David N. Reshef

Factors Related to Increasing Prevalence of Resistance to Ciprofloxacin and Other Antimicrobial Drugs in Neisseria gonorrhoeae, United States

What would you do if you had a sexually transmitted disease that was untreatable with antibiotics? That is the situation we may be heading toward. In the United States, gonorrhea is the second most common reportable infection. Over the years, the organism that causes it, N. gonorrhoeae, has acquired resistance to several classes of antibiotics including, most recently, the fluoroquinolones. In fact, widespread resistance led CDC to stop recommending fluoroquinolones for gonorrhea treatment in 2007. Today, cephalosporin-based combination therapy is the last remaining option currently recommended for gonorrhea treatment. Understanding of the causes of drug resistance is needed so that control measures can be improved and the effectiveness of the few remaining drugs can be maintained. This article investigates possible causes for the emergence of fluoroquinolone-resistant N. gonorrhoeae that occurred several years ago. Fluoroquinolone-resistant strains spread in the United States in the late 1990s and spread more rapidly among men who have sex with men (MSM) than among heterosexual men. One possible explanation for the rise in drug resistance, especially among heterosexuals, is acquisition of resistant gonorrhea through travel. Certain drug-resistant strains of N. gonorrhoeae, particularly the multidrug resistant strains (also resistant to penicillin and tetracycline) circulating among MSM, seemed to be able to reach high prevalence levels through domestic transmission, rather than through frequent importation. After resistance emerged in a geographic area, resistant strains appeared among MSM and heterosexuals within several months. When resistance is detected in either MSM or heterosexuals, prevention efforts should be directed toward both populations.

Oseltamivir for treatment and prevention of pandemic influenza A/H1N1 virus infection in households, Milwaukee, 2009.

BackgroundDuring an influenza pandemic, a substantial proportion of transmission is thought to occur in households. We used data on influenza progression in individuals and their contacts collected by the City of Milwaukee Health Department (MHD) to study the transmission of pandemic influenza A/H1N1 virus in 362 households in Milwaukee, WI, and the effects of oseltamivir treatment and chemoprophylaxis.Methods135 households had chronological information on symptoms and oseltamivir usage for all household members. The effect of oseltamivir treatment and other factors on the household secondary attack rate was estimated using univariate and multivariate logistic regression with households as the unit of analysis. The effect of oseltamivir treatment and other factors on the individual secondary attack rate was estimated using univariate and multivariate logistic regression with individual household contacts as the unit of analysis, and a generalized estimating equations approach was used to fit the model to allow for clustering within households.ResultsOseltamivir index treatment on onset day or the following day (early treatment) was associated with a 42% reduction (OR: 0.58, 95% CI: 0.19, 1.73) in the odds of one or more secondary infections in a household and a 50% reduction (OR: 0.5, 95% CI: 0.17, 1.46) in the odds of a secondary infection in individual contacts. The confidence bounds are wide due to a small sample of households with early oseltamivir index usage - in 29 such households, 5 had a secondary attack. Younger household contacts were at higher risk of infection (OR: 2.79, 95% CI: 1.50-5.20).ConclusionsEarly oseltamivir treatment may be beneficial in preventing H1N1pdm influenza transmission; this may have relevance to future control measures for influenza pandemics. Larger randomized trials are needed to confirm this finding statistically.

Cleaning up the record on the maximal information coefficient and equitability.

Although we appreciate Kinney and Atwal’s interest in equitability and maximal information coefficient (MIC), we believe they misrepresent our work. We highlight a few of our main objections below. Fig. 1. Equitability of MIC and mutual information under a range of noise models. The equitability of MIC and mutual information across a subset of noise models analyzed in refs. 1 and 4. For each noise model, the relationships tested are as in ref. 4. In each ... Regarding our original paper (1), Kinney and Atwal (2) state “MIC is said to satisfy not just the heuristic notion of equitability, but also the mathematical criterion of R2 equitability,” the latter being their formalization of the heuristic notion that we introduced. This statement is simply false. We were explicit in our paper that our claims regarding MIC’s performance were based on large-scale simulations: “We tested MIC’s equitability through simulations….[These] show that, for a large collection of test functions with varied sample sizes, noise levels, and noise models, MIC roughly equals the coefficient of determination R2 relative to each respective noiseless function.” Although we mathematically proved several things about MIC, none of our claims imply that it satisfies Kinney and Atwal’s R2 equitability, which would require that MIC exactly equal R2 in the infinite data limit. Thus, their proof that no dependence measure can satisfy R2 equitability, although interesting, does not uncover any error in our work, and their suggestion that it does is a gross misrepresentation. Kinney and Atwal seem ready to toss out equitability as a useful criterion based on their theoretical result. We argue, however, that regardless of whether “perfect” equitability is possible, approximate notions of equitability remain the right goal for many data exploration settings. Just as the theory of NP completeness does not suggest we stop thinking about NP complete problems, but instead that we look for approximations and solutions in restricted cases, an impossibility result about perfect equitability provides focus for further research, but does not mean that useful solutions are unattainable. Similarly, as others have noted (3), Kinney and Atwal’s proof requires a highly permissive noise model, and so the attainability of R2 equitability under more limited noise models such as those in our work remains an open question. Finally, the authors argue that mutual information is more equitable than MIC. However, they provide as justification only a single noise model, only at limiting sample sizes (n≥5,000). As we’ve shown in follow-up work (4), which they themselves cite but fail to address, MIC is more equitable than mutual information estimation under many other realistic noise models even at a sample size of 5,000. Kinney and Atwal have stated, “…it matters how one defines noise” (5), and a useful statistic must indeed be robust to a wide range of noise models. Equally importantly, we’ve established in both our original and follow-up work that at sample size regimes less than 5,000, MIC is more equitable than mutual information estimates across all noise models tested. MIC’s superior equitability in these settings is not an “artifact” we neglected—as Kinney and Atwal suggest—but rather a weakness of mutual information estimation and an important consideration for practitioners. We expect that the understanding of equitability and MIC will improve over time and that better methods may arise. However, accurate representations of the work thus far will allow researchers in the area to most productively and collectively move forward.

An empirical study of the maximal and total information coefficients and leading measures of dependence

In exploratory data analysis, we are often interested in identifying promising pairwise associations for further analysis while filtering out weaker ones. This can be accomplished by computing a measure of dependence on all variable pairs and examining the highest-scoring pairs, provided the measure of dependence used assigns similar scores to equally noisy relationships of different types. This property, called equitability and previously formalized, can be used to assess measures of dependence along with the power of their corresponding independence tests and their runtime. Here we present an empirical evaluation of the equitability, power against independence, and runtime of several leading measures of dependence. These include the two recently introduced and simultaneously computable statistics MICe, whose goal is equitability, and TICe, whose goal is power against independence. Regarding equitability, our analysis finds that MICe is the most equitable method on functional relationships in most of the settings we considered. Regarding power against independence, we find that TICe and Heller and Gorfine’s SDDP share state-of-the-art performance, with several other methods achieving excellent power as well. Our analyses also show evidence for a trade-off between power against independence and equitability consistent with recent theoretical work. Our results suggest that a fast and useful strategy for achieving a combination of power against independence and equitability is to filter relationships by TICe and then to rank the remaining ones using MICe. We confirm our findings on a set of data collected by the World Health Organization.