MEASURING POPULATION HEALTH: A Review of Indicators€¦ · Toronto, Ontario, Canada M5G 1L5; email: [email protected] Erica Di Ruggiero Canadian Institutes of Health Research,

15 Feb 2006 10:49 AR ANRV269-PU27-02.tex XMLPublishSM(2004/02/24) P1: KUV

10.1146/annurev.publhealth.27.021405.102141

Annu. Rev. Public Health 2006. 27:29–55doi: 10.1146/annurev.publhealth.27.021405.102141

Copyright c© 2006 by Annual Reviews. All rights reservedFirst published online as a Review in Advance on October 28, 2005

MEASURING POPULATION HEALTH:A Review of Indicators

Vera EtchesSudbury & District Health Unit, Sudbury, Ontario, Canada P3E 3A3;email: [email protected]

John FrankCanadian Institutes of Health Research, Institute of Population and Public Health,Toronto, Ontario, Canada M5G 1L5; email: [email protected]

Erica Di RuggieroCanadian Institutes of Health Research, Institute of Population and Public Health,Toronto, Ontario, Canada M5G 1L5; email: [email protected]

Doug ManuelInstitute for Clinical Evaluative Sciences, Toronto, Ontario, Canada M4N M5;email: [email protected]

Key Words history, public health, epidemiological methods, framework, ethics

■ Abstract This article reviews the historical development of population health in-dicators. We have long known that environmental, socioeconomic, early life conditions,individual actions, and medical care all interact to affect health. Present quantitative re-porting on the impact of these factors on population health grew out of Bills of Mortalitypublished in the 1500s. Since then, regular censuses, civil registration of vital statistics,and international classification systems have improved data quality and comparability.Regular national health interview surveys and application of administrative data con-tributed information on morbidity, health services use, and some social determinants ofhealth. More recently, traditional health databases and datasets on “nonhealth” sectordeterminants have been linked. Statistical methods for map-making, risk adjustment,multilevel analysis, calculating population-attributable risks, and summary measuresof population health have further helped to integrate information. Reports on the healthof populations remain largely confined to focused areas. This paper suggests a con-ceptual framework for using indicators to report on all the domains of populationhealth. Future ethical development of indicators will incorporate principles of justice,transparency, and effectiveness.

0163-7525/06/0421-0029$20.00 29

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30 ETCHES ET AL.

INTRODUCTION

Whether to ensure fit soldiers and laborers, to improve the plight of the poor, fornormative or other purposes, interest in measuring the health of populations has along history. This chapter traces the historical development and use of populationhealth indicators to the present. To improve the health of populations and increaseopportunities for comparability, more valid, comprehensive, transparent, and stan-dardized ways of measuring and reporting on population health are needed. Suchmetrics will need to be connected to a clear conceptual framework, integrating rela-tionships between the different elements of population health. This paper proposessuch a framework.

Uses of Health Indicators

The uses of health indicators should contribute to overall population health goals,namely improving the health of populations and reducing health inequalities.Health indicators support this goal through the following key applications: ad-vocacy, accountability, system management, quality improvement, and research(3). Although users generally accept that indicators have been helpful with respectto these approaches, evidence or well-developed evaluation of indicators is oftenlacking. The section on evaluation of indicators will further this point below.

Advocacy, one of the first applications of indicators of population health, re-mains one of the most important for improving population health. For instance,baptism and death registration led to a comparison of the indicator “infant mortal-ity” in Halifax and Saint John, and in Massachusetts and England in the mid-1800s,to shame municipalities into taking measures to improve sanitation (26).

Indicators can be used, or not used, to advocate for different ideologies. Propo-nents of sanitary measures, again in the mid-nineteenth-century, demonstrate thispoint. Some used the discrepancy in mortality rates between rich and poor areasto argue that a more egalitarian society is healthier. Others presented mortalityinformation to advocate for healthier laborers and soldiers and to maintain socialstability, through the control of contagious diseases (74). Later, “eugenics soci-eties” developed social Darwinist ideas and blamed the rise of diseases among thepoor on people with poor heredity (83). Nazi ideology based its program of racehygiene on ideas of sociopathology partly by drawing on indicators of populationhealth (83, p. 595).

Indicators are therefore not often employed neutrally; they can classify situa-tions as “unfavorable” or “favorable,” to show “improvement” or “deterioration”over time (81). Different indicators are chosen not only for their different uses, butalso because different societies—and authorities—will value/acknowledge someaspects of health or determinants of health more than others (75).

The next major use of indicators of population health is to achieve accountabil-ity. Reporting on population health can satisfy the needs of government, healthprofessionals, voluntary agencies, and the public for information on risks, patterns,

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POPULATION HEALTH INDICATORS 31

and trends related to health and whether or not expectations for performance aremet (29). In a related use, population-level indicators of health may inform sys-tem management to improve health. For example, in seventeenth-century London,England, parish clerks made regular weekly reports of the number of burials andthe causes of death. Any indication of spreading plague resulted in systemic ac-tions to limit its spread, and the weekly reports provided feedback on measurestaken (74).

Indicators may provide information to improve the quality of initiatives forpopulation health, even to improve the quality of measurement of population healthitself. Indicators have often been suggested on a theoretical basis, before data areavailable to calculate them, to guide development of systems that provide better-quality information. Such was the case with indicators of mortality based on civilregistration of vital statistics. Early World Health Organization (WHO) lists ofindicators to be obtained from health interview surveys were meant to guide thedevelopment of new and improved information systems (98); greater internationalcomparability of data collected did result.

Research may be stimulated by reports of population health indicators. Geo-graphic disparities in health status, apparent from reporting of mortality, promptedresearch into causal explanations in the eighteenth century (47), as it still does to-day. Researchers also use indicators to evaluate the effectiveness of interventions.

Finally, indicators of population health can be applicable in other contexts, suchas improving professional training (73). Development of indicators of populationhealth became the core training of students of public health in the late-nineteenthcentury (82). Suitable indicators of population health can also be used more broadlyfor education in schools, workplaces, and institutions.

