Background

Brazil has made progress towards a more equitable distribution of health care, but gains may be threatened by economic instability resulting from the 2008 global financial crisis. This study measured predictors of health care utilization and changes in horizontal inequity between 2008 and 2013.

Method

Data were from two nationally representative surveys that measured a variety of sociodemographic, health behaviors and health care indicators. We used Poisson regression models to estimate adjusted prevalence ratios and the Horizontal Equity Index (HEI) standardized by health needs to measure inequity in the utilization of doctor and dentist visits, hospitalizations and reporting of a usual source of care (USC) for those 18 and older. To estimate the HEI, we ranked the population from the poorest to the richest using a wealth index. We also decomposed the HEI into its different components and assessed changes from 2008 to 2013.

Results

The population proportion with doctor and dentist visits in the past year and a USC increased between 2008 and 2013, while hospitalizations declined. In 2013, pro-rich inequity in doctor visits increased significantly while the distribution of hospitalizations shifted from pro-rich in 2008 to neutral in 2013. Dentist visits were highly pro-rich and USC was slightly pro-rich; the distribution of dentist visits and USC did not change over time. Health need was a strong predictor of health care utilization regardless of the type of coverage (public or private). Education, wealth, and private health plans were associated with the pro-rich orientation of doctor and dentist visits. Private health plans contributed to the pro-rich orientation of all outcomes, while the Family Health Strategy contributed to the pro-poor orientation of all outcomes.

Conclusion

The results of this study support the claim that Brazil's population continued to see absolute gains in access to care despite recent economic crises. However, gains in equity have slowed and may even decline if investments are not maintained as the country enters deeper financial and political crises.

Firearms are a major source of preventable morbidity and mortality in the United States, contributing to over 48,000 deaths in 2022 and generating societal costs in excess of $500 billion. A body of work has examined the relationship between US state level firearm laws and health outcomes, generally finding that some firearm regulations are associated with lower firearm-related mortality. Alcohol has been identified as an additional risk factor for both homicides and suicide and stronger state alcohol laws have been associated with lower rates of suicide. To date, there are no empirical studies that have investigated the impact of laws over a long period of time that target the intersection of alcohol and firearm. One reason for this may be because there is no existing dataset that includes the range of these state laws over time. This study describes the protocol for collecting, coding and operationalizing these legal data.

Introduction

Ignition interlock devices installed after conviction for driving under the influence of alcohol (DUI) have been shown to reduce subsequent DUI arrests (specific deterrence). However, there is little evidence on how interlock-device penalties might affect general deterrence, that is, deterring people from driving after consuming alcohol prior to a DUI conviction.

Methods

A discrete choice experiment was conducted and data were analyzed in 2023 with 583 U.S.-based adults who consume alcohol at least once in the past week to assess the deterrent effects of five different penalties (fine, jail time, interlock device, license suspension, alcohol treatment) for alcohol-impaired driving under randomized sequential scenarios of high (20% chance of being caught) and low (1%) police enforcement. Participants resided in 46 states.

Results

Deterrent effects of an interlock penalty, operationalized as having to install an interlock device for 1 year, are large and on par with a 20-fold increase in police enforcement activity (from 1% chance of being caught to 20%), or a $2,000 increase in the DUI fine under the status quo enforcement regime. On average, a 1-year interlock penalty had the same deterrent effect as a 10-day increase in jail time.

Conclusions

Wider use of interlock devices as a DUI penalty could have large deterrent effects, independent of their ability to physically prevent the motor vehicle of an intoxicated driver from starting. The deterrent effect documented here adds to evidence on interlock devices' overall effectiveness as well as their potential to shift DUI penalties away from criminalization (jail time) and toward immobilization and rehabilitation.

Background

There are significant differences in the meaning and use of the term 'Reverse Innovation' between industry circles, where the term originated, and health policy circles where the term has gained traction. It is often conflated with other popularized terms such as Frugal Innovation, Co-development and Trickle-up Innovation. Compared to its use in the industrial sector, this conceptualization of Reverse Innovation describes a more complex, fragmented process, and one with no particular institution in charge. It follows that the way in which the term 'Reverse Innovation', specifically, is understood and used in the healthcare space is worthy of examination.

