Counterfactual Analysis Research Articles

Counterfactual Mediation Analysis with a Latent Class Exposure

Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method “updated pseudo class draws (uPCD)” to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome (“one-step,” “bias-adjusted three-step,” “modal class assignment,” “non-inclusive pseudo class draws,” and “inclusive pseudo class draws”) and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach.

The multiple effects of farmland infrastructure investment on agrifood systems in China—an interdisciplinary model analysis

PurposeAs one of its major strategies, China has made a new plan to further expand High Standard Farmland (HSF) to all permanent basic farmland (80% of total farmland) for grain security over the next decade. Yet, what will be the impact of farmland infrastructure investment on agrifood systems? The paper aims to systematically evaluate the multiple effects (food security, economy, nutrition and environment) of expanding HSF construction under the context of the “Big Food vision” using an interdisciplinary model.Design/methodology/approachAn interdisciplinary model – AgriFood Systems Model, which links the China CGE model to diet and carbon emission modules, is applied to assess the multiple effects of HSF construction on agrifood systems, such as food security and economic development, residents’ diet quality and carbon emissions. Several policy scenarios are designed to capture these effects of the past HSF investment based on counterfactual analysis and compare the effects of HSF future investment at the national level under the conditions of different land use policies – restricting to grain crops or allowing diversification (like vegetables, and fruit).FindingsThe investments in HSF offer a promising solution for addressing the challenges of food and nutrition security, economic development and environmental sustainability. Without HSF construction, grain production and self-sufficiency would decline significantly, while the agricultural and agrifood systems’ GDP would decrease. The future investment in the HSF construction will further increase both grain production and GDP, improve dietary quality and reduce carbon emissions. Compared with the policy of limiting HSF to planting grains, diversified planting can provide a more profitable economic return, improve dietary quality and reduce carbon emissions.Originality/valueThis study contributes to better informing the impact of land infrastructure expanding investment on the agrifood systems from multiple dimensions based on an interdisciplinary model. We suggest that the government consider applying diversified planting in the future HSF investment to meet nutritional and health demands, increase household income and reduce carbon emissions.

Resource utilization potential of municipal solid waste affects the sustainable development goals progress in China

In the past decades, municipal solid waste (MSW) has generally been recognized as a substance that poses risks to human health and the ecosystem. With the emergence of the sustainable idea, MSW began to be recognized as a renewable resource and technological methods were used to make MSW as a resource utilization potential (RUP) source. SDGs realization relies on MSW's sustainable management. However, how the MSW's RUP affects to SDGs and how the two are coupled still needs to be explored. In this study, we took China, the largest MSW producer in developing countries, as an example. First, we selected 17 SDG indicators (including 83 nested indicators) and assessed China's SDG progress from 2005 to 2021 based on the SDG weighting calculation method. Regression analysis and structural equation modeling (SEM) were then used to explore the direct and indirect impacts of MSW's RUP on the SDGs. Finally, a counterfactual analysis was used to determine the regional heterogeneity of the impact of MSW's RUP on SDGs. The results show that China made greater progress in its SDG scores overall between 2005 and 2021, with particularly strong progress in SDG1 (poverty eradication) and SDG3 (healthy living). The MSW's RUP contributes positively to SDGs 1, 3, 6, 8, 9, 10, 11, and 17 (economy, health, water, industry, community, and global partnerships). The response of SDGs 2, 7, and 16 (zero hunger, energy, and justice) to MSW's RUP showed a threshold when the MSW's RUP value was 0.3 GJ. The MSW's RUP has a negative impact on SDG13 and SDG15 (climate change and ecosystems). Regional heterogeneity exists in the relationship between MSW's RUPs and the SDGs, therefore the promotion of MSW's RUPs is targeted to different provinces. The MSW's RUP provides significant potential for improvement in energy production and material production, and adherence to managing MSW using a sustainable mindset will contribute to the goal of carbon neutrality by 2060.

Monetary policy and renewable energy production

This paper examines the relationship between monetary policy and renewable energy. Using US quarterly data from 1990:Q1 to 2023:Q2, we employ a stylized monetary policy structural vector autoregressive model and provide evidence that monetary tightening reduces the production of renewable energy. Among different sources of renewable energy, solar energy shows the most significant response. Finally, a counterfactual policy analysis provides evidence that monetary policy can either offset or exaggerate other structural shocks on renewable energy production, depending on the sources of these shocks.

