For fellow travelers in public health economics, this post provides a quick update on resources for publishing and funding research on these topics. First, the publishing frontier: I am guest-editing an upcoming supplemental issue of the American Journal of Public Health devoted to the topic of advances in public health services and systems research (PHSSR). This issue is being developed with support provided by the Robert Wood Johnson Foundation and the U.S. Centers for Disease Control and Prevention. We are especially interested in studies examining health and economic consequences of organizational strategies, funding mechanisms, workforce models, policy/legal approaches, and health IT applications deployed in public health settings. The extended deadline for submitting manuscripts is May 15, 2014, and more information is available here.

Next, the funding frontier: I have recently added a page to this blog that highlights research funding opportunities relevant to public health economics. This page is far from a comprehensive inventory at this stage, but I will be building it over time as I have time.

Speaking of research funding, for those who may have missed it, an interesting item was included in the President’s FY2015 budget request submitted to Congress earlier this month. A $5 million request for public health systems research at the U.S. Centers for Disease Control and Prevention was included in the HHS budget request, as authorize by Section 4301 of the ACA. As the request explains: “CDC will undertake research that seeks to identify the economic and budgetary impacts of public health interventions; expand data on health care utilization and effectiveness; and inform how public health should evolve and public health and health care should collaborate as the health care delivery system transforms.” This blog will keep track of any research findings that flow from this federal initiative, should it be implemented.

Can investments in public health infrastructure help solve big-ticket problems within the medical care system? A new study suggests an affirmative answer when it comes to healthcare associated infections. The controversial federal stimulus package known as the American Recovery and Reinvestment Act of 2009 included a small program aimed at the big problem of HAIs, the potentially lethal and increasingly drug-resistant pathogens that people acquire as a byproduct of receiving care in hospitals and other health care settings. Stimulus allocated approximately $36 million to a problem that generates an estimated $30 billion in hospital costs alone each year. Researchers from CDC and Emory analyzed the effects of this stimulus spending in a new paper appearing in the current issue of AJPH.

Notably, this federal HAI prevention program did not funnel resources directly to hospitals and other health care settings. The pool of funds was far too small for that. Rather, the outlays flowed to state public health agencies to allow them to build state-wide surveillance infrastructure for detecting and reporting HAIs, conduct trainings for health care personnel on infection control practices, form collaboratives among hospitals and other health care stakeholders to collectively plan and implement evidence-based prevention strategies, and monitor and evaluate progress.

The study finds that stimulus-funding significantly improved public health agency capacity to support HAI prevention activities, resulting in reductions in catheter-associated urinary tract infections (CAUTIs). In particular, states that received higher levels of program funding to implement CAUTI prevention collaboratives achieved larger reductions in CAUTI infection rates compared to states without these collaboratives. Infection rates for two other types of HAIs also declined during the study period, but these changes appeared to occur independently of stimulus funding levels and program activities.

That state public health agencies appear to have achieved significant reductions in HAI rates by mobilizing multi-organizational collaboratives is particularly notable in view of the fact that prior research has failed to find an effect for other high powered policy mechanisms such as mandatory HAI public reporting laws and hospital payment penalties tied to HAI events. This pattern of findings suggests that when it comes to HAI prevention, collective action by multiple community stakeholders may trump individual effort fueled by competition, public pressure or financial incentives. Moreover, the findings suggest that public health agencies can be effective in stimulating and supporting collective action through broad multi-organizational collaboratives (see my last post and IOM paper on this topic).

As is common in many PHSSR studies, the authors had to MacGyver a research strategy together from little more than kite string and chewing gum, and for this they deserve high praise. Pre-intervention measures of HAI prevention activity had to be scraped from the grant applications that state agencies submitted to compete for the funding. All states received some level of program funding, so lacking a control group the study had to exploit variation in the amount of funding received and the types of activities implemented in order to estimate possible program effects. State-specific data on HAI infection rates were not uniformly available prior to the initiation of the stimulus funding, making a strong pre-post design or difference-in-difference estimation infeasible. And then there is the problem of surveillance bias: program funding might have strengthened states abilities to detect and report HAIs, the very outcomes that the program seeks to reduce. These and other caveats indicate that the results must be interpreted with caution and should be confirmed with further study.

