Data is supposed to settle arguments. It tells us what is happening, how serious a problem is, who is affected and, importantly, what governments should do about it. But what happens when data itself becomes part of the problem?
One of the clearest examples comes from the tobacco industry. For decades, tobacco companies did not necessarily need to prove that smoking was safe. They only needed to create enough uncertainty for people to believe that the science was still unsettled. That distinction is important. When evidence cannot be defeated, confusing the public about the evidence can be almost as effective.
In Kenya, the push to pass the Tobacco Control (Amendment) Bill has sparked intense public and political conflict. The bill’s primary aim is to close loopholes in the original 2007 Act by introducing stringent restrictions on modern non-traditional products, including a ban on most flavours, limits on nicotine concentrations, and strict bans on digital marketing to protect young people.
An investigation reported that a tobacco company successfully lobbied for reduced health-warning requirements on nicotine pouches. That case is primarily about policy influence rather than statistical manipulation, but it illustrates the wider ecosystem: shaping what policymakers see, what consumers are told and how risk is framed.
Independent research compiled by the Tobacco Control Data Initiative (TCDI) and health groups shows that tobacco use results in a massive net economic loss. In Kenya, for every dollar collected or earned from tobacco, the economy loses between KES 297 and KES 405 in healthcare spending and lost productivity due to tobacco-related diseases.
Investigations of previously confidential tobacco-industry documents found that companies cultivated networks of sympathetic scientists, funded organisations that appeared independent, promoted non-peer-reviewed research and suppressed unfavourable findings. Internal documents also showed that the industry knew nicotine was addictive while publicly resisting that conclusion. The lesson is bigger than tobacco: data can be manipulated without necessarily inventing a single number.
Researchers can be selected because they produce favourable results. An inconvenient finding can be excluded. A denominator can be changed. A short-term outcome can be presented while long-term consequences are ignored. A statistically significant association can be described as meaningless, while a favourable association is presented as proof. And perhaps most deceptively, uncertainty can be exaggerated until the public concludes that “we simply do not know.”
To terrify policymakers into killing the flavour bans and nicotine restrictions, industry-backed campaigns have flooded Kenyan mainstream media and parliamentary submissions with a highly specific statistic: claiming that an illicit black market already accounts for 45% to 50% of Kenya’s tobacco market. Why does this matter? Because the objective is not always to deny harm directly. Sometimes the objective is to make regulation appear harmful. If an industry can persuade policymakers that taxation will cause an enormous explosion in illicit trade, for example, the debate shifts from “How do we protect health?” to “Can we afford to regulate?”
Kenya therefore provides an important reminder: we should never confuse an industry’s data with independent evidence simply because the numbers are presented in a professional report.
The tactics are also evolving. In 2024, investigations reported that a major global tobacco company had used third parties and concealed funding arrangements to promote research and advocacy around heated tobacco products in Japan. The company disputed the allegations and described its activities as legitimate regulatory engagement.
Governments, researchers and the media should ask simple but uncomfortable questions: Who funded the study? Who designed it? What data were excluded? Can the methodology be independently replicated? Were alternative findings considered? Does the conclusion actually follow from the evidence?
The tobacco experience teaches us that misinformation does not always look like a lie. Sometimes it looks like a sophisticated spreadsheet, a respectable expert, a carefully selected statistic or a legitimate-sounding report.
And that is precisely why manipulated data can be so dangerous: it does not merely distort what we know; it can distort what governments decide to do about what we know.
Simon Mwangi is the Deputy Director of Corporate Communications at NACADA.