Role of cardiovascular health factors in mediating social inequalities in the incidence of dementia in the UK: two prospective, population-based cohort studies

The contribution of modifiable risk factors to social inequalities in dementia, observed in longitudinal studies, remains unclear. We aimed to quantify the role of cardiovascular health factors, assessed using Life's Essential 8 (LE8) score, in mediating social inequalities in incidence of dementia and, for comparison, in incidence of stroke, coronary heart disease, and mortality. In this prospective, population-based cohort study, we collected data from the UK Whitehall II Study and UK Biobank databases. Participants were included if data were available on SEP, outcomes and LE8 (smoking, physical activity, diet, body mass index, blood pressure, fasting blood glucose, lipid levels, sleep duration). The primary outcome was incident dementia and secondary outcomes were stroke, coronary heart disease, and mortality. Outcomes were derived from electronic healthcare records. Socioeconomic position (SEP) was measured by occupation in Whitehall II and education in UK Biobank. Counterfactual mediation analysis was used to quantify the extent to which LE8 score explained the associations of SEP with all outcomes. Analyses involved Cox regression, accelerated failure time models, and linear regression; and were adjusted for age, sex, and ethnicity. Between 10.09.1985 and 29.03.1988, a total of 9688 participants (mean age±SD 44.9±6.0; 67% men) from the Whitehall II study, and between 19.12.2006 and 01.10.2010, 278,215 participants (mean age±SD 56.0±8.1; 47% men) from the UK Biobank were included. There were 606 and 4649 incident dementia cases over a median (interquartile range) follow-up of 31.7 (31.1-32.7) and 13.5 (12.7-14.1) years respectively in Whitehall II and UK Biobank. In Whitehall II, the hazard ratio was 1.85 [95% CI 1.42, 2.32] for the total effect of SEP on dementia and 1.20 [1.12, 1.28] for the indirect effect via the LE8, the proportion mediated being 36%. In UK Biobank, the total effect of SEP on dementia was 1.65 [1.54, 1.78]; the indirect effect was 1.11 [1.09, 1.12], and the proportion mediated was 24%. The proportions mediated for stroke, coronary heart disease, and mortality were higher, ranging between 34% and 63% in Whitehall II and between 36% and 50% in UK Biobank. In two well-characterised cohort studies, up to one third of the social inequalities in incidence of dementia was attributable to cardiovascular health factors. Promotion of cardiovascular health in midlife may contribute to reducing social inequalities in risk of dementia, in addition to cardiovascular diseases and all-cause mortality. This study used adult measures of SEP, further research is warranted using lifecourse measures of SEP. NIH (RF1AG062553).

A location discrete choice model of crime: Police elasticity and optimal deployment.

Despite the common belief that police presence reduces crime, there is mixed evidence of such causal effects in major Latin America cities. In this work we identify the casual relationship between police presence and criminal events by using a large dataset of a randomized controlled police intervention in Bogotá D.C., Colombia. We use an Instrumental Variables approach to identify the causal effect of interest. Then we consistently estimate a Conditional Logit discrete choice model with aggregate data that allow us to identify agents' utilities for crime location using Two Stage Least Squares. The estimated parameters allow us to compute the police own and cross-elasticities of crime for each of the spatial locations and to evaluate different police patrolling strategies. The elasticity of crime to police presence is, on average across spatial locations, -0.26 for violent crime, -0.38 for property crime and -0.38 for total crime, all statistically significant. Estimates of cross-elasticities are close to zero; however, spillover effects are non-negligible. Counterfactual analysis of different police deployment strategies show, for an optimal allocating algorithm, an average reduction in violent crime of 7.09%, a reduction in property crimes of 8.48% and a reduction in total crimes of 5.15% at no additional cost. These results show the potential efficiency gains of using the model to deploy police resources in the city without increasing the total police time required.

Hourly Long-Term Traffic Volume Prediction with Meteorological Information Using Graph Convolutional Networks

Hourly traffic volume prediction is now emerging to mitigate and respond to hourly-level traffic congestion augmented by deep learning techniques. Incorporating meteorological data into the forecasting of hourly traffic volumes substantively improves the precision of long-term traffic forecasts. Nonetheless, integrating weather data into traffic prediction models is challenging due to the complex interplay between traffic flow, time-based patterns, and meteorological conditions. This paper proposes a graph convolutional network to predict long-term traffic volume with meteorological information. This study utilized a four-year traffic volume and meteorological information dataset in Chung-ju si to train and validate the models. The proposed model performed better than the other baseline scenarios with conventional and state-of-the-art deep learning techniques. Furthermore, the counterfactual scenarios analysis revealed the potential negative impacts of meteorological conditions on traffic volume. These findings will enable transportation planners predict hourly traffic volumes for different scenarios, such as harsh weather conditions or holidays. Furthermore, predicting the microscopic traffic simulation for different scenarios of weather conditions or holidays is useful.

