Ryan T. Moore

Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme

BACKGROUND We assessed aspects of Seguro Popular, a programme aimed to deliver health insurance, regular and preventive medical care, medicines, and health facilities to 50 million uninsured Mexicans. METHODS We randomly assigned treatment within 74 matched pairs of health clusters-ie, health facility catchment areas-representing 118 569 households in seven Mexican states, and measured outcomes in a 2005 baseline survey (August, 2005, to September, 2005) and follow-up survey 10 months later (July, 2006, to August, 2006) in 50 pairs (n=32 515). The treatment consisted of encouragement to enrol in a health-insurance programme and upgraded medical facilities. Participant states also received funds to improve health facilities and to provide medications for services in treated clusters. We estimated intention to treat and complier average causal effects non-parametrically. FINDINGS Intention-to-treat estimates indicated a 23% reduction from baseline in catastrophic expenditures (1.9% points; 95% CI 0.14-3.66). The effect in poor households was 3.0% points (0.46-5.54) and in experimental compliers was 6.5% points (1.65-11.28), 30% and 59% reductions, respectively. The intention-to-treat effect on health spending in poor households was 426 pesos (39-812), and the complier average causal effect was 915 pesos (147-1684). Contrary to expectations and previous observational research, we found no effects on medication spending, health outcomes, or utilisation. INTERPRETATION Programme resources reached the poor. However, the programme did not show some other effects, possibly due to the short duration of treatment (10 months). Although Seguro Popular seems to be successful at this early stage, further experiments and follow-up studies, with longer assessment periods, are needed to ascertain the long-term effects of the programme.

Human Cancer Cells Commonly Acquire DNA Damage during Mitotic Arrest

The mitotic checkpoint is a mechanism that arrests the progression to anaphase until all chromosomes have achieved proper attachment to mitotic spindles. In cancer cells, satisfaction of this checkpoint is frequently delayed or prevented by various defects, some of which have been causally implicated in tumorigenesis. At the same time, deliberate induction of mitotic arrest has proved clinically useful, as antimitotic drugs that interfere with proper chromosome-spindle interactions are effective anticancer agents. However, how mitotic arrest contributes to tumorigenesis or antimitotic drug toxicity is not well defined. Here, we report that mitotic chromosomes can acquire DNA breaks during both pharmacologic and genetic induction of mitotic arrest in human cancer cells. These breaks activate a DNA damage response, occur independently of cell death, and subsequently manifest as karyotype alterations. Such breaks can also occur spontaneously, particularly in cancer cells containing mitotic spindle abnormalities. Moreover, we observed evidence of some breakage in primary human cells. Our findings thus describe a novel source of DNA damage in human cells. They also suggest that mitotic arrest may promote tumorigenesis and antimitotic toxicity by provoking DNA damage.

Blocking for Sequential Political Experiments.

In typical political experiments, researchers randomize a set of households, precincts, or individuals to treatments all at once, and characteristics of all units are known at the time of randomization. However, in many other experiments, subjects “trickle in” to be randomized to treatment conditions, usually via complete randomization. To take advantage of the rich background data that researchers often have (but underutilize) in these experiments, we develop methods that use continuous covariates to assign treatments sequentially. We build on biased coin and minimization procedures for discrete covariates and demonstrate that our methods outperform complete randomization, producing better covariate balance in simulated data. We then describe how we selected and deployed a sequential blocking method in a clinical trial and demonstrate the advantages of our having done so. Further, we show how that method would have performed in two larger sequential political trials. Finally, we compare causal effect estimates from differences in means, augmented inverse propensity weighted estimators, and randomization test inversion.

Innovator Organizations in New Drug Development: Assessing the Sustainability of the Biopharmaceutical Industry

The way new medicines are discovered and brought to market has fundamentally changed over the last 30 years. Our previous analysis showed that biotechnology companies had contributed significantly to the US Food and Drug Administration approval of new molecular entities up to the mid-1980s, when the trends started to decline. Although intriguing, the focus on biotechnology necessarily precluded the wider question of how the biopharmaceutical industry has been delivering on its goals to develop new drugs. Here, we present a comprehensive analysis of all biopharmaceutical innovators and uncover unexpected findings. The present biopharmaceutical industry grew steadily from 1800 to 1950 and then stagnated for two decades, before a burst of growth attributable to the biotechnology revolution took place; but consolidation has reduced the number of active and independent innovators to a level not experienced since 1945. The trajectories and trends we observe raise fundamental questions about biopharmaceutical innovators and the sustainability of the drug-development enterprise.

