Most downloaded biology preprints, all time
in category health policy
399 results found. For more information, click each entry to expand.
225 downloads medRxiv health policy
This paper investigates the role of socioeconomic considerations in the formation of official COVID-19 reports. To this end, we employ a dataset that contains 1,159 preprocessed indicators from the World Bank Group GovData360 and TCdata360 platforms and an additional 8 COVID-19 variables generated based on reports from 138 countries. During the analysis, a rank-correlation-based complex method is used to identify the time- and space-varying relations between pandemic variables and the main topics of World Bank Group platforms. The results not only draw attention to the importance of factors such as air traffic, tourism, and corruption in report formation but also support further discipline-specific research by mapping and monitoring a wide range of such relationships. To this end, an R Notebook is attached that allows for the customization of the analysis and provides up-to-date results.
225 downloads medRxiv health policy
The COVID-19 pandemic has induced large-scale social, economic, and behavioral changes, presenting a unique opportunity to study how air pollution is affected by unprecedented societal shifts. At each of 455 PM2.5 monitoring sites across the United States, we conduct a causal inference analysis to determine the impacts of COVID-19 interventions and behavioral changes ("lockdowns") on PM2.5 concentrations. Our approach allows for rigorous confounding adjustment and provides highly spatio-temporally resolved effect estimates. We find that, with the exception of the Southwest, most of the US experienced increases in PM2.5 during lockdown, compared to the concentrations expected under business-as-usual. To investigate possible drivers of this phenomenon, we use regression to characterize the relationship of many environmental, geographical, meteorological, mobility, and socioeconomic factors with the lockdown-attributable changes in PM2.5. Our findings have immense environmental policy relevance, suggesting that large-scale mobility and economic activity reductions may be insufficient to substantially and uniformly reduce PM2.5.
221 downloads medRxiv health policy
Background Several countries paused their rollouts of the Oxford-AstraZeneca COVID-19 vaccine in mid-March 2021 due to concerns about vaccine-induced thrombosis and thrombocytopenia. Many warned that this risked damaging public confidence during a critical period of pandemic response. This study investigated whether the pause in the use of the Oxford-AstraZeneca vaccine had an impact on subsequent vaccine uptake in European countries. Methods We used a difference-in-differences approach capitalizing on the fact that some countries halted their rollouts whilst others did not. A longitudinal panel was constructed for European Economic Area countries spanning 15 weeks in early 2021. Media reports were used to identify countries that paused the Oxford-AstraZeneca vaccine and the timing of this. Data on vaccine uptake were available through the European Centre for Disease Control and Prevention COVID-19 Vaccine Tracker. Difference-in-differences linear regression models controlled for key confounders that could influence vaccine uptake, and country and week fixed effects. Further models and robustness checks were performed. Results The panel included 28 countries, with 19 in the intervention group and 9 in the control group. Pausing the Oxford-AstraZeneca vaccine was associated with a 0.52% decrease in uptake for the first dose of a COVID-19 vaccine and a 1.49% decrease in the uptake for both doses, comparing countries that paused to those that did not. These estimates are not statistically significant (p=0.86 and 0.39 respectively). For the Oxford-AstraZeneca vaccine only, the pause was associated with a 0.56% increase in uptake for the first dose and a 0.07% decrease in uptake for both doses. These estimates are also not statistically significant (p= 0.56 and 0.51 respectively). All our findings are robust to sensitivity analyses. Conclusion As new COVID-19 vaccines emerge, regulators should be cautious to deviate from usual protocols if further investigation on clinical or epidemiological grounds is warranted.
220 downloads medRxiv health policy
ObjectivePublic response monitoring is critical to reducing COVID-19 infections and developing effective public health strategies. This study explored Google search trends to understand public responses to COVID-19 concerns in Bangladesh. MethodsWe used country-level Google search trends data to examine the association between Google search terms related to COVID-19 deaths, face masks, and COVID-19 vaccines and the actual and one-week lagged actual COVID-19 death counts from February 2, 2020, to December 19, 2020, in Bangladesh. Results: Search terms related to COVID-19 deaths, face masks, and COVID-19 vaccines increased and peaked during March and April, but then began declining gradually after June 2020. The mean relative search volume for face masks (35 points) was higher than for death information (8 points) and vaccines (16 points) throughout the study period, and searching for masks peaked (100 points) during the third week of March. Search interests for death information and face masks were negatively correlated with the actual and one-week lagged actual COVID-19 death counts. ConclusionIn response to declining trends in COVID-19-related google search terms, policymakers should strengthen ongoing risk communication and preventive information dissemination programs to control and prevent COVID-19 cases and deaths.