The impact of water rights trading on water resource use efficiency: Evidence from China's water rights trading pilots

With the increasing conflict between the supply and demand of water resources, enhancing water resource use efficiency has gradually become a focus of attention. As a new way to manage water resources, water rights trading pilots have been implemented in China. It is important to explore the impact of water rights trading on regional water resource use efficiency and possible impact paths. This paper employs a global nonradial directional distance function (GNDDF) model to measure water resource use efficiency, which is more accurate and reliable. To discern the effect of China's water rights trading pilot policy on enhancing regional water resource use efficiency, we apply the regression control method (RCM) for counterfactual analysis and test the possible pathways through which the water rights trading policy affects water resource use efficiency. This article draws the following conclusions. (i) China's overall water resource use efficiency has not reached a high level over the past decade, and water resource use efficiency has exhibited substantial regional disparities. (ii) There is some evidence to suggest that the water rights trading policy may have a positive impact on improving water resource use efficiency in the pilot provinces. However, the improvement effect exhibits regional variations, which are likely attributed to the initial water resource use levels in the pilot provinces and the specific implementation modes of water rights trading. (iii) Water rights trading policies can enhance water resource use efficiency through two pathways, including facilitating reclaimed water reuse and promoting water use restructuring.

Improving well‐being and reducing deforestation in Indonesia's protected areas

AbstractProtected areas (PAs) are central to sustainability targets, yet few evaluations explore outcomes for both conservation and development, or the trade‐offs involved. We applied counterfactual analyses to assess the extent to which PAs maintained forest cover and influenced well‐being across >31,000 villages in Sumatra and Kalimantan, Indonesia. We examined multidimensional aspects of well‐being, tracking education, health, living standards, infrastructure, environment, and social cohesion in treatment and control villages between 2005 and 2018. Overall, PAs were effective at maintaining forest cover compared to matched controls and were not detrimental to well‐being. However, impacts were highly heterogeneous, varying by island and strictness of protection. While health, living standards, and infrastructure aspects of well‐being improved, education access, environmental conditions, and social cohesion declined. Our analysis reveals the contexts through which individual PAs succeed or fail in delivering multiple benefits and provides insights into where further on‐ground support is needed to achieve conservation and development objectives.

Does pain tolerance mediate the effect of physical activity on chronic pain in the general population? The Tromsø Study.

Knowledge is needed regarding mechanisms acting between physical activity (PA) and chronic pain. We investigated whether cold pain tolerance mediates an effect of leisure-time physical activity on the risk of chronic pain 7 to 8 years later using consecutive surveys of the population-based Tromsø Study. We included participants with information on baseline leisure-time PA (LTPA) and the level of cold pressor-assessed cold pain tolerance, who reported chronic pain status at follow-up as any of the following: chronic pain for ≥3 months, widespread chronic pain, moderate-to-severe chronic pain, or widespread moderate-to-severe chronic pain. We included 6834 participants (52% women; mean age, 55 years) in counterfactual mediation analyses. Prevalence decreased with severity, for example, 60% for chronic pain vs 5% for widespread moderate-to-severe chronic pain. People with one level higher LTPA rating (light to moderate or moderate to vigorous) at baseline had lower relative risk (RR) of 4 chronic pain states 7 to 8 years later. Total RR effect of a 1-level LTPA increase was 0.95 (0.91-1.00), that is, -5% decreased risk. Total effect RR for widespread chronic pain was 0.84 (0.73-0.97). Indirect effect for moderate-to-severe chronic pain was statistically significant at RR 0.993 (0.988-0.999); total effect RR was 0.91 (0.83-0.98). Statistically significantly mediated RR for widespread moderate-to-severe chronic pain was 0.988 (0.977-0.999); total effect RR was 0.77 (0.64-0.94). This shows small mediation of the effect of LTPA through pain tolerance on 2 moderate-to-severe chronic pain types. This suggests pain tolerance to be one possible mechanism through which PA modifies the risk of moderate-to-severe chronic pain types with and without widespread pain.

Metal and energy price uncertainties and the global economy

We construct novel monthly measures of energy and metal price uncertainty based on the one-year-ahead price forecast dispersion for twenty commodities. Using a simple VAR model, we show both metal and energy price uncertainty shocks are contractionary for the world economy, and these results are largely robust to a battery of alternative specifications. However, our results highlight the differences between metal and energy price uncertainties, both in terms of their propagation channels, as well as the driving forces behind them. Counterfactual analysis reveals that metal price uncertainty has a mainly direct impact on global economic activity while the impact of energy price uncertainty is indirectly mediated through global financial and economic uncertainties. Variance decomposition shows fluctuations of both metal and energy price uncertainties are mainly self-driven, however global financial uncertainty plays a significant role in driving metal price uncertainty in the short run while global economic activity uncertainty plays a significant role in driving energy price uncertainty in the medium to long run.

