Public Health Stimulus Spending and Healthcare Associated Infections

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.

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