219 downloads medRxiv health policy
AO_SCPLOWBSTRACTC_SCPLOWStepped wedge designs (SWDs) are designs for cluster randomized trials that feature staggered, unidirectional cross-over, typically from a control to a treatment condition. Existing literature on statistical power for SWDs primarily focuses on designs with a single treatment. However, SWDs with multiple treatments are being proposed and conducted. We present a linear mixed model for a SWD with two treatments, with and without an interaction between them. We derive closed form solutions for the standard errors of the treatment effect coefficients for such models along with power calculation methods. We consider repeated cross-sectional designs as well as open and closed cohort designs and different random effect structures. Design features are examined to determine their impact on power for main treatment and interaction effects.
218 downloads medRxiv health policy
Objectives: To assess the extent to which public support for outbreak containment policies varies with respect to the severity of an infectious disease outbreak. Methods: A web-enabled survey was administered to 1,017 residents of Singapore during the COVID-19 pandemic, and was quota-sampled based on age, gender and ethnicity. A fractional-factorial design was used to create hypothetical outbreak vignettes characterised by morbidity and fatality rates, and local and global spread of an infectious disease. Each respondent was asked to indicate which response policies (among 5 policies restricting local movement and 4 border control policies) they would support in 5 randomly-assigned vignettes. Binomial logistic regressions were used to predict the probabilities of support as a function of outbreak attributes, personal characteristics and perceived policy effectiveness. Results: Likelihood of support varied across government response policies; however, was generally higher for border control policies compared to internal policies. The fatality rate was the most important factor for internal policies while the degree of global spread was the most important for border control policies. In general, individuals who were less healthy, had higher income and were older were more likely to support these policies. Perceived effectiveness of a policy was a consistent and positive predictor of public support. Conclusions: Our findings suggest that campaigns to promote public support should be designed specifically to each policy and tailored to different segments of the population. They should also be adapted based on the evolving conditions of the outbreak in order to receive continued public support.
217 downloads medRxiv health policy
Background: Antimicrobial resistance (AMR) is a complex multifactorial outcome of health, socio-economic and geopolitical factors. Therefore, tailored solutions for mitigation strategies could be more effective in dealing with this challenge. Knowledge-synthesis and actionable models learned upon large datasets are critical in order to diffuse the risk of entering into a post-antimicrobial era. Objective: This work is focused on learning Global determinants of AMR and predicting susceptibility of antibiotics at isolate level (Local) for WHO (world health organization) declared critically important pathogens Pseudomonas aeruginosa, Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, Enterobacter cloacae, Staphylococcus aureus. Methods: In this study, we used longitudinal data (2004-2017) of AMR having 633820 isolates from 72 Middle and High-income countries. We integrated the Global burden of disease (GBD), Governance (WGI), and Finance data sets in order to find the unbiased and actionable determinants of AMR. We chose a Bayesian Decision Network (BDN) approach within the causal modeling framework to quantify determinants of AMR. Finally Integrating Bayesian networks with classical machine learning approaches lead to effective modeling of the level of AMR. Results: From MAR (Multiple Antibiotic Resistance) scores, we found that developing countries are at higher risk of AMR compared to developed countries, for all the critically important pathogens. Also, Principal Components Analysis(PCA) revealed that governance, finance, and disease burden variables have a strong association with AMR. We further quantified the impact of determinants in a probabilistic way and observed that heath system access and government effectiveness are strong actionable factors in reducing AMR, which was in turn confirmed by what-if analysis. Finally, our supervised machine learning models have shown decent performance, with the highest on Staphylococcus aureus. For Staphylococcus aureus, our model predicted susceptibility to Ceftaroline and Oxacillin with the highest AUROC, 0.94 and 0.89 respectively.
