measles in africa, vaccination africa, measles

Measles deaths in Africa

The following article is by Greg Beattie, author of Vaccination: A Parent’s Dilemma and the more recent Fooling Ourselves on the Fundamental Value of Vaccines. It was originally published on the REAL Australian Sceptics blog and bears repeating.

This information and the graphs included are excerpted from Mr Beattie’s latest book. It demonstrates very clearly that a true sceptic will not necessarily believe in headlines such as “Measles deaths in Africa plunge by 91%” without seeing the proof of those claims. Question everything – accept nothing at face value – that is the credo of the true sceptic.

Man is a credulous animal, and must believe something; in the absence of good grounds for belief, he will be satisfied with bad ones.
Bertrand Russell

Africa, measles africa, vaccination africa
0.450–0.499 0.400–0.449 0.350–0.399 0.300–0.349 under 0.300 n/a (Photo credit: Wikipedia)

If you are not one to follow the news, you may have missed it. Others will have undoubtedly seen a stream of good-newsstories over the past five years, such as:

Measles Deaths In Africa Plunge By 91%[1],[2]

There have been many versions on the theme; the percentage rates have changed over time. However, the bodies of the stories leave us in no doubt as to the reason for their headlines. Here are some direct quotes:

In a rare public health success story on the world’s most beleaguered continent, Africa has slashed deaths from measles by 91 per cent since 2000 thanks to an immunization drive.

An ambitious global immunization drive has cut measles deaths…

Measles deaths in Africa have fallen as child vaccination rates have risen.

These stories represent a modern-day version of the belief that vaccines vanquished the killer diseases of the past. There is something deeply disturbing about the stories, and it is not immediately apparent. The fact is: no-one knows how many people died of measles in Africa. No-one! Not last year and not ten years ago.

I will repeat that. No-one knows how many measles deaths have occurred in Africa. So, where did these figures come from? I will explain that in this blog. In a nutshell, they were calculated on a spreadsheet, using a formula. You may be surprised when you see how simple the method was.

We all believe these stories, because we have no reason to doubt them. The only people who would have questioned them were those who were aware that the deaths had not been counted. One of these was World Health Organisation (WHO) head of Health Evidence and Statistics, who reprimanded the authors of the original report (on which the stories were based) in an editorial published in the Bulletin of the WHO, as I will discuss shortly. Unfortunately, by then the train was already runaway. The stories had taken off virally through the worldwide media.


First, an overview of the formula. The authors looked at it this way: for every million vaccines given out, we hope to save ‘X’ lives. From that premise, we simply count how many million vaccines we gave out, and multiply that by ‘X’ to calculate how many lives (we think) we have saved. That is how the figures were arrived at.

The stories and the formula are both products of a deep belief in the power of vaccines. We think the stories report facts, but instead they report hopes.

The nuts and bolts

Hardly any of the willing participants in spreading the stories bothered to check where the figures came from, and what they meant. That was possibly understandable. Why would we need to check them? After all, they were produced by experts: respected researchers, and reputable organisations such as UNICEF, American Red Cross, United Nations Foundation, and the World Health Organisation.

However, I did check them. I checked because I knew the developing world wasn’t collecting cause of death data that could provide such figures[3]. In fact, it is currently estimated that only 25 million of the 60 million deaths that occur each year are even registered, let alone have reliable cause-of-death information[4]. Sub-Saharan Africa, where a large proportion of measles deaths are thought to occur, still had an estimated death registration of only around 10%[5] in 2006, and virtually no reliable cause-of-death data. Even sample demographic surveys, although considered accurate, were not collecting cause-of-death data that allowed for these figures to be reported. Simply put, this was not real data: the figures had to be estimates.

I was curious as to how the estimates were arrived at, so I traced back to the source—an article in The Lancet, written by a team from the Measles Initiative[6]. After reading the article, I realised the reports were not measles deaths at all. They were planning estimates, or predictions. In other words, they represented outcomes that the Measles Initiative had hoped to achieve, through conducting vaccination programs.

Don’t get me wrong. We all know that planning and predicting are very useful, even necessary activities, but it is obvious they are not the same as measuring outcomes.