HISTORICAL DEVELOPMENT OF POPULATION HEALTHCONCEPTS AND RELATED INDICATORS

Current concepts of population health recognize that many interconnected aspectsof society, the environment, and individuals all contribute to health. Several authorshave pointed out that this recognition is not new (56, 82, 103). For example, theChinese have acknowledged and written about multiple influences on health formillennia (99). The Greek distinction between the god of medicine’s two daughtersHygeia—the goddess of prevention and wellness, and Panacea—the goddess oftreatment (although our current use of the term is strikingly broader!) suggests thatpeople have long believed that there is more to health than health care (74). And,the imposition of quarantine in Marseilles (in 1377) and Venice (in 1403) demon-strated recognition of the need for population-level actions to improve health,above and beyond individual medical treatments (74). Environmental variablesproducing endemic disease identified by Hippocrates (500 BC) included climate,soil, and water (82). “Corrupting” the environment/air was the subject of the firstEnglish Sanitary Act passed in 1388 to control practices at slaughterhouses (74).

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32 ETCHES ET AL.

Regarding social variables, Johann Peter Frank (1745–1821) emphasized the needto transform social conditions to render them compatible with health and longlife for all (30). Rudolf Virchow (1821–1902) felt the right to work safely, andto responsible government, was central to a healthy society (82). Among the de-mands for other health-promoting measures, nineteenth-century sanitarians calledfor greater efforts to promote healthy infancy and childhood development as afoundation for later life, education of women, town planning, and new housing forthe poor (102). In summary, the beliefs that environmental, social, early life con-ditions, individual actions, and medical care all contribute to health long predatedthe availability of much quantitative data to support these views. Reformers in themid-eighteenth century, however, began to call for more data collection in orderto better promote the health of the population.

The debate between Farr and Chadwick, discussed in Hamlin’s insightful article,demonstrates the conflict over the meaning imputed to the numbers generated byanalyses of routinely collected mortality data (46). Farr listed “poverty” as the causeof death for 63 people and commented, “hunger destroys a much higher proportionthan is indicated by the registers in this and in every other country, but its effects,like the effects of excess, are generally manifested indirectly, in the production ofdiseases of various kinds” (46, pp. 857–58). Chadwick, on the other hand, believedthe poor needed to behave more morally, and that environments required sanitation,to reduce mortality. The collection of data and use of indicators did not in itselflead to agreement over solutions; similarly, despite the proliferation of indicatorsof population health today, debate continues over the strategies to improve healthamong groups with lower incomes.

Social scientists and medical and other reformers in the nineteenth century bothapplied and collected new data on populations. By the 1870s, “it is clear that thesocial body had acquired a firmer existence and that it was increasingly understoodto be a medical object” (26, p. 97). The influence of the population-level analysesof Marx, Engels, and Durkheim and the application of quantitative measures ofdeterminants of health, in the late 1800s and early 1900s, continue. Porter (82)gives credit to the rise of the social sciences’ application to health and disease tothe rise of “social medicine,” on both sides of the Atlantic, in the 1930s and 1940s.

Monitoring of the causes of death quantified populations’ experiences of thedecline of air and waterborne “classical” diseases by the middle of the twentiethcentury, and their replacement with noncommunicable diseases (e.g., cardiovascu-lar, diabetes, arthritis, depression). Researchers proposed theoretical frameworksfor the shift that alluded to broad determinants of health-impacting physiologicalprocesses in individuals’ bodies (51, 78, 82). In this context, in 1974, Canada’sLalonde Report revitalized the concept that multiple underlying causes of mortalityand morbidity operate completely outside of the health care system per se, but con-tributed to public health practice that focused on changing individuals’ lifestyles(59, 102). In 1993, Porter rather ironically suggested that public health practicehad “come full circle” to apply the same strategies used by people, like Chadwickin the nineteenth century, who urged the poor to “behave” more healthily (82).

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Nevertheless, research on the impacts of broader and more “upstream” (69)determinants of health continued to grow. The WHO’s Ottawa Charter for HealthPromotion (109) and its accompanying Canadian document Achieving Health forAll led to a framework for the multifactoral determinants of human health. Theconceptual framework developed by the Canadian Institute for Advanced Re-search’s (CIAR) Population Health Program also received wide acceptance as anapproach to research and reporting on population health (35). Critics pointed toa lack of theoretical understanding of the impacts of global political regimes andsocial structures on health (24), as well as a lack of recognition of the agency ofindividuals and populations (85). A forthcoming and final book by the CIAR Pop-ulation Health Program addresses some of these critics’ claims (49). Additionalconceptual frameworks for population health, highlighted by Evans & Stoddart’s2003 review (34), suggest a trend towards multiple “subframeworks” focusing onspecific topics (e.g., occupational health).

Indicator development based on conceptual models have also tended to take a“focused area” approach. For example, the dimension of human developmentalstages throughout life was one key factor lacking in the early CIAR framework,though early studies by Barker & Osmond (6) clearly showed associations betweencharacteristics of infancy and childhood, and adult chronic disease. Hertzman &Wiens (48) later promoted the inclusion of what has come to be called the “lifecourse” dimension, more explicitly to models of population health. A recentlypublished glossary for life course epidemiology demonstrates the proliferation ofmeasures in this area of focus (58).

Strongly influenced by CIAR’s work, Figure 1 reflects a current synthesis ofthe broad determinants of population and individual health. The return to a focuson poverty, inequalities in the distribution of resources, and other societal andenvironmental determinants of health may be seen as another turn of the full-circle wheel to which Porter (82) referred. This suggests that concepts of whatmakes a population healthy will develop, recede, and re-emerge in the future.Nonetheless, the ideas that health should be measured in multiple ways, at thepopulation level, and that multiple nonmedical factors influence health are likelyto remain. In contrast to the fairly stable uses of broad concepts of populationhealth, data sources for constructing indicators have steadily improved over time,and are the subject of the next section.

TYPES OF DATA

Censuses and Vital Statistics

Counting people dates back to ancient times and is central to a population’s gover-nance. The United States was the first country to pass an act for regular, periodicalenumeration in 1790 (114), yet such efforts were not true censuses in the mod-ern sense (4). Initially, censuses simply provided population counts, including

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34 ETCHES ET AL.

Figure 1 Canadian Institutes of Health Research—Institute of Population and Public Health

(CIHR-IPPH) conceptual framework of population health.

numbers of people dying in the census year. Wright (114) notes that in the UnitedStates, early enumeration was used to ascertain the military and industrial strengthof the country, since free males (16 years of age or older) were enumerated sep-arately. Higgs (50) demonstrates how the censuses in England were constructednot only for social and economic purposes, but also to study the health effects ofwork in various settings, through the occupational classification system chosen.Difficulties of relying on censuses for mortality data included a large proportion ofmissing data; the migration of people; low-quality records for cause of death; a lackof knowledge and slowed recall of people supplying the data; and inadequatelytrained or reimbursed enumerators (36). Present-day censuses collect much otherdata on basic determinants of health useful for calculating indicators of popula-tion health: income, work, housing, immigration/language, and family/householdrelations.