Methods

Between September and December 2014, we conducted eleven in-depth face-to-face or telephone interviews with key informants from innovation, health and social policy circles, experts in international comparative policy research and leaders in the Reverse Innovation space in the United States. Interviews were open-ended with guiding probes into the barriers and enablers to Reverse Innovation in the US context, specifically also informants' experience and understanding of the term Reverse Innovation. Interviews were recorded, transcribed and analyzed thematically using the process of constant comparison.

Results

We describe three main themes derived from the interviews. First, 'Reverse Innovation,' the term, has marketing currency to convince policy-makers that may be wary of learning from or adopting innovations from unexpected sources, in this case Low-Income Countries. Second, the term can have the opposite effect - by connoting frugality, or innovation arising from necessity as opposed to good leadership, the proposed innovation may be associated with poor quality, undermining potential translation into other contexts. Finally, the term 'Reverse Innovation' is a paradox - it breaks down preconceptions of the directionality of knowledge and learning, whilst simultaneously reinforcing it.

Conclusions

We conclude that this term means different things to different people and should be used strategically, and with some caution, depending on the audience.

Death due to motor vehicle collisions (MVCs) remains a leading cause of death in the US and alcohol plays a prominent role in a large proportion of these fatalities nationwide. Rates for these incidents vary widely among states and over time. Here, we explore the extent to which driving volume, alcohol consumption, legislation, political ideology, and geographical factors influence MVC deaths across states and time. We specify structural equation models for extracting associations between the factors and outcomes for MVC deaths and compute correlation functions of states' relative geographic and political positions to elucidate the relative contribution of these factors. We find evidence that state-level variation in MVC deaths is associated with time-varying driving volume, alcohol consumption, and legislation. These relationships are modulated by state spatial proximity, whereby neighboring states are found to share similar MVC death rates over the thirty-year observation period. These results support the hypothesis that neighboring states exhibit similar risk and protective characteristics, despite differences in political ideology.

A universal question in network science entails learning about the topology of interaction from collective dynamics. Here, we address this question by examining diffusion of laws across US states. We propose two complementary techniques to unravel determinants of this diffusion process: information-theoretic union transfer entropy and event synchronization. In order to systematically investigate their performance on law activity data, we establish a new stochastic model to generate synthetic law activity data based on plausible networks of interactions. Through extensive parametric studies, we demonstrate the ability of these methods to reconstruct networks, varying in size, link density, and degree heterogeneity. Our results suggest that union transfer entropy should be preferred for slowly varying processes, which may be associated with policies attending to specific local problems that occur only rarely or with policies facing high levels of opposition. In contrast, event synchronization is effective for faster enactment rates, which may be related to policies involving Federal mandates or incentives. This study puts forward a data-driven toolbox to explain the determinants of legal activity applicable to political science, across dynamical systems, information theory, and complex networks.

Background

In the past 40 years, a variety of factors might have impacted motor vehicle (MV) fatality trends in the US, including public health policies, engineering innovations, trauma care improvements, etc. These factors varied in their timing across states/localities, and many were targeted at particular population subgroups. In order to identify and quantify differential rates of change over time and differences in trend patterns between population subgroups, this study employed a novel analytic method to assess temporal trends in MV fatalities between 1968 and 2010, by age group and sex.

Methods

Cause-specific MV fatality data from traffic injuries between 1968 and 2010, based on death certificates filed in the 50 states, and DC were obtained from Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Long-term (1968 to 2010) and short-term (log-linear piecewise segments) trends in fatality rates were compared for males and females overall and in four separate age groups using joinpoint regression.

Results

MV fatalities declined on average by 2.4% per year in males and 2.2% per year in females between 1968 and 2010, with significant declines observed in all age groups and in both sexes. In males overall and those 25 to 64 years, sharp declines between 1968 and mid-to-late 1990s were followed by a stalling until the mid-2000s, but rates in females experienced a long-term steady decline of a lesser magnitude than males during this time. Trends in those aged <1 to 14 years and 15 to 24 years were mostly steady over time, but males had a larger decline than females in the latter age group between 1968 and the mid-2000s. In ages 65+, short-term trends were similar between sexes.

Conclusions

Despite significant long-term declines in MV fatalities, the application of Joinpoint Regression found that progress in young adult and middle-aged adult males stalled in recent decades and rates in males declined relatively more than in females in certain age groups. Future research is needed to establish the causes of these observed trends, including the potential role of contemporaneous MV-related policies and their repeal. Such research is needed in order to better inform the design and evaluation of future population interventions addressing MV fatalities nationally.

