Data and Methods
Jeanne Altmann • Elizabeth Armstrong • Dalton Conley • Noreen Goldman • Bryan Grenfell • Sara F. McLanahan • C. Jessica E. Metcalf • Germán Rodríguez • Marta Tienda • James Trussell • Tom S. Vogl • Yu Xie
Jeanne Altmann collaborated on “Female and Male Life Tables for Seven Wild Primate Species,” published in Scientific Data, with Bronikowski, A. (Iowa State University) et al. The authors provide male and female census count data, age-specific survivorship, and female age-specific fertility estimates for populations of seven wild primates that have been continuously monitored for at least 29 years: sifaka (Propithecus verreauxi) in Madagascar; muriqui (Brachyteles hypoxanthus) in Brazil; capuchin (Cebus capucinus) in Costa Rica; baboon (Papio cynocephalus) and blue monkey (Cercopithecus mitis) in Kenya; chimpanzee (Pan troglodytes) in Tanzania; and gorilla (Gorilla beringei) in Rwanda. Using one-year age-class intervals, they computed point estimates of age-specific survival for both sexes. In all species, their survival estimates for the dispersing sex are affected by heavy censoring. They also calculated reproductive value, life expectancy, and mortality hazards for females. They used bootstrapping to place confidence intervals on life-table summary metrics (R0, the net reproductive rate; ?, the population growth rate; and G, the generation time). These data have high potential for reuse; they derive from continuous population monitoring of long-lived organisms and will be invaluable for addressing questions about comparative demography, primate conservation and human evolution.
Elizabeth Armstrong and Miranda Waggoner are working on a book manuscript that looks at the uses of data from the Dutch Hunger Winter. During the winter of 1944-45, Nazi forces occupied the western provinces of the Netherlands, cutting off food and fuel shipments to the area. A severe famine ensued, which came to be known as the Dutch Hunger Winter, affecting some 4-5 million people. The health consequences of the famine have been extensively studied; in particular, data on the effects of exposure to famine in utero collected through the Dutch Famine Birth Cohort Study have become paradigmatic within epidemiology and in the emerging field of epigenetics. In addition, these data have been discussed extensively in the obstetric literature, the popular press, and increasingly, in social sciences like economics. This project examines patterns of dissemination and interpretation of evidence from the Dutch Hunger Winter through time and disciplinary space.
Dalton Conley, Domingue, B. (Stanford University), Wedow, R. (University of Colorado), McQueen, M. (New York University), Hoffman, T. (University of California, San Francisco), and Boardman, J. (University of Colorado) published “Genome-Wide Estimates of Heritability for Social Demographic Outcomes,” in Biodemography and Social Biology. An increasing number of studies that are widely used in the demographic research community have collected genome-wide data from their respondents. It is therefore important that demographers have a proper understanding of some of the methodological tools needed to analyze such data. This article details the underlying methodology behind one of the most common techniques for analyzing genome-wide data, genome-wide complex trait analysis (GCTA). GCTA models provide heritability estimates for health, health behaviors, or indicators of attainment using data from unrelated persons. The authors’ goal was to describe this model, highlight the utility of the model for bio-demographic research, and demonstrate the performance of this approach under modifications to the underlying assumptions. The first set of modifications involved changing the nature of the genetic data used to compute genetic similarities between individuals (the genetic relationship matrix). They then explored the sensitivity of the model to heteroscedastic errors. In general, GCTA estimates are found to be robust to the modifications proposed here, but they also highlight potential limitations of GCTA estimates.