Though some censuses predate the registration of vital statistics (births anddeaths), vital statistics are the oldest continuous and most established type ofpopulation health data. That stated, many developing countries have yet to establishreliable systems for recording vital statistics (28).

Over the centuries, there have been many ways of, and reasons for, collectingvital statistics data. The first London Bills of Mortality, prepared in 1532, reliedon data collected by churches (29). Parish records remained the main system ofregistration of baptisms, marriages, and deaths until the mid-nineteenth to earlytwentieth century in Europe and its colonies. However, baptisms served as a poor

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proxy for births, since early infant mortality was high and many children diedbefore baptism; others were not baptized through the church or were not baptizedat all (89). Civil registration of births and deaths was advanced for reasons beyondmerely obtaining more accurate health data: to calculate annuities on lives moreaccurately (32); for political and moral uses (10); for more complete and accurateinformation on the population for setting electoral boundaries and direct taxation(36); and to address concerns about inherited property rights (26). Clinicians inthe mid-1800s did not generally acknowledge the importance of understanding thedrivers of population “average” illness or death rates. Curtis (26) and Emery (33a)concur that the initiation of registrations of deaths and births, and censuses in acountry, cannot be regarded as the culmination of “progress in relation to projectsof medical mastery.”

Practical and jurisdictional barriers also had to be overcome before vital statis-tics registries could be applied to measuring the health of the population. The needfor a uniform classification of causes of death became apparent. In 1768, JohnFothergill declared the Bills of Mortality for London as inaccurate because, “thelist of diseases was a very injudicious one,” and, “present labours, therefore, an-swered very little useful purpose, either to themselves or the community. . .” (42,p. 293). He blamed the “ignorant poor women,” who were the “common searchers”appointed to view dead bodies in order to prevent the concealment of violence,for making too many diagnoses of consumption based on emaciation alone. In1855, the International Statistics Congress chose to follow Farr’s classificationbased on anatomical site. The first conference for the revision of the InternationalClassification of the Causes of Death (ICD) was held in 1900 (36). Since 1948, theWHO has been entrusted with updating the International Statistical Classificationof Diseases and Related Health Problems.

Surveys

Early health surveys were conducted of restricted populations to gather informationon specific topics. For example, John Haygarth (1740–1827) conducted pioneersurveys in the 1770s, believing that greater knowledge of “natural and artificialvariables” would suggest practical policies for disease containment and control(83). Samuel Hitch carried out one of the earliest surveys in the area of mentalhealth, of the “pauper insane” in 1844 in Leicester (20). By 1917, a survey au-thorized by the U.S. federal government examined households’ use of physicians,medicines, nurses and hospitals (52). However, not until after World War II weresurveys conducted on a large scale, representative of national populations. TheUnited States performed the first National Health Survey in 1935 and has imple-mented health interview surveys regularly since 1957 (29). Japan started regularsurveys even earlier, in 1948 (61).

Design and analysis of survey results were aided by developments in mathe-matics and statistics. Thomas Bayes (1706–61) was one of the first to performstatistical inference. Later, Adolphe Quetelet (1796–1874) and Francis Galton

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(1822–1911) applied statistics to human populations and developed correlationand simple regression techniques. As more complex data on populations be-came available, statistical methods to infer population characteristics, from a sub-sample, began to be developed in the late-nineteenth century (80). Karl Pearson(1857–1936) argued that statistical tools allowed clinical and preventive medicinepractitioners to “conduct experiments” with records already available, especiallyto provide insights into epidemic disease. Sir Bradford Hill (1897–1991) wasmost successful in getting medicine to adopt statistics as part of its regulararmamentarium.

By 1960, enough countries were conducting national health surveys that theWHO commissioned a study that proposed a template of “factors bearing on thedesign of health interview surveys” for the benefit of other countries (61). By1990, the WHO was seeking data for indicators of progress toward Health forAll goals and pressed for greater comparability of survey data between countries(27). Differences existed between nations in terms of which subpopulations wereexcluded (e.g., nursing homes, prisons, military), the method of data collection,and the wording of questions that might otherwise provide data for the sameindicator.

As was previously the case for mortality, people began to seek common def-initions and measures of morbidity used in surveys. The WHO developed an In-ternational Classification of Impairment, Disabilities and Handicaps in 1980. TheInternational Classification of Functioning, Disability and Health (ICF), publishedin 2001, now supersedes the former. The new emphasis on capacity and social par-ticipation in 2001, rather than on disability and handicap in 1980, demonstrates howclassification systems evolve according to new needs, understandings, and values.

As another example, early censuses recorded information on “idiocy” or “in-sanity,” and later differentiated specific types of mental illness. The Diagnosticand Statistical Manual (DSM) has promoted much greater consistency in diagno-sis and reporting on mental health and illness since it was first published in 1952(92). Yet, the DSM-IV (1994) still faces questions about the validity and reliabilityof diagnoses. Noting the socially constructed nature of disease classification sys-tems, Lee (62) discusses the classification of mental disorders in China and arguesthat international systems must not displace regional systems, which serve valu-able functions locally. The advent of international quality-of-life surveys servesas the latest example of the trend toward seeking comparable data on morbidity.A recent review examined application of quality-of-life measures in developingcountries, highlighting the lack of effective assessments of equivalence acrosscountries (13).

Limits to international classifications exist. The desire to continue collectingdata that are strictly comparable with past survey information leads to reluctanceto adopt new classifications and definitions. Buratta & Egdili (17) consider thenecessity of achieving universally acceptable definitions and questions on healthinterview surveys. Instead, they advocate constructing instruments that are clearlydirected toward specific aims, for specific users of the data.

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Administrative Data

Although administrative data are generally collected for reasons other than generat-ing health statistics to reflect population health status, the data can be applied to thelatter. Some of the oldest health administrative data comes from hospitals. JohannPeter Frank (1745–1821) records that numbers of people treated in hospitals, undervarious diagnoses, were first incorporated to try and assess population health statusin continental Europe around the turn of the nineteenth century. Florence Nightin-gale (1820–1910) also vigorously promoted the use of hospital statistics (65).

Interest in gathering data on entire health care systems grew with pressuresfrom social democratic and labor parties to establish national health insurancesystems in the early twentieth century, though many countries did not implementsuch systems until after World War II (105). The United Kingdom made use ofNational Health Insurance data for surveillance by 1911 (29).

With the rise of national health insurance plans in many countries and thecomputerization of databases, an extensive body of administrative data has becomeavailable for analysis, but many developing countries continue without publiclyadministered health insurance systems (29). Created in developed countries in the1950s and 1960s, applications of computerized health information systems (HIS)in developing countries did not take off until the 1970s. A 1982 conference askedwhether developing countries could afford to spend scarce resources on informaticswhen many millions of people did not have access to basic primary health care(38). Developers of HIS realized that “the dream of one’s own computer will notautomatically provide the answer to all problems and may even give rise to newones” (38, p. 8). The goal remains the accurate and efficient assessment of thehealth of populations, not computerized systems as an end in themselves.

Elements of the health care system based outside of hospitals have also devel-oped electronic databases that aid indicator development. Following the exampleof the first Cancer Registry, established in 1943 in Holland, many jurisdictions nowcollate diverse sources of cancer records centrally (29). Other disease-specific reg-istries have been created but there is little international consistency. Medical clinicsare now able to perform more complex analysis of their records electronically, in-cluding linking patients’ primary care information with socioeconomic data (21).

The rising costs associated with publicly funded health care systems providedthe largest stimulus for creating data banks of health system information to comparespending across countries (84). Poullier (1987) notes that the rising costs werelikely primarily due to behavioral, cultural, and environmental factors leadingto increased chronic diseases, but that a paucity of readily available data led toneglecting these “upstream” factors in formal statistical analyses (84, p. 36).

Interest in the quality and cost-effectiveness of interventions also grew. In addi-tion to the money spent on interventions, their expected benefits in the populationcovered had to be estimated. Cost-benefit analyses in the health field were firstcrucially performed by social reformers in the 1850s, in an attempt to demonstratethat sanitary measures would save lives and thus money, through decreased lossof fit and productive laborers (26).

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38 ETCHES ET AL.

In the context of evaluating health care, Donebedian (33) made a fundamentaldistinction between health demands, health needs, and the use of health services,which should inform the design of indicators of health care system functioning.Perceptions of health needs and of the ability of interventions to improve them, aswell as observations regarding the care experiences of other people, lead to indi-viduals’ health demands. The use of health services may not meet all demands anddoes not (often) meet the actual health needs of patients or populations. Simplyreporting the number of procedures that different populations receive may not cor-relate with the health outcomes achieved. For instance, the OECD’s 2003 healthindicators report presents the high and escalating coronary-artery bypass and an-gioplasty rates in the United States with no evidence of an associated marginaldecrease in mortality from coronary artery disease (77). Finally, those with greatesthealth needs often have the least access to care or health care coverage. Therefore,data on health care services alone cannot provide adequate indicators of populationhealth. The conceptual framework for population health proposed in this paper (seebelow) points to the need to put cost, quality, and coverage of health care servicesin context: They are useful but inherently very “downstream” in nature.

Looking beyond health care systems, “public health systems” are intended toprotect and promote health at the population level, a mandate that dates back to atleast the nineteenth century. For over a century, public health administrative datahave been used to calculate indicators of population health, such as vaccinationcoverage and incidence of certain reportable infectious diseases. However, mea-suring the contribution of public health systems to population health requires abroader set of indicators. Examples of indicators used by public health systems ineight different countries can be found in a recent review (1).

Woodward et al. (111) suggested a “balanced scorecard” with four domains formeasuring the functioning of a public health system, in relation to the system’soverall goal to improve population health. First, “health determinants and status”would include intervention impact measures. Second, “community engagement”would measure perceptions and degree of involvement of residents, “high-risk”groups, health care providers, policy makers, and staff. Third, “resources and ser-vices” would include per capita measures, human resources data, and the propor-tion spent on administration. And fourth, “integration and responsiveness” wouldinclude measures of partnership, collaboration, and coordination. The National Ad-visory Committee on SARS pointed out that Canada is among the countries missingnational public health goals, priorities, strategies, and reporting mechanisms (74),although plans are underway to remedy these gaps. In fact, administrative data forall four quadrants do not currently exist in most Canadian jurisdictions.

Non-Health (Sector) Data

Meterological data are one type of non-health sector data with a long history ofmeasurement, at times for health purposes. Galen’s medical writings from around170 AD included attempts to devise a standard temperature scale (84). Environ-mental monitoring and regulatory data continue to be related to health indicators,such as that of air quality, for population health (101).

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Historically, data on income was sought, in some cases, to study its relation-ship with health; housing quality was sometimes used as a surrogate (82). Withre-emerging interest in the societal determinants of health, researchers are now ex-amining data collected from economic, social service, and education sectors, mostnotably to ascertain socioeconomic status. However, as Lynch & Kaplan report,the use of occupation, income, education, or wealth as indicators of socioeconomicstatus poses methodological challenges (64). For example, data in an educationdatabase in Manitoba showed small differences in success on exams at the end ofsecondary school between the highest and lowest socioeconomic groups. Linkagewith a health database allowed researchers to follow the entire birth cohort. Thedifference in successful completion of grade 12 tests between highest and lowestsocioeconomic groups was much more striking (77% pass rate compared with27%) using the linked databases (16).

Both non-health and health data are used together to produce comprehensiveindicators of population health. For instance, the OECD 2005 report on socialindicators presents indicators to reflect self-sufficiency, equity, and social cohe-sion, involving financial, employment, social spending, and well-being measures,permitting comparisons among the most economically developed nations (76).Sweden provides another example. The overall national public health aim is to“create social conditions that will ensure good health for the entire population”(63) (see Box 1). The new model for monitoring the impact of policy makes use ofnon-health sector indicators that are correlated with health outcomes. Examplesof these indicators are

� election turnout in municipal elections, as a measure of the population’sparticipation and influence in society; and

� how pupils are treated by teachers, other grown-ups, and fellow pupils (mea-sured at the national level) as an indicator of secure and favorable conditionsin childhood and adolescence.

