**Africa Fighting Malaria**

The blogger, Tim Lambert (aka Deltoid), regularly engages in the DDT debate by making ad hominem attacks on those who defend DDT in an effort to undermine their credibility. AFM has often been the target of such attacks and as a general policy, has not considered it a constructive use of our time to engage in these often misguided and pointless discussions. However, as Lambert recently blogged about a recent peer-reviewed paper that we published, we consider his comments too important to ignore.

Lambert recently posted a commentary on our peer-reviewed scientific paper investigating false claims made by UNEP and GEF about insecticide-free malaria control interventions in Mexico and Central America, accessible here.

Lambert begins his commentary with the statement "Roberts and Tren's key argument is that reductions in malaria in the Americas were not the result of Global Environmental Facility interventions but were caused by increased use of antimalarial drugs."

This opening comment misstates our argument entirely. The most important message of our paper was that UNEP/GEF/Stockholm Convention officials were promoting false information when they claimed that their project had controlled malaria with environmentally sound methods. From his comments, it seems as though Lambert never took the time to actually read the paper; rather it appears he has just tried to discredit data we included in one of the tables.

Evidence for a conclusion is presented in the 'Results' section of scientific papers; however, Lambert ignores the results section of our paper entirely. Our conclusion about false claims of UN officials is explained clearly in the results subsection "Claims about effectiveness of GEF project interventions." The falsehood of UNEP claims rests entirely on epidemiological analyses conducted by independent scientists, which we describe. The analyses were not ours, and our conclusion does not rely on data in the table at all. Data presented in the table is nothing more than an effort to explain how countries actually exerted control over malaria once environmentalists forced them to stop using insecticides. The tabular data had a limited and secondary role in proving UN officials were making fraudulent claims. Had Lambert actually read the paper, he could not possibly have missed that basic fact.

Lambert goes on to state reductions of more than 100% are impossible. Actually, there is no mathematical reason for not having a positive or negative percent value and you can have a percent value greater than 100. The validity of numerical values is dependent on the reader having a clear understanding of what the numerical values represent. This is true whether you call the value a percent, a proportion, or some other relative unit of measure.

The caption of our table in question reads: Table 1 Numbers of chloroquine pills distributed per diagnosed case of malaria in Mexico and seven countries of Central America for 1990 versus 2004 and percent change in numbers of pills per case and percent change in numbers of cases from 1990^35 to 2004^36

Lambert uses the example of Panama to inform his followers that we erred. He states "I checked the source for the column "pills/case in 2004" and found that all these numbers were incorrect, being too high by a factor 10. The correct number for Panama, for example, was 13.99, not 140."

If he had checked our literature citation (see reference 36), he would have discovered we cited data from two tables, Tables 7 and 8. Lambert pulled his stats from Table 8 and from the column heading "Number of first-line treatments available per case reported." Lambert obviously saw no disconnect in what is stated in our caption versus that column's heading. Our caption states, "The number of chloroquine pills distributed per diagnosed case of malaria," not "The number of first-line treatments."

The reason we cited two tables of data is because our values are derived from two tables, not one. The number of diagnosed cases was in Table 8 and the number of pills distributed per diagnosed case was in Table 7. So tabular data for number of pills distributed per diagnosed case in Panama, which Lambert claimed was incorrect, is composed of two variables—number of pills from Table 7 divided by number of cases from Table 8. Thus, for Panama in 2004, 712,852 pills (Table 7) divided by 5,095 cases (Table 8) equals 140 pills distributed per diagnosed case; the same value in our table.

In attacking data values in the table, Lambert states "The column appears to show the bigger number divided by the smaller." He is precisely correct. We did it that way so any reader could immediately understand what those values represented.

In the table we present number of pills per case for 1990 and 2004. For Panama, the number in 1990 was 202 and 140 in 2004. By dividing 202 (larger number) by 140, we get a quotient of 1.4428. This value multiplied by 100 is 144. Since fewer pills per case were distributed in 2004 than in 1990, we used a negative sign to show direction of change, -144. That is to say, there was a negative change of 144% in number of pills distributed per case in 2004 than in 1990. Although one might argue this is an 'improper' percent value, it is, nevertheless, a legitimate value. Since both dividend and divisor are presented in the table, even the most obdurate will understand how the quotient was derived, and how it was then converted to a percentage value. For clarity, with reversals in increasing or decreasing numbers of pills per case for different countries from 1990 to 2004, the role of dividend and divisor could be reversed. We noted such switches in parts of the equations by using a - or + symbol to indicate the direction of change. A plus meaning that number of pills per case increased from 1990 to 2004 (i.e., value for 1990 divided into 2004 value), and a negative value meaning number of pills per case decreased from 1990 to 2004 (i.e., value for 2004 divided into 1990 value). There is nothing mathematically wrong with this and the method was used for purposes of simplicity and clarity. For consistency, the same process was used for generating all endpoint data in Table 1.

Lambert can rage about these values but the real question is: did they present a mathematically valid, clear and succinct message? The answer is: yes, they did.

Lambert states, "all these numbers were incorrect." In fact, as we have demonstrated, the numbers were precisely correct and the misunderstanding was a consequence of his carelessness. Lambert does not understand that evidence for our conclusion was not in the table in the first place, it was in the results section of the paper.

Lambert's claim about decreasing numbers of malaria cases invalidating our conclusions is total nonsense. Obviously, Lambert does not understand most countries distribute drugs according to a ratio of one curative treatment per diagnosed case. When those ratios change and a program is distributing far more drugs than needed for cure of diagnosed cases then, by definition, drugs are being used to suppress malaria, not just treat infections, per se. This relationship is true regardless of numbers of diagnosed cases and regardless of the scale of numbers of excess drugs distributed per diagnosed case. We will not dignify further his meaningless commentary.

Some of Lambert's devoted readers have encouraged him to write a rebuttal to the journal. We hope he follows this advice, as we would greatly appreciate the opportunity of writing a formal response. For far too long Lambert has relied on false and tendentious arguments to launch personal attacks on those with whom he disagrees. His campaign against DDT harms malaria control and feeds into an agenda that has imposed great harm on the world's poor.

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