Noreen Goldman’s co-authored paper with Downer, B (University of Texas, Galveston), González-González ,C. (University of Colima, Mexico), Pebley, A. (University of California, Los Angeles), and Rebeca Wong (University of Texas Galveston), “The Effect of Adult Children Living in the United States on the Likelihood of Cognitive Impairment for Older Parents Living in Mexico,” was published in Ethnicity and Health. The increased risk for poor physical and mental health outcomes for older parents in Mexico who have an adult child living in the United States may contribute to an increased risk for cognitive impairment in this population. The objective of this study was to examine if older adults in Mexico who have one or more adult children living in the United States are more or less likely to develop cognitive impairment over an 11-year period compared to older adults who do not have any adult children living in the United States. Data for this study came from Wave I (2001) and Wave III (2012) of the Mexican Health and Aging Study. The final sample included 2609 participants aged 60 and over who were not cognitively impaired in 2001 and had one or more adult children (age =15). Participants were matched using a propensity score that was estimated with a multivariable logistic regression model that included sociodemographic characteristics and migration history of the older parents. The results found that having one or more adult children living in the United States is associated with lower socioeconomic status and higher number of depressive symptoms, but greater social engagement for older parents living in Mexico. No significant differences in the odds for developing cognitive impairment according to having one or more adult children living in the United States were detected. In summary, having one or more adult children living in the United States was associated with characteristics that may increase and decrease the risk for cognitive impairment. This may contribute to the non-significant relationship between migration status of adult children and likelihood for cognitive impairment for older parents living in Mexico.
Noreen Goldman, Glei, D. (Georgetown University), and Weinstein, M. (Georgetown University) published “What Matters Most for Predicting Survival? A Multinational Population-based Cohort Study,” in PLoS ONE. Despite myriad efforts among social scientists, epidemiologists, and clinicians to identify variables with strong linkages to mortality, few researchers have evaluated statistically the relative strength of a comprehensive set of predictors of survival. Here, they determine the strongest predictors of five-year mortality in four national, prospective studies of older adults. They analyze nationally representative surveys of older adults in four countries with similar levels of life expectancy: England (n = 6113, ages 52+), the U.S (n = 2023, ages 50+), Costa Rica (n = 2694, ages 60+), and Taiwan (n = 1032, ages 53+). Each survey includes a broad set of demographic, social, health, and biological variables that have been shown previously to predict mortality. They rank 57 predictors, 25 of which are available in all four countries, net of age and sex. They use the area under the receiver operating characteristic curve and assess robustness with additional discrimination measures. They demonstrate consistent findings across four countries with different cultural traditions, levels of economic development, and epidemiological transitions. Self-reported measures of instrumental activities of daily living limitations, mobility limitations, and overall self-assessed health are among the top predictors in all four samples. C-reactive protein, additional inflammatory markers, homocysteine, serum albumin, three performance assessments (gait speed, grip strength, and chair stands), and exercise frequency also discriminate well between decedents and survivors when these measures are available. They identify several promising candidates that could improve mortality prediction for both population-based and clinical populations. Better prognostic tools are likely to provide researchers with new insights into the behavioral and biological pathways that underlie social stratification in health and may allow physicians to have more informed discussions with patients about end-of-life treatment and priorities.
In “Why are Well-Educated Muscovites More Likely to Survive? Understanding the Biological Pathways,” Noreen Goldman, Todd, M., Shkolnikov, V. (Max Planck Institute for Demographic Research) state there are large socioeconomic disparities in adult mortality in Russia, although the biological mechanisms are not well understood. With data from the study of Stress, Aging, and Health in Russia (SAHR), the authors use Gompertz hazard models to assess the relationship between educational attainment and mortality among older adults in Moscow and to evaluate biomarkers associated with inflammation, neuroendocrine function, heart rate variability, and clinical cardiovascular and metabolic risk as potential mediators of that relationship. They do this by assessing the extent to which the addition of biomarker variables into hazard models of mortality attenuates the association between educational attainment and mortality. They find that an additional year of education is associated with about 5% lower risk of age-specific all-cause and cardiovascular mortality. Inflammation biomarkers are best able to account for this relationship, explaining 25% of the education-all-cause mortality association, and 35% of the education-cardiovascular mortality association. Clinical markers perform next best, accounting for 13% and 23% of the relationship between education and all-cause and cardiovascular mortality, respectively. Although heart rate biomarkers are strongly associated with subsequent mortality, they explain very little of the education-mortality link. Neuroendocrine biomarkers fail to account for any portion of the link. These findings suggest that inflammation may be important for understanding mortality disparities by socioeconomic status.
Bryan Grenfell’s research is at the interface between theoretical models and empirical data. Grenfell and his lab members investigate the population dynamics of infectious diseases, focusing on their epidemiological and evolutionary dynamics and control by vaccination. They approach these problems by working at the interface of theoretical models and empirical data. They are especially interested in understanding the nonlinear spatio-temporal dynamics of acute immunizing infections and how these are affected by control strategies. They are continuing focus on measles and exploring comparative dynamics of a range of pathogens, including influenza, rotavirus, RSV, Norovirus, HIV, HCV, and veterinary morbilliviruses. The lab also explores phylodynamics, in particular how pathogen phylogenies are affected by host immunity, transmission bottlenecks and epidemic dynamics at scales from individual host to the population level. Finally, they are keen on exploring ‘cross-scale’ dynamics of pathogens: from within-host dynamics to the population scale and especially the impact of human behavioral dynamics.
Bryan Grenfell's Lab project, “Population Dynamics of Infectious Disease ~ Infectious Disease Modeling Project,” studies the spatio-temporal dynamics of infectious disease. Apart from its public health importance, measles provides an ideal paradigm for understanding nonlinear population interactions. Their lab has made significant developments in epidemic modeling methodology, time series methods, and analysis of large spatio-temporal data sets. In collaboration with others, they have used the resulting methods to quantify the dynamics of measles epidemics, in a range of settings and the impact of vaccination strategies against measles and other childhood infections. Their analyses reveal a wide spectrum of dynamic behavior in large populations, spanning limit cycles in pre-vaccination U.K. to chaotic dynamics in more seasonally-driven contexts.
They use gravity models, adapted from transportation theory, to capture and explain key features of measles metapopulation dynamics in developed countries (i.e. England and Wales). Recent development includes refinement of these gravity models to better understand the relative importance of core-satellite dynamics and local metapopulations to the persistence of measles. A big recent thrust of the group has been generalizing these results to explore comparative dynamics of a range of pathogens, including influenza, rotavirus, RSV, Norovirus, HIV, HCV and veterinary morbilliviruses.
The lab also looks at the dynamics of viral evolution – phylodynamics. In particular, they are interested in the question of how pathogen phylogenies are affected by host immunity, transmission bottlenecks, and epidemic dynamics at scales from individual host to population. Grenfell and colleagues coined the term phylodynamics, describing the feedback between epidemiological and evolutionary dynamics of pathogens, in a paper on immune escape in influenza. They are currently exploring how the impact of vaccination impacts the phylodynamics of a range of infections.
Their recent work includes:
• Linking within-host, individual level and population dynamics of measles and other infections;
• Exploring epidemiological and evolutionary implications of novel broad spectrum influenza vaccines;
• Population dynamics and control of rotavirus;
• Synthesizing epidemic dynamics of immunizing infections with the spatiotemporal economic dynamics of vaccination and the impact of vaccine hesitancy;
• The dynamics and control of HIV, typhoid and hand foot and mouth disease (HFMD).
The Great Recession was marked by severe negative shocks to labor market conditions. Sara McLanahan, Schneider, D. (University of California, Berkeley), and Harknett, K. (University of Pennsylvania) combined longitudinal data from the Fragile Families and Child Wellbeing Study with U.S. Bureau of Labor Statistics data on local area unemployment rates to examine the relationship between adverse labor market conditions and mothers’ experiences of abusive behavior between 2001 and 2010. Unemployment and economic hardship at the household level were positively associated with abusive behavior. Further, rapid increases in the unemployment rate increased men’s controlling behavior toward romantic partners even after adjusting for unemployment and economic distress at the household level. The authors hypothesized that the uncertainty and anticipatory anxiety that go along with sudden macroeconomic downturns have negative effects on relationship quality, above and beyond the effects of job loss and material hardship.
C. Jessica Metcalf, Lessler, J. (Johns Hopkins University), Cutts, F. (London School of Hygiene and Tropical Medicine), and Grenfell, B. published “Impact on Epidemic Measles of Vaccination Campaigns Triggered by Disease Outbreaks or Serosurveys: A Modeling Study,” in Plos Medicine. Background - Routine vaccination supplemented by planned campaigns occurring at 2–5 y intervals is the core of current measles control and elimination efforts. Yet, large, unexpected outbreaks still occur, even when control measures appear effective. Supplementing these activities with mass vaccination campaigns triggered when low levels of measles immunity are observed in a sample of the population (i.e., serosurveys) or incident measles cases occur may provide a way to limit the size of outbreaks.
Measles incidence was simulated using stochastic age-structured epidemic models in settings conducive to high or low measles incidence, roughly reflecting demographic contexts and measles vaccination coverage of four heterogeneous countries: Nepal, Niger, Yemen, and Zambia. Uncertainty in underlying vaccination rates was modeled. Scenarios with case- or serosurvey-triggered campaigns reaching 20% of the susceptible population were compared to scenarios without triggered campaigns. The best performing of the tested case-triggered campaigns prevent an average of 28,613 (95% CI 25,722–31,505) cases over 15 y in our highest incidence setting and 599 (95% CI 464–735) cases in the lowest incidence setting. Serosurvey-triggered campaigns can prevent 89,173 (95% CI, 86,768–91,577) and 744 (612–876) cases, respectively, but are triggered yearly in high-incidence settings. Triggered campaigns reduce the highest cumulative incidence seen in simulations by up to 80%. While the scenarios considered in this strategic modeling exercise are reflective of real populations, the exact quantitative interpretation of the results is limited by the simplifications in country structure, vaccination policy, and surveillance system performance. Careful investigation into the cost-effectiveness in different contexts would be essential before moving forward with implementation.
Serologically triggered campaigns could help prevent severe epidemics in the face of epidemiological and vaccination uncertainty. Hence, small-scale serology may serve as the basis for effective adaptive public health strategies, although, in high-incidence settings, case-triggered approaches are likely more efficient.
C. Jessica Metcalf, Gandon, S. (CNRS–Université de Montpellie), Day, T. (Queen's University, Canada), and Grenfell, B. published “Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases,” in Trends Ecology & Evolution. Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. The authors review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting.
C. Jessica Metcalf, Wesolowski, A. (Johns Hopkins University), Buckee, C. (Harvard University), and Engø-Monsen, K. (Telenor Research) co-authored “Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data,” which was published in the Journal of Infectious Diseases. Human travel can shape infectious disease dynamics by introducing pathogens into susceptible populations or by changing the frequency of contacts between infected and susceptible individuals. Quantifying infectious disease–relevant travel patterns on fine spatial and temporal scales has historically been limited by data availability. The recent emergence of mobile phone calling data and associated locational information means that we can now trace fine scale movement across large numbers of individuals. However, these data necessarily reflect a biased sample of individuals across communities and are generally aggregated for both ethical and pragmatic reasons that may further obscure the nuance of individual and spatial heterogeneities. Additionally, as a general rule, the mobile phone data are not linked to demographic or social identifiers, or to information about the disease status of individual subscribers (although these may be made available in smaller-scale specific cases). Combining data on human movement from mobile phone data–derived population fluxes with data on disease incidence requires approaches that can tackle varying spatial and temporal resolutions of each data source and generate inference about dynamics on scales relevant to both pathogen biology and human ecology. Here, they review the opportunities and challenges of these novel data streams, illustrating our examples with analyses of two different pathogens in Kenya, and conclude by outlining core directions for future research.
