Does the Swiss Debt Break Induce Sound Federal Finances? A Synthetic Control Analysis

2016 ◽  
Author(s):  
Christian Friedrich Pfeil ◽  
Lars P. Feld
Keyword(s):  
Control Analysis ◽  
Synthetic Control

scholarly journals 1620. Effectiveness of the 2016 California Policy Eliminating Non-Medical Exemptions on Vaccine Coverage: A Synthetic Control Analysis

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S591-S591
Author(s):  
Sindiso Nyathi ◽  
Hannah Karpel ◽  
Kristin L Sainani ◽  
Yvonne Maldonado ◽  
Peter J Hotez ◽  
...  
Keyword(s):  
Control Method ◽  
Vaccine Coverage ◽  
The United States ◽  
State Policies ◽  
Vaccine Hesitancy ◽  
Control Analysis ◽  
Synthetic Control ◽  
Permutation Testing ◽  
Personal Belief ◽  
Post Period

Abstract Background Vaccine hesitancy in low vaccine coverage “hot spots” has led to recent outbreaks of vaccine-preventable diseases across the United States. State policies to improve vaccine coverage by restricting non-medical (personal belief) exemptions are heavily debated and their effectiveness is unclear due to limited rigorous policy analysis. In 2016, a California policy (SB 277) eliminated non-medical exemptions from kindergarten requirements. To address the ongoing debate on such policies, we performed a quasi-experimental, controlled analysis of the policy’s impact on vaccine and exemption outcomes. Methods We used state vaccine coverage and exemption data (2011–2017) from the CDC and health data from public sources. We prespecified a primary outcome of MMR coverage (%) and secondary outcomes of medical and non-medical exemptions (%). We included covariates related to socioeconomic and health measures (e.g., insurance, well child visits) and pre-2016 mean coverage. Using the synthetic control method, with 2016 as the treatment year and a 2-year post-policy period, we constructed a “control” California, from a weighted sum of states. We used permutation testing to repeat the process for each of the other states and their unique synthetic control, to determine whether there was a meaningful difference in California (i.e., a change in California’s coverage relative to its control in the top 5th percentile of states). We tested the model’s sensitivity to various analytical assumptions. Results Of 43 control states, synthetic California was predominantly comprised of Idaho, Mississippi, and Arkansas, and had a good pre-policy match on outcomes. MMR coverage in California increased by 3.2% relative to synthetic California in the post period (Top 1 of 44 states, Figure 1). Medical exemptions increased by 0.4%, while non-medical exemptions decreased by 2.2% in the post-period (Top 1 of 43 states). The model was robust to changes in covariates and control states. Conclusion The policy resulted in a meaningful increase in MMR coverage and reduction in non-medical exemptions. We measured a modest increase in medical exemptions, but this was offset by the larger reduction in non-medical exemptions. State policies removing non-medical exemptions can be effective in increasing vaccination coverage. Disclosures All authors: No reported disclosures.

scholarly journals Effects of statewide stay-at-home orders on COVID-19 cases and deaths in the central USA

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gary W. Reinbold
Keyword(s):  
Health Service ◽  
Nearest Neighbor ◽  
Control Analysis ◽  
Synthetic Control ◽  
Content Type ◽  
Service Areas ◽  
Nearest Neighbor Matching ◽  
The Usa ◽  
Central Usa ◽  
At Home

PurposeThis study seeks to determine the effects of stay-at-home orders in Spring 2020 on COVID-19 cases and deaths in the Central USA by comparing counties and health service areas that were and that were not subject to statewide orders.Design/methodology/approachThis study estimates the effects of statewide stay-at-home orders on new COVID-19 cases and deaths within 19 central states, of which 14 had stay-at-home orders. It uses synthetic control analysis and nearest neighbor matching to estimate the effects at two geographic levels: counties and health service areas.FindingsStatewide stay-at-home orders significantly reduced the number of new COVID-19 cases in the Central USA starting about three weeks after their effective dates; during the fourth week after their effective dates, the orders reduced the number of new cases per capita by 31%–57%. Statewide stay-at-home orders did not reduce the number of new COVID-19 deaths in the Central USA.Social implicationsThe main purpose of stay-at-home orders in Spring 2020 was to “flatten the curve” so that hospitalizations would not exceed capacity. It is likely that stay-at-home orders in the Central USA reduced hospitalizations to some extent, although the effect on hospitalizations was likely smaller than the effect on cases.Originality/valueThis is the first study of stay-at-home orders in the USA to limit the population to a group of interior states. All coastal states had statewide stay-at-home orders and comparing coastal states with orders to interior states without them may be problematic.

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