215 downloads medRxiv health policy
Emily Breza, Fatima Cody Stanford, Marcela Alsan, Burak Alsan, Abhijit Banerjee, Arun G Chandrasekhar, Sarah Eichmeyer, Traci Glushko, Paul Goldsmith-Pinkham,, Kelly Holland, Emily Hoppe, Mohit Karnani, Sarah Liegl, Tristan Loisel, Lucy Ogbu-Nwobodo, Benjamin Olken, Carlos Torres, Pierre-Luc Vautrey, Erica T. Warner, Susan Wootton, Esther Duflo
During the COVID-19 epidemic, many health professionals started using mass communication on social media to relay critical information and persuade individuals to adopt preventative health behaviors. Our group of clinicians and nurses developed and recorded short video messages to encourage viewers to stay home for the Thanksgiving and Christmas Holidays. We then conducted a two-stage clustered randomized controlled trial in 820 counties (covering 13 States) in the United States of a large-scale Facebook ad campaign disseminating these messages. In the first level of randomization, we randomly divided the counties into two groups: high intensity and low intensity. In the second level, we randomly assigned zip codes to either treatment or control such that 75% of zip codes in high intensity counties received the treatment, while 25% of zip codes in low intensity counties received the treatment. In each treated zip code, we sent the ad to as many Facebook subscribers as possible (11,954,109 users received at least one ad at Thanksgiving and 23,302,290 users received at least one ad at Christmas). The first primary outcome was aggregate holiday travel, measured using mobile phone location data, available at the county level: we find that average distance travelled in high-intensity counties decreased by -0.993 percentage points (95% CI -1.616, -0.371, p-value 0.002) the three days before each holiday. The second primary outcome was COVID-19 infection at the zip-code level: COVID-19 infections recorded in the two-week period starting five days post-holiday declined by 3.5 percent (adjusted 95% CI [-6.2 percent, -0.7 percent], p-value 0.013) in intervention zip codes compared to control zip codes.
214 downloads medRxiv health policy
Importance: Nationally stated goals for distributing SARS-CoV-2 vaccines included to reduce COVID-19 mortality, morbidity, and inequity using prioritization groups. However, the impact of these prioritization strategies is not well understood, particularly their effect on health inequity in COVID-19 burden for historically marginalized racial and ethnic populations. Objective: To assess the impact of vaccination prioritization and operational strategies on disparities in COVID-19 burden among historically marginalized populations, and on mortality and morbidity by race and ethnicity. Design: We use an agent-based simulation model of North Carolina to project SARS-CoV-2 infections and COVID-19-associated deaths (mortality), hospitalizations (morbidity), and cases over 18 months (7/1/2020-12/31/2021) with vaccine distribution beginning 12/13/2020 to frontline medical and people 75+, assuming initial uptake similar to influenza vaccine. We study two-stage subsequent prioritization including essential workers (essential), adults 65+ (age), adults with high-risk health conditions, HMPs, or people in low income tracts, with eligibility for the general population in the third stage. For age-essential and essential-age strategies, we also simulated maximal uptake (100% for HMP or 100% for everyone), and we allowed for distribution to susceptible-only people. Results: Prioritizing Age then Essential had the largest impact on mortality (2.5% reduction from no prioritization); Essential then Age had the lowest morbidity and reduced infections (4.2% further than Age-Essential) without significantly impacting mortality. Under each prioritization scenario, the age-adjusted mortality burden for HMPs is higher (e.g., 33.3-34.1% higher for the Black population, 13.3%-17.0% for the Hispanic population) compared to the White population, and the gap grew under some prioritizations. In the Age-Essential strategy, the burden on HMPs decreases only when uptake is increased to 100% in HMPs. However, the Black population still had the highest mortality rate even with the Susceptible-Only distribution. Conclusions and Relevance: Simulation results show that prioritization strategies have differential impact on mortality, morbidity, and disparities overall and by race and ethnicity. If prioritization schemes were not paired with increased uptake in HMPs, disparities did not improve and could worsen. Although equity was one of the tenets of vaccine distribution, the vaccination strategies publicly outlined are insufficient to remove and may exacerbate disparities between racial and ethnic groups, thus targeted strategies are needed for the future.
214 downloads medRxiv health policy
Calls for eliminating prioritization for SARS-CoV-2 vaccines are growing amid concerns that prioritization reduces vaccination speed. We use an SEIR model to study the effects of vaccination distribution on public health, comparing prioritization policy and speed under mitigation measures that are either eased during the vaccine rollout or sustained through the end of the pandemic period. NASEMs recommended prioritization results in fewer deaths than no prioritization, but does not minimize total deaths. If mitigation measures are eased, abandoning NASEM will result in about 134,000 more deaths at 30 million vaccinations per month. Vaccination speed must be at least 53% higher under no prioritization to avoid increasing deaths. With sustained mitigation, discarding NASEM prioritization will result in 42,000 more deaths, requiring only a 26% increase in speed to hold deaths constant. Therefore, abandoning NASEMs prioritization to increase vaccination speed without substantially increasing deaths may require sustained mitigation.