Germán Rodríguez and Croft, T. (The DHS Program, ICF International, USA) published “Research Material Extracting and Reshaping World Fertility Survey Data in Stata,” in Demographic Research. The Demographic and Health Surveys (DHS) program has made available online a large number of public-use files from its predecessor, the World Fertility Survey (WFS) program. To encourage and facilitate the use of this data, Rodríguez and Croft provide a Stata command that can be used to extract and reshape the data, using local copies or working directly with the DHS data archive pages.
Matthew Salganik and Feehan, D. (University of California, Berkeley) published “Generalizing the Network Scale-up Method. A New Estimator for the Size of Hidden Populations,” in Sociological Methodology. The network scale-up method enables researchers to estimate the sizes of hidden populations, such as drug injectors and sex workers, using sampled social network data. The basic scale-up estimator offers advantages over other size estimation techniques, but it depends on problematic modeling assumptions. The authors propose a new generalized scale-up estimator that can be used in settings with nonrandom social mixing and imperfect awareness about membership in the hidden population. In addition, the new estimator can be used when data are collected via complex sample designs and from incomplete sampling frames. However, the generalized scale-up estimator also requires data from two samples: one from the frame population and one from the hidden population. In some situations these data from the hidden population can be collected by adding a small number of questions to already planned studies. For other situations, the authors develop interpretable adjustment factors that can be applied to the basic scale-up estimator. The authors conclude with practical recommendations for the design and analysis of future studies.
“Quantity Versus Quality: A Survey Experiment to Improve the Network Scale-up Method,” co-authored by Feehan, D. (University of California, Berkeley), Umubyeyi, A. (University of Rwanda), Mahy, M. (Joint United Nations Programme on HIV/AIDS, Switzerland), Hladik, W. (Centers for Disease Control and Prevention), and Matthew Salganik was published in American Journal of Epidemiology. The network scale-up method is a promising technique that uses sampled social network data to estimate the sizes of epidemiologically important hidden populations, such as sex workers and people who inject illicit drugs. Although previous scale-up research has focused exclusively on networks of acquaintances, they show that the type of personal network about which survey respondents are asked to report is a potentially crucial parameter that researchers are free to vary. This generalization leads to a method that is more flexible and potentially more accurate. In 2011, they conducted a large, nationally representative survey experiment in Rwanda that randomized respondents to report about one of 2 different personal networks. The results showed that asking respondents for less information can, somewhat surprisingly, produce more accurate size estimates. The authors also estimated the sizes of 4 key populations at risk for human immunodeficiency virus infection in Rwanda. Their estimates were higher than earlier estimates from Rwanda but lower than international benchmarks. Finally, in this article they develop a new sensitivity analysis framework and use it to assess the possible biases in our estimates. Their design can be customized and extended for other settings, enabling researchers to continue to improve the network scale-up method.
The Open Review Toolkit is a project led by Matthew Salganik which received financial support from the Alfred P. Sloan Foundation. The Open Review Tooklit grew out of a desire to improve the way that academic books are published. In particular, it sought to develop a process that could simultaneously lead to better books, higher sales, and increased access to knowledge. Authors, publishers, and he public all share these goals, although they might prioritize them differently. Rather than seeing these goals as being in conflict, the Open Review process seeks to use new technology to advance all of them.
The first Open Review website, which was inspired by earlier innovations in academic publishing, was built for Salganik’s book, Bit by Bit: Social Research in the Digital Age, which will be published in hardcopy by Princeton University Press in 2017. Finally, the Open Review builds on some amazing open source software.
The Open Review edition of Matthew Salganik’s book, Bit By Bit: Social Research in the Digital Age was published by Princeton University Press. Bit by Bit is the essential guide to mastering the key principles of doing social research in this fast-evolving digital age. In this comprehensive book, Salganik explains how the digital revolution is transforming how social scientists observe behavior, ask questions, run experiments, and engage in mass collaborations. He provides a wealth of real-world examples throughout and also lays out a principles-based approach to handling ethical challenges.
