Nicholas W. Stine

Opportunity in Austerity — A Common Agenda for Medicine and Public Health

Government austerity is likely to hit public health programs hard, as these programs compete for funds against the health care delivery juggernaut. But such an approach seems likely to hamper efforts to improve population health and reduce medical spending.

Improving population health in US cities.

Interest in population health and the promotion of greater collaboration between medical, public health, and social service institutions has surged in recent months. (1) This approach adopts a comprehensive notion of health determinants that are spread across domains of behavioral risk, social and economic circumstances, environmental exposures, and medical care. The balance and effects of many of these determinants, eg, availability of healthy foods, parks and other safe places to play and exercise, exposure to environmental irritants, and safe housing, are specific to geographic locale. (2) Although there is general enthusiasm for efforts to advance population health, strategies for tailoring approaches to specific locales are not well established. Of particular strategic interest is the need for developing population health approaches for the 80% of US residents who live in urban environments. While the diversity and fragmentation of services within cities pose formidable organizational challenges, there are several key attributes of urban settings, if harnessed strategically, that offer opportunities for potentially effective population health strategies.

J-Shaped Curves and Public Health.

Interventions that alter population-level risk exposure have yielded a number of improvements in public health. Tobaccotaxesareanexampleofsuchpopulation-basedapproaches to disease prevention. In the case of tobacco, the harms of shifting total population exposure through taxationareminimal,becausethereisnosafelevelofconsumption. However, other risk factors do not exhibit the same linear relationship between exposure and mortality—and thereforemayintroducenewcomplexitiesincommunicating with individuals and the public. In particular, many risk factors, such as alcohol consumption, exhibit a J-shaped association when plotting health effects like mortality on the vertical axis against the magnitude of the risk factor on the horizontal axis (Figure). Settingasidepopulationrisk,anyclinicianwhohastried tocounselapatientaboutalcoholusehasencounteredthe question: “But I thought a couple of drinks a night is good for my health?” In this way, the strategies of preventive medicine—bothindividualandpopulationbased—thathave proven quite successful for tobacco control may be less effective when confronting the epidemiologic and perceptional challenges presented by the J-shaped curve.

Elimination of Lipid Levels From Quality Measures: Implications and Alternatives

On October 31, 2014, the Centers for Medicare & Medicaid Services (CMS) issued an update to the set of quality measures for accountable care organizations under the Medicare Shared Savings Program. The new rule will retire 8 of the 33 measures and add 8 new measures. Notably, the revision will remove all 3 quality measures addressing low-density lipoprotein cholesterol (LDL-C), including (1) percentage of patients aged 18 to 75 years with diabetes and an LDL-C level lower than 100 mg/dL within the past 12 months; (2) percentage of patients 18 years or older with ischemic vascular disease and an LDL-C level lower than 100 mg/dL within the past 12 months; and (3) percentage of patients 18 years or older with a diagnosis of coronary artery disease and an LDL-C level lower than 100 mg/dL within the past 12 months or an LDL-C level of 100 mg/dL or higher and a documented plan of care to achieve a level lower than 100 mg/dL, including at minimum the prescription of a statin.1

Reconsidering the politics of public health.

A central dilemma in public health is reconciling the role of the individual with the role of the government in promoting health. On the one hand, governmental policy approaches—taxes, bans, and other regulations—are seen as emblematic of “nanny state” overreach. In this view, public health regulation is part of a slippery slope toward escalating government intrusion on individual liberty. On the other hand, regulatory policy is described as a fundamental instrument for a “savvy state” to combat the conditions underlying an inexorable epidemic of chronic diseases. Proponents of public health regulation cite the association of aggressive tobacco control, physical activity, and nutritional interventions with demonstrable increases in life expectancy.1 Under Mayor Michael Bloomberg, New York City has served as a crucible for the fractious politics of public health. In the most recent example, the Bloomberg administration sparked a firestorm of controversy with its “portion cap rule” limiting the serving size of sugary drinks. Detractors such as the Center for Consumer Free-

Multifactorial risk assessment for atherosclerotic cardiovascular disease--reply.