This study also fuels further thoughts about how best to design and finance public health programs, particularly those with uncertain effectiveness and constrained resources. This HAI program, like many federally-funded public health programs, awarded funding using competitive grant mechanisms that gave preference to settings that already had relevant resources, skills, and program capabilities in place. By attempting to maximize the chances of success, these funding mechanisms may blunt the impact of programs by failing to channel resources where they are needed most. Moreover, competitive funding mechanisms may exacerbate existing disparities in health services and health outcomes between low-resource and higher-resource settings. In this study, one can only wonder what program effects would have been observed if resources were targeted to states based on their observed HAI rates and other measures of need.

Using explicit and objective funding criteria and formulae for such programs, rather than competitive award processes, would also allow for more rigorous research on program effects. If program funding levels are determined based on objective measures, then strong quasi-experimental research designs can be used such as the regression discontinuity design to mitigate selection bias and confounding of the program exposure variable of interest. These types of designs are also advantageous when program implementation resources are constrained, allowing the targeting of resources to high-need settings based on observable and measurable criteria.

Despite its caveats, this study provides a revealing example of how modest investments in public health infrastructure and activities can have meaningful effects on a $2.8 trillion dollar health care system. More PHSSR research is underway to help elucidate and improve the interplay between public health and medical care, so stay tuned to this blog for further updates.

A new empirical paper from the University of Michigan examines behavioral responses to two forms of governmental authority that have direct relevance in public health: (1) the authority to act by exercising legal powers and duties that facilitate public goods production; and (2) authority as a presumed source of expert knowledge and information. While governmental public health agencies routinely use both mechanisms to promote health and prevent disease and injury, the authors of this new paper point out that the research community has paid insufficient attention to distinguishing these two forms of authority and studying how they affect behavior alone and in combination. In this post, I suggest that these distinct forms of governmental authority have particular relevance for the discussions about public health roles within population health improvement strategies and the increasingly popular concept of collective impact.

First, a quick recap of the public and policy discussions surrounding population health strategies. The Affordable Care Act has ushered in a period of enhanced enthusiasm for and experimentation with strategies designed to improve health status on a broad, population-wide basis – at the level of a city, county, neighborhood or other group definition. These strategies contrast with the one-person-at-a-time approach used in much of clinical health care delivery. Former CMS administrator Dr. Donald Berwick characterized these strategies as the third element of his Triple Aim approach to reforming the U.S. health system. Population health strategies aim to address fundamental determinants of health, often through the collective actions of multiple stakeholders that extend far beyond the traditional boundaries of medical care and public health programming – an approach popularized recently in a widely-read issue of the Stanford Social Innovation Review. The growing interest in population health strategies post-ACA even can be seen in Google Trends data for the term “population health”:

As I wrote in a recent discussion paper for the Institute of Medicine’s Roundtable on Population Health Improvement, governmental public health agencies have long advocated for population-wide approaches to health improvement, but they have not always been successful in securing the financial, human, and political capital necessary to implement such approaches successfully. After all, population health strategies are public goods. Convincing organizations to depart from their own institutional interests to undertake collective actions can be difficult, as economic game theorists and governance theorists have long cautioned. A growing discussion has focused on which actors are best positioned to function as integrators for population health strategies, providing the necessary convening, motivating and enabling processes that stimulate and support public goods production in health. As part of this discussion, some observers have questioned whether governmental public health agencies – particularly those functioning at state and local levels – can function successfully as integrators. My discussion paper suggests some economic principles that could inform how these agencies adapt to population health strategies, including adaptation through substitution, synergy, and independence. But, admittedly, empirical data on these issues are hard to come by.

Enter stage right, the new paper from the University of Michigan’s group of experimental economists and social scientists. The authors have strong theoretical reasons to hypothesize that the two types of authorities described at the beginning of this post – the “authority to” exercise legal powers and the “authority in” a relevant body of knowledge – may be important in motivating and enabling voluntary contributions to public goods projects. These same authorities are used widely in public health policy and practice, particularly the “authority to” powers in areas such as taxing tobacco products, licensing retail food vendors, and issuing quarantine and isolation orders for infectious disease control. The “authority in” elements of public health are perhaps a bit more subtle, such as issuing recommendations for vaccination and use of other preventive services, and collecting surveillance data on disease and risk factor prevalence so as to motivate and inform health improvement strategies.