In just the past several years, we have witnessed the birth and rapid spread of social media, mobile phones, and numerous other digital marvels. In addition to changing how we live, these tools enable us to collect and process data about human behavior on a scale never before imaginable, offering entirely new approaches to core questions about social behavior. Bit by Bit is the key to unlocking these powerful methods and will fundamentally change how the next generation of social scientists and data scientists explores the world around us.
For example, the proliferation of the internet and other technological advances has opened the door for researchers to use huge caches of data on user behavior collected by companies such as Facebook and Google. While such “found data” has drawn the interest of many researchers, it is far different from the “designed data” social scientists have generally collected under controlled conditions, said Salganik.
Brandon Stewart, Roberts, M. (University of California, San Diego), and Airoldi, E. (Harvard University) published “A Model of Text for Experimentation in the Social Sciences,” in the Journal of the American Statistical Association. Application and Case Studies. Statistical models of text have become increasingly popular in statistics and computer science as a method of exploring large document collections. Social scientists often want to move beyond exploration, to measurement and experimentation, and make inference about social and political processes that drive discourse and content. In this article, they develop a model of text data that supports this type of substantive research. Their approach is to posit a hierarchical mixed membership model for analyzing topical content of documents, in which mixing weights are parameterized by observed covariates. In this model, topical prevalence and topical content are specified as a simple generalized linear model on an arbitrary number of document level covariates, such as news source and time of release, enabling researchers to introduce elements of the experimental design that informed document collection into the model, within a generally applicable framework. They demonstrate the proposed methodology by analyzing a collection of news reports about China, where they allow the prevalence of topics to evolve over time and vary across newswire services. Their methods quantify the effect of news wire source on both the frequency and nature of topic coverage.
“The Civic Mission of MOOCs: Measuring Engagement across Political Differences in Forums, authored by Brandon Stewart, Reich, J. (Massachusetts Institute of Technology), Mavon, K. (Harvard University), and Tingley, D. (Harvard University) won Best Paper Award for the Proceedings of the Third (2016) ACM Conference on Learning @ Scale. In this study, the authors develop methods for computationally measuring the degree to which students engage in MOOC forums with other students holding different political beliefs. They examine a case study of a single MOOC about education policy, Saving Schools, where they obtain measures of student education policy preferences that correlate with political ideology. Contrary to assertions that online spaces often become echo chambers or ideological silos, they find that students in this case hold diverse political beliefs, participate equitably in forum discussions, directly engage (through replies and upvotes) with students holding opposing beliefs, and converge on a shared language rather than talking past one another. Research that focuses on the civic mission of MOOCs helps ensure that open online learning engages the same breadth of purposes that higher education aspires to serve.
During the coming two years, Marta Tienda’s research will focus on a collaborative study with Rachel Goldberg (University of California, Irvine) that uses web and mobile technology to study the emergence and evolution of adolescent relationships. By pairing a yearlong diary study with a 15-year longitudinal birth cohort study, the mDiary Study will permit innovative investigations into the content, quality, and evolution of teens’ romantic and sexual attachments; the childhood and adolescent circumstances conducive to healthy partnerships; and the health and developmental consequences of teen relationships. To administer the diary study on mobile platforms, they designed and extensively tested an automated, high-security website and back-end database linked through an Application Programming Interface (API) to Qualtrics survey software. During fall of Tienda’s sabbatical year, they extensively tested the 26-survey sequence to verify the accuracy of skip patterns across and within surveys, and also to ensure the linking of relationships across surveys. The diary is designed to trace both individual teens and relationships. In addition to the aforementioned research about education and intermarriage, Tienda will focus her energies on analyzing the mDiary surveys beginning in summer, 2017.