The title of the original report from the Measles Initiative reads, “Has the 2005 measles mortality reduction goal been achieved? A natural history modelling study.[7] The authors took one and a half pages to explain how natural history modelling applied here. I will simplify it in about ten lines. I realise that in doing so, some may accuse me of editorial vandalism, however I assure you what follows captures the essence of the method. The rest is detail. If you are interested in confirming this, I urge you to read the original article for that detail. Here we go… the formula at the heart of the stories:

My interpretation of the Measles Natural History Modelling Study

  1. Open a blank spreadsheet
  2. Enter population data for each year from 2000 to 2006
  3. Enter measles vaccine coverage for each of the years also
  4. Assume all people develop measles if not vaccinated
  5. Assume vaccination prevents 85-95% of measles cases
  6. Calculate how many measles cases were ‘prevented’ each year (using the above figures)
  7. Calculate how many measles deaths were ‘prevented’ each year (using historical case-fatality ratios)

There, simple. As you can see, this is a typical approach if we are modelling,for predictive purposes. Using a spreadsheet to predict outcomes of various plans helps us set targets, and develop strategies. When it comes to evaluating the result of our plan however we need to go out into the field, and measure what happened. We must never simply return to the same spreadsheet. But this is precisely what the Measles Initiative team did. And the publishing world swallowed it—hook, line and sinker.

As mentioned earlier, WHO Health Evidence and Statistics head, Dr Kenji Shibuya, saw the problem with this method. Writing editorially in the Bulletin of the WHO, under the title “Decide monitoring strategies before setting targets”, Shibuya had this to say[8]:

Unfortunately, the MDG[9] monitoring process relies heavily on predicted statistics.

…the assessment of a recent change in measles mortality from vaccination is mostly based on statistics predicted from a set of covariates… It is understandable that estimating causes of death over time is a difficult task. However, that is no reason for us to avoid measuring it when we can also measure the quantity of interest directly; otherwise the global health community would continue to monitor progress on a spreadsheet with limited empirical basis. This is simply not acceptable. [emphasis mine]

This mismatch was created partly by the demand for more timely statistics …and partly by a lack of data and effective measurement strategies among statistics producers. Users must be realistic, as annual data on representative cause-specific mortality are difficult to obtain without complete civil registration or sample registration systems

If such data are needed, the global health community must seek indicators that are valid, reliable and comparable, and must invest in data collection (e.g. adjusting facility-based data by using other representative data sources).

Regardless of new disease-specific initiatives or the broader WHO Strategic Objectives, the key is to focus on a small set of relevant indicators for which well defined strategies for monitoring progress are available. Only by doing so will the global health community be able to show what works and what fails.

In simple terms, Shibuya was saying:

  • We know it is difficult to estimate measles deaths, but
  • You should have tried, because you attracted a lot of interest
  • Instead, you simply went back to the same spreadsheet you used to make the plan—and that is unacceptable!
  • If you want to make a claim about your results, you need to measure the outcomes and collect valid data
  • Until you do, you cannot say whether your plan ‘worked’

Unfortunately, by the time Shibuya’s editorial was published, the media had already been trumpeting the stories for more than a year, because the Measles Initiative announced its news to a waiting media before subjecting it to peer-review. So, without scientific scrutiny, the stories were unleashed into a world hungry for good news, especially concerning the developing world. The result… the reports were welcomed, accepted, and regurgitated to a degree where official scrutiny now seems to have the effect of a drop in a bucket.

The question of who was responsible for this miscarriage of publishing justice plagued me for a while. Was it the architects of the original report? Or was it the robotic section of our media (that part that exists because of a lack of funds for employing real journalists) who spread the message virally to every corner of the globe, without checking it?

One quote which really stands out in the stories is from former director of the United States Centers for Disease Control (CDC).

“The clear message from this achievement is that the strategy works,” said CDC director Dr. Julie Gerberding

What strategy works? Is she talking about modelling on a spreadsheet? Or, using the predictions in place of real outcomes? More recent reports from the Measles Initiative indicate the team are continuing with this deceptive approach. In their latest report[10] it is estimated 12.7 million deaths were averted between 2000-2008. All were calculated on their spreadsheet, and all were attributed to vaccination, for the simple reason that it was the only variable on the spreadsheet that was under their control. And still there is no scrutiny of the claims. Furthermore, the authors make no effort to clarify in the public mind that the figures are nothing but planning estimates.