213 downloads medRxiv health policy
Objective: To evaluate the impact of the first round of the National Centralized Drug Procurement (NCDP) pilot (referred to as "4+7" policy) on the use of policy-related original and generic drugs. Methods: Drug purchase data from the China Drug Supply Information Platform (CDSIP) database were used, involving nine "4+7" pilot cities and 12 non-pilot provinces in China. "4+7" policy-related drugs were included, which consisted of 25 "4+7" List drugs and 97 alternative drugs that have an alternative relationship with "4+7" List drugs in clinical use. "4+7" List drugs were divided into bid-winning and non-winning products according to the bidding results. Purchase volume, purchase expenditures, daily costs were selected as outcome variables, and were measured using Defined Daily Doses (DDDs), Chinese Yuan (CNY), and Defined Daily Drug cost (DDDc), respectively. Difference-in-Difference (DID) method was employed to estimate the net effect of policy impact. Results: After policy intervention, the DDDs of original drugs among "4+7" List drugs significantly reduced by 124.59%, while generic drugs increased by 52.12% (all p-values <0.01). 17.08% of the original drugs in DDDs were substituted by generic drugs. Prominent reductions of 121.69% and 80.54% were observed in the expenditure of original and generic drugs, with a total cost-saving of 5036.78 million CNY for "4+7" List drugs. The DDDc of bid-winning original and generic drugs, as well as non-winning original drugs, significantly decreased by 33.20%, 75.74%, and 5.35% (all p-values <0.01), while the DDDc of non-winning generic drugs significantly increased by 73.66% (p<0.001). The use proportion of bid-winning products and non-winning original drugs raised prominently from 39.66% to 91.93% Conclusions: "4+7" policy promoted the substitution use of generic drugs against original drugs, which conducive to drug costs saving. The overall quality level of drug use of public medical institutions significantly increased after "4+7" policy, especially in primary medical institutions.
212 downloads medRxiv health policy
Introduction: The objective of this study is to estimate the effects of the national immunisation strategy for Covid-19 in Italy on the national healthcare system. Methods: An epidemiological scenario analysis was developed in order to simulate the impact of the Covid-19 pandemic on the Italian national healthcare system in 2021. Hospitalisations, ICU admissions and death rates were modelled based on 2020 data. We forecast the impact of the introduction of a primary prevention strategy on the national healthcare system by considering vaccine efficacy, availability of doses and potential population coverage over time. Results: In the absence of immunisation, between 57,000 and 63,000 additional deaths are forecast in 2021. Based on the assumptions underlying the two epidemiological scenarios from the 2020 data, our model predicts that cumulative hospital admissions in 2021 will range from 3.4 to 3.9 million. The deployment of vaccine immunisation has the potential to control the evolution of 2021 infections and avoid from 60 to 67 percent of deaths compared to not vaccinating. Conclusions: In order to inform Italian policymakers on delivering a mass vaccination programme, this study highlights and detects some key factors that must be controlled to ensure that immunisation targets will be met in reasonable time.
212 downloads medRxiv health policy
The White House issued Guidelines for Opening Up America Again to help state and local officials when reopening their economies. These included a 'downward trajectory of positive tests as a percent of total tests within a 14-day period.' To examine this rule, we computed the probability of observing continuous decline in positivity when true positivity is in decline using data-driven simulation. Data for COVID-19 positivity reported in New York state from April 14 to May 5, 2020, where a clear reduction was observed, were used. First, a logistic regression model was fitted to the data, considering the fitted values as true positivity. Second, we created observed positivity by randomly selecting 25,000 people per day from a population with those true positivity for 14 days. The simulation was repeated 1,000 times to compute the probability of observing a consecutive decline. As sensitivity analyses, we performed the simulation with different daily numbers of tests (10 to 30,000) and length of observation (7 and 21 days). We further used daily hospitalizations as another metric, using data from the state of Indiana. With 25,000 daily tests, the probability of a consecutive decline in positivity for 14 days was 99.9% (95% CI: 99.7% to 100%). The probability dropped with smaller numbers of tests and longer lengths of consecutive observation, because there is more chance of observing an increase in positivity with smaller numbers of tests and longer observation. The probability of consecutive decline in hospitalizations was ~0.0% regardless of the length of consecutive observation due to large variance. These results suggest that continuous declines in sample COVID-19 test positivity and hospitalizations may not be observed with sufficient probability, even when population probabilities truly decline. Criteria based on consecutive declines in metrics are unlikely to be useful for making decisions about relaxing COVID-19 mitigation efforts.