Box 1. Sweden’s domains of objectives for population health (63)

1. Participation and influence in society

2. Economic and social security

3. Secure and favorable conditions during childhood and adolescence

4. Healthier working life

5. Healthy and safe environments and products

6. Health and medical care that more actively promotes good health

7. Effective prevention against communicable diseases

8. Safe sexuality and good reproductive health

9. Increased physical activity

10. Good eating habits and safe food

11. Reduced use of tobacco and alcohol, a society free from illicit drugsand doping, and a reduction in the harmful effects of excessive gambling

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The Swedish example demonstrates that high-level acceptance can be achievedon the importance of collecting non-health data to measure a population’s progresstoward better health for all. Future applications of a conceptual framework for pop-ulation health should continue to draw on new “non-health” sector data sources.

Map Data

Maps are “permanent, graphic depictions, which link places in the world to whatcomes with them” (110). Mapmaking has many historical origins, mainly in soci-eties where people lived in large groups with centralized bureaucracies. Early mapswere constructed with large scales but were infrequently made, in part because therewas no perceived utility for them: traditional cultures did not lack “spatial percep-tion intelligence” or the ability to find their way (110). In the sixteenth century,maps became strikingly more common as navigation needs increased for globalcommerce and exploration. Small-scale maps were used as tools of governance indeveloping nation-states to assess taxes, wage war, facilitate communications, andexploit resources, as well as for more humanistic purposes (110).

Computer technology has prompted more widespread use of mapping (71).Presently, public health practitioners, spatial researchers, and policy analysts areincreasingly applying Geographic Information Systems (GIS) and the related sci-ences (25, 88). Mullner et al. suggest the greatest appeal of GIS is its ability todepict clearly with maps the results of complex analyses (71).

The WHO has developed a computer application called HealthMapper, whichhas been successfully used to present health indicators in developing countries.Health administrators can obtain a quick, visual representation of the availabilityand use of specific health and social services (28). Information depicting otherhealth services or risk factors for disease can be overlaid on the maps. Apparently,the maps have “a more visceral and direct impact” on the reader than tables or listsof numbers (28).

Qualitative Data

Although this paper has focused largely on quantitative data for indicators ofpopulation health, some indicators do incorporate qualitative information. Forinstance, neighborhood- or community-level characteristics that may affect healthhave been collected qualitatively—from the time of the eighteenth century, whenthey were sometimes assessed by smell, to the present (54, 96). A full discussionof qualitative data for indicators of population health is beyond the scope of thispaper. However, significant downsides of quantitative approaches should be noted.Numbers in quantitative health indicators are often presented as “the truth” withoutacknowledgment of the assumptions, methodological and epistemological, that ledto their creation. The loss of contextual information in quantitative data increasesthe risk of misinterpretation of meanings. In discussing attributions of causes ofdeath in about 1840, Hamlin suggests that, “to require that the narrative history ofa patient’s constitution be condensed to a single word (i.e., the “cause of death”)

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was to give up the possibility of a medicine that would take an interest in, and see asproblematic, the full variety of pathological influences a person encounters” (46,p. 863). Quantitative indicators can also be said to remove the “voice” or a senseof the “lived experience” of the individuals or populations that generated them.Thus, particular populations may be labeled without an opportunity to presenttheir interpretative view. Survey instruments with closed questions and establishedtemplates for data collection provide little opportunity for gaining new insights onfactors important to population health. For reasons such as these, some research andreports on population health now sometimes present quantitative and qualitativedata together (59a).

INTEGRATING INDICATORS

Basket Presentation

Comprehensive consideration of the relationships between indicators can be en-couraged by simply presenting different types of indicators, under different cate-gories, in one “basket” or report. For example, the 2003 OECD report on healthplaces indicators into these five categories: (a) health status, (b) health care re-sources and utilization, (c) health expenditure and financing, (d) nonmedical de-terminants of health, and (e) demographic and economic context. The categories,and indicators selected, reveal what the authors value as well as their underly-ing concepts of population health. For instance, the “nonmedical determinantsof health” section consists entirely of indicators of “lifestyle” behaviors—for to-bacco and alcohol consumption and body weight. Indicators of the state of theenvironment or social context are notably absent.

Placing indicators for different subpopulations in the same basket can high-light inequities [see Braverman’s review of measuring health disparities in thisissue (8)]. However, Szreter (103) warns that data can be collected in such a waythat information on subpopulations cannot be properly captured or disaggregated.WHO, OECD, and national reports on health generally present information on sub-populations by sex, age group, or health region, but not often by socioeconomicstatus, or ethnic group, or other category related to the social determinants ofhealth.

Advantages of the basket approach include the straightforward use of readilyavailable data and the ability to prompt testable hypotheses concerning relation-ships between indicators. However, the precise relationships between indicatorsare virtually always left unspecified, preventing policy makers from determininghow best to maximize contributions to population health. Another downside ofan unstandardized basket approach may be a tendency to choose indicators basedon what will appear favorable in a report rather than what will more fully depictactual population health status. Next, linked databases are discussed as a way toquantify relationships between diverse sectors and their impacts on health.

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42 ETCHES ET AL.

Linked Databases

Linking health status databases with databases on other determinants of health canhelp increase understanding of causal pathways in both directions. Roos et al.’s (94)review suggests that record linkage promotes policy applications, when databasesallow researchers to (a) study interventions longitudinally; (b) compare regions,areas, and hospitals; (c) combine information on patients and physicians; (d) add upexpenditures for different services within the health-care system; and (e) examinethe determinants of health using education and family services data in conjunctionwith health-related information. Upon reviewing possible “non-medical” datasetsto monitor community well-being, Saunders et al. (97) concluded, however, thatno one dataset will contain information on all the determinants of health. Fur-thermore, many populations will not have as complete datasets for linkage as the“information-rich environments” highlighted by Roos et al.

Multilevel Analysis

Linking data from different domains may involve individual- and-community-levelinformation. In her 1998 landmark article on the value and methodological chal-lenges of multilevel analysis, Diez-Roux noted that following the vague holisticnotions of the causes of ill health in the early nineteenth century, and then the rise ofthe unicausal model of the germ theory, a “web of causation” approach developed,but with the focus remaining largely on individual-level risk factors (31). Krieger(57) asked, “Where is the spider?” implying that simply collecting informationon multiple individual-level causes of ill health is not enough. Multilevel analysisenables examination of specific processes and pathways linking individual andcommunity characteristics to good or poor health.

Risk Adjustment

An international example of risk adjustment comes from a comparative study of 14OECD countries based on household surveys from 1996. The researchers adjustedthe number of physician visits across different income groups for need (based onself-reported health status). The results showed that most countries had achievedequity. Only in Portugal, the United States, Austria, and Greece was significantinequity demonstrated (106a).