No proof

Supporters of vaccination might argue that this does not prove vaccines are of no use. I agree. In fact,let me say it first: none of this provides any evidence whatsoever of the value of vaccination. That is the crux of the matter. The media stories have trumpeted the success of the plan, and given us all a pat on the back for making it happen. But the stories are fabrications. The only aspect of them which is factual is that which tells us vaccination rates have increased.

Some ‘real’ good-news?

General mortality rates in Africa are going down. That means deaths from all causesare reducing. How do we know this? Because an inter-agency group, led by UNICEF and WHO, has been evaluating demographic survey data in countries that do not have adequate death registration data. These surveys have been going on for more than 50 years. One of the reasons they do this is to monitor trends in mortality; particularly infant, and under-five mortality.

Although the health burden in developing countries is inequitably high, there is reason to be positive when we view these trends. Deaths are declining and, according to the best available estimates, have been steadily doing so for a considerable time; well over 50 years.

One of the most useful indicators of a country’s health transition is its under-5 mortality rate: that is, the death rate for children below five years old. The best estimates available for Africa show a steady decline in under-5 mortality rate, of around 1.8% per year, since 1950[11]. Figure 1 shows this decline from 1960 onward[12]. It also shows the infant mortality rate[13]. Both are plotted as averages of all countries in the WHO region of Africa.

Figure 1. Child mortality, Africa

This graph may appear complex, but it is not difficult to read. The two thick lines running horizontally through the graph are the infant (the lower blue line) and under-5 (the upper black line) mortality rates per 1000 from 1960 to 2009. The handful of finer lines which commence in 1980, at a low point, and shoot upward over the following decade, represent the introduction of the various vaccines. The vertical scale on the right side of the graph shows the rate at which children were vaccinated with each of these shots.

The primary purpose of this graph (as well as that in Figure 2) is to deliver the real good-news. We see a slowly, but steadily improving situation. Death rates for infants and young children are declining. I decided to add the extra lines (for vaccines) to illustrate that they appear to have had no impact on the declining childhood mortality rates; at least, not a positive impact. If they were as useful as we have been led to believe, these vaccines (covering seven illnesses) would surely have resulted in a sharp downward deviation from the established trend. As we can see, this did not occur.

In Africa, the vaccines were introduced at the start of the 1980s and, within a decade, reached more than half the children. The only effect observable in the mortality rates, is a slowing of the downward trend. In other words, if anything were to be drawn from this, it would be that the introduction of the vaccines was counter-productive. One could argue that the later increase in vaccine coverage (after the year 2000) was followed by a return to the same decline observed prior to the vaccines. However, that does not line up. The return to the prior decline predates it, by around five years.

With both interpretations we are splitting hairs. Since we are discussing an intervention that has been marketed as a modern miracle, we should see a marked effect on the trend. We don’t.

The WHO region of Africa (also referred to as sub-Saharan Africa) is where a substantial portion of the world’s poor-health burden is thought to exist. The country that is believed to share the majority of worldwide child mortality burden with sub-Saharan Africa is India, in the WHO south-east Asia region. Together, the African and South-east Asian regions were thought in 1999 to bear 85% of the world’s measles deaths[14]. Figure 2 shows India’s declining infant and under-5 mortality rates, over the past 50 years. Again, the introduction of various vaccines is also shown.

Figure 2. Child mortality, India

And again, vaccines do not appear to have contributed. Mortality rates simply continued their steady decline. We commenced mass vaccination (for seven illnesses) from the late 1980s but there was no visible impact on the child mortality trends.

In a nutshell, what happened in the developed world is still happening in the yet-to-finish-developing world, only it started later, and is taking longer. The processes of providing clean water, good nourishment, adequate housing, education and employment, freedom from poverty, as well as proper care of the sick, have been on-going in poor countries.

I would have loved to go back further in time with these graphs but unfortunately I was not able to locate the data. I did uncover one graph in an issue of the Bulletin of the WHO, showing the under-5 mortality rate in sub-Saharan Africa to be an estimated 350 in 1950[15]. It subsequently dropped to around 175 by 1980, before vaccines figured. It continued dropping, though slower, to 129 by 2008[16].