Using a series of randomized laboratory experiments, the authors show for the first time that exercising both types of authorities in combination generates greater public goods contributions than using either authority by itself. How did the study show this? During the experiments, people recruited into the study were randomly sorted into groups, given an allotment of resources with actual cash value, and then confronted with a series of decisions over time regarding how to allocate resources between a group project (the public good) vs. a private project, with the pay-offs from these decisions being partly contingent on how other subjects in the experiment use their resources. In some decisions, subjects faced explicit incentives and penalties designed to steer decisions toward the group project (“authority to”), while in other decisions subjects were given information about suggested contribution amounts based on expert opinions about the pay-offs that would result (“authority in”), with different sources of expert opinion assigned randomly. In all decisions, the true production function that determined the payoffs from investments in group and individual projects remained unspecified to the subjects. The results from a series of decision-making experiments show that “penalizing non-social behavior without expert explanation does not increase voluntary contributions, nor does expert explanation without the threat of penalty, but together they induce more contributions than any other combination of policies.”

If these results generalize to the public goods problem of population health strategies, then they suggest governmental public health agencies may have key roles to play in mobilizing collective action. In many cases, public health agencies hold relevant regulatory and enforcement powers within their jurisdictions, and they also have the potential to function as neutral and credible sources of information about health risks and prevention strategies. Armed with the resources and training needed to use these authorities in support of population health strategies, public health agencies could function as powerful integrators.

Of course, all of the usual caveats surrounding laboratory decision-making experiments apply here, particularly the uncertainties about how the results may apply to real-world decision-making in public health policy and practice. Studies supported by our PHSSR research center highlight other complicating factors: governmental public health agencies vary widely in the legal authorities they are empowered to exercise (see for example this Minnesota PBRN study); and agencies also vary widely the resources they are given to carry out their authorities (see for example this Ohio PBRN study). PHSSR research has shown that agencies having both legal and fiscal discretion in exercising public health authorities tend to perform best in implementing the authorities.

Taken as a whole, the available research suggests that governmental public health agencies are well positioned to play key roles as population health strategy integrators using both their legal authority and their scientific authority. These roles, however, must be tailored to fit the degree of legal and fiscal discretion available to the agency. Moreover, the field of public health services and systems research (PHSSR) has important roles to play in strengthening the scientific authority of these agencies by producing reliable knowledge about the effectiveness of public health strategies and the “pay-offs” that can be expected. We also need more comparative research that examines public health agency roles within population health strategies and the health and economic results that are achieved, so as to determine whether and how Michigan’s laboratory results apply to real-world public health settings.

This week brings more signals that the pediatric obesity epidemic may be in retreat. A new study using data from NHANES shows a much-celebrated 43% reduction in obesity prevalence among 2-5 year old children between 2004-2011. Before we jump to the why and how questions, we should exercise some caution in interpreting this trend estimate. First, the absolute change in prevalence is numerically quite modest, falling from 13.9% in 2003-4 to 8.4% in 2011-12 for an absolute change of 5.5 percentage points over 8 years. Second, the sampling variability surrounding this trend estimate is relatively large compared to the trend estimate itself, introducing considerable statistical uncertainty. The sample size of children in this age cohort in NHANES is not very large, and obesity prevalence is not very high in this cohort to begin with (a total of 91 kids in this subgroup were classified as obese in 2011-12), so we have some serious sampling error to contend with. Add in the distortions caused by the authors’ use of linear trend estimates, along with the problem of multiple comparisons that were not adjusted for in this analysis, and suddenly a p-value of 0.03 doesn’t seem all that definitive anymore. Of the 8 age cohorts examined in the study, these pre-K kids were the only group to exhibit a statistically significant reduction in obesity (but women over 60 years exhibited a significant increase in obesity).

Even if the headliner 43% reduction seems less impressive after taking a closer look at the numbers, the accumulating body of research suggests that finally we may have reached the downward-sloping face of the obesity epi curve, at least for young children. The next questions we need to ask are why and how. Of the myriad programs and policies used for obesity prevention and control across the U.S., which combination of approaches is working for whom, and under what circumstances? And readers of this blog surely want to know whether (and which) components of the public health delivery system – that constellation of governmental agencies and private actors that jointly implement public health programs and policies – are helping to generate these effects. By distinguishing the active ingredients from the instructive failures, we can rapidly and efficiently scale up what works in obesity prevention and limit future losses in quality and quantity of life, not to mention significant economic costs.