210 downloads medRxiv health policy
Breast density is known to increase breast cancer risk and decrease mammography screening sensitivity. Breast density notification laws (enacted in 38 states as of September 2020), require physicians to inform women with high breast density of these potential risks. The laws usually require healthcare providers to notify patients of the possibility of using more sensitive supplemental screening tests (e.g., ultrasound). Since the enactment of the laws, there have been controversial debates over i) their implementations due to the potential radiologists bias in breast density classification of mammogram images and ii) the necessity of supplemental screenings for all patients with high breast density. In this study, we formulate a finite-horizon, discrete-time partially observable Markov chain (POMC) to investigate the effectiveness of supplemental screening and the impact of radiologists bias on patients outcomes. We consider the conditional probability of eventually detecting breast cancer in early states given that the patient develops breast cancer in her lifetime as the primary and the expected number of supplemental tests as the secondary patients outcome. Our results indicate that referring patients to a supplemental test solely based on their breast density may not necessarily improve their health outcomes and other risk factors need to be considered when making such referrals. Additionally, average-skilled radiologists performances are shown to be comparable with the performance of a perfect radiologist (i.e., 100% accuracy in breast density classification). However, a significant bias in breast density classification (i.e., consistent upgrading or downgrading of breast density classes) can negatively impact a patients health outcomes.
210 downloads medRxiv health policy
The crisis induced by the Coronavirus pandemic has severely impacted educational institutes. Even with vaccination efforts underway, it is not clear that sufficient confidence will be achieved for schools to reopen soon. This paper considers the impact of vaccination rates and testing rates to reduce infections and hospitalizations and evaluates strategies that will allow educational institute in urban settings to reopen. These strategies are also applicable to businesses and would help plan reopening in order to help the economy. Our analysis is based on a graph model where nodes represent population groups and edges represent population exchanges due to commuting populations. The commuting population is associated with edges and is associated with one of the end nodes of the edge during part of the time period and with the other node during the remainder of the time period. The progression of the disease at each node is determined via compartment models, that include vaccination rates and testing to place infected people in quarantine along with consideration of asymptomatic and symptomatic populations. Applying this to a university population in Chicago with a substantial commuter population, chosen to be 80% as an illustration, provides an analysis which specifies benefits of testing and vaccination strategies over a time period of 150 days.
208 downloads medRxiv health policy
Analyzing data from a large, nationally distributed group of Japanese hospitals, we found a dramatic decline in both inpatient and outpatient volumes over the three waves of the COVID-19 pandemic in Japan from February-December 2020. We identified three key reasons for this fall in patient demand. First, COVID-19-related hygiene measures and behavioral changes significantly reduced non-COVID-19 infectious diseases. Second, consultations relating to chronic diseases fell sharply. Third, certain medical investigations and interventions were postponed or cancelled. Despite the drop in hospital attendances and admissions, COVID-19 is said to have brought the Japanese health care system to the brink of collapse. In this context, we explore longstanding systematic issues, finding that Japan's abundant supply of beds and current payment system may have introduced a perverse incentive to overprovide services, creating a mismatch between patient needs and the supply of health care resources. Poor coordination among health care providers and the highly decentralized governance of the health care system have also contributed to the crisis. In order to ensure the long-term sustainability of the Japanese health care system beyond COVID-19, it is essential to promote specialization and differentiation of medical functions among hospitals, to strengthen governance, and to introduce appropriate payment reform.
207 downloads medRxiv health policy
Determining how best to allocate resources to be used during a pandemic is a strategic decision that directly affects the success of pandemic response operations. However, government agencies have finite resources, so they cant monitor everything all of the time: they have to decide how best to allocate their scarce resources (i.e., budget for antivirals and preventive vaccinations, Intensive Care Unit (ICU), ventilators, non-intensive Care Unit (non-ICU), doctors) across a broad range of risk exposures (i.e., geographic spread, routes of transmission, overall poverty, medical preconditions). This paper establishes a comprehensive risk-based emergency management framework that could be used by decision-makers to determine how best to manage medical resources, as well as suggest patient allocation among hospitals and alternative healthcare facilities. A set of risk indexes are proposed by modeling the randomness and uncertainty of allocating resources in a pandemic. The city understudy is modeled as a Euclidean complex network, where depending on the neighborhood influence of allocating a resource in a demand point (i.e., informing citizens, limit social contact, allocate a new hospital) different network configurations are proposed. Finally, a multi-objective risk-based resource allocation (MoRRA) framework is proposed to optimize the allocation of resources in pandemics. The applicability of the framework is shown by the identification of high-risk areas where to prioritize the resource allocation during the current COVID-19 pandemic in Bogota, Colombia.