Attributable Proportions

The amount of health or ill-health attributable to an intervention or determinantof health is often of interest to decision-makers. A common approach is to cal-culate “population attributable risk” (62a), which is affected by the population’sprevalence of the causative exposures or determinants being examined, as well asthe strength of their association with the health outcome. Such attributable propor-tions should be interpreted with caution as confounding variables and importantmore “upstream” determinants may not be measured. For instance, the recent

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calculations by Allison et al. (2), which attributed nearly 300,000 deaths in a yearto obesity in the United States, have been criticized for only controlling for age,sex, and smoking status, and assuming all remaining excess mortality associatedwith elevated BMI was causally related to obesity per se. Other researchers haveshown that obesity relates to socioeconomic status (116) and race as well as tocommunity disadvantage in multilevel analyses (90). In short, obesity itself isnot an “upstream” determinant of health but rather an intermediary physiologicaloutcome affected by the whole spectrum of cultural, economic, social, and otherforces, as well as genes, that jointly influence human caloric intake and energyexpenditure.

Modeling

Statistical models are increasingly used to attribute health outcomes to multiple riskfactors. For example, in the INTERHEART study authors recently assessed ninevariables related to the risk of acute myocardial infarction in 52 countries (95,115). From data collected using a case-control design, incremental population-attributable risks (PAR) were calculated using logistic regression models. Afteradjustment for confounders, the top three population-level risk factors in all theregions of the world were (a) the ratio of blood apolipoproteins, with a PARof 49.2% (95% confidence interval: 43.8%–54.5%); (b) current or former smok-ing status, 35.7% (32.5%–39.1%); followed by (c) psychosocial factors, 32.5%(25.1%–40.8%). Altogether, the nine risk factors were associated with 90.4%(88.1%–92.4%) of the risk for myocardial infarction.

However, difficulties arise in interpreting these PARs. First, the total of allthe attributable fractions, as is usual in multifactorial disease, adds to more than100%, due to complex interactions between factors, presenting a credibility hurdleto policy makers.

Second, the risk factors modeled were at various levels in the “web” of mul-tistep causal pathways that lead from truly exogenous forces acting in society—such as cultural and economic drivers of smoking, eating and physical activitypatterns, and individual, “intraorganismal”-level factors, such as blood pressureand lipid levels. Furthermore, these latter sorts of risk factors are in turn themselvesa homeostatically controlled result of many different genes and aspects of environ-mental factors, many of which operate differentially over the life-course and arepoorly understood. To their great credit, however, the investigators do compare their52 study settings around the world for their relative degree of urbanization andhypothesize that this is a key “upstream driver” of the new coronary heart diseasepandemic engulfing low- and middle-income countries.

Summary Measures

Building on data in life tables, Summary Measures of Population Health (SMPH)include information on mortality and morbidity. Joshua Milne presented the firstlife table with accurate data in 1815 (70). Life table methods were further developed

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44 ETCHES ET AL.

in the 1930s to explore how long individuals could be expected to work in theirlifetimes (58a). Multistate life tables in the 1970s integrated information on statesof health lying in between the pure presence and absence of a health condition,as well as the probability of moving between these states. The data for multistatelife tables ideally arises from longitudinal surveys, or may be calculated fromcross-sectional data using certain assumptions.

SMPH fall into two general categories: (a) health expectancies (HE) and(b) health gaps; many specific types have been summarized (45, 92). Health ex-pectancies go beyond purely mortality-based calculations of life expectancy, to de-termine how long people can expect to live without certain diseases or limitations ontheir normal activities. Health gaps choose a norm or goal for the population (suchas an average age at death of 75), and quantify the difference between the actualhealth of the population and that goal. Disability-adjusted life years (DALYs) re-main the most commonly used health gap measure since their first publication inthe World Bank’s 1993 World Development Report: Investing in Health (73, 107).

In general, methodological issues for SMPH arise from conceptual differencesin approach and variations in data sources, collection, and analysis (17). Method-ological challenges related to vital statistics and survey and administrative systemdata also apply here, since summary measures are built up from these databases.

Theoretically, SMPH can facilitate integration of the dimensions of populationhealth, from social determinants, through proximal determinants such as physio-logical risk factors, because they can be adjusted to show the impact of various riskfactor modifications, or hypothesized interventions, at different levels of intensity.Furthermore, impacts at different ages may be derived from the calculations in thelife tables. However, there is often difficulty finding enough high-quality epidemi-ological data for individuals in various subgroups for all required domains (7).Currently, most calculations that break down summary measures of populationhealth status into their contributing causes are done for rates of medically diag-nosed diseases, such as coronary heart disease mortality. For this reason, McDowellet al. (68) correctly assert that SMPH describe population health status but havelimitations in analytic studies examining the “whys” and the “hows”/processesthat create population health. They propose a classification of indicators of popu-lation health based on four applications: description, prediction, explanation, andevaluation, which should all be reflected in indicators of population health.

A Unifying Conceptual Framework

A framework for measuring population health assembles different types of indi-cators together, provides an integrated picture of health, and clarifies at what leveldifferent indicators are measuring population health. As Krieger (55, 56) suggests,a conceptual framework as part of a theory helps structure ideas to explain causalconnections between, within, and across specified domains. A clear conceptualframework for the selection and use of indicators may help point to the dimen-sions of population health of import, and lead to more balanced discussions about

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what indicators should be targeted by interventions in order to impact populationhealth. The authors offer the Canadian Institutes of Health Research—Instituteof Population and Public Health (CIHR-IPPH) conceptual framework for deter-minants of individual and population health (Figure 1) as a basis for organizingindicators of population health. Ideally, reports on population health would presentand integrate indicators from all seven key domains depicted in Figure 1.

PERSISTENT ETHICAL ISSUES

Early quantitative indicators of population health raised ethical issues of individu-als’ privacy and confidentiality, and the just distribution of resources, which persisttoday. Concern over ethical conduct by medical practitioners dates back to at least1700 BC, in the Code of Hammurabi, and the Hippocratic Oath in 460 BC (29).Early census enumerators were required to take an oath of secrecy (36). Legislationto protect individuals’ privacy now extends to surveys, administrative health data,and linkable databases used to construct indicators of population health, and canvary across jurisdictions. Other ethical questions, such as how to place appropri-ate value on different populations and health states, often become evident in thedevelopment of newer indicators (73). Recent papers examine the difficulties ofextending bioethical principles to the field of population and public health (14,53, 79). However, the developing literature on population and public health ethicspoints to some consensus on ethical principles that may guide the construction anduse of indicators of population health: justice, transparency, and effectiveness.