Drought influences habitat associations and abundances of birds in California's Central Valley

AbstractAimAs climate change increases the frequency and severity of droughts in many regions, conservation during drought is becoming a major challenge for ecologists. Droughts are multidimensional climate events whose impacts may be moderated by changes in temperature, water availability or food availability, or some combination of these. Simultaneously, other stressors such as extensive anthropogenic landscape modification may synergize with drought. Useful observational models for guiding conservation decision‐making during drought require multidimensional, dynamic representations to disentangle possible drought impacts, and consequently, they will require large, highly resolved data sets. In this paper, we develop a two‐stage predictive framework for assessing how drought impacts vary with species, habitats and climate pathways.LocationCentral Valley, California, USA.MethodsWe used a two‐stage counterfactual analysis combining predictive linear mixed models and N‐mixture models to characterize the multidimensional impacts of drought on 66 bird species. We analysed counts from the eBird participatory science data set between 2010 and 2019 and produced species‐ and habitat‐specific estimates of the impact of drought on relative abundance.ResultsWe found that while fewer than a quarter (16/66) of species experienced abundance declines during drought, nearly half of all species (27/66) changed their habitat associations during drought. Among species that shifted their habitat associations, the use of natural habitats declined during drought while use of developed habitat and perennial agricultural habitat increased.Main ConclusionsOur findings suggest that birds take advantage of agricultural and developed land with artificial irrigation and heat‐buffering microhabitat structure, such as in orchards or parks, to buffer drought impacts. A working lands approach that promotes biodiversity and mitigates stressors across a human‐induced water gradient will be critical for conserving birds during drought.

Measuring Switching Costs in the Italian Residential Electricity Market

Residential electricity markets in European countries are still characterized by low consumer engagement, especially where regulated and liberalized markets coex- ist. Using an original dataset on 2015–2018 prices for the Italian electricity mar- ket, augmented with the number of residential consumers, we study the presence and magnitude of switching costs—i.e., time-based and cognitive-based costs on consumers changing providers—in the liberalized market. We find that switch- ing from the incumbent involves high costs—almost as high as the yearly energy expenditure—while switching from competitors is less expensive. We also carry out two counterfactual analyses. In the first, we show that consumers would have incurred lower average switching costs over the years had the market been less concentrated. In the second, we simulate how switching costs could evolve once regulated prices are phased out, and the market is fully liberalized.

Pension policies and early retirement: New evidence from a counterfactual analysis in Iran

AbstractCompared to the global average, the exit rate of old-aged Iranian labourers is significantly higher than that of middle-aged, raising the hypothesis that social security generosity pulls older workers into early retirement. We used a unique individual dataset of Iran’s Social Security Organization (ISSO), including 267, 000 newly retired in 2016 and 2018, to assess the impact of ISSO’s pension policies on employees’ retirement age. In a counterfactual evaluation design, this study first estimated the implicit tax on work continuation and then applied the Heckman two-stage selection model. The findings show that ISSO’s retirement rules determining the age of exit and benefit eligibilities significantly increase the retirement probability and simultaneously decline the retirement age. Moreover, the implicit tax on work continuation, which reflects ISSO’s benefit formula, has a significant positive effect on retirement probability. The replacement rate also has a significant negative impact on retirement age. The retirement probability in hazardous jobs is higher than in normal ones, while exemptions significantly fall the outflow age of hazardous job holders. To maintain the scheme’s sustainability, reforms have to target an increase in the statutory retirement age, a reduction in the accrual rate, the calculation of reference salary for more extended periods, and the decline of the exemption coefficient for hazardous jobs.

Enhancing the Efficiency of Enterprise Shutdowns for Environmental Protection: An Agent-Based Modeling Approach with High Spatial–Temporal Resolution Data

Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises. This study developed a universal dynamic agent-based supply chain model to achieve tradeoffs between environmental risk reduction and economic sustainability. The model was used to conduct high-resolution daily simulations of the dynamic shifts in enterprise operations and their cascading effects on supply chain networks. It includes production, consumption, and transportation agents, attributing economic features to supply chain components and capturing their interactions. It also accounts for adaptive responses to daily external shocks and replicates realistic firm behaviors. By coupling high spatial–temporal resolution firm-level data from 18 916 chemical enterprises, this study investigates the economic and environmental impacts of an environmental policy resulting in the closure of 1800 chemical enterprises over three years. The results revealed a significant economic loss of 25.8 billion USD, ranging from 23.8 billion to 31.8 billion USD. Notably, over 80% of this loss was attributed to supply chain propagation. Counterfactual analyses indicated that implementing a staggered shutdown strategy prevented 18.8% of supply chain losses, highlighting the importance of a gradual policy implementation to prevent abrupt supply chain disruptions. Furthermore, the study highlights the effectiveness of a multi-objective policy design in reducing economic losses (about 29%) and environmental risks (about 40%), substantially enhancing the efficiency of the environmental policy. The high-resolution simulations provide valuable insights for policy designers to formulate strategies with staggered implementation and multiple objectives to mitigate supply chain losses and environmental risks and ensure a sustainable future.

ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation.

Great progress in the development of computational strategies for drug design applications has revolutionized the process of searching for new drugs. Although the focus of in silico strategies is still put on the provision of the desired activity of a compound to the considered target, characterization of a compound in terms of its physicochemical and ADMET properties becomes an indispensable element of computer-aided drug design protocols. In the study, an online application ADMET-PrInt for in silico assessment of selected compound features: cardiotoxicity, solubility, genotoxicity, membrane permeability, and plasma protein binding was prepared. In addition to the prediction of particular property, ADMET-PrInt enables also the identification of compound features influencing this property thanks to the application of two explainability approaches: local interpretabile model-agnostic explanations and counterfactual analysis. It is an important factor for medicinal chemists, as it greatly facilitates the process of optimization of the compound structure in terms of the evaluated properties. The intuitive webpage, available at admet.if-pan.krakow.pl, allows making use of all predictive and interpretability models also by nonexperts and nonprogrammers.

The influence of industrial robots on firm-level pollution emissions: Evidence from China

Industrial robots are crucial in the production process, but limited literature uncovers the comprehensive channels through which they affect firms’ emissions. This paper investigates the influence of industrial robots on firm-level emissions and emissions-intensity. Using comprehensive firm-level SO2 emissions and robot import data in China during 2000–2013, we find that the robot-adoption manifests a negative effect on both firm-level emissions and emissions-intensity. Meanwhile, this reduction effect exhibits an increasing dynamic effect. Furthermore, the mechanism analysis indicates that improving abatement efficiency, increasing the share of intermediate inputs, and reducing SO2 generations per output contribute 43.25%, 22.81%, and 17.54%, respectively to the reduction effect. Finally, the counterfactual analysis implies that shutting off the abatement efficiency, share of intermediate inputs, and SO2 generation channels, will lower the reduction effect of robots on emissions by 4.83%, 2.85% and 1.96%, respectively.

Spatioethnic Household Carbon Footprints in China and the Equity Implications of Climate Mitigation Policy: A Machine Learning Approach

This article relies on the first and only representative survey data to estimate household carbon footprints (CFs) of China’s large yet vastly understudied ethnic minority population, documenting for the first time significant ethnic disparities in CFs driven by ethnic minorities’ relatively worse-off living standards: From 2010 to 2020, China’s ethnic minority population contributed less than 6 percent of residential emissions, about 50 percent less than expected based on population share alone. Next, results from a counterfactual policy analysis find that the distributive effects of a carbon tax are regressive in urban areas but not in rural areas, increasing within and between ethnic group inequality in urban China. A carbon tax with revenue-neutral schemes, by contrast, helps to mitigate existing inequalities in society, reducing income- and ethnic-based forms of inequality. Results are robust to machine learning techniques employed to simulate potential heterogeneous household abatement scenarios. The findings emphasize the potential benefits of a carbon tax, contributing to a more comprehensive understanding of climate justice and informing policy decisions that promote equitable outcomes for vulnerable segments of society.

Low‐rise buildings in big cities: Theory and evidence from China

AbstractThis article explores the determinants of floor area ratio (FAR) limit, a major form of construction density regulation, in China. I develop a spatial equilibrium framework to study local governments’ optimal FAR design and investigate over 400,000 residential land transactions between 2007 and 2019 to perform the empirical analysis. Exploiting the exogenous variations generated by administrative adjustments and applying a propensity score matching approach, I find that a one standard deviation increase in local budgetary revenue decreases FAR limits by 0.29. Further counterfactual analysis suggests that the land finance model contributes to housing affordability issues and spatial inequality in China.

Evaluating completion rates of COVID-19 contact tracing surveys in New York City

ImportanceContact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs.ObjectiveTo identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021.MethodsWe analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used.ResultsThe percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation – compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% – 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% –2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% – 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%.ConclusionThese findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.