202 downloads medRxiv health policy
Health inequality in maternal health is one of the serious challenges currently faced by public health experts. Maternal mortality in Empowered Action Group (EAG) states is highest and so are the health inequalities prevalent. We have made a comprehensive attempt to understand maternal health inequality and the risk factors concerning the EAG states in India, using recent data of Demography Health Survey of India (2015-16). Bi-variate, multivariate logistic regression, and concentration indices were used. The study has measured the four outcome variables of maternal health namely antenatal care of at least 4 visits, institutional delivery, contraceptive use, and unmet need. The study revealed that better maternal health is heavily concentrated among the richer households, while the negative concentration index of unmet need clearly reflected the greater demand for higher unmet need among the poor households in the EAG states of India. Challenges of inequalities still persist at large in maternal health, but to achieve better health these inequalities must be reduced. Since inequality mainly affects the poor households due to a lower level of income. Therefore, specific measures must be taken from a demand-side perspective in order to enhance their income and reduce the disparities in the EAG states of India.
201 downloads medRxiv health policy
Background. Hip fracture (HF) requires an intensive healthcare resources utilization. Long-term morbidity related to poor fracture management is associated with a significant increase in healthcare costs. Many factors may affect the costs and outcomes in patients with HF. Using a definition of integrated Continuum-Care Episode (CCE) that encompasses the hospital phase and the post-acute rehabilitation after a surgical procedure for HF, we investigated the costs of CCEs for HF and their determinants, with particular regard to the contribution of different rehabilitation settings. Methods. We conducted a retrospective observational cohort study using data extracted from administrative databases of 5094 consecutive patients hospitalized for HF in 2017, aged [≥]65 years, and resident in Emilia Romagna, Italy. To evaluate the overall costs of the CCE, we calculated the acute and post-acute costs from the date of the first hospital admission to the end of the integrated CCE. The determinants of costs were investigated using generalized linear regression models. Results. After adjusting for demographic and clinical characteristics, type of surgery (b=-0.340, p<0.001), and hospital bed-based rehabilitation in public or private healthcare facilities either followed by rehabilitation in a community hospital/temporary nursing home beds (b=0.372, p<0.001) or not (b=0.313, p<0.001) were the strongest determinants of costs, while rehabilitation in intermediate care facilities alone was associated with lower costs (0.163, p<0.001). Conclusions. Our findings suggest that CCE cost and its variability is mainly related to the rehabilitation settings. Cost-wise, intermediate care resulted to be an appropriate setting for providing post-acute rehabilitation for HF, representing the one associated with the lower cost of the overall CCE. Therefore, intermediate care settings should be privileged when planning HF rehabilitation pathways.
201 downloads medRxiv health policy
The Centers for Disease Control and Prevention reported 70 630 drug overdose deaths for 2019 in the United States, 70.5% of which were opioid-related. Preliminary estimates now warn that drug overdose deaths likely surpassed 86 000 during 2020. Despite a 57.4% decrease in opioid prescribing since a peak in 2012, the opioid death rate has increased 105.8% through 2019, as the share of those deaths involving fentanyl increased from 16.4% to 72.9%. This letter seeks to determine whether the opioid prescribing and mortality paradox is robust to accepted methods of causal policy analysis and if prescribing rates mediate the effects of policy interventions on overdose deaths. Using loge-loge ordinary least squares with three different specifications as sensitivity analyses for all 50 states and Washington, DC for the period 2001-2019, the elasticities from the regressions with all control variables report operational prescription drug monitoring programs (PDMPs) reduce prescribing rates 8.7%, while mandatory PDMPs increase death rates from opioids 16.6%, heroin and fentanyl 19.0%, cocaine 17.3% and all drugs 10.5%. There is also weak evidence that recreational marijuana laws reduce prescribing, increases in prescribing increase pain reliever deaths, pill mill laws increase cocaine deaths, and medical marijuana laws increase total overdose deaths, with demographic variables suggesting states with more male, less non-Hispanic white, and older citizens experience more overdoses. Weak mediation effects were observed for pain reliever, cocaine, and illicit opioid deaths, while broad reductions in prescribing have failed to reduce opioid overdoses.
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