The decline represents a substantial health transition, and a lot of lives saved. When cause-of-death data improves, or at least some genuine effort is made to establish credible estimates of measles deaths, it will undoubtedly be found they are dropping as well. Why wouldn’t they? This is good news, and all praise needs to be directed at the architects and supporters of the international activities that are helping to achieve improvements in the real determinants of health. In the midst of all the hype, I trust we will not swallow attempts to give the credit to vaccines… again.

I am not confident, however. I feel this is simply history repeating itself. Deaths from infectious disease will reach an acceptable “low” in developing countries, at some point in time. And although this will probably be due to a range of improvements in poverty, sanitation, nutrition and education, I feel vaccines will be given the credit. To support the claim, numerous pieces of evidence will be paraded, such as:

Measles Deaths In Africa Plunge By 91%

We need to purge these pieces of “evidence” if we are to have rational discussion. The public have a right to know that these reports are based on fabricated figures.  Otherwise, the relative importance of vaccines in future health policy will be further exaggerated.

[1]    Medical News Today 30Nov 2007;

[2]    UNICEF Joint press release;

[3]    Jaffar et al. Effects of misclassification of causes of death on the power of a trial to assess the efficacy of a pneumococcal conjugate vaccine in The Gambia; International Journal of Epidemiology 2003;32:430-436

[4]    Save lives by counting the dead; An interview with Prof Prabhat Jha, Bulletin of the World Health Organisation 2010;88:171–172

[5]    Counting the dead is essential for health: Bull WHO Volume 84, Number 3, March 2006, 161-256

[6]    Launched in 2001, the Measles Initiative is an international partnership committed to reducing measles deaths worldwide, and led by the American Red Cross, CDC, UNICEF, United Nations Foundation, and WHO. Additional information available at

[7]    Wolfson et al. Has the 2005 measles mortality reduction goal been achieved? A natural history modelling study; Lancet 2007; 369: 191–200 Available from

[8]    Kenji Shibuya. Decide monitoring strategies before setting targets; Bulletin of the World Health Organization June 2007, 85 (6)

[9]    MDG – Millennium Development Goals, to be discussed shortly in this chapter.

[10]  Dabbagh et al. Global Measles Mortality, 2000–2008; Morbidity & Mortality Weekly Report. 2009;58(47):1321-1326

[11]  Garenne & Gakusi. Health transitions in sub-Saharan Africa: overview of mortality trends in children under five years old (1950-2000);  Bull WHO June 2006, 84(6) p472

[12]  If you perform a ‘google’ search for ‘infant mortality rate’ or ‘under-5 mortality rate’ you will locate a google service that provides most of this data. It is downloadable in spreadsheet form by clicking on the ‘More info’ link. :Vaccine coverage data is available from the WHO website

[13]  Infant mortality rate is “under-1 year of age” mortality rate.

[15]  Garenne & Gakusi. Health transitions in sub-Saharan Africa: overview of mortality trends in children under five years old (1950-2000);  Bull WHO June 2006, 84(6) p472

Reducing the incidence of grand claims

This is the second post in our series critiquing the new booklet “The Science of Immunisation”, from the Australian Academy of Science. Here Greg Beattie takes a look at the opening statement from the summary.

“The widespread use of vaccines globally has been highly effective in reducing the incidence of infectious diseases and their associated complications, including death.”

– The Science of Immunisation (Australian Academy of Science)
The claim here is that vaccines reduced cases of infectious disease, and therefore, associated death and disability. This sounds good. It may or may not be true, but it certainly sounds good. One would expect it to be backed with solid evidence. Let’s have a look.

A good part of this has already been dealt with in a recent post by Meryl Dorey. Death-rates in Australia from some of the diseases we vaccinate against were discussed in the post, however, much more Australian data can be viewed in the following four posts I made to a debate on the issue:





Death graphs for USA and England can be found HERE, as well as HERE.