In Reply We agree with Dr Legrand and colleagues that our study providesfurtherindirectevidencethatnonhemodynamicfactors maybeimportanttothepathogenesisofAKIaftercardiacsurgery. We also agree that increases in serum creatinine level represent an already advanced level of decreased GFR, at which time interventions may be less likely to succeed in preserving GFR. However, in a previous randomized double-blind clinical trial of fenoldopam in which the drug was given at anesthesia induction, we also failed to see a beneficial effect.1 Moreover, although we have conducted several studies of renal biomarkers in the setting of cardiac surgery and the development of AKI,2-4 we are not aware of any studies conducted among cardiac surgery patients in which interventions triggered by novel biomarkers instead of creatinine level delivered better functional outcomes. Thus, the putative advantage of biomarkertriggered interventions, although interesting and perhaps logical, remains theoretical at this stage.

Nonlinear Exposure-Outcome Associations and Public Health Policy—Reply

In Reply Ms Zhu and colleagues express concerns regarding potential bias and confounding in the study evaluating the safety of repeated tetanus-containing vaccines in pregnancy. First, they point to the accuracy of methods for identifying gestational age. The pregnancy episode algorithm was used to identify live births in our study but was not the method used for identifying gestational age. The validation data presented were specific to the pregnancy episode algorithm for identifying live births. We stated in the article that we limited the cohort for birth outcomes to “records that contained information on the neonate (ie, weight and gestational age).” In the Vaccine Safety Datalink, gestational age data come from electronic medical record and state birth registry data, which are based on clinician assessment. The clinician uses all available data to determine this estimate, including estimated due date based on ultrasound and last menstrual period data and can adjust this estimate based on the infant’s appearance at birth. Zhu and colleagues also raise concerns about risk factors that were used in the analysis of birth outcomes. Despite the change in direction of the relative risk with adjustment, both the unadjusted and adjusted risk estimates were nonsignificant, and it is inappropriate to overinterpret nonsignificant point estimates on either side of the null. After adjustment for adequacy of prenatal care, comorbidities, and pregnancy complications (which were clinically similar between the groups compared), in addition to gestational age at vaccination, health care site, maternal age, and length of enrollment, we still did not find an association. These adjustments strengthen the overall findings, especially because we had substantial statistical power to detect a difference in birth outcomes. A healthy user effect would not be expected, as all of the women in our study were vaccinated, and women during their prime childbearing years are generally healthy and seek preventive care during pregnancy. Finally, Zhu and colleagues suggest that because most Tdap vaccinations in our study were given in the third trimester, our results might not apply to vaccinations given earlier in pregnancy. Although it is true that 67.4% of the pregnancies had Tdap in the third trimester, there were 9505 pregnancies in which vaccinations were given in the first or second trimester. The Advisory Committee on Immunization Practices currently recommends Tdap at any time during pregnancy with a preference for third trimester administration to “provide the highest concentration of maternal antibodies to be transferred closer to birth.”1 This optimal timing for administration corresponds to the majority of vaccinations given in our study. Therefore, our findings are applicable to pregnant women whose clinicians are following recommendations. However, we agree that additional data for first trimester vaccination will further strengthen the evidence base regarding Tdap safety in early pregnancy.

Health-weighted Composite Quality Metrics Offer Promise to Improve Health Outcomes in a Learning Health System.

Health system leaders sometimes adopt quality metrics without robust supporting evidence of improvements in quality and/or quantity of life, which may impair rather than facilitate improved health outcomes. In brief, there is now no easy way to measure how much “health” is conferred by a health system. However, we argue that this goal is achievable. Health-weighted composite quality metrics have the potential to measure “health” by synthesizing individual evidence-based quality metrics into a summary measure, utilizing relative weightings that reflect the relative amount of health benefit conferred by each constituent quality metric. Previously, it has been challenging to create health-weighted composite quality metrics because of methodological and data limitations. However, advances in health information technology and mathematical modeling of disease progression promise to help mitigate these challenges by making patient-level data (eg, from the electronic health record and mobile health (mHealth) more accessible and more actionable for use. Accordingly, it may now be possible to use health information technology to calculate and track a health-weighted composite quality metric for each patient that reflects the health benefit conferred to that patient by the health system. These health-weighted composite quality metrics can be employed for a multitude of important aims that improve health outcomes, including quality evaluation, population health maximization, health disparity attenuation, panel management, resource allocation, and personalization of care. We describe the necessary attributes, the possible uses, and the likely limitations and challenges of health-weighted composite quality metrics using patient-level health data.