Fortunately, we have an accumulating body of research about the public health system’s roles in obesity prevention thanks to the growing field of public health services & systems research (PHSSR). For example, work from Frank Chaloupka’s group at UIC showed us that a decade ago much of the governmental public health infrastructure was not positioned to address the rapidly advancing obesity epidemic (recall my earlier posts on Chaloupka’s obesity and tobacco research). This group found that in 2003 less than half of the nation’s local health departments provided, supported, or advocated for obesity prevention programs targeted at youth. The CDC’s Xinzhi Zhang and colleagues used NACCHO Profile data to show that by 2005 this number had risen to 56%, although considerable geographic variation persisted in the availability of these programs. Better financed agencies were more likely to undertake obesity prevention activities, particularly those agencies that receive larger shares of their revenue from state government sources, which tend to be more flexible. Katie Stamatakis and colleagues at Washington University-St. Louis took a closer look at geographic variation in the NACCHO data, finding that the availability of obesity prevention activities in 2005 was not associated with the prevalence of obesity in the community. This finding suggests that any efforts to target obesity prevention funding and program activities to high-need areas are not working very well. Houbin Lou and his colleagues from CDC used NACCHO data from both 2005 and 2008 to better characterize the nature of obesity prevention activities supported by local health departments, finding that by 2008 nearly 70% of the nation’s local health departments were engaged in the most prevalent activity of disseminating information about obesity risks and prevention strategies to the public, health professionals, and policy decision-makers. The proportion of agencies that had no obesity prevention activities fell from 28% to 17% over the three year period.

What do we know about the impact of public health’s obesity activities? Here the evidence is still quite thin but encouraging. A paper published last year in the respected journal Health Services Research combined NACCHO data with county-level estimates of obesity prevalence from BRFSS to examine the connection between activities and outcomes. Using a careful econometric estimation approach, Adam Chen and colleagues from CDC found that local health departments that implemented obesity prevention programs in 2005 experienced significant reductions in obesity prevalence between 2004 and 2005, compared to agencies that did not implement the programs. Consistent with expectations, the reductions in obesity were larger among low-income population subgroups than for the populations as a whole, suggesting that health department-delivered programs may be particularly effective in reaching low-income populations and reducing socioeconomic disparities in obesity risks. This study also used one of my favorite tricks – a nonequivalent dependent variable approach – to show that the estimated effects could not be attributed to general temporal trends or simple forms of selection bias.

These population-level results are backed up by person-level results from a pragmatic randomized trial published last year in the journal Obesity. In this study by investigators at UNC’s CDC-funded Prevention Research Center, six North Carolina local health departments were trained to deliver a behavioral weight loss intervention tailored for low-income, mid-life women. The 16-week program produced significant weight loss among the women when compared to a delayed-intervention control group that received only general health education materials. All this at a cost of less than $350 per participant.

Intervention studies of specific obesity prevention policies and programs like the North Carolina trial are becoming more numerous, making it increasingly important to examine how multiple strategies interface and interact at the community and system level to impact health. Research on a wide variety of specific interventions continues to accumulate, including strategies for: promoting and supporting breastfeeding; reducing screen time; improving the food and physical activity environments of schools, child care settings and residential neighborhoods; expanding BMI screening and healthy lifestyle education in schools, child care, and health care settings; and changing menu labeling, food product pricing, and marketing practices in restaurants and retail outlets, worksites, schools, and community settings. While evidence supporting some of these strategies is already quite strong (like breastfeeding support), evidence for other strategies is preliminary, mixed or otherwise inconclusive. A missing element in many of these single-intervention studies is the ability to examine the characteristics of the surrounding public health delivery systems that may support or impede the obesity strategy’s reach, intensity, quality, efficiency, and its synergy with other prevention activities.

While the above studies are far from a comprehensive review of the evidence, it is clear that there is much more we need to learn about the public health strategies that work best to prevent and control obesity, and about the public health delivery system characteristics that best enable and support these strategies. For example, much of the existing work in PHSSR has focused heavily on governmental public health contributions to obesity prevention – an important but incomplete view of the strategies underway within the larger delivery system to tackle obesity. A large and diverse body of obesity prevention work is occurring through multi-sectoral initiatives and public-private ventures, including schools, worksites, churches, transportation programs, land use and zoning processes, and many other contributors. There is much to be learned from the careful study of these types of complex and multi-faceted strategies. Some of this research is already underway through the PHSSR Program supported by the Robert Wood Johnson Foundation, including results from the recently completed Multi-Network Practice and Outcome Variation (MPROVE) study that has been implemented in six diverse states through practice-based research networks (PBRNs). Stay tuned to this blog for updates on emerging findings related to obesity prevention and public health strategies.

