Predictors of Inactivity: An Analysis of the Ontario Health Survey

Predictors of Inactivity: An Analysis of the Ontario Health SurveyAuthor(s): Kenneth R. AllisonSource: Canadian Journal of Public Health / Revue Canadienne de Sante'e Publique, Vol. 87, No.5 (SEPTEMBER / OCTOBER 1996), pp. 354-358Published by: Canadian Public Health AssociationStable URL: http://www.jstor.org/stable/41993821 .

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Predictors of Inactivity: An Analysis

of the Ontario Health Survey

Kenneth R. Allison, PhD1

Increasing empirical evidence supports the role of physical activity in disease prevention and health promotion. Regular physical activity and fitness have been shown to be important in the reduction of all-cause mortality1 and lower rates of such diseases and conditions as coronary heart disease, stroke, diabetes, certain types of cancer, hypertension and obesity.1"3 Physical activity has also been shown to contribute to mental health and enhanced feelings of well-being.4"6 An important public health implication of these findings is the development of policies and programs to promote physical activity, thus contributing to an improvement in the health status and longevity of the population. It is now widely believed that, in terms of disease prevention, moving the most sedentary (inactive) group of the population to a moderate level of physical activity would have the greatest impact.7

Numerous reports in Canada have docu- mented patterns of physical activity participation and some of the primary factors associated with it.8"11 The purpose of this paper is to develop a profile of the inactive population in Ontario. The identification of factors predictive of inactive status will indicate those population groups in greatest need of interventions and support.

1. Career Scientist of the Ontario Ministry of Health, Health Research Personnel Development Program, School of Physical and Health Education and Department of Behavioural Science, University of Toronto.

Correspondence and reprint requests: K.R. Allison, Ph.D., School of Physical and Health Education, University of Toronto, 320 Huron Street, Toronto, Ontario, M5S 3J7, Tel: 416-978-6954. This paper is based partly on a report prepared by the author for the Population Health Service, Public Health Branch, Ontario Ministry of Health.

METHODS

Survey design, data collection, and measures used

The Ontario Health Survey, conducted January to December, 1990, utilized a multi-stage stratified cluster sample in order to obtain data for both provincial and health unit level analysis.12 In the first stage of data collection, an interview was conducted with one respondent from each selected household concerning the health status of all household members. The second stage involved a self-administered questionnaire completed by all members of the household aged 12 or over. The response rate for the interview phase of the survey was 87%, while the response rate for the self-administered questionnaire phase was 77%. The current paper is based on analysis of the 49,164 respondents completing the questionnaire.

For purposes of this paper, Physical Activity Index was used as a measure of leisure time physical activity status. This measure is similar to the one used in the Canada Fitness Survey 8 which was based on the Minnesota Leisure Time Activity Questionnaire.13 The questionnaire item used to construct the Physical Activity Index asked the question, "Have you participated in the following physical activities during the last month?" For a list of 20 activities, respondents indicated the number of times (frequency) and time spent on each occasion (duration) for each activity participated in. Using information on the type, frequency, and duration of combined leisure activities, energy expenditure was calculated. In order to estimate the intensity of activity for this calculation, values for the metabolic energy cost (MET) of specific activities were obtained, based on the

354 REVUE CANADIENNE DE SANTÉ PUBLIQUE VOLUME 87, NO. 5

ABSTRACT

This paper develops a profile of the inactive population in Ontario. The findings were based on an analysis of data from the 1990 Ontario Health Survey. A Physical Activity Index, including frequency, duration, and an estimate of intensity, was used to classify individuals on the basis of their leisure time physical activity levels. Inactives comprised the group estimated to average less than 1.5 kcals/kg/day of energy expenditure. Multiple logistic regression was used to estimate the odds of inactivity for a number of predictor variables. Age, gender, friends' participation, perceived health status, and perceived future health problems were the strongest predictors of physical inactivity. The findings contribute to a further under- standing of the factors influencing physical activity participation and promotion.

ABRÉGÉ

Cet article présente un profil de la population inactive en Ontario. Les résultats sont basés sur une analyse des données provenant de l'Enquête sur la santé en Ontario de 1990. Un indice de l'activité physique - fréquence, durée et estimation de l'intensité - a servi à classer les individus selon leurs niveaux d'activité et de temps libre. Les individus inactifs constituent le groupe dont on estime que la dépense moyenne d'énergie est inférieure à 1,5 kcals/kg/jour. Une régres- sion logistique multiple a servi à évaluer la probabilité d'inactivité en fonction d'un certain nombre de variables explicatives. L'âge, le genre, