Quantitative estimation in Health Impact Assessment: Opportunities and challenges

Environmental Impact Assessment Review 31 (2011) 301–309

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Environmental Impact Assessment Review j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e i a r

Quantitative estimation in Health Impact Assessment: Opportunities and challenges Rajiv Bhatia a,⁎, Edmund Seto b a b

San Francisco Department of Public Health, CA, United States University of California at Berkeley, CA, United States

a r t i c l e

i n f o

Article history: Received 15 June 2010 Received in revised form 22 August 2010 Accepted 25 August 2010 Keywords: Health Impact Assessment Environmental health Social determinants of health Quantitative estimation Public policy Health equity

a b s t r a c t Health Impact Assessment (HIA) considers multiple effects on health of policies, programs, plans and projects and thus requires the use of diverse analytic tools and sources of evidence. Quantitative estimation has desirable properties for the purpose of HIA but adequate tools for quantification exist currently for a limited number of health impacts and decision settings; furthermore, quantitative estimation generates thorny questions about the precision of estimates and the validity of methodological assumptions. In the United States, HIA has only recently emerged as an independent practice apart from integrated EIA, and this article aims to synthesize the experience with quantitative health effects estimation within that practice. We use examples identified through a scan of available identified instances of quantitative estimation in the U.S. practice experience to illustrate methods applied in different policy settings along with their strengths and limitations. We then discuss opportunity areas and practical considerations for the use of quantitative estimation in HIA. © 2010 Elsevier Inc. All rights reserved.

1. Introduction Health Impact Assessment (HIA) is a growing sub-discipline of impact assessment that uses diverse and often novel qualitative or quantitative methods to judge the impacts of policies, programs, plans, and projects on population health or its determinants (WHO, 1999; Kemm et al., 2004; Collins and Koplan, 2009). The quality of evidence and analytic methods and validity of predictions are prominent concerns for practice quality (Mindell et al., 2001; Veerman et al., 2007). While quantitative estimates in HIA provide measures of the magnitude of impacts, they also generate greater demands on information needs and may generate additional questions or controversies over the validity of methods or choice of methodological assumptions (O'Connell and Hurley, 2009). As HIA practice evolves, there is a need to identify the opportunities for and the practical limitations of quantitative health impact estimation in a range of policy sectors. HIA may be conducted independently or integrated into a social, environmental, or strategic impact assessment (Bhatia and Wernham, 2008). Regardless of the context, the overarching goal of HIA is that decisions, on balance, be more aligned with the needs of population health. Unlike traditional health risk assessments utilized for regulatory management of environmental hazards, a single HIA may assess multiple effects on human health associated with multiple social, economic, or environmental effects. Accordingly, HIA may use diverse types of evidence, expertise, and qualitative and quantitative methods. The International Association of Impact Assessment (IAIA) ⁎ Corresponding author. Tel.: + 415 252 3982; fax: + 415 252 3818. E-mail address: [email protected] (R. Bhatia). 0195-9255/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.eiar.2010.08.003

defines HIA as a “… combination of procedures, methods and tools that systematically judges the potential, and sometimes unintended, effects of a policy, plan, program or project on the health of a population and the distribution of those effects within the population. HIA identifies appropriate actions to manage those effects (Quigley et al., 2006).” The spectrum of available analytic methods used in HIA includes but is not limited to quantitative estimation tools. Other sources of knowledge used in making impact assessment judgments can include measures of baseline health status and vulnerability, empirical studies and original qualitative research, such as focus groups, structured and unstructured interviews, and group or expert consensus processes. Local knowledge of community organizations and residents can also raise hypotheses or serve to corroborate findings. Despite the potential breadth of impacts and tools, there is a clear need for methodological rigor in HIA. To support valid judgments, practitioners recommend a careful examination of uncertainty and transparency of assumptions and limitations (Quigley et al., 2006; North America Practice Standards Working Group, 2009). Quantitative estimates help characterize the magnitude and therefore the significance of potential health impacts. Quantification may support evaluation of impacts relative to numerical significance thresholds or social or public health objectives, provide inputs for economic valuation, and allow for “apples to apples” comparison among alternatives. While these benefits are clearly important, there are few standardized tools for quantitative health effects estimation and these tools are applicable to a limited range of policy and decision settings (Mindell et al., 2001; Veerman et al., 2005; O'Connell and Hurley, 2009). Human health risk assessment (HRA) is one of the