Governmental public health agencies have long played extensive roles in delivering personal health services to people who lack access to mainstream medical providers. And for just as long, these roles have generated policy controversy because of their potential to dominate agency budgets and attention, crowding out the ability to support activities with larger, population-wide health impact. A new paper by UCLA’s Charleen Hsuan and Hector Rodriguez offers some important insight into these dynamics and their implications for population health. But first, let’s recap a few of the basic issues in play.

Some of the best insight into the origins of this controversy and its longer-term dynamics comes from Princeton’s Paul Starr and his revealing sociological analysis ( book and paper) of how public health has evolved over time in tandem and in conflict with the U.S. medical profession. In recent years, many public health agencies have scaled back the delivery of personal health services, and such delivery is expected to decline even further as health insurance coverage expands with the Affordable Care Act (ACA) implementation.

Public health’s movement away from direct provision of personal health services has some serious possible advantages, but probably only under certain circumstances. If agencies that discontinue these services are able to retain some of the resources that formerly supported clinical care and reinvest them in the delivery of effective disease prevention and health protection programs and policies, then real gains in population health are possible. This type of reinvestment – which was recommended in a recent report from the Institute of Medicine on public health financing – is likely to be possible for many agencies that currently must use some of their discretionary funding from state and local appropriations to fill the gaps between clinical revenues and clinical expenditures. As many safety-net providers know, the delivery of clinical services to low-income and underserved populations is often far from self-financing.

At the same time, there are some serious potential downsides to reducing the joint production of public health and personal health services. Synergies in the production and consumption of the two types of services seem quite plausible, although solid empirical evidence of such is generally lacking. Disease and injury risks tend to cluster in the low-income populations who most often receive personal health services from public health agencies, so the opportunity exists to layer on the delivery of prevention programs like tobacco prevention and cessation, nutrition education, physical activity promotion, and injury and violence screening and prevention interventions when people seek care from public health agencies. However, most public health agencies offer only selected personal health services (e.g. immunizations, sexually transmitted disease testing and treatment, maternal and child health support services) rather than comprehensive primary care medical services to their clients, creating natural limits to the possible synergies. In theory, even greater synergies in production and consumption would be possible if full-service primary care providers were to take up the task of jointly producing these types of prevention programs, using models like the medical home, the medical community and the accountable care organization. Such synergies could also be realized through models that closely coordinate and integrate the services delivered by public health and primary care providers (models that yield what Nobel economics prize winner Elinor Ostrom termed coproduction synergies). But if public health agencies scale back the joint production while medical providers fail to take up joint production in equal measure, then a net loss in synergy could result, causing overall reductions in the reach, effectiveness and efficiency of prevention programs.

Adverse effects are of particular concern in communities where an adequate supply of medical providers is not available or not willing to serve the underserved. Of particular concern are the millions of U.S. residents who will not gain insurance coverage under ACA because they reside in a state that has chosen not to expand Medicaid, because they lack residency status in the U.S., or because coverage otherwise remains unaffordable. Scaling back personal health services can also be problematic for public health agencies that rely on earnings from clinical reimbursements to cross-subsidize other high-value public health activities such as population-based disease prevention and injury prevention programs. Again, empirical evidence for this type of cross-subsidization is scarce, but it remains a possibility for agencies that succeed in making selected clinical services self-financing.

The new paper by UCLA’s Charleen Hsuan and Hector Rodriguez examines changes in the delivery of personal health services by local health departments between 1997 and 2006, taking a careful look at the characteristics of agencies and communities that either scale back or ramp up their joint production during this period. Using data from our own National Longitudinal Survey of Public Health Systems, the analysis offers some reassuring evidence that local agencies are able to make context-specific decisions about joint production in ways that mitigate adverse effects on service delivery. While most agencies reduced the delivery of clinical services during this time period, a sizable minority (22%) maintained or increased these services. Those that discontinued services tended to do so in communities that experienced growth in the availability of other providers of services to underserved populations. Agencies that increased clinical services delivery tended to do so primarily for Medicaid-reimbursable services that offered revenue and cross-subsidy possibilities.

