Did private election administration funding advantage Democrats in 2020?

Private donors contributed more than $350 million to local election officials to support the administration of the 2020 election. Supporters argue these grants were neutral and necessary to maintain normal election operations during the pandemic, while critics worry these grants mostly went to Democratic strongholds and tilted election outcomes. How much did these grants shape the 2020 presidential election? To answer this question, we collect administrative data on private election administration grants and election outcomes. We then use advances in synthetic control methods to compare presidential election results and turnout in counties that received grants to counties with similar election results and turnout before 2020. While Democratic counties were more likely to apply for a grant, we find that the grants did not have a noticeable effect on the presidential election. Our estimates of the average effect on Democratic vote share range from 0.03 to 0.36 percentage points. Our estimates of the average effect of receiving a grant on turnout range from 0.03 to 0.14 percentage points. Across specifications, our 95% CIs typically include negative effects and all fail to include effects on Democratic vote share larger than 0.58 percentage points and effects on turnout larger than 0.40 percentage points. We characterize the magnitude of our effects by asking how large they are compared to the margin by which Biden won the 2020 election. In simple bench-marking exercises, we find that the effects of the grants were likely too small to have changed the outcome of the 2020 presidential election.

Public health implications of opening National Football League stadiums during the COVID-19 pandemic.

SignificanceUsing data from 2020, we measure the public health impact of allowing fans into sports stadiums during the COVID-19 pandemic; these results may inform future policy decisions regarding large outdoor gatherings during public health crises. Second, we demonstrate the utility of robust synthetic control in this context. Synthetic control and other statistical approaches may be used to exploit the underlying low-dimensional structure of the COVID-19 data and serve as useful instruments in analyzing the impact of mitigation strategies adopted by different communities. As with all statistical methods, reliable outcomes depend on proper implementation strategies and well-established robustness tests; in the absence of these safeguards, these statistical methods are likely to produce specious or misleading conclusions.

Estimated population-level impact of pneumococcal conjugate vaccines against all-cause pneumonia mortality among unvaccinated age groups in five Latin American countries.

BACKGROUND: Pneumococcal conjugate vaccines (PCVs) provide strong direct protection in children, while limited data are available on their indirect effect on mortality among older age groups. This multi-country study aimed to assess the population-level impact of pediatric PCVs on all-cause pneumonia mortality among ≥5 years of age, and invasive pneumococcal disease (IPD) cases in Chile. METHODS: Demographic and mortality data from Argentina, Brazil, Chile, Colombia, and Mexico were collected considering the ≥ 5-year-old population, from 2000-2019, with 1,795,789 deaths due to all-cause pneumonia. IPD cases in Chile were also evaluated. Time series models were employed to evaluate changes in all-cause pneumonia deaths during the post-vaccination period, with other causes of death used as synthetic controls for unrelated temporal trends. RESULTS: No significant change in death rates due to all-cause pneumonia was detected following PCV introduction among most age groups and countries. The proportion of IPD cases caused by vaccine serotypes decreased from 29% (2012) to 6% (2022) among ≥65 years in Chile. DISCUSSION: While an effect of PCV against pneumonia deaths (a broad clinical definition that may not be specific enough to measure indirect effects) was not detected, evidence of indirect PCV impact was observed among vaccine-type-specific IPD cases.

Quasi-experimental methods for pharmacoepidemiology: difference-in-differences and synthetic control methods with case studies for vaccine evaluation.

Difference-in-differences and synthetic control methods have become common study designs for evaluating the effects of changes in policies, including health policies. They also have potential for providing real-world effectiveness and safety evidence in pharmacoepidemiology. To effectively add to the toolkit of the field, however, designs-including both their benefits and drawbacks-must be well understood. Quasi-experimental designs provide an opportunity to estimate the average treatment effect on the treated without requiring the measurement of all possible confounding factors, and to assess population-level effects. This requires, however, other key assumptions, including the parallel trends or stable weighting assumptions, a lack of other concurrent events that could alter time trends, and an absence of contamination between exposed and unexposed units. The targeted estimands are also highly specific to the settings of the study, and combining across units or time periods can be challenging. Case studies are presented for 3 vaccine evaluation studies, showcasing some of these challenges and opportunities in a specific field of pharmacoepidemiology. These methods provide feasible and valuable sources of evidence in various pharmacoepidemiologic settings and can be improved through research to identify and weigh the advantages and disadvantages in those settings. This article is part of a Special Collection on Pharmacoepidemiology.