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most common tools for quantitative estimation in HIA (O'Connell and Hurley, 2009) and is also used in EIA practice (Steinemann, 2000) and environmental risk management decisions (NRC, 2009). However, sufficient information to develop robust quantitative estimation methods does not exist for many policy sectors whose actions also have potential health impacts. Efforts to develop standardized quantitative or predictive models broadly applicable in HIA practice also have been met with limitations, including inabilities to estimate effects on different populations, assumptions of steady-state health, over-complexity, and lack of transparency in the causal pathways (Lhachimi et al., 2010). Despite these limitations, quantitative forecasts have been generated in a number of HIAs internationally on a range of policies, including those that effect environmental pollutants, traffic hazards, infectious disease risks, housing conditions, and tobacco and alcohol consumption (Veerman et al., 2005; O'Connell and Hurley, 2009). In the United States, quantification has only been recently used in HIAs and there has been no review of methodological approaches used in these HIAs. This article aims to characterize the approaches used for quantitative health effects estimation in HIA based on a scan of the first decade of U.S. practice (1999 to 2009). We first consider the general information requirements for quantitative estimation of health effects. Second, we describe available examples of quantitative estimation in HIA from a range of policy contexts in the United States in order to illustrate the methods used as well as to identify the strengths and limitations of these methods. Finally, we offer opportunity areas and practical considerations for the use of quantitative estimation in HIA. 2. Materials and methods To assess contemporary applications of quantitative estimation in U. S. HIA practice, we identified publicly available reports on HIAs to ascertain whether they included quantitative estimates of health effects. It is important to note that no widely accepted criteria or minimum standard to define HIA exists in the U.S., and HIAs are typically identified as such by their authors. Many forms of practice may also characterize quantitative estimates of prospective health impacts of policy change; however, these may not be labeled as HIAs. We identified HIA reports from our own practice experience, two published reviews of U.S. HIA practice (Dannenberg et al., 2008; Cole and Fielding, 2007), a search of PubMed using the phrase “Health Impact Assessment”, a list of completed HIAs maintained by the CDC and a data base of U.S. HIA and HIA methods maintained by UCLA (www.ph.ucla.edu/hs/hiaclic/). We excluded from our review examples of HRA in Environmental Impact Statements (EISs) or environmental management decisions and regulatory impact assessments conducted for environmental standard setting. We focus on U.S. practice given by the recent reviews on the European practice of quantitative estimation (Veerman et al., 2005; O'Connell and Hurley, 2009). Based on a screening of all reports, we accessed publicly available HIA reports that quantitatively predicted impacts on health status measures (e.g. mortality, disease incidence) or established behavioral determinants of health (e.g., physical activity, food consumption) to

ascertain the outcome measures and estimation methodology. We included individual-level behavioral determinants because these may be considered equivalent to health outcomes. We did not include examples of HIA that quantified only environmental-level changes (e.g., impacts on air pollution concentrations, housing conditions, and employment). We identified strengths and limitations of methods based on our own professional judgments and informed by general theoretical principles from public health and environmental health science. 3. Concepts and general challenges for quantitative health effects estimation Typically, policy, program, or project decisions do not affect health directly; rather, these decisions change the determinants of human health which indirectly impact health status as illustrated below. Making prospective quantitative estimates of future health effects of policies or projects first require a logical, plausible model linking the decision to health outcomes (Mindell et al., 2001).

Health determinants refer to the range of personal, social, economic and environmental factors which determine the health status of individuals or populations (WHO, 1998). Several examples of health determinants that may be affected, directly or indirectly, by policies, projects, or plans are listed in Table 1. Many of these health determinants are associated empirically with health status measures, including life-expectancy, disease and injury rates, and measures of health care utilization, through empirical research (Marmot and Wilkinson, 2006; Kawachi and Berkman, 2003). Prior reviews have established the essential information requirements for quantitative estimation in HIA (Hertz-Picciotto, 1995; Mindell et al., 2001; Veerman et al., 2005; O'Connell and Hurley, 2009). First, evidence must be sufficient to be confident in the causal relationship(s) between the policy decision and the health status outcome. If the causal nature of the relationship is uncertain, precise prospective impact estimates cannot be reasonably made from measures of association. Quantification also requires data on affected populations and on exposures and changes to exposures as well as valid effect measures, models, or exposure–response (E–R) functions or relationships to relate policy effects with health effects (Hertz-Picciotto, 1995; Mindell et al., 2001; O'Connell and Hurley, 2009). The evaluation of health care policy including bio-medical interventions and the management and regulation of environmental hazards both utilize quantitative estimates of health impacts, and experience from these settings is instructive for HIA. Controlled, randomized intervention trials usually provide the basis for estimates of the efficacy of a pharmaceutical or other therapy in the case of biomedical interventions. In environmental management, health risk assessments (HRA) typically use E–R relationships, based on animal

Table 1 Examples of health determinants potentially impacted by policies, projects, or plans. Health behaviors

Physical infrastructure

Environmental conditions

Social and economic conditions

Diet

Education access, facilities, and quality

Income or livelihood

Physical activity Smoking Alcohol and drug addictions Sexual practices

Housing facilities Transportation network and services Health care services Access to parks and natural spaces

Contamination of air, soil or water resources with hazardous substances Community noise Presence of or habitats for disease vectors Agricultural resources Geographical hazards from floods, fires, landslides, earthquakes

Water, solid waste, and sanitation systems

Wealth or resources distribution Social networks and support Inclusive political participation