These results offer encouraging news about the ability of public health agencies to balance public health and personal health services needs during ACA implementation. Because these results are based exclusively on pre-ACA data, it will be important to continue monitoring the trends in joint production and their health and economic consequences. This study was supported through the Robert Wood Johnson Foundation’s program in Public Health Services and Systems Research. Our research center has ongoing and forthcoming studies on this topic, and new waves of data from the National Longitudinal Survey, so stay tuned to this blog for further discussions of emerging findings.

Much of the value of governmental public health practice derives from the value of the information that public health agencies generate and disseminate. This information allows for the early detection and containment of disease outbreaks, for the targeting of health interventions to populations at greatest risk, for the tailoring health communication and education strategies to diverse population groups in ways that facilitate comprehension and informed decision-making, and for efficient inter-organizational coordination in the delivery of health promotion and disease prevention activities. Indeed, public health agencies accomplish much of their work through information acquisition, aggregation, analysis, and dissemination. This is why a new study documenting large structural gaps in information flow between state and local public health agencies is so troubling.

The new study by Cornell’s Joshua Vest and UNC-Charlotte’s Michele Issel analyzes measures of the structural capacity to share information between state and local public health agencies across the U.S. This type of intergovernmental information flow is particularly important in public health because the earliest opportunities to acquire health-related information and the earliest opportunities to act on this information may arise at different locations within the state-local public health system at any given point in time – and timing can be everything when it comes to mounting an effective public health response. The authors find that structural barriers to intergovernmental information flow are highly prevalent across the U.S., existing in 34% of communities for immunization records, in 70% of communities for vital records, and in 82% of communities for reportable communicable diseases.

These findings imply that the potential health and economic value of investments in U.S. public health programs and services may go partially unrealized because of deficits in intergovernmental information infrastructure. These findings, however, should be interpreted with caution given the study’s reliance on indirect measures of structural barriers rather than data on actual information flows, and given that the measures were collected in 2007-08 prior to the latest wave of federal investments in health information technology. Nevertheless, the results clearly signal that opportunities exist for improving the effectiveness and efficiency of public health practice through improvements in intergovernmental information flow.

This research study was supported through the predoctoral and postdoctoral research award program of our own National Coordinating Center for Public Health Services & Systems Research, based at the University of Kentucky and funded by the Robert Wood Johnson Foundation. The new study appears in the February 2014 special issue of the journal Health Services Research devoted to health information technology.

We received the devastating news that Dr. Eleanor “Lynn” Jenkins passed away on Friday January 17. Lynn was a powerful but graceful force within CDC’s National Center for Injury Prevention and Control, and before that the National Institute for Occupational Safety and Health. A policy researcher by training with a PhD in Public Policy Analysis from West Virginia University, Lynn’s work spanned many important public health issues including unintentional injury prevention, traumatic brain injury, trauma systems, violence prevention, motor vehicle injuries, and health care associated injuries. She was a valued colleague and friend to our Public Health Practice Based Research Networks program. All of this accomplished during her brief 48 years. Let us carry Lynn in our hearts and push forward with the health improvement agenda she championed.

Investments in research and development remain among the most powerful tools of government and the private sector to achieve gains in performance through innovation. Studies suggest that R&D investments are at least as important as investments in human capital, infrastructure, physical equipment and technology in producing long-term growth in economic and organizational performance. As the amount of R&D investment increases within an organization or industry, so too does the level of product and process innovation. However, the importance of R&D appears to vary widely across sectors and professions. Data from the National Science Foundation’s periodic Survey of Industrial Research and Development shows that the percent of revenue devoted to R&D varies more than fivefold across sectors (Table above). Not surprisingly, the sectors that face the greatest external pressures to innovate tend to invest much more in R&D than do steady-state sectors, as the table above shows. Such high-pressure sectors often invest more than 10% of revenue in R&D.