Contamination bias in the estimation of child maltreatment causal effects on adolescent internalizing and externalizing behavior problems.

BACKGROUND: When unaddressed, contamination in child maltreatment research, in which some proportion of children recruited for a nonmaltreated comparison group are exposed to maltreatment, downwardly biases the significance and magnitude of effect size estimates. This study extends previous contamination research by investigating how a dual-measurement strategy of detecting and controlling contamination impacts causal effect size estimates of child behavior problems. METHODS: This study included 634 children from the LONGSCAN study with 63 cases of confirmed child maltreatment after age 8 and 571 cases without confirmed child maltreatment. Confirmed child maltreatment and internalizing and externalizing behaviors were recorded every 2 years between ages 4 and 16. Contamination in the nonmaltreated comparison group was identified and controlled by either a prospective self-report assessment at ages 12, 14, and 16 or by a one-time retrospective self-report assessment at age 18. Synthetic control methods were used to establish causal effects and quantify the impact of contamination when it was not controlled, when it was controlled for by prospective self-reports, and when it was controlled for by retrospective self-reports. RESULTS: Rates of contamination ranged from 62% to 67%. Without controlling for contamination, causal effect size estimates for internalizing behaviors were not statistically significant. Causal effects only became statistically significant after controlling contamination identified from either prospective or retrospective reports and effect sizes increased by between 17% and 54%. Controlling contamination had a smaller impact on effect size increases for externalizing behaviors but did produce a statistically significant overall effect, relative to the model ignoring contamination, when prospective methods were used. CONCLUSIONS: The presence of contamination in a nonmaltreated comparison group can underestimate the magnitude and statistical significance of causal effect size estimates, especially when investigating internalizing behavior problems. Addressing contamination can facilitate the replication of results across studies.

Synthetic Controls for Implementation Science: Opportunities for HIV Program Evaluation Using Routinely Collected Data.

PURPOSE OF REVIEW: HIV service delivery programs are some of the largest funded public health programs in the world. Timely, efficient evaluation of these programs can be enhanced with methodologies designed to estimate the effects of policy. We propose using the synthetic control method (SCM) as an implementation science tool to evaluate these HIV programs. RECENT FINDINGS: SCM, introduced in econometrics, shows increasing utility across fields. Key benefits of this methodology over traditional design-based approaches for evaluation stem from directly approximating pre-intervention trends by weighting of candidate non-intervention units. We demonstrate SCM to evaluate the effectiveness of a public health intervention targeting HIV health facilities with high numbers of recent infections on trends in pre-exposure prophylaxis (PrEP) enrollment. This test case demonstrates SCM's feasibility for effectiveness evaluations of site-level HIV interventions. HIV programs collecting longitudinal, routine service delivery data for many facilities, with only some receiving a time-specified intervention, are well-suited for evaluation using SCM.

Doubly robust proximal synthetic controls.

To infer the treatment effect for a single treated unit using panel data, synthetic control (SC) methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is subsequently used to impute the counterfactual outcomes of the treated unit had it not been treated in the post-treatment period, and used to estimate the treatment effect. Existing SC methods rely on correctly modeling certain aspects of the counterfactual outcome generating mechanism and may require near-perfect matching of the pre-treatment trajectory. Inspired by proximal causal inference, we obtain two novel nonparametric identifying formulas for the average treatment effect for the treated unit: one is based on weighting, and the other combines models for the counterfactual outcome and the weighting function. We introduce the concept of covariate shift to SCs to obtain these identification results conditional on the treatment assignment. We also develop two treatment effect estimators based on these two formulas and generalized method of moments. One new estimator is doubly robust: it is consistent and asymptotically normal if at least one of the outcome and weighting models is correctly specified. We demonstrate the performance of the methods via simulations and apply them to evaluate the effectiveness of a pneumococcal conjugate vaccine on the risk of all-cause pneumonia in Brazil.

Evaluating quasi-experimental approaches for estimating epidemiological efficacy of non-randomised field trials: applications in Wolbachia interventions for dengue.

BACKGROUND: Wolbachia symbiosis in Aedes aegypti is an emerging biocontrol measure against dengue. However, assessing its real-world efficacy is challenging due to the non-randomised, field-based nature of most intervention studies. This research re-evaluates the spatial-temporal impact of Wolbachia interventions on dengue incidence using a large battery of quasi-experimental methods and assesses each method's validity. METHODS: A systematic search for Wolbachia intervention data was conducted via PUBMED. Efficacy was reassessed using commonly-used quasi-experimental approaches with extensive robustness checks, including geospatial placebo tests and a simulation study. Intervention efficacies across multiple study sites were computed using high-resolution aggregations to examine heterogeneities across sites and study periods. We further designed a stochastic simulation framework to assess the methods' ability to estimate intervention efficacies (IE). RESULTS: Wolbachia interventions in Singapore, Malaysia, and Brazil significantly decreased dengue incidence, with reductions ranging from 48.17% to 69.19%. IEs varied with location and duration. Malaysia showed increasing efficacy over time, while Brazil exhibited initial success with subsequent decline, hinting at operational challenges. Singapore's strategy was highly effective despite partial saturation. Simulations identified Synthetic Control Methods (SCM) and its variant, count Synthetic Control Method (cSCM), as superior in precision, with the smallest percentage errors in efficacy estimation. These methods also demonstrated robustness in placebo tests. CONCLUSIONS: Wolbachia interventions exhibit consistent protective effects against dengue. SCM and cSCM provided the most precise and robust estimates of IEs, validated across simulated and real-world settings.

Impact of organised colorectal cancer screening on age-specific population incidences: evidence from a quasi-experimental study in Sweden.

Colorectal cancer (CRC) incurs a significant disease burden globally. Organised CRC screening programmes have been widely implemented for early detection and prevention. To understand the public health impact of these programmes, quantitative evidence of changes in overall and age-specific population incidences is fundamental. We aimed to provide such evidence by exploiting a time lag in the implementation of organised screening in Sweden: two out of 21 regions (these two regions comprise nearly 20% of the total Swedish population) have offered organised screening since 2008; the other regions have offered CRC screening since 2021. Using registry data on diagnosed CRC cases and socio-demographics for all regions in Sweden over the period 1970-2019, Bayesian structural time series modelling and difference-in-differences were applied to analyse the impact of screening on age-specific population incidences over time (CRC cases per 100.000 persons/year). After inviting birth-year cohorts aged 60-69 years for stool-based testing, the incidence rate in the 70-74-year age group decreased significantly over time, with an average reduction of - 44·40 (95% CI - 58·15 to - 31·31) from 2011 to 2019 in the intervention regions. In the overall population aged 60-74 years, there was a net incidence decrease of - 7·99 (95% CI - 13·85 to - 2·39) since the initiation of organised screening in the intervention regions (2008-2019). Organised CRC screening for 60-69-year-olds generated a change in age-specific incidence patterns with a long-lasting incidence decrease in the 70-74-year-old population, implying reductions in the excess mortality and burden of the disease.

Revisiting the Occupational Health Impact of Right-to-Work Laws: A Research Note.

This research note reevaluates the occupational health impact of right-to-work (RTW) legislation, incorporating recent developments in causal inference techniques. In an era marked by an uptick in the adoption of anti-union legislation and increases in workplace fatalities and injuries, it is particularly urgent to examine the extent to which RTW laws affect workers' health. Using a state-year-level dataset spanning 28 years and collected from multiple data sources, we apply an innovative generalized synthetic control method to overcome several limitations of the traditional two-way fixed-effects approach to examine the effect of RTW laws on occupational fatal injuries as well as various other health outcomes. Robustness checks were conducted using a wide range of alternative methods for two-way fixed-effects adjustments. In contrast with findings from previous studies, we found null effects on occupational fatal injuries, as well as on all other health outcomes. Overall, our results indicate that findings from previous studies are based on very thin empirical evidence, with potentially underestimated standard errors and unobserved confounders. Our results highlight the importance of revisiting research questions using updated methodological tools.

External Control Arms in Idiopathic Pulmonary Fibrosis Using Clinical Trial and Real-World Data Sources.