James Trussell with Abigail Aiken (Princeton University; University of Texas, Austin), James Scott (University of Texas, Austin), Catherine Aiken (University of Cambridge), and Jeremy Brockelsby (University of Cambridge), published: “Weekend Working: a Retrospective Cohort Study of Maternal and Neonatal Outcomes in a Large NHS Delivery Unit” in European Journal of Obstetrics & Gynecology and Reproductive Biology. The conclusion states that under current working arrangements, women who would benefit from consultant-led delivery are equally likely to receive one on weekends compared to weekdays. Weekend delivery has no effect on maternal or neonatal morbidity. Adopting mandatory 7-day contracts is unlikely to make any difference to either consultant-led delivery during weekends or to patient outcomes.
Abigail Aiken (Princeton University; University of Texas, Austin) and James Trussell co-authored a paper titled, “High hopes versus harsh realities: the population impact of emergency contraceptive pills” in An International Journal of Obstetrics and Gynaecology. The authors found two findings striking in the Black et al. paper that examines changes in the prevalence of emergency contraception (EC) use in Britain between 2000 and 2010, a period of major change in the availability of emergency contraceptive pills (ECPs).
First, despite increased availability, there was no meaningful increase in the proportion of sexually active women aged 16–44 years not intending pregnancy who used any type of EC in the past year between 1999–2001 (2.3%) and 2010–2012 (3.6%). Second, there was a shift towards obtaining ECPs from retail outlets (mostly pharmacies). These twin findings replicate earlier results from Marston and colleagues (BMJ 2005; 331:271), who found no increase in use in the past year from 2000 (8.4%) to 2001 (7.9%), and to 2002 (7.2%), but who found a shift towards obtaining ECPs from a pharmacist (a third in 2002). It is puzzling that Black et al. found much lower use (based on the second and third National Survey of Sexual Attitudes and Lifestyles, NATSAL 2 and 3) than did Marston et al. (based on the Omnibus Survey), particularly because the latter study included all women in the denominator, whereas the former study included sexually active women only.
Abigail Aiken (Princeton University; University of Texas, Austin), James Scott (University of Texas, Austin), Rebecca Gomperts (Women on Web, Amsterdam, the Netherlands), Marc Worrell (Women on Web, Amsterdam, the Netherlands), James Trussell, and Catherine Aiken (University of Cambridge) corresponded to the editor of the New England Journal of Medicine. The comments entitled, “Requests for Abortion in Latin America Related to Concern About Zika Virus Exposure,” received worldwide attention.
On November 17, 2015, the Pan American Health Organization (PAHO) issued an epidemiologic alert regarding Zika virus in Latin America. Several countries subsequently issued health advisories, including cautions about microcephaly, declarations of national emergency, and unprecedented warnings urging women to avoid pregnancy. Yet in most Latin American countries, abortion is illegal or highly restricted, leaving pregnant women with few options.
For several years, one such option for women in Latin America has been Women on Web (WoW), a nonprofit organization that provides access to abortion medications (mifepristone and misoprostol) outside the formal health care setting through online telemedicine in countries where safe abortion is not universally available. They analyzed data with respect to requests for abortion through WoW between January 1, 2010, and March 2, 2016, in 19 Latin American countries. Using a regression-discontinuity design, they assessed whether requests for abortion increased after the PAHO alert, as compared with preannouncement trends.
In Latin-American countries that issued warnings to pregnant women about Zika-related complications, requests for abortion through WoW increased substantially. This approach may underestimate the impact of the advisories on demand for abortion, since many women may have used an unsafe method, accessed misoprostol via local pharmacies or the black market, or visited local underground providers. The data provides a window on how Zika has affected the lives of pregnant women in Latin America.
World Health Organization (WHO) mathematical models predict 3–4 million Zika cases across the Americas over the next year, and Zika will inevitably spread to other countries where access to safe abortion is restricted. Official information and advice about Zika must be accompanied by efforts to ensure that all reproductive choices are safe, legal, and accessible. To do otherwise would be irresponsible public-health practice and unjust policy.