Background

Country-of-origin of a product can negatively influence its rating, particularly if the product is from a low-income country. It follows that how non-traditional sources of innovation, such as low-income countries, are perceived is likely to be an important part of a diffusion process, particularly given the strong social and cognitive boundaries associated with the healthcare professions.

Methods

Between September and December 2014, we conducted eleven in-depth face-to-face or telephone interviews with key informants from innovation, health and social policy circles, experts in international comparative policy research and leaders in Reverse Innovation in the United States. Interviews were open-ended with guiding probes into the barriers and enablers to Reverse Innovation in the US context, specifically also to understand whether, in their experience translating or attempting to translate innovations from low-income contexts into the US, the source of the innovation matters in the adopter context. Interviews were recorded, transcribed and analyzed thematically using the process of constant comparison.

Results

Our findings show that innovations from low-income countries tend to be discounted early on because of prior assumptions about the potential for these contexts to offer solutions to healthcare problems in the US. Judgments are made about the similarity of low-income contexts with the US, even though this is based oftentimes on flimsy perceptions only. Mixing levels of analysis, local and national, leads to country-level stereotyping and missed opportunities to learn from low-income countries.

Conclusions

Our research highlights that prior expectations, invoked by the Low-income country cue, are interfering with a transparent and objective learning process. There may be merit in adopting some techniques from the cognitive psychology and marketing literatures to understand better the relative importance of source in healthcare research and innovation diffusion. Counter-stereotyping techniques and decision-making tools may be useful to help decision-makers evaluate the generalizability of research findings objectively and transparently. We suggest that those interested in Reverse Innovation should reflect carefully on the value of disclosing the source of the innovation that is being proposed, if doing so is likely to invoke negative stereotypes.

Detecting and explaining the relationships among interacting components has long been a focal point of dynamical systems research. In this paper, we extend these types of data-driven analyses to the realm of public policy, whereby individual legislative entities interact to produce changes in their legal and political environments. We focus on the U.S. public health policy landscape, whose complexity determines our capacity as a society to effectively tackle pressing health issues. It has long been thought that some U.S. states innovate and enact new policies, while others mimic successful or competing states. However, the extent to which states learn from others, and the state characteristics that lead two states to influence one another, are not fully understood. Here, we propose a model-free, information-theoretical method to measure the existence and direction of influence of one state's policy or legal activity on others. Specifically, we tailor a popular notion of causality to handle the slow time-scale of policy adoption dynamics and unravel relationships among states from their recent law enactment histories. The method is validated using surrogate data generated from a new stochastic model of policy activity. Through the analysis of real data in alcohol, driving safety, and impaired driving policy, we provide evidence for the role of geography, political ideology, risk factors, and demographic and economic indicators on a state's tendency to learn from others when shaping its approach to public health regulation. Our method offers a new model-free approach to uncover interactions and establish cause-and-effect in slowly-evolving complex dynamical systems.

Excessive alcohol consumption contributes significantly to premature mortality, injuries and morbidity, and a range of U.S. state policies have been shown to reduce these behaviors. Monitoring state alcohol policy environments is essential, but methodologically challenging given that new laws may be passed (or repealed) each year, resulting in considerable variation across states. Existing measures have not been made public or have only a single year available. We develop a new replicable measure, the state alcohol policy score, for each state and year 2004-2009, that captures the essential features of a state's evidence-based alcohol policies. We evaluate its similarity to two existing alcohol policy measures and validate it by replicating findings from a previous study that used one of those measures to assess its relationship with several binge drinking outcomes. Estimates of the association between one-year lagged state alcohol policy scores and state binge drinking outcomes, obtained from the 2005-2010 Behavioral Risk Factor Surveillance System surveys (n = 440,951, 2010), were produced using Generalized Linear Models that controlled for state and individual-level co-variates, with fixed effects for year and region. We find a 10-percentage point increase in the state alcohol policy score was associated with a 9% lower odds of binge drinking (aOR = 0.91, 95% CI 0.89, 0.92; N = 1,992,086), a result consistent for men, women and for most age and race subgroups. We find that gender gaps in binge drinking behaviors narrowed in states with higher state alcohol policy scores. These results were nearly identical to those found in other studies using different scores obtained with the aid of expert opinions. We conclude that the score developed here is a valid measure that can be readily updated for monitoring and evaluating the variation and impact of state alcohol policies and make available our state scores for the years of the study.