Rationale: Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease for which novel therapies are needed. External controls (ECs) could enhance IPF trial efficiency, but the direct comparability of ECs versus concurrent controls is unknown. Objectives: To develop IPF ECs by fit-for-purpose data standards to historical randomized clinical trial (RCT), multicenter registry (Pulmonary Fibrosis Foundation Patient Registry), and electronic health record (EHR) data and to evaluate endpoint comparability among ECs and the phase II RCT of BMS-986020. Methods: After data curation, the rate of change in FVC from baseline to 26 weeks among participants receiving BMS-986020 600 mg twice daily was compared with the BMS-placebo arm and ECs using mixed-effects models with inverse probability weights. Measurements and Main Results: At 26 weeks, the rates of change in FVC were -32.71 ml for BMS-986020 and -130.09 ml for BMS-placebo (difference, 97.4 ml; 95% confidence interval [CI], 24.6-170.2), replicating the original BMS-986020 RCT. RCT ECs showed treatment effect point estimates within the 95% CI of the original BMS-986020 RCT. Pulmonary Fibrosis Foundation Patient Registry ECs and EHR ECs experienced a slower rate of FVC decline compared with the BMS-placebo arm, resulting in treatment-effect point estimates outside of the 95% CI of the original BMS-986020 RCT. Conclusions: IPF ECs generated from historical RCT placebo arms result in comparable primary treatment effects to that of the original clinical trial, whereas ECs from real-world data sources, including registry or EHR data, do not. RCT ECs may serve as a potentially useful supplement to future IPF RCTs.

Considerations for master protocols using external controls.

There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.

Estimating treatment effect in randomized trial after control to treatment crossover using external controls.

In clinical trials, it is common to design a study that permits the administration of an experimental treatment to participants in the placebo or standard of care group post primary endpoint. This is often seen in the open-label extension phase of a phase III, pivotal study of the new medicine, where the focus is on assessing long-term safety and efficacy. With the availability of external controls, proper estimation and inference of long-term treatment effect during the open-label extension phase in the absence of placebo-controlled patients are now feasible. Within the framework of causal inference, we propose several difference-in-differences (DID) type methods and a synthetic control method (SCM) for the combination of randomized controlled trials and external controls. Our realistic simulation studies demonstrate the desirable performance of the proposed estimators in a variety of practical scenarios. In particular, DID methods outperform SCM and are the recommended methods of choice. An empirical application of the methods is demonstrated through a phase III clinical trial in rare disease.

The Impacts of New York State's Paid Family Leave Policy on Parents' Sleep and Exercise.

OBJECTIVES: To assess changes in young parents' health behaviors following implementation of New York State's Paid Family Leave Program (NYSPFL). METHODS: We used synthetic control (N = 117,552) and difference-in-differences (N = 18,973) models with data from the nationally representative Behavioral Risk Factor Surveillance System (BRFSS) from 2011 to 2019 to provide individual-level estimates of the effects of NYSPFL on self-reported exercise in the past month and average daily sleep of adults aged 21-30 years living with one or more children under 18 years of age in New York and comparison states. RESULTS: Synthetic control model results indicate that the NYSPFL increased the likelihood of exercise in the past month among mothers, single parents, and low-income parents by 6.3-10.3% points (pp), whereas fathers showed a decrease in exercise (7.8 pp). Fathers, single parents, and those with two or more children showed increases in daily sleep between 14 and 21 min per day. CONCLUSIONS FOR PRACTICE: State paid family and medical leave laws may provide benefits for health behaviors among young parents with children under 18, particularly those in low-income and single-parent households.

The effect of ending public funding for abortion: evidence from West Virginia.

OBJECTIVES: This study aimed to evaluate the immediate impact of West Virginia's 2018 policy change prohibiting state Medicaid funds from covering abortions on the state's abortion rate. STUDY DESIGN: We use the synthetic control method with a weighted combination of control states to estimate West Virginia's counterfactual abortion rate without the policy change. METHODS: We obtained abortion data from the Centers for Disease Control and Prevention. We used a synthetic control approach to construct a weighted average of control states that resembled West Virginia in various pre-policy characteristics. We then compared the actual abortion rate in West Virginia after the policy change with the estimated counterfactual rate based on the control states. RESULTS: We found that the abortion rate per 1000 women of reproductive age in West Virginia was approximately 1.3 abortions lower in 2019 compared to the estimated counterfactual rate in the absence of the policy change (3.8 vs. 5.1). CONCLUSIONS: Our findings suggest that West Virginia's policy change prohibiting Medicaid-funded abortions in 2018 may have led to a reduction in the state's abortion rate in 2019.