“Comparison of a Timing-Based Measure of Unintended Pregnancy and the London Measure of Unplanned Pregnancy” published in Perspectives on Sexual and Reproductive Heath, Guttmacher Institute written by Abigail Aiken (Princeton University; University of Texas, Austin), Carolyn Westhoff (Columbia University), James Trussell and Paula Castaño (Columbia University) examined unintended pregnancy as a universal benchmark for reproductive health. Whether variations reflect differences in measurement and how well measures predict pregnancy outcomes warrant further examination. U.S. and British measures of unintended and unplanned pregnancy offer a useful comparison.
Some 220 women seeking pregnancy testing at the Columbia University Medical Center in 2005 responded to three pregnancy measures: a binary timing-based measure of unintended pregnancy (TMUP); a multi-item measure of timing-based intentions and planning behaviors, the London Measure of Unplanned Pregnancy (LMUP); and a measure combining intentions (from the TMUP) and how women would feel about a positive pregnancy test. Six-month pregnancy status was assessed among 159 respondents. Estimates of unintended and unplanned pregnancy were calculated using the TMUP and the LMUP, and receiver operating characteristic (ROC) curves were generated to assess congruence.
According to the TMUP, 76% of pregnancies were unintended; by contrast, LMUP scores categorized 39% as unplanned. The ROC curve indicated that expanding the range of scores for classifying pregnancies as unplanned on the LMUP would achieve greater congruence between these measures. At six months, the proportion of pregnancies that had ended in abortion was 42% of those classified as unintended using the TMUP, 60% of those classified as unplanned using the LMUP and 71% of those that women said they had not intended and were very upset about.
U.S. and British measures of unintended pregnancy are not directly comparable, and a measure combining intentions and feelings may better predict pregnancy outcomes.
In their article, “Habit Formation in Voting: Evidence from Rainy Elections,” published in the American Economic Journal: Applied Economics, Tom Vogl, Fujiwara, T. (Princeton University, Politics Department) and Meng, K. (University of California, Santa Barbara), estimate habit formation in voting-the effect of past on current turnout-by exploiting transitory voting cost shocks. Using county-level data on U.S. presidential elections from 1952-2012, they found that rainfall on current and past election days reduced voter turnout. Further analyses suggested that habit formation operates by reinforcing the direct consumption value of voting and that their estimates may be amplified by social spillovers.
Yu Xie’s China-related projects review important social changes that have been taking place in China. He has been leading a heroic effort in documenting these social changes with the China Family Panel Studies (CFPS) conducted by the Peking University in China. CFPS is a nationally representative, annual longitudinal survey of Chinese communities, families, and individuals launched in 2010. The CFPS is designed to collect individual-, family-, and community-level longitudinal data in contemporary China. The studies focus on the economic, as well as the non-economic, wellbeing of the Chinese population, with a wealth of information covering such topics as economic activities, education outcomes, family dynamics and relationships, migration, and health.
The CFPS is funded by the Chinese government through Peking University. The CFPS promises to provide to the academic community the most comprehensive and highest-quality survey data on contemporary China. Yu Xie and his colleagues have been analyzing the CFPS data and data from other sources in China to understand social determinants of important social and demographic outcomes in China, such as education, marriage, cohabitation, work, earnings, and health.
In one project, “Economic Inequality in Contemporary China,” funded by the National Natural Science Foundation of China, Yu Xie and his collaborators examine structural factors accounting for high levels of economic inequality in today’s China.
In another project, “Life Course and Family Dynamics in a Comparative Perspective,” also funded by the National Natural Science Foundation of China, Yu Xie and his Chinese colleagues collaborate with researchers in the United Kington, Germany, and the Netherlands to understand differences between China and the European countries in life-course processes.