The U.S. public health system now finds itself facing mounting external pressure for innovation. After a remarkable century of progress in improving human health through prevention, the public health enterprise faces many new and escalating health threats – ranging from obesity to emerging infectious diseases. Public health faces fierce competition for public resources from other worthy policy domains, including the ever-growing medical care system, while still weathering the fiscal constraints triggered by the economic recession. And in the midst of health reform implementation spurred by the Affordable Care Act, public health is in the process of renegotiating its roles and responsibilities with many other stakeholders that contribute to population health, including medical providers, insurers, employers, nonprofits, and other government agencies. Public health is expected to help the medical care sector bend its cost curve and produce more health for the dollars it consumes.

These growing imperatives for innovation suggest a need for increased investment in public health R&D. Available data indicate that the federal government’s lead public health agency, CDC, spent about $363 million on public health R&D activities in 2012. However, the 10% rule of thumb for high-innovation sectors indicates that public health probably requires at least $7.5 billion annually in R&D investments. Even if we assume that some small portion of NIH and AHRQ funding supports public health R&D, and that additional public health R&D investments derive from state, local, and philanthropic sources, the total investment certainly falls far short of what is likely to be required for successful public health innovation. For these reasons, a recent IOM report called for an expanded, federally-funded program of research devoted to public health services and systems research.

The constrained budget for public health R&D also indicates a need for more efficient and effective mechanisms for conducting research in public health settings. The fact is that many innovations in public health organization, financing, and delivery are occurring throughout the U.S. in response to policy, economic, and institutional changes. Public health professionals and policy-makers are routinely called to act against health threats for which few if any evidence-based strategies exist, or to act in settings where evidence-based strategies are logistically, politically or economically infeasible. In these situations, innovations in public health practice and policy occur but without the comparative research necessary to determine their impact and value. By building pragmatic research designs around these naturally occurring innovations, new evidence can be generated at relatively low marginal cost.

Last month’s issue of the American Journal of Preventive Medicine features a series of studies conducted through a mechanism that holds considerable promise for improving the quality and efficiency of public health R&D: practice-based research networks (PBRNs). Borrowing the concept from primary care physician-researchers, our research center has been working to develop PBRNs in public health settings for more than five years now with the support of the Robert Wood Johnson Foundation. We now have public health PBRNs up and running in 30 states, engaging more than 1200 state and local public health organizations in the design, implementation, and translation of research studies. Our research suggests that PBRNs can be quite effective in engaging public health professionals in the implementation and translation of valuable research studies. For example, we find that public health agencies affiliated with PBRNs are 2 to 3 times more likely to engage in research activities than a comparable sample of agencies without such affiliations. Given NSF’s research showing a strong connection between R&D involvement and successful innovation, our findings suggest that PBRNs can serve as powerful engines of innovation for the public health enterprise.

The PBRN studies published in the December AJPM issue showcase the wide range of research that can be accomplished through these networks. The issue includes studies test the effectiveness of practice innovations designed to improve the delivery of evidence-based prevention programs, explore the use of evidence-based decision-making strategies among public health administrators, elucidate the roles of fiscal policies and financing mechanisms in shaping public health delivery, evaluate decision-making strategies for public health resource allocation during the economic recession, and test the influence of the PBRN model itself on the scope and intensity of R&D activities undertaken public health practice settings.

To be sure, there is much more to come from the public health PBRNs, including the Public Health Delivery and Cost Studies (DACS) now underway, which should be of special interest to readers of this blog. And there are other powerful engines for public health R&D, including the CDC’s Prevention Research Centers program, the embedded R&D units found within in some particularly progressive state and local health departments, the Academic Health Department model, and the many public health quality improvement initiatives spreading across the U.S. The capacity to accelerate public health innovation through R&D clearly exists. Let’s hope that the Institute of Medicine’s call for expanded investments in public health R&D can gain traction as part of the nation’s push for health system transformation.

After a bit of mid-winter break, those of us interested in all things health and economic get a belated holiday treat this week in the form of updated estimates from the National Health Expenditure Accounts. Included in this data series, but too often overlooked, are the federal government’s official estimates of governmental public health spending – now updated to 2012. Of course, we know the underlying data sources suffer from errors of omission and co-mission when it comes to defining and measuring governmental public health activity, but at least the data are longitudinally consistent – giving us reasonable metrics for tracking trends over time.

Total governmental public health spending reached almost $75.0 billion in 2012, which in nominal dollars is about $1.7 billion more than in 2011 but still about $300 million below the nominal level in 2010. In constant dollars, however, 2012 public health expenditures were about 3.6% below the level in 2009 when using the GDP deflator.