First, the principle of seeking justice must guide decisions about what to mea-sure, which is related to decisions about health goals and uses of indicators. Justicelooks different, and will be reflected in different types of indicators, under differentphilosophical traditions (41, 91). For example, measures that focus on health out-comes, such as quality-adjusted life years (QALYs), lend themselves to utilitariananalysis. Indicators of the equity of distribution of resources are necessary for aliberal rights assessment of justice. Indicators developed based on a community’sideas of “the good” would be supported by people with a communitarian approach.Some feminists would seek indicators of preferential support for those who pro-vide care (91). Ethicists are still working to articulate the underlying philosophicalbasis for a population health approach that balances resource allocation to addressthe worst off, with expenditures on the rest of the population (11, 14, 106b). Ahuman rights basis for a bioethics of population health is one option (12, 106b),though not without problems (79).

Bayer & Fairchild declare that a moral obligation exists to prevent avoidablesuffering and death, a proposition with which most people pursuing populationhealth would agree. Thus, indicators that present “preventable” fractions of diseasehighlight the ethical work to be done. Ethicists argue that for people to haveequitable health opportunities, important social determinants of health should beas much our concern as the differences in health arising from inequities in accessto health care (12, 14, 106b). Starting from agreed-upon obligations of certain

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46 ETCHES ET AL.

groups or authorities, to provide specific products or services may be less contested(79). Ethical measures of population health would then pointedly indicate whatobligations of whom remain unfulfilled. The literature on ethics and populationhealth also demands looking beyond state borders and creating internationallycomparable indicators (79, 106b).

Second, the principle of transparency must guide the development and use ofindicators of population health (22, 106). Insistence on transparency underlinesthe fundamental belief that people have a right to be involved in decisions affectingthem. Advocates for the first systems of civil registration stipulated that the datashould be available to the public—open to consultation by all citizens (26). Publiccommunication strategies must be considered. Early Bills of Mortality were pub-lished with poetry, such as a sonnet on the subject of death by William Wordsworth,which perhaps gave the publication more popular appeal (70). Once the GeneralRegister Office was established in London, weekly and quarterly bulletins wereused to heighten awareness of preventable deaths, while annual and decennialreports presented more rigorous scientific analysis (104).

Ideally, indicators promote information sharing in multiple directions. Advo-cates for knowledge transfer and exchange seek engagement of members of thepublic, policy-makers, and professionals, together with researchers, but barriers re-main (23). Attention must be paid to what public participates, with those least welloff included and the interests of powerful groups contained. A decision-makingprocess open to input from different affected subpopulations can recognize thescientific expertise required for developing population health indicators. Further-more, public participation to define public health problems and goals can increaseawareness of the social determinants of health and empower people to pressureauthorities to address them (18; http://www.healthy-canadians.ca). The ethicaldevelopment of population health indicators in the future will involve greater dia-logue with a broad range of stakeholders to reflect social values, as well as moralquestions of justice.

Finally, the principle of effectiveness suggested by Childress et al. (22) high-lights the ethical need to evaluate indicators (also see 53). Resources spent ondeveloping indicators of population health must be in proportion to the evidenceof expected benefits. Potential harms, such as infringement on an individual’s pri-vacy, are also weighed against the benefits. Evaluations of the development and useof indicators may assess whether a consultative, ethical approach itself promoteshealth (11, 53).

EVALUATION OF INDICATORS

Have indicators of population health, and their use, improved over time? Howshould this be assessed? One way to determine the quality of indicators may beto compare them to a set of ideal characteristics, such as those in Box 2. TheWorking Party on Community Health Data and Indicators in Denmark produced

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a template for evaluating indicators along these lines (112, 113). Few studieshave quantified the intelligibility of different indicators for different stakehold-ers. Since methods exist to assess routinely the “readability” of documents (67a),standard tests of “indicator intelligibility” could be developed for routine use. Theavailability of data and local capacity to perform data analysis—the feasibility ofconstructing an indicator—often take precedence over other qualities (63, 111a).However, indicators that are merely “feasibility-driven” or “available-data-driven”are less preferable than those with an underlying clear and widely accepted con-ceptual framework (86). A concept-driven selection process should result in moremethodologically sound indicators.

Box 2. Ideal indicators are■ Built on consensus■ Based on a conceptual framework■ Valid■ Sensitive■ Specific■ Feasible■ Reliable and sustainable■ Understandable■ Timely■ Comparable■ Flexible for use at different organizational levels

and include measures of all these aspects

■ Incidence and prevalence■ Central tendency (e.g., mean, median, etc.) and distribution■ Stratification by subpopulations

Another way to assess whether indicators have improved over time is to brieflyreview indicators actually used by, for example, societal decision-makers. Manuel& Goel (66) first suggested the following framework when summarizing the de-velopment of indicators in the health care sector. Also, see Table 1 for a summaryof the links between population health concepts, types of data, and indicators used.

The first indicators of population health were simple reports of information atthe population level. Reporting of infant mortality rates (IMR) and life expectanciesfrom the 1800s are relevant examples. Improvements after some time included thecalculation of age-standardized mortality rates. Currently, many countries calculatean extension of mortality rates, potential years of life lost (PYLL), by individualcauses, first suggested in the literature as an indicator to guide health planningby Romeder & McWhinnie in 1977 (93). Differences in registering practices ofpremature and especially very low-birth-weight infants (i.e., whether they arereported as live births or not) still affect international comparability of IMRs (77).

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48 ETCHES ET AL.

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Second, indicators of population health began to be mapped to frameworksin focused areas. For example, one can present a comprehensive picture of theprevalence of coronary heart disease (CHD), CHD mortality, DALYs lost due toCHD (81a), CHD risk factors, prevalence and CHD-related health services use,for males and females, rural and urban populations (5).

Third, indicators began to reflect the interwoven nature of population healthdomains (Figure 1) by more clearly creating connections between those domains.Examples of this approach include stratified reporting by subpopulations, suchas cardiovascular mortality by socioeconomic status, after adjusting for knownbehavioral risk factors (67). Current reporting on prevalence of diseases and riskfactors by “peer group” recognizes that multiple common community factors affectthe health of populations in similar ways (102a).

