tuberculosis
Key information to evaluate TB control strategies
Measurement methods develop and change over time
◦ Old estimates are updated regularly
Previous WHO reports no longer digitally available
24. www.aighd.org
Reaching consensus
Subjective
Open discussion not always possible
Error prone due to “blind spots”
Example of Tanzania
◦ WHO: 48% of cases notified
◦ Programme: 85% cases notified
◦ Applied on next year data: case detection > 100%
40. www.aighd.org
Conclusions
Estimating TB burden requires multiple methods
Estimates are done with uncertainty
More precise estimates require improved surveillance and vital registration systems
Full insight in TB burden requires adequate
◦ Identification patients with presumptive TB
◦ Proper diagnostic procedures
◦ Complete reporting and notification to national bodies
◦ Timely reporting to international organizations (WHO)
41. Thank you for the attention
slides posted on frankvanleth.com
Notas do Editor
We are currently reporting more TB patients that ever before.
But there are differences per region.
Of the six WHO regions, three report an increasing number of TB patients, while the European region shows as only region a clear decline
But looking at absolute numbers is just half the story.
If we look at the number of patients in relation to the population size, then we see a modest decrease in TB incidence over the last 18 years
Where the absolute numbers show a complete lack of TB control, the incidence shows us a small but consistent decrease in the burden of TB
But what we also see is that there is uncertainty: the green are around the estimate.
The targets in WHO’s EndTB strategy are fully based on measurement of the burden of TB
It is therefore important that we understand how the burden of TB is measured
That is what I want to tlak about in the coming 30 to 40 minutes
Having insight in the burden of TB is of importance when evaluating TB control activities.
The methods of estimation change regularly, making that the estimates are also updated
For that reason it s not possible to find old WHO report on-line anymore
Let us start with the definition of incidence: The number of new case per time period.
In the field of TB a year
The important words are new and time period
We can measure it in different ways:
Direct, through surveys, and through notification data.
The direct measurement uses a cohort approach.
We have a population and we follow everybody his population for a full year.
At the end of the year we assess who has a new diagnosis of TB.
This proportion is then the incidence
This approach is extremely difficult
First: It requires large populations
Second: it is impossible to have every person in direct observation for a full year
This method is therefore never used in real life
The direct measurement uses a cohort approach.
We have a population and we follow everybody his population for a full year.
At the end of the year we assess who has a new diagnosis of TB.
This proportion is then the incidence
This approach is extremely difficult
First: It requires large populations
Second: it is impossible to have every person in direct observation for a full year
This method is therefore never used in real life
A more feasible approach would be the survey approach
We have a population in which we assess the prevalence of TB: all persons that have TB at that moment in time
After a few years, we repeat this.
Some persons of the first survey might be in the second, other persons are new.
When we have inforamtion on who leves and who enters the first population, we can estimate the incidence.
Repeat surveys are done, but the extra inforamtion to get incience is diffictlt
A more feasible approach would be the survey approach
We have a population in which we assess the prevalence of TB: all persons that have TB at that moment in time
After a few years, we repeat this.
Some persons of the first survey might be in the second, other persons are new.
When we have inforamtion on who leves and who enters the first population, we can estimate the incidence.
Repeat surveys are done, but the extra inforamtion to get incience is diffictlt
A more feasible approach would be the survey approach
We have a population in which we assess the prevalence of TB: all persons that have TB at that moment in time
In the same survey we assess the duration of disease.
We can then translate the prevalence of disease to the incidence of disease
Such a survey is a huge undertaking.
I have been directly involved in the design and implementation of two: In Tanzania and in Bangladesh.
It requires knocking on doors, interviewing people, and performing laboratory tests in difficult circumstances
But most of all: it requires a lot of people.
Bot surveys needed over 50,000 participants to obtain a reasonable precise prevalence estimate
The third methods is through notification.
This is the form you use in Russia to record information on diagnosed TB patients.
The formal notification data is nothing else that the total of diagnosed TB pa.tients within a time period
How does this work?
If we are sure that all TB patients in the country are identified and notified, the number of notifications is a direct estimate of the incidence
If we know that only a few TB patients are not identified or notified, we can use a correction factor.
This factor corrects the number of notifications in a way that the corrected numbers are an estimate of the incidence
If we suspect a large number of patients not being identified or notified, we can not use a correction factor.
Then we use detailed information from expects to correct the number of notifications
To start with the expert.