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toxicity or epidemiological studies, to assess hazards and population impacts of these risks (NRC, 2009). HRA is also occasionally utilized for health analysis conducted within EIA (Steinemann, 2000). In both settings, meta-analysis of studies or pooled data may be employed to increase the confidence in the precision and validity of the effect measure or E–R relationship. In contrast, in many other public policy and decision-making settings, there has been little development or application of methods to estimate health impacts. Measures of association among health outcomes and risk factors for disease, illness, or injury have been estimated through epidemiological research, but the evidence on how structural conditions (e.g. environment, culture) and policy change influence the risk factors is scant (Dow et al., 2010). Further challenging quantitative estimation, the links between policy change and health may be complex with multiple causal steps and additional mediating factors, both behavioral and environmental, outside of the scope of a policy decision. 4. Results: quantitative health effects estimation in U.S. HIA practice Our scan identified 14 examples where HIAs conducted in the U. S. included quantitative estimates of impacts on health status or health behavioral outcomes. Most examples of HIA with quantitative estimates also analyzed other impacts separately not using quantitative methods. Examples of HIA with quantitative estimation were conducted by a limited number of practitioners with the majority occurring in California. Table 2 briefly describes the outcome measures, the methods used, and the principal limitations of these quantitative analyses. The following synthesis, organized by health determinant, describes in more detail the approaches used along with their strengths and limitations. 4.1. Physical and chemical disease agents The most common approach to quantitative estimation in the U. S. HIA practice experience relates to discrete physical, chemical, biological, and radiological hazards, follows the approach of human health risk assessment (HRA), and utilizes available, published E–R relationships often borrowed from those used in regulatory environmental impact analysis. Several HIAs used the HRA approach (Table 2) to evaluate health risks from air pollution and noise caused by locating new residential development adjacent to new, expanded, or existing roadways (UCBHIG, 2007a,b; HIP, 2008, 2009) Others examined the health impacts of existing roadways and industrial activities on area residents (UCBHIG, 2010). For example, the Pittsburg Railroad Avenue HIA focused on a new transit village in Northern California that was proposed immediately adjacent and downwind of a regional freeway and commuter rail line, generating concerns about vehicle and train air and noise emissions hazards for future residents (HIP, 2008). The HIA on the proposed development used a Gaussian physical dispersion model (CAL3QHCR) and available highway traffic counts and vehicles emissions data to predict fine particulate matter (PM2.5) concentrations. The analysis then applied changes in ambient concentrations to E–R functions used by air pollution regulators to predict impacts on avoidable pre-mature mortality. Based on the analysis, residential development near to the highway was associated with predicted exposures about 10 times higher than development further away with proportional differences in the hazard of pre-mature mortality. The HIA also modeled noise levels in indoor and outdoor environments and used available noise E–R functions to predict impacts on sleep disturbance and subjective annoyance. For air pollution and noise hazards, available data are well-suited for application in HIA. E–R functions used in regulatory impact analysis exist for several air pollutants and this data has been applied both

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in regulatory impact assessment and in HIA internationally (CARB, 2002; Boldo et al., 2006). Well-characterized E–R functions based on meta-analysis of pooled data from published studies also estimate the proportion of the population annoyed or sleep disturbed as a function of road or rail noise (Miedema and Oudshoorn, 2001; Miedema and Vos, 2007). These methods for estimating health impacts from changes in noise and ambient air pollutants appear widely applicable to land use development, transportation infrastructure projects, and other policies (e.g., road pricing; building construction standards). While well-established, HRA methods also have important limitations (Hertz-Picciotto, 1995; NRC, 2009). HRA practice relies on E–R data that is extrapolated from epidemiological or toxicological studies often with unverifiable assumptions about low-dose effects and intraspecies and inter-species variation. Available regulatory standards, which might be used to judge the significance of health impacts, may not be adequate to protect health in all circumstances. Comparisons of future impacts against baseline conditions also need to account for expected secular trends, such as future emissions changes associated with population growth, technology and regulations. It is critical to consider cumulative impacts from the additive effects of new and existing exposures. Particular population vulnerabilities (e.g. sensitivity to exposures, higher disease prevalence, unemployment, poverty, and poor sanitation) also may modify the E–R relationships (Pope and Dockery, 2006; Makria and Stilianakis, 2008). 4.2. Road safety Road traffic collisions are significant and avoidable. Predictive models have been developed to estimate the generation of traffic collisions as a function of causal environmental factors including vehicle volume, vehicle speeds, pedestrian volumes, and roadway design (Naderjan and Shahi, 2010; Harwood et al., 2008; Lee and Abdel-Aty, 2005; LoukaitouSideris et al., 2007). The type, fit and parameters in road collision prediction models varies with context. Several HIAs estimated quantitative impacts on traffic injuries (Table 2) (SFDCP, 2007; UCBHIG, 2007a,b; Rutt et al., 2010; Bhabka and Nagev, 2009). One HIA used a multivariate regression model of pedestrian-vehicle collisions validated with data from the same local context (SFDCP, 2007). In that model, nine environmental-level variables predicted 71% of the variation in vehicle-pedestrian injury collision frequencies among San Francisco census-tracts (Wier et al., 2009). These variables included traffic volume; proportion of arterial streets; neighborhood and residential-neighborhood commercial land use; land area (square miles); employee and resident populations; proportion of households in poverty; and proportion of residents older than 65. The HIA applied this model to a rezoning plan that anticipated changes to several model parameters, including a 15% increase in traffic volume and a 16% percent change in population, and estimated a cumulative 17% increase in pedestrian injury collisions or over 30 additional collisions each year. Forecasts varied by individual neighborhoods demonstrating the relative importance of transportation infrastructure, traffic volume, and population variables. Both the prior development and validation of similar models and available local data enabled the application of this model in HIA. There was also a clear and plausible causal nexus. While this modeling effort had a particular focus on vehicle volumes and pedestrian injury aggregated at the census tract level, the approach could be extended to look at other crash types (e.g. bicycle collisions), other environmental determinants (e.g. speed), and to smaller geographies (e.g. streets and intersections). This application also had several important limitations. Specifically, the regression model used crude proxies for pedestrian flow and traffic speeds — two important predictors of vehicle-pedestrian collisions and injury severity. Furthermore, the land use plans also could not presume the demographic characteristics of the future populations to inform quantitative estimation.