Finally, indicators are beginning to be used to explicitly address populationhealth goals. WHO’s 2000 World Health Report stands out in making this attemptusing SMPH, though data were lacking to properly execute the task worldwide(108). Even critics agree that the addition of health outcome data to the previouslypredominant focus on process indicators of health care systems is a desirable step(75). Because improving population health is the ultimate goal/use of indicators,this approach to the use of indicators does seem to be an improvement over earlierapproaches, although more rigorous methods are still required.

If successful use of indicators directs resources to public policies that improvepopulation health, then indicator evaluations logically need to examine policy im-plementation and their effects. Evans & Stoddart reviewed the uptake of populationhealth concepts in Canada in 2003. Lavis found that economists in particular, espe-cially those at high levels in public policy making, have not assimilated populationhealth concepts (60). In addition, a recent evaluation revealed that most Canadiansare largely unaware of population health concepts, such as the importance of non-biomedical determinants of health (19). The question remains, however, “Has theuse of population health indicators improved the health of populations?” Quanti-fying the differential impact of the use of various specific indicators of populationhealth may be a fruitful area for further research.

CONCLUSION

Population health indicators in common use today are built on mortality measuresinitiated in the 1500s, regular censuses started in the 1800s, civil registration ofvital statistics commencing in the 1850s, regular national surveys first initiatedin the 1950s, and health system and other administrative databases facilitatedby computer applications used on a wide scale since the 1960s. An explicit andcomprehensive conceptual framework, such as the one outlined in this chapter, willacknowledge the complexity of the determinants of health and clarify what is beingmeasured, thus promoting comparability of indicators and the use of appropriatestatistical models to analyze them. Finally, to improve indicators of population

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50 ETCHES ET AL.

health, both indicators and the frameworks from which they stem must be betterevaluated, to determine whether they effectively and efficiently serve their intendedpurposes.

The Annual Review of Public Health is online athttp://publhealth.annualreviews.org

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P1: JRX

February 11, 2006 21:44 Annual Reviews AR269-FM

Annual Review of Public HealthVolume 27, 2006

CONTENTS

EPIDEMIOLOGY AND BIOSTATISTICS

Effective Recruitment and Retention of Minority Research Participants,

Antronette K. Yancey, Alexander N. Ortega, and Shiriki K. Kumanyika 1

Measuring Population Health: A Review of Indicators, Vera Etches,John Frank, Erica Di Ruggiero, and Doug Manuel 29

On Time Series Analysis of Public Health and Biomedical Data,

Scott L. Zeger, Rafael Irizarry, and Roger D. Peng 57

The Promise and Pitfalls of Systematic Reviews, Patricia Dolan Mullenand Gilbert Ramırez 81

Hypertension: Trends in Prevalence, Incidence, and Control, Ihab Hajjar,Jane Morley Kotchen, and Theodore A. Kotchen 465

ENVIRONMENTAL AND OCCUPATIONAL HEALTH

Environmental Justice: Human Health and Environmental Inequalities,

Robert J. Brulle and David N. Pellow 103

Speed, Road Injury, and Public Health, Elihu D. Richter, Tamar Berman,Lee Friedman, and Gerald Ben-David 125

The Big Bang? An Eventful Year in Workers’ Compensation,

Tee L. Guidotti 153

Shaping the Context of Health: A Review of Environmental and Policy

Approaches in the Prevention of Chronic Diseases, Ross C. Brownson,Debra Haire-Joshu, and Douglas A. Luke 341

PUBLIC HEALTH PRACTICE

Health Disparities and Health Equity: Concepts and Measurement,

Paula Braveman 167

The Politics of Public Health Policy, Thomas R. Oliver 195

Vaccine Shortages: History, Impact, and Prospects for the Future,

Alan R. Hinman, Walter A. Orenstein, Jeanne M. Santoli,Lance E. Rodewald, and Stephen L. Cochi 235

What Works, and What Remains to Be Done, in HIV Prevention in the

United States, David R. Holtgrave and James W. Curran 261

vii

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February 11, 2006 21:44 Annual Reviews AR269-FM

viii CONTENTS

SOCIAL ENVIRONMENT AND BEHAVIOR

A Public Health Success: Understanding Policy Changes Related to Teen

Sexual Activity and Pregnancy, Claire D. Brindis 277

An Ecological Approach to Creating Active Living Communities,

James F. Sallis, Robert B. Cervero, William Ascher, Karla A. Henderson,M. Katherine Kraft, and Jacqueline Kerr 297

Process Evaluation for Community Participation, Frances Dunn Butterfoss 323

Shaping the Context of Health: A Review of Environmental and Policy

Approaches in the Prevention of Chronic Diseases, Ross C. Brownson,Debra Haire-Joshu, and Douglas A. Luke 341

Stress, Fatigue, Health, and Risk of Road Traffic Accidents Among

Professional Drivers: The Contribution of Physical Inactivity,

Adrian H. Taylor and Lisa Dorn 371

The Role of Media Violence in Violent Behavior, L. Rowell Huesmann andLaramie D. Taylor 393

HEALTH SERVICES

Aid to People with Disabilities: Medicaid’s Growing Role,

Alicia L. Carbaugh, Risa Elias, and Diane Rowland 417

For-Profit Conversion of Blue Cross Plans: Public Benefit or Public Harm?

Mark A. Hall and Christopher J. Conover 443

Hypertension: Trends in Prevalence, Incidence, and Control, Ihab Hajjar,Jane Morley Kotchen, and Theodore A. Kotchen 465

Preventive Care for Children in the United States: Quality and Barriers,

Paul J. Chung, Tim C. Lee, Janina L. Morrison, and Mark A. Schuster 491

Public Reporting of Provider Performance: Can Its Impact Be Made

Greater? David L. Robinowitz and R. Adams Dudley 517

Health Disparities and Health Equity: Concepts and Measurement,

Paula Braveman 167

INDEXES

Subject Index 537

Cumulative Index of Contributing Authors, Volumes 18–27 565

Cumulative Index of Chapter Titles, Volumes 18–27 570

ERRATA

An online log of corrections to Annual Review of Public Healthchapters may be found at http://publhealth.annualreviews.org/

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Documents

MEASURING POPULATION HEALTH: A Review of Indicators€¦ · Toronto, Ontario, Canada M5G 1L5; email: [email protected] Erica Di Ruggiero Canadian Institutes of Health Research,