The WHO uses the onion model to estaimte the number of missed patients at each step.
Which is not an easy task.
I have been in a team that assessed this for Tanzania. It is detailed work looking at all kind of data sources and interviewing different people in the country.
But in the end, you have to come up with a decision
And that is just by sitting together, sharing opinions and come up with a consensus.
This process is very subjective.
Discussions are often not fully open, especially if the head of the TB programme is present.
And there are clear blind spot on which even the experts have no reliable information.
Which is specifically true if there is a large private health care system that is not engaged with the national TB program
In Tanzania, the local experts decided that 85% of the TB cases was notified.
This was much larger than the 48% that we as a team calculated form available data
When the 85% estimate was used the next year in the official WHO report, The case det4ction rte for Tanzania was above 100%.
Clearly a wrong estimate
A more formal way to assess under-reporting is the use of capture recapture analyses
This is a technique that stems from the field of biology to estimate the size of an animal population.
First described in detail by Petersen in 1894, although there are indications that it is used even earlier
Petersen needed to estimate the population size of eel at a Danish observation system.
He catched fish at different places, marked them and recorded if a marked fish was captured again at a different place
It very intuitive; if the population is large, you will catch only a few fish for a second time
You need a minimal of two independent sources for TB patients
For example the national disease register and the national laboratory system
You count how many patients are in each of the sources, and how many are in. both sources
The formula to estimate the total number of patients uses the total number in each of the registers and divides this by the number of those who are in both
From here you can calculate how many cases are missed
This was done in 2007 in the Netherlands
Although a country that was considered having a very good notification system, it turned out that arounnd 13% of the TB patients were not notified.
For this analysis, the investigators used 3 sources
.What is striking is that only 388 patients were in all 3 sources. This was 26%
The analysis looks straightforward but has some major difficulties.
But it is not always easy to prove that the sources are really independent
Matching can be a problem given the absence of personal data or restrictions by data protection laws.
In an analyses to assess under-reporting of TB-HIV co-infection it took me almost 3 years to get permission to merge two national databases.
But if a system is in place, it can easily be repeated for updated estimates and measure the impact of strategies for improved reporting
Having discussed the different methodologies, we can see which one is used where.
There are clear differences with the poorest method of expert opinion used in the majority of African countries
While formal capture-recapture analyses are performed in just a few countries.
And this does not mean that these studies are done very year.
In general: estimating TB incidence is guess work leading to uncertain estimates
Apart from incidence, the EndTB strategy has a target on TB mortality
compared to 1990, the number of deaths need to be decreased by 35% in 2020, and 95% in 2035
Mortality is measured for a long time.
The first systematic appoach was in the 17th century by John Graunt. A parish priest who recorded all births and death in the parishes in London.
But not he added the causes of death and published this in the famous book: Bills of Mortality
Here is just a page, and we notice that cough and consumption is listed, with clearly the largest numbers of deaths form the disease.
This is tuberculosis
We do nothing different today and just want to count the number of deaths form tuberculosis.
For that we use different data sources
Vital registration refers to official government records
Verbal autopsies are interviews in which we try to identify from what disease relatives have died
We can perform formal surveys
or just come up with a wild guess by using other data like incidence and death rate
Assessing TB mortality ahs some major problems.
Quite a number of countries do not have a reliable vital registration
Difficult to assign a cause of death, as all of doctors might relate to.
A good example is TB in HIV. This is recorded often as an HIV death, not a TB death
Suveys are always a small sample making coverage an important issue
The diffent methods are use in different parts of the world
The two types of red refer to the use of vital registration
Tb mortality estimates are produced by two different sources.
The WHO and the Institute for Health Metrics and Evaluation.
Known from the Global Burden of Disease project
And these measures can differed greatly.
This is the result form an analysis from a colleague at AIGHD
There are countries where the WHO estimates are higher (the left side of the graph)
And counties where the IHME estimates are higher (right side)
More importantly: there is not an single correction factor that can bring the estimates in line
This graph shows the differences between the two methods
In the orange countries the WHO estimates are highest and in the green countries the estimates from IHME
Estimating TB mortality becomes very difficult
In conclusion.
We need different methods to estimate the burden of TB in the world
This leads to uncertain estimates
For more precise estimates we need better surveillance and vital registration systems
But it starts with adequate identification of patients with presumptive TB
Proper diagnostic procedures
Good record keeping
And timely reporting to international organizations.