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Table 2 Quantitative health effects estimates in HIAs conducted in the U.S. (1999–2009). Subject / Title

Health outcomes and Related health behaviors estimated determinants affected quantitatively by policy or project

Method to quantify health outcomes

Limitations of estimates

Living Wage Ordinance, San Francisco (Bhatia and Katz, 2001)

Avoidable mortality High school graduation rates Teenage pregnancy Alcohol consumption Sick days Avoidable mortality

Using a literature review, analysis identified effect measures from controlled, prospective empirical studies on income and mortality, morbidity, and child development outcomes then applied effect measures to predicted change in income. Applied effect measures from an epidemiologic study to predict mortality impact from change in income; compared effect to benefit associated with health insurance. Applied effect measure from meta-analysis of traffic noise dose–response relationships to measured noise levels. Applied empirically derived collision model to predicted changes in traffic flow. Developed and validated a regression model for pedestrian injury collisions for San Francisco (Wier et al., 2009). Estimated changes in population and traffic volume from the plans then applied to the model. Applied modeled sound levels to effect measures from meta-analysis of traffic noise and subjective annoyance. Applied measured peak noise levels to sleep disturbance prediction equation. Applied modeled particulate matter concentrations to E–R functions from regulatory impact assessments (CARB, 2002). Applied estimated traffic volume change to empirically derived collision model Physical activity changes based on estimates of program participation, distance walked, and activity level. Physical activity change then applied to physical activity-BMI effect measure from prospective intervention study. Calculations based on authors' assumptions of participation rates, level of activity, and policy compliance Applied modeled sound levels to effect measures from meta-analysis of traffic noise and subjective annoyance Applied measured peak noise levels to sleep disturbance prediction equation Applied modeled particulate matter concentrations to E–R functions from regulatory impact assessments (CARB, 2002) Applied published crash reduction factors associated with transportation design interventions to existing collision rates Applied empirical association between perceived neighborhood pedestrian quality and minute of walking to planned physical changes based on neighborhood plan

Prospective studies were based on baseline income not income change Intervention may not change immutable life-course effects of chronic poverty

Living Wage Ordinance, Los Angeles (Cole et al., 2005)

Oak to Ninth Avenue Plan (UCBHIG, 2007a)

Noise-related annoyance Pedestrian injuries

Neighborhoods Rezoning Plan, San Francisco, CA (SFDCP, 2007)

Vehicle–pedestrian injury collisions

Macarthur BART Transit Village (UCBHIG, 2007b)

Sacramento Safe Routes To Schools Program (Cole, 2004)

California Physical Educational Policy (Fielding et al., 2007) Pittsburg Railroad Avenue Specific Plan (HIP, 2008)

Income

Income Health Insurance

Ambient noise Motor vehicle flow

Traffic volumes Residential and commercial populations Land use mix Street type Avoidable mortality Particulate matter Lung cancer risk Diesel exhaust Sleep disturbance Ambient noise Subjective annoyance Peak train noise Pedestrian injuries Vehicle traffic volumes

Body mass index

Physical activity

Physical activity

Avoidable mortality Asthma hospitalizations Lower respiratory disease Sleep disturbance Subjective annoyance

Ambient fine particulate matter (PM 2.5) concentrations Ambient Noise

Re-design of Buford Highway, Atlanta (Rutt et al., 2009)

Fatal and Injury Collisions Weekly minutes of walking

Streetscape design elements

San Pablo Avenue Corridor Plan (HIP, 2009) Menu Labeling Law (Kuo et al., 2009)

Avoidable mortality

Ambient fine particulate matter (PM 2.5) concentrations Chain restaurant menu choices

Population weight

California Maximum Speed Limit Reduction (Bhabka and Negev, 2009)

Fatal motor vehicle Highway vehicle volumes collisions All highway collisions Vehicle speeds Speed limits

Federal Paid Sick Days Law (HIP and SFDPH, 2009)

Emergency room use Worker access to paid Delayed medical care sick days Hospital care

Applied modeled particulate matter concentrations to E–R functions from regulatory impact assessments (CARB, 2002) Multiplied County-level estimated frequency of fast food meal consumption by estimated reduction in consumer meal-calories and calories per pound of weight to estimate population weight averted. Ascertained baseline highway speed / traffic volume distribution from California Department of Transportation highway traffic database. Then applied speed limit change to effect measures from observational studies relating speed limits to changes in highway speeds, collision rates, and fatality rates. Estimated difference in population prevalence of utilization measures based analysis of National Health Interview Survey data.

Prospective studies were based on baseline income not income change Intervention may not change immutable life-course effects of chronic poverty External validity of traffic volume — pedestrian collision prediction equation

Crude proxies for pedestrian flow and vehicle speeds in the model No reliable information on demographic changes External validity of traffic volume — pedestrian collision prediction equation External validity of E–R functions from inter-regional studies applied to small area impacts

External validity of effect measure linking physical activity change to change in body mass index

None identified

External validity of E–R functions from inter-regional studies applied to small area impacts

External validity of exposure and effect measure linking environment and walking External validity of FHWA crash reduction factors Estimates assumed no changes in pedestrian volumes, traffic volumes, and speeds External validity of E–R functions from inter-regional studies applied to small area impacts Limited and conflicting evidence on consumer behavior Incomplete consideration of compensatory dietary and metabolic effects Aggregated effects on responders and non-responders Secular trends in the relationships among speed limits, speed, collisions, and fatalities.

Estimate based on single cross-sectional study with limited control for confounding Cross-sectional data

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Table 2 (continued) Subject / Title

Health outcomes and Related health behaviors estimated determinants affected quantitatively by policy or project

Port of Oakland (UCBHIG, 2010)

Avoidable mortality Occupational lung cancer Sleep disturbance Subjective annoyance Sleep disturbance Myocardial infarction

Ambient fine particulate matter (PM 2.5) concentrations Ambient diesel exhaust concentrations Ambient noise

Another approach to predicting impacts on traffic collisions is illustrated in a HIA that applied quantitative crash reduction factors (CRFs) to planned roadway safety improvements proposed to occur in the redevelopment of a neighborhood corridor (Rutt et al., 2009). CRFs estimate the percentage crash reduction that might be expected after implementing a given countermeasure based on empirical studies. The approach relied on a published database of CRFs along with sources, standard errors, and study quality rankings for a diverse set of interventions and conditions (FHWA, 2007). However, the FHWA does not rate the external validity of the studies and did not anticipate the application of CRFs to quantitative predictive estimation. The HIA also looked at the effect of the countermeasures independent of other changes (pedestrian volume, traffic volume) that would occur in the course of redevelopment and likely effect injury rates. A third HIA estimated changes to the number of fatal and non-fatal collisions resulting from a hypothetical reduction in the Maximum Speed Limit in California (Bhabka and Nagev, 2009). The HIA ascertained the joint distribution of highway traffic volume and speed based on a California Department of Transportation traffic database then applied empirical, before and after studies of the effect of speed limit changes on highway speeds, total collision rates, and fatal injury rates. The key strength of this HIA was the availability and use of evaluation data from real-world national policy experience; still, unmeasured secular trends may have altered the relationship between speed limits, speeds, and fatalities.

4.3. Employment, income, and labor standards Social policies that facilitate economic development (e.g., taxes or subsidies, employment training, and loan guarantees), establish labor standards (e.g., minimum wages), or offer social safety nets (e.g., unemployment insurance) have direct effects on individual economic status and thus indirect effects on health. Epidemiological studies have quantified measures of association between health or disease outcomes and health determinants, including employment status, income, education, and social support, potentially allowing quantitative effects estimation of social and economic policies (Marmot and Wilkinson, 2006; Kawachi and Berkman, 2003). Effect measures (e.g. relative risks) from these epidemiological studies along with data on changes in population prevalence of relevant determinants of health allow estimation of changes in the population fraction of disease incidence attributable to these determinants (Rockhill et al., 1998). Two HIAs assessed the impacts of changes to statutory minimum wages (Bhatia and Katz, 2001; Cole et al., 2005). An HIA on a proposed living wage ordinance for San Francisco provided quantitative estimates of the impacts of the adoption of a living wage of $11.00 per hour on adult health and children's development outcomes (Bhatia and Katz, 2001). Based on the wage change, the estimated income distribution of the affected population, and effect measures based on a systematic review of peer-reviewed studies, the HIA estimated a decrease in the risk of pre-mature death by 5% for adults 24–44 years of age in households whose current income was around

Method to quantify health outcomes

Limitations of estimates

Applied modeled sound levels to effect measures from meta-analysis of traffic noise and subjective annoyance, sleep disturbance, and myocardial infarction Applied modeled particulate matter concentrations to E–R functions from regulatory impact assessments (CARB, 2002);

Completeness of air pollution emissions sources Exclusion of non-transportation noise sources Causal relationship between noise and myocardial infarction not established

$20,000. For the offspring of these workers, the analysis estimated that a living wage would result in an increase of a quarter of a year of completed education, a 34% increased odds of high school completion, and a 22% decrease in the risk of early childbirth. A retrospective analysis on a Living Wage implemented in Los Angeles used a similar approach to estimate impacts on mortality (Cole et al., 2005). The strengths of these estimates build on a strong and consistent literature on income and health; effects of income are found for diverse health outcomes and populations and clear plausible pathways support the causal effects. Effect measures used in these HIAs came from prospective studies that controlled for numerous social and economic characteristics and some behavioral risk factors. There are also several sources of uncertainty in the estimates. For example, unmeasured factors at the neighborhood level associated with income may not change with a prospective change in income. Similarly, chronic poverty may result in immutable life-course effects that are not amenable to a change in income (Dow et al., 2010). In another case, a recent HIA utilized data from the 2007 National Health Interview Survey (NHIS) to estimate impacts on health care utilization for workers who would gain paid sick days benefits from a proposed Federal legislative mandate (HIP and SFDPH, 2009). While the published empirical research on paid sick leave was limited, the NHIS data set allowed the practitioners to conduct an original analysis of potential policy impacts. Among workers without paid sick days, the HIA estimated an increased prevalence of individuals using emergency room (ER) services and families experiencing delayed medical care. While these estimates represented findings from a single data source, the representative population sample in the NHIS and the plausibility of causal effects supported their validity.

4.4. Diet, physical activity, and obesity: the challenges of predicting policy impacts on health behaviors Physical activity and diet are well-recognized determinants of human health that have proved resistant to behavioral public health interventions. Public health practitioners are increasingly calling for environmental policy change to improve physical activity and dietary behaviors. Several U.S. HIAs have quantified the impacts of policies, programs, or projects on physical activity levels (Cole, 2004; Fielding et al., 2007; Rutt et al., 2009). In some cases, the methods applied are straightforward calculations based on reasonably expected changes in activity levels resulting from direct programmatic interventions on behaviors like physical activity education and walking to school (Cole, 2004; Fielding et al., 2007). In another case, changes in populationlevel physical activity were predicted by generalizing a quantitative environment-physical activity association from an empirical study to planned changes in neighborhood infrastructure in a different location (Rutt et al., 2009). While the approach was innovative there appeared to be limited external validity of the empirical study used to establish the relationship between the environment and physical activity. Equally important, studies on which to base these predictions of built environment changes on physical activity

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currently provide only weak support for causal inference (TRB, 2005). Many studies are ecological or cross-sectional analyses and cannot presume individual-level associations or their temporal direction. Future research may address these limitations. The case of an HIA conducted on legislation requiring “point of sale” labeling of calories in restaurants in California is similarly illustrative of the current limits of quantification in predicting impacts of policy change on dietary behavior. This HIA aimed to estimate the effects of the calorie labeling law on the future weight gain of all Los Angeles county residents (Kuo et al., 2009). The authors' conceptual model proposed that the net effect on population-level weight would be the mathematical product of annual chain restaurant meals consumed, the proportion of consumers responding to the menu information, the expected average differences in energy content of affected meal choices, and the energy surplus required to gain one pound of weight. The authors thus estimated that, if 10% of chain restaurant consumers responded to the new calorie information, the menu labeling law would result in the avoidance of 40% of the future annual weight gain in the county population. This menu labeling HIA made several assumptions that limited the plausibility of these findings. First, the data relating behavioral changes to meal energy content were from a single non-experimental study of purchase choices at one restaurant chain (Subway) in one city (New York City) that had voluntarily posted calorie information. These data were extrapolated to Los Angeles without a clear assessment of the external validity of this estimate when applied to another population. Further, the HIA assumed that that the population of consumers responding to calorie labeling restaurants would be the same as those at risk for gaining weight. The calculations also assumed changes in calories consumed at meals would translate directly into an equivalent change in net annual energy balance, an assumption not supported by research on short or long-term food restriction (Levitsky, 2005; Martin et al., 2007; Levitsky and Derosimo, 2010). Finally, the HIA applied the estimated weight loss among responding individuals to the entire population, including non-responding consumers. Predicting real-world menu labeling impacts on population weight is clearly challenged by limited available evidence. While some research from prospective studies support a causal association between more frequent fast food consumption, increased energy intake, and weight gain (Rosenheck, 2008), experimental studies of menu labeling interventions have not demonstrated a substantial or consistent effect (Harnack et al., 2008). Only recently have empirical evaluations of real-world menu labeling interventions been published and these studies have demonstrated quite heterogeneous effects (Elbel et al., 2009; Downs et al., 2009; Bollinger et al., 2010). While the HIA authors acknowledged some of the data limitations, their results suggest that attempts to quantify effects with insufficient data may generate unreliable projections. 4.5. Taxation and subsidies and the price of consumer goods and services Policies that change the cost of certain market goods or services may affect consumption and thus have indirect health effects including effects on health behaviors. Taxes, fees, and subsidies are commonly used policy mechanisms that affect price. Goods or services with established relationships to health include tobacco and alcohol, household energy, transportation, and some categories of foods and beverages. Conceptually, data on price elasticity (the change in consumption relative to the change in price) may allow quantitative estimates of change in population consumption and epidemiological studies may be able to relate consumption to health effects. In the U.S., one HIA conducted as a graduate school project analyzed the impact of a hypothetical state-wide carbon tax on health-related purchases (Beltre et al., 2009). The HIA considered the effect of the tax, levied on fossil fuel energy production inputs, household energy, and transportation energy, on consumer prices and

indirectly on health effects of changes in consumption. This HIA estimated that fuel consumption would decrease approximately 3% with an approximately similar decline in particulate matter (PM10) emissions and related reductions in noise and traffic collisions. The HIA concluded that the small change in food price would be unlikely to affect dietary patterns and, because of relative price inelasticity, home energy price changes would result in a modest increase of household expenditures. Future trends in energy supply or demand, economic and technological conditions, and heterogeneity in price elasticity based on economic status or geography might be among the sources of uncertainty in these estimates. 5. Discussion: considerations for useful and valid quantitative estimation 5.1. Opportunity areas for quantitative health effects estimation in HIA This scan of practice illustrates that there is substantial scope and opportunity for quantitative estimation in HIA. Estimation in other circumstances may be limited by insufficient evidence causally linking policy to health determinants or health outcomes and by robust quantitative effect measures or E–R relationships. Cases where a policy or project affects human exposure to a known physical environmental hazard, including air, soil and water pollutants, noise, radiation, and biologic contaminants, currently appear most amenable to quantitative estimation both in the U.S. and internationally (Veerman et al., 2005). In these cases, available E–R functions or epidemiological effect measures (e.g., Relative Risks) can be applied in policy or project scenarios that change exposures. Other physical, mechanistic hazards, such as traffic hazards, are also tractable using quantitative predictive models (Harwood et al., 2008). Behaviors, including tobacco use, addictions, sexual practices, physical activity, and diet, are well-established determinants of health status. While there is relatively high confidence in the causal effects of these behavioral factors; there is substantial uncertainty in the efficacy of novel policy change on health behaviors. For example, food consumption and net energy balance are clear determinants of weight; nevertheless, few societal-level interventions have yet succeeded in changing energy balance at a population-level. In general, policy efforts to affect behavioral determinants of health (e.g., addictions, overeating, and physical activity) will require more generalizable evaluations of efficacy in order to conduct quantitative estimation. However, there appears to be specific opportunities for HIA to study the impacts of economic instruments (e.g. taxes, fees, and subsidies) on consumption of goods and services that relate to health behaviors. HIAs conducted in Europe have analyzed how policies that change alcohol or tobacco prices affect related health endpoints (Veerman et al., 2005). U.S. studies, not identified as HIA, have analyzed the impact of tobacco policy on health behaviors. For example, using a computer model, one study reported that a 40% tax-induced increase in cigarette price could reduce smoking prevalence from 21% to 15.2% with large gains in cumulative life years over 20 years (Ahmad and Franz, 2008). Evaluations of programmatic behavioral interventions funded through tax measures have also estimated impacts on smoking prevalence (Weintraub and Hamilton, 2002). There is emerging evidence that sugared beverages and energydense fast-foods are contributing causes of obesity (Gibson, 2008; Pereira et al., 2005). Recently, health advocates have proposed fees, taxes, and subsidies on agricultural or food products to support nutritional objectives (Brownell et al., 2009). Conceptually, HIAs on policies that propose subsidies for or taxes on “healthy” or “unhealthy” foods could link elasticity data with measures of consumption effects on health outcome data to predict health effects. A recent review found that price elasticity for major food categories and nonalcoholic beverages varied from 0.27 to 0.81, with larger elasticity for food away from home, soft drinks, juice, and meats (0.7–0.8)

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(Andreyeva et al., 2010). Other studies have also estimated price elasticity for foods with higher nutritional quality such as fresh fruits and vegetables (Powell et al., 2009). Some applied analyses have already utilized elasticity data to estimate the impacts of soft drink taxes on childhood obesity (Sturm et al., 2010). Another recent applied analysis estimated the effect of price changes of soda and away from home pizza on energy intake, weight, and insulin resistance (Duffey et al., 2010). Changes in environmental and social conditions can affect the distribution and transmission of infectious diseases (Eisenberg et al., 2007). No HIA in the U.S. has yet quantitatively estimated impacts of a policy or program on infectious diseases, although the Paid Sick Days HIA referenced findings from pandemic influenza modeling studies on the impact of social distancing policies (HIP and SFDPH, 2009). Recent mathematical models describe the spread of infectious diseases both within and among populations (Riley, 2007; Grassly and Fraser, 2008). Some of these models account for environmental change and social dynamics (Remais et al., 2010), and these models may be used to explore the impact of projects as well as alternative infectious disease control strategies (Liang et al., 2007). HIAs conducted on diverse policies programs, or projects including employment leave, school closure, or other social distancing policies, vaccine access or requirements, resource extraction, road building sanitation, agriculture, water management, and climate change adaptations or mitigations could potentially apply modeling or epidemiological approaches to predict effects on infectious disease (Eisenberg et al., 2006). HIA is concerned with the distribution of health impacts and impacts on health equity (Quigley et al., 2006); however, no HIA examined in our review provided quantitative estimates of the distribution of health impacts or provided analysis of socio-economic gradients in impacts (i.e., an equity analysis). Distributional impacts may be described qualitatively, mapped spatially, or quantified using a statistical measure. Several statistical techniques are available to evaluate the relationship among impact inequalities and potentially related socioeconomic or demographic factors (Kakwani et al., 1997) For example, one analysis examined environmental equity quantitatively for alternative road pricing schemes in Leeds, England using linear regression to estimate the relationship between exposure and an area-level deprivation measure (Mitchell, 2005). 5.2. Managing validity in quantitative estimation Predictive validity is a challenge for all judgments in HIA and the demands on validity can increase with efforts to quantify impacts in HIA (Mindell et al., 2001; Veerman et al., 2007;). For example, quantitative estimation raises new questions about the precision of estimates and the transparency or validity of assumptions used in models (O'Connell and Hurley, 2009). In most cases, it is not feasible, in the context of decision-making, to validate quantitative estimates of health impacts by real-world outcome data (Veerman et al., 2007). There may be substantial latency between a decision, its implementation, and its effects on health. Health outcomes are also driven by multiple factors, not all of which can be anticipated or controlled by the decision at hand. However, the validity of quantitative estimates in HIA can be strengthened through a robust and transparent accounting of the assumptions and the limitations of estimation methods (O'Connell and Hurley, 2009). Allowing outside experts and stakeholders to criticize HIA findings through opportunities for public comments can help identify such limitations. When using complex mathematical models to make quantitative estimates, sensitivity analysis (SA) can help examine the relative importance of uncertain data inputs on predicted outcomes. Based on this scan, four issues critical to validity and precision should be thoughtfully considered in all applications of quantitative estimation: causation, external validity, heterogeneous effects, and

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secular trends. These issues are not unique to quantitative estimation in HIA, yet their consideration will help to ensure appropriate application and defensible interpretation and may also provide specific direction for research and methods development. The interpretation of a numerical effect estimate is ambiguous if causal relationships are uncertain. An effect measure or E–R relationship based on statistical analysis of empirical data does not itself demonstrate a cause and effect relationship, and it is critical that quantitative application in HIA critically consider causal inference based on the weight of the evidence of the relationship between the policy, health determinants, and health outcomes. Reviews of evidence used to establish a causal relationship should use a priori study inclusion criteria reflecting the outcomes, exposure variables, populations or time periods of interest and should be attentive to limited study power, and biases due to selection error, loss to followup, analytic methods, and confounding (Mindell et al., 2006). Criteria for causal inference may help evaluate whether the weight of evidence lends support for a cause and effect relationships (Susser, 1986; Rothman and Greenland, 1998; Weed, 2005). As illustrated in several cases above, external validity should always be considered when generalizing an effect measure or E–R function from one context to another. An assessment of external validity should consider the applicability of an empirically based effect estimates across time, place, or demographic subgroup, as well as other measured or unmeasured context specific factors that may mediate or moderate effects. Developing or using a summary effect measure from a quantitative meta-analysis of empirical studies from many different contexts may build greater confidence in the health effect estimate. Estimates in HIA must be attentive to heterogeneity of effects among populations, particularly vulnerable populations. Effect measures from epidemiological studies or E–R functions generally reflect the independent effect of an exposure on an average member of the population. In many cases, HIA is applied to local or neighborhood settings where the population characteristics may be substantially different from the average in a city, region, or country. Vulnerability or resilience factors associated with population subgroups may thus influence the magnitude of health effects. A subpopulation may have greater susceptibility to a specific health impact because of a demographic characteristic (e.g., poverty, the susceptibility of the young to pedestrian injuries); a higher prevalence of certain health conditions (e.g., asthma); environmental hazards or stressors (e.g., noise); or cultural dependence on natural resources (e.g., sustenance consumption of local wildlife). With adequate data on interactions between exposures and demographic factors, it may be possible to adjust quantitative effect estimates to account for population characteristics. Secular trends, including demographic, political and cultural changes, represent an ever present immeasurable influence on judgments in impact assessment. The application of all empirically or experimentally derived analytic models to real-world predictions assumes a condition of ceteris paribus or “all other things being equal.” No model will be able to account for all potentially influential variables and many important variables will likely remain unmeasured. Relationships between causes and health effects may also change over time, for example, through interventions that affect other causes or vulnerabilities. Projects and policies themselves can induce contextual changes. For example, small or large scale development projects can result in in- or out-migration and resulting changes to community composition and social relationships. Cultural factors specific to population migrating into an area may increase or decrease population-level vulnerability or disease risks. The dynamics between physical and social environments and health effects make it difficult to account for these complexities in quantitative models. For example, the growth in the population or change in their economic or political power in areas with existing environmental hazards might lead to new political demands for improvement of environmental quality.

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It is important to recognize that the practice of HIA in the U.S. and internationally is developing. Many of the methods applied in the U.S. HIA practice experience were not built upon an established methodological literature, but rather are novel and innovative efforts to respond to the policy questions at hand. While these creative responses to methodological gaps are strengths of HIA practice, there has been little formal critique or evaluation of individual analytic methods or approaches from within or outside the field. Rigorous independent evaluation of methods beyond the limited approach used in this scan will benefit the legitimacy of the practice. As the practice of HIA matures, HIA practitioners also may also identify opportunities (e.g. natural experiments) to validate predictive methods in the HIA toolkit. In different policy contexts, such research would elucidate whether the validity of quantitative estimates are most sensitive to problems related to ambiguous causality and a limited body of evidence, problems associated with external validity (extrapolating evidence from specific regions/populations to larger domain), problems of effect heterogeneity (applying general effect measures to specific, possibly vulnerable populations), and/or problems with a priori unmeasured/uncontrollable factors. Such evaluation is all part of a formative research agenda for HIA practice.

5.3. Is quantification required for HIA? HIA strives to inform stakeholder and decision-makers, support policy design, and to support more transparent and participatory governance institutions. Beyond issues of methodological feasibility, validity, and precision, it is important to consider how quantitative estimation supports the overarching purposes and objectives of a particular HIA and what value quantification, in particular, would add relative to the use of other methods (Mindell et al., 2001). The strength of HIA is its use of more than one analytic approach to answer inherently complex questions (O'Connell and Hurley, 2009). Diverse forms of evidence, from existing literature to stakeholder interviews and expert consensus, can provide adequate sources for reasoned judgments, and these approaches may be particularly important for scenarios where proposed decisions have no comparable antecedents. A number of HIAs that made quantitative estimates described in this review were associated with design changes to policy or projects. For example, HIAs that predicted impacts on noise or air pollutant outcomes led to acoustical and air quality protections incorporated into project design (SFDCP, 2007; HIP, 2008). However, in these cases it is hard to ascertain the specific influence of the numerical estimates in the HIA. In the case of the living wage HIAs, the association between higher wages and health might have been revealed with equal effectiveness through a qualitative interpretation of the epidemiologic literature. Quantification may have a more direct influence on decisionmaking where effect estimates are evaluated against a specific numerical health-based standard or performance indicator. Established quantitative criteria for impact significance and in some cases statutory or regulatory standards exist for but not all health impacts. National public health objectives, health indicators and targets may compliment these standards (DHHS, 2020). If there is a greater use of quantitative methods in HIA, there may be complimentary need to develop thresholds for health effects estimated using these methods. On the other hand, HIAs without quantification appears also to have been influential on decisions. Qualitative judgments of health impacts on indigenous populations based on expert judgments and empirical evidence in a recent HIA on Alaskan oil lease agreements provided sufficient basis for protective mitigations (Wernham, 2007). In the case of the HIA of the Trinity Plaza Apartment redevelopment, a summary of empirical evidence along with tenant focus groups was sufficient to demonstrate the health effects of forced eviction and to justify re-design of the redevelopment to accommodate the existing residents (Bhatia, 2007).

A single policy may have multiple, even competing effects on health determinants. For example, in the context of land scarcity, the development of new housing might reduce overcrowding but also limit industrial development — both factors that influence health. All important health effects of a policy choice may not be amenable to quantification. Many HIAs include mixed quantitative and qualitative methods and multiple health outcomes; only three included in this review focused exclusively on quantitative outcomes (Cole et al., 2005; Fielding et al., 2007; Kuo et al., 2009). Utilizing quantitative methods exclusively may preclude a full accounting of health effects. (Mindell et al., 2001; O'Connel and Hurley, 2009). For example, a different HIA on the menu labeling law might have also explored effects on product formulation and product pricing.

6. Conclusions Overall, quantitative estimates may be useful in HIA as a way to convey the magnitude of population-level health impacts. However, the quality of predictive quantitative estimates rests on the strength of the causal model, the availability and validity of inputs and parameters, and the transparency of the application and its assumptions. This review of US HIA practice illustrates several current opportunities for quantification and further scope for methodological development and application. It also suggests that estimates should be made with caution and should be balanced with equally important but nonquantifiable effects. The U.S. experience does not suggest that quantitative estimates provide substantial advantages over non-quantitative judgments with regards to the purpose and effectiveness of HIA.

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