Using the Synthetic Control Method to Evaluate the Impact of a Land-Based Gambling Ban on Crime.

We use the synthetic control method to examine whether land-based gambling ban has an effect on crime in a given city. In a sample of four Czech cities where the ban was introduced, we show that these cities had roughly the same crime rate development in the years after the ban as the control cities without the ban, showing that there is no significant impact. As this is, to our knowledge, the first use of the synthetic control method in the context of gambling, the main contribution of this study lies in demonstrating the possibilities in its use. Employing this versatile method may improve the current situation where many gambling impact studies suffer from the absence of a control group or an identification strategy to confirm causal relationship. Last but not least, the results of this study make an important contribution to the debate on blanket measures in gambling regulation; although pathological gambling is linked to crime, banning this activity may not lead to the expected consequences.

Can China's carbon generalized system of preferences reduce urban residents' carbon emissions? Evidence from a quasi-natural experiment.

The carbon generalized system of preferences (CGSP) is an innovative incentive mechanism implemented by the Chinese government, which has also become an important part of carbon emission reduction at the living end, and it is of great significance to study whether the Pilot Policy can reduce the carbon emissions of residents. This study firstly accounts for the total carbon emissions and per capita carbon emissions of the residents of 284 cities in China, and on this basis, adopts the SCM method to quantitatively study and analyze the overall and local implementation effects of CGSP in China by taking the first batch of CGSP pilots in China as an example, and further applies the mediation effect model to test the pathways of the role of CGSP. The main findings of the study are as follows: (1) During the period of 2010-2020, the total carbon emissions from urban residents' living in China showed a yearly growth trend, from 36,623.98 ×10-2Mt in 2010-85,241.20 ×10-2Mt in 2020, an increase of 8.83%. Total carbon emissions present a structural difference of "electricity consumption > central heating > private transport > gas (oil, natural gas) consumption". (2) Overall, the implementation of the CGSP had a robust positive impact on the overall carbon emission reduction in the pilot cities, with an average annual emission reduction effect value of 36.53 ×10-2Mt. Locally, the annual net policy effect values of Dongguan, Zhongshan, Heyuan, and Guangzhou are 6169.79 ×10-2, 26,600.17 ×10-2, 17,081.34 ×10-2 and 9393.36 ×10-2Mt respectively. (3) CGSP has a good carbon emission reduction effect by suppressing the impact on residents' carbon emissions through enhancing the city's innovation capacity and promoting electricity saving and consumption reduction, while the mediating effect played by the promotion of green and low-carbon travel in the pilot policy is not significant. Finally, based on the research findings, relevant suggestions are targeted.

Impact of Changes to Concealed-Carry Weapons Laws on Fatal and Nonfatal Violent Crime, 1980-2019.

The United States faces rapidly rising rates of violent crime committed with firearms. In this study, we sought to estimate the impact of changes to laws that regulate the concealed carrying of weapons (concealed-carry weapons (CCW) laws) on violent crimes committed with a firearm. We used augmented synthetic control models and random-effects meta-analyses to estimate state-specific effects and the average effect of adopting shall-issue CCW permitting laws on rates of 6 violent crimes: homicide with a gun, homicide by other means, aggravated assault with a gun, aggravated assault with a knife, robbery with a gun, and robbery with a knife. The average effects were stratified according to the presence or absence of several shall-issue permit provisions. Adoption of a shall-issue CCW law was associated with a 9.5% increase in rates of assault with a firearm during the first 10 years after law adoption and was associated with an 8.8% increase in rates of homicide by other means. When shall-issue laws allowed violent misdemeanants to acquire CCW permits, the laws were associated with higher rates of gun assaults. It is likely that adoption of shall-issue CCW laws has increased rates of nonfatal violent crime committed with firearms. Harmful effects of shall-issue laws are most clear when provisions intended to reduce risks associated with civilian gun-carrying are absent.

Confounding adjustment in the analysis of augmented randomized controlled trial with hybrid control arm.