But what about developing nations? Well, it’s a bit trickier. Where Australia, USA, England, and Europe have meticulously recorded all deaths (and their causes) since the mid 1800s, the story was entirely different in the developing world. Deaths were rarely recorded. Even when they were there was virtually no information on what caused them. The World Bank overcame this missing data to an extent by conducting sample surveys over the past half century. These surveys estimate the infant mortality rate and the under-5 mortality rate. Here’s what they show in Africa and India:

Child death rates versus vaccination, Africa.
Child death rates versus vaccination, India

As we can see, the big push for vaccines from the 1980s onward (the finer lines shooting upward) appears to have had little if any effect on the trend in death rates in children (the two thick lines running from left to right). After viewing all the above my guess is you’ll feel we have little reason to credit vaccines with any role in saving lives. You’re free, however, to come to your own conclusions about that.

But what about incidence? That is, the number of cases of the illness, regardless of whether the affected person died. Did vaccines reduce this? The answer is… who knows? It’s actually impossible to tell: at least, not statistically. To explain, I’ll start by taking you back, almost 70 years, to a special book written by Darrell Huff. Regarded as one of the biggest selling books on statistics ever, “How to Lie with Statistics” was commonly used as an introductory textbook for statistics students.

It covers most of the pitfalls that await us when confronted with claims based on sample statistics. And what are ‘sample statistics’? Well, that’s what we work with when we don’t have the resources to measure the whole population. We take a sample and extrapolate our findings to the wider population. With deaths, we don’t use ‘samples’ because we are working with the whole set. As mentioned above, all are recorded (except in developing countries). With ‘cases’ of illness, however, it’s impossible to work with the whole set. No one knows how many cases of illness occur. We can only take a sample. Of course it’s important that our sample is representative: that is, it represents the whole population. We’ll have a look at this shortly, but first, let’s see what Huff had to say about sample statistics:

“The ‘population’ of a large area in China was 28 million. Five years later it was 105 million. Very little of that increase was real; the great difference could be explained only by taking into account the purposes of the two enumerations and the way people would be inclined to feel about being counted in each instance. The first census was for tax and military purposes, the second for famine relief.”

This was one of many examples he used to illustrate problems frequently lurking behind grand statistical claims. Huff takes us through the things we need to keep in mind, including non-representative samples and biased or poorly collected data, all of which lead to erroneous conclusions. He urges us to take a close look. Is the sample a true representation of the population, or is it skewed? Are the measurements free of bias? Are the investigators free of bias? Regarding ‘incidence’ data, he tells us:

“Many statistics, including medical ones that are pretty important to everybody, are distorted by inconsistent reporting at the source. There are startlingly contradictory figures on such delicate matters as abortions, illegitimate births, and syphilis. If you should look up the latest available figures on infuenza and pneumonia, you might come to the strange conclusion that these ailments are practically confined to three southern states, which account for about 80% of the reported cases. What actually explains this percentage is the fact that these three states required reporting of the ailments after other states had stopped doing so.

Some malaria figures mean as little. Where before 1940 there were hundreds of thousands of cases a year in the American South there are now only a handful, a salubrious and apparently important change that took place in just a few years. But all that has happened in actuality is that cases are now recorded only when proved to be malaria, where formerly the word was used in much of the South as a colloquialism for a cold or chill.”

Then there’s polio. Here’s what Huff had to say about polio figures BEFORE the first polio vaccine came into use:

“You may have heard the discouraging news that 1952 was the worst polio year in medical history. This conclusion was based on what might seem all the evidence anyone could ask for: There were far more cases reported in that year than ever before.

But when experts went back of these figures they found a few things that were more encouraging. One was that there were so many children at the most susceptible ages in 1952 that cases were bound to be at a record number if the rate remained level. Another was that a general consciousness of polio was leading to more frequent diagnosis and recording of mild cases. Finally, there was an increased financial incentive, there being more polio insurance and more aid available from the National Foundation for Infantile Paralysis. All this threw considerable doubt on the notion that polio had reached a new high, and the total number of deaths confirmed the doubt.”

Of course Huff couldn’t know the fate that awaited polio notifications afterward. The first polio vaccine was introduced in the same year the book was published, and after a few years in which polio numbers rose (yes, you read that correctly) the case definition for the illness was changed. It became more restrictive. This was the first of a series of revisions which led to a drop in cases being notified. This rendered the data gathered prior to the changes totally irreconcilable with that gathered after.