The Job Market's First Steps: Using Research Tools to Simplify the Process

Excitement about the political science job market builds around the time of the Labor Day Annual Meeting of the APSA, when schools start to post their openings for the next year. As we entered the job market, we found ourselves repeatedly collecting information about available positions as we prepared application materials. We monitored APSA’s eJobs website, cut and pasted relevant job information into a single spreadsheet, and assembled letters using that information. Here, we introduce free and open-source tools to automate these data collection and letter generation procedures using R and LaTeX. Our system minimizes manual data entry by extracting and creating a spreadsheet from APSA’s eJobs information. We walk applicants through the initial job search steps, including using eJobs, compiling position information, and producing attractive letters. As we entered the job market, we found ourselves spending hours collecting information about job openings and preparing applications to send to hiring committees. First, we repeatedly transferred information about dozens of jobs from the web to a single spreadsheet. This process involved line-by-line cutting and pasting for every position to which we applied (most of which were drawn from APSA’s eJobs). Second, we manually created customized letters that drew information from our spreadsheet. As users of LaTeX, a free and open-source platform for creating professionally typeset documents, we found no off-the-shelf mailmerge procedure in LaTeX that accepted a spreadsheet as an input.1 LaTeX is increasingly used by social scientists and taught to graduate students in political science programs because of its flexibility, quality, and affordability (it’s free!).We found ourselves wishing we could automate these processes to populate our jobs spreadsheet more quickly and then generate attractive mail-merged letters for potential employers. To save job-seekers time and effort, we here introduce muRL,2 a set of tools for collecting job information and preparing cover letters and mailing labels. These tools can be applied to any mailmerge task (e.g., letters of recommendation), and we provide guidance on special methods to simplify job searches in the field of political science. There are several benefits to our approach. We are able to automate the data entry of job information from APSA’s eJobs listings. Using R and LaTeX, we can create handsome documents and minimize the effort dedicated to word processing and formatting tasks, allowing applicants to focus on creating highquality content to send to hiring committees. Finally, by automating the creation of letters, we can help prevent small mistakes with potentially large consequences: no search committee member from University X wants to read a cover letter that touts an applicant as “a great match for the position at University Y”! The next section briefly sketches the job market process and the importance of the cover letter. We then detail the benefits of our approach and how it can be used in your own political science job search. Along the way, we introduce the eJobs interface to readers who may be unfamiliar with this tool. OVERVIEW OF THE JOB MARKET PROCESS The process of applying for an academic job follows predictable rhythms, and we advise applicants to review the work of other scholars that thoroughly details these patterns in political science (Carter and Scott 1998; Drezner 1998; Simien 2002). We provide a synopsis here, focusing on the creation of the application packet. Departments begin posting jobs through APSA’s eJobs listing service toward the end of the summer. Applications are generally due between early September and December, with most deadlines occurring in October and November. The eJobs listing is a primary source for U.S. academic jobs, although other sources may be consulted.3 After selecting a list of potential jobs, the applicant prepares a packet of materials that search committees will consult in making their decisions about which candidates to bring in for interviews and ultimately hire.4 The typical job market packet consists of a cover letter, a curriculum vitae, three letters of recommendation, and one or two writing samples. As Carter and Ryan T. Moore is an assistant professor of political science at Washington University in St. Louis. He is currently a Robert Wood Johnson Foundation Scholar in Health Policy Research at the University of California, Berkeley, and the University of California, San Francisco. He can be reached at rtm@wustl.edu. Andrew Reeves is an assistant professor of political science at Boston University. His research focuses on elections and political behavior. He can be reached at areeves@bu.edu. T h e P r o f e s s i o n ............................................................................................................................................................................................................................................................. doi:10.1017/S1049096511000230 PS • April 2011 385 Scott have noted, “Without being overly ostentatious, the package should be attractive and effectively organized, and the most relevant and important information that you want to convey should jump out at the reader who just ‘skims’ the materials” (1998, 617). The applicant will assemble the packets and mail them to the institutions to which he or she is applying. The administrative task of mailing 10, 20, 50, or even hundreds of these documents can be daunting; muRL tames the process by automating several of the most time-intensive tasks.

Public Opinion and the State Politics of the Affordable Care Act

Since the passage of the Patient Protection and Affordable Care Act, several attempts have been made to block its provisions in the states. Among these, ballot propositions challenging the individual mandate have occurred in five states, with four more scheduled for November 2012. We first provide state-level estimates of public opinion on the ACA since the beginning of 2010, and we show that, consistent with models of partisan resonance, polarization of public opinion is greatest near elections that politicize health care. We then use synthetic control methods to estimate the causal effects of the high-profile public campaigns surrounding these proposition elections, finding these effects to be conditioned by the broader political context of the campaign. In Ohio, where the campaign took place without simultaneous major candidate elections, we find effects on opinion of about seven percentage points. These effects are fairly short-lived, persisting a few months.