The augmented randomized controlled trial (RCT) with hybrid control arm includes a randomized treatment group (RT), a smaller randomized control group (RC), and a large synthetic control (SC) group from real-world data. This kind of trial is useful when there is logistics and ethics hurdle to conduct a fully powered RCT with equal allocation, or when it is necessary to increase the power of the RCT by incorporating real-world data. A difficulty in the analysis of augmented RCT is that the SC and RC may be systematically different in the distribution of observed and unmeasured confounding factors, causing bias when the two control groups are analyzed together as hybrid controls. We propose to use propensity score (PS) analysis to balance the observed confounders between SC and RC. The possible bias caused by unmeasured confounders can be estimated and tested by analyzing propensity score adjusted outcomes from SC and RC. We also propose a partial bias correction (PBC) procedure to reduce bias from unmeasured confounding. Extensive simulation studies show that the proposed PS + PBC procedures can improve the efficiency and statistical power by effectively incorporating the SC into the RCT data analysis, while still control the estimation bias and Type I error inflation that might arise from unmeasured confounding. We illustrate the proposed statistical procedures with data from an augmented RCT in oncology.

An impact evaluation of conservation investments targeting long-distance migratory species.

We evaluated the impact of a philanthropic program investing in the conservation of sites along the Pacific Americas Flyway, which spans >16,000 km of coastline and is used by millions of shorebirds. Using a quasi-experimental, mixed methods approach, we estimated what would have happened to shorebird populations at 17 wintering sites without the sustained and additional investment they received. We modeled shorebird populations across the entire flyway and at sites with and without investment. Combining shorebird abundance estimates with a land-cover classification model, we used the synthetic control method to create counterfactuals for shorebird trends at the treatment sites. We found no evidence of an overall effect across three outcome variables. Species- and site-level treatment effects were heterogeneous, with a few cases showing evidence of a positive effect, including a site with a high level of overall investment. Results suggest six shorebirds declined across the entire flyway, including at many Latin American sites. However, the percentage of flyway populations present at the sites remained stable, and the percentage at the treatment sites was higher (i.e., investment sites) than at control sites. Multiple mechanisms behind our results are possible, including that investments have yet to mitigate impacts and negative impacts at other sites are driving declines at the treatment sites. A limitation of our evaluation is the sole focus on shorebird abundance and the lack of data that prohibits the inclusion of other outcome variables. Monitoring infrastructure is now in place to design a more robust and a priori shorebird evaluation framework across the entire flyway. With this framework, it will prove easier to prioritize limited dollars to result in the most positive conservation outcomes.

Evaluación del impacto de la inversión para la conservación enfocada en especies migratorias de largo recorrido Resumen Evaluamos el impacto de un programa filantrópico que invierte en la conservación de sitios a lo largo de la Ruta Migratoria Pacífico-Américas, la cual abarca >16,000 km de la línea costera y millones de aves playeras la usan. Estimamos con una estrategia cuasiexperimental y de métodos mixtos lo que habría pasado con las poblaciones de estas aves en 17 sitios invernales sin la inversión adicional y continua que recibieron. Modelamos estas poblaciones en toda la ruta y en sitios con y sin inversión. Combinamos las estimaciones de aves playeras con el modelo de clasificación de la cobertura del suelo y usamos el método de control sintético para crear contrafactuales para las tendencias de las aves playeras en sitios de tratamiento. No encontramos evidencia alguna de un efecto generalizado en las tres variables de los resultados. Los efectos del tratamiento de especies y de sitio fueron heterogéneos, con unos cuantos casos que mostraron evidencia de un efecto positivo, incluido un sitio con un nivel elevado de inversión general. Los resultados sugieren que seis especies de aves playeras declinaron a lo largo de toda la ruta, incluyendo en varios sitios de América Latina. Sin embargo, el porcentaje de poblaciones de la ruta presentes en los sitios permaneció estable y el porcentaje en los sitios de tratamiento (sitios de inversión) fue más elevado que en los sitios control. Muchos mecanismos son posibles detrás de nuestros resultados, incluidas las inversiones que todavía no han mitigado impactos y los impactos negativos en otros sitios que están causando las declinaciones en los sitios de tratamiento. Una limitación en nuestra evaluación es el enfoque único en la abundancia de aves playeras y la falta de datos que impiden la inclusión de otras variables de los resultados. El monitoreo de la infraestructura ahora está en una posición en la que puede diseñar un marco de evaluación más robusto y a priori de las aves playeras a lo largo de toda la ruta. Con este marco, será más fácil priorizar los dólares limitados para que los resultados de conservación sean lo más positivos posible.