Huff’s conclusion:

“It is an interesting fact that the death rate or number of deaths often is a better measure of the incidence of an ailment than direct incidence figures — simply because the quality of reporting and record-keeping is so much higher on fatalities. In this instance, the obviously semiattached figure is better than the one that on the face of it seems fully attached.”

So what exactly are ‘incidence’ figures? How did we collect them, and why were there so many problems with them? Well, all good questions. Basically, we don’t have true incidence data. Instead we use something quite different, called notifications. We asked doctors to ‘notify’ certain illnesses when they saw them, so we could track cases. In other words, we asked them to send a record to their local health authority whenever one of their patients turned up with what looked like one of the diseases. But for a start, one obvious problem is these ‘notifications’ only included cases which visited a doctor. In the USA it’s been estimated only 3% of adult whooping cough cases are reported to the system. But even more concerning, doctors didn’t always consider it important to report cases they did see. One study in the USA, where reporting is mandated by law, found the rate ranged from 9% to 99%. The likelihood of a case being reported to the system depended largely on publicity.

Notifications had one purpose only: to enable a quick response to outbreaks. They were never meant to be used for retrospective assessment the impact of vaccination programs. One only needs to look at the history of whooping cough notification in Australia to confirm this. When mass vaccination for whooping cough commenced in the 1950s ALL STATES except South Australia stopped collecting notifications. Why would health authorities stop collecting figures which were supposed to record the great change?

But there are other major problems with the data. Some of these also apply to deaths figures, although to a lesser extent. First there was the problem of diagnosis. Doctors could seldom be sure which illness their patients had. Often it was a choice between whooping cough and bronchiolitis, croup or whatnot. Or between measles and roseola, rubella, rocky mountain spotted fever, and a host of others.

To complicate matters doctors were taught to use the vaccination status of a patient to help them make the decision. Textbooks would encourage them to diagnose other illnesses if the patient had been vaccinated. Governments (through their health bureaucracies) also encouraged this, and continue to do so. In this example the UK National Health Service exhorts doctors to check the patient’s vaccination history before diagnosing measles, mumps, rubella and whooping cough.

This is a no-no in statistics. It’s a cut and dried example of bias, obviously slanting the data and supporting the notion that vaccines reduced case numbers. How much did it slant the data? We’ll never know. All we know is it’s one of those big problems Huff warned us about.

Finally there was the problem of changing case definitions, as mentioned above with polio. We hear a lot about laboratory confirmation nowadays, but it wasn’t always so. For example, prior to the 1990s measles was diagnosed clinically: that is, it was decided after physical examination by a doctor. Since then, however, a measles case needed to be tested in a laboratory to ‘prove’ it was measles. When inexpensive testing first became available during the 1990s it was found that only a few percent of the cases initially diagnosed as measles passed the test ( link ). Again this led to the impression something had brought about a ‘real’ decline in measles.

In summary, it is perhaps impossible to know how much, if at all, vaccination influenced the rates of infectious disease. However the claim that it has substantially done so forms the backbone of the whole case for vaccination. Death trends appear to offer no support for this claim, and we have no properly collected incidence data. Without good evidence we’re left with little reason to vaccinate our children or ourselves.

Greg Beattie is author of “Vaccination: a Parent’s Dilemma” and “Fooling Ourselves on the Fundamental Value of Vaccines”. He can be contacted via his website:  

Experts call WHO & Bill Gates Foundation’s role in India’s polio eradication campaign unethical – Pharmabiz | The Refusers

It’s amazing that India can be declared polio-free by simply redefining polio and ignoring the 47,500 cases of acute flaccid paralysis and other forms of paralytic diseases which, prior to the vaccination campaign, would have been classed as polio. Presto-chango – the country is polio free and you no longer have polio though it looks and feels like you do. Oh, and the fact that you’re twice as likely to die from what you’ve got as you would if you had polio – that’s just the price you have to pay to keep big pharma in business.

The authors noted that while India was polio-free in 2011, in the same year, there were 47500 cases of NPAFP. While data from India’s National Polio Surveillance Project showed NPAFP rate increased in proportion to the number of polio vaccine doses received, independent studies showed that children identified with NPAFP “were at more than twice the risk of dying than those with wild polio infection.”’

via Experts call WHO & Bill Gates Foundation’s role in India’s polio eradication campaign unethical – Pharmabiz | The Refusers.

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