Factoring in the growing size of the U.S. population requiring health protection, public health spending stood at almost $240 per capita in 2012, about 5.5% lower than the 2009 level. As a share of total health expenditures, public health spending stood at 2.7%, the lowest level seen since 1988. As a percent of total GDP, public health spending is down 10.2% from its 2009 level.

The lion’s-share of governmental public health spending derives from state and local government sources, as opposed to federal sources, as the red line in the figure above shows (be careful of the rescaling in this figure, and see my earlier post on this topic). The state and local share has edged up significantly in recent years, likely attributable in part to the expiration of federal stimulus funds for the short-lived Putting Prevention to Work initiative. Sequestration will be unkind to these estimates as well, as next year’s NHEA data will show.

The big unknown centers on the future health and economic effects of stagnant and declining public health spending. Existing studies like David Grembowski’s, Paul Erwin’s, Tim Brown’s (which I posted about last month), and my own, suggest that if these diminished levels of spending persist, the adverse health effects are likely to be sizable. Moreover, a forthcoming paper (presented at the University of Michigan last year) suggests that, over time, diminished public health spending can lead to significantly higher levels of medical care spending. Perhaps most troublesome, some of our latest work presented recently at APHA shows that if these aggregate spending reductions are disproportionately concentrated in low-resource communities (and they probably are to some extent), the adverse health and economic effects are likely to be much larger.

For those who don’t like these prospects, one can always quibble with the data. And this is a significant problem for research and for policy. The ugly truth is that a high degree of uncertainty surrounds our best estimates of public health spending. Public health administrators, policy makers, and the public at large need – and truly deserve – clearer answers to several basic questions of financing: how much does the nation currently spend on public health activities, who pays for these activities, and how are funds allocated and used across programs and population groups? Not to mention, we need better data to produce better science on the benefits and burdens of public health spending. I will take this up in a future post, so stay tuned.

A new paper by UC-Berkeley’s Tim Brown adds to the growing evidence about the health effects attributable to public health investments. A key innovation in Brown’s approach is the use of Koyck distributed lag models that allow exploration of the time paths that determine how spending in one period influences health outcomes in subsequent periods. This innovation is important given that many public health programs and policies target chronic diseases and risk factors with relatively long incubation and progression periods.

Brown’s study uses annual data on the expenditures of California’s county public health departments during 2001-2008, linked with county-level estimates of all-cause mortality. By exploiting both cross-sectional and longitudinal variation in county public health agency spending per capita, he finds that a $10 increase in agency spending per capita reduces the all-cause mortality rate by 9.1 deaths per 100,000 over this 8 year period. At current levels of spending in California, these results suggest that local public health agencies have averted a total of 27,000 deaths per year in California during this period, generating a total economic value of $212 billion – more than $100 in benefit for every $1 invested.

Another notable feature of this study is its approach for addressing endogeneity bias in estimates of how spending influences mortality. Addressing the endogeneity of spending is absolutely essential for supporting causal inferences about the health effects of public health spending, as my own work has shown. Eliminating this bias generally requires using an instrumental-variables (IV) approach that hinges on finding instruments that induce exogenous variation in spending but that have no direct effect on the health outcomes of interest. These IVs function as a stand-in for randomization, allowing researchers to approximate results that would be obtained if it were possible to randomly assign communities and agencies to different levels of spending (with certain caveats of course).

The Brown study employs a method recently proposed by Lewbel to reduce endogeneity bias when traditional instrumental variables are not available. The “Lewbel IV approach” relies purely on heteroskedasticity in the model to identify unbiased estimates. By relying on distributional assumptions about the error term, this approach is likely to be less reliable than traditional IV methods, and the results must be interpreted with caution. However, in the absence of traditional IVs, this approach may be the only feasible strategy available to address endogeneity bias.

This new work, supported through the Robert Wood Johnson Foundation’s PHSSR research portfolio, is an important contribution to the growing evidence base about the value of public health programs and policies. For my fellow travelers in empirical public health research, I would urge caution in employing some of the methods illustrated here – particularly the Lewbel approach, as it is not a shortcut to a quicker and easier IV strategy. There is no substitute for using knowledge of the underlying data generating process to find the strongest possible research design and analytic approach for an observational study, and traditional IV methods usually offer the best alternative to an experimental study. But in certain circumstances, the methods demonstrated in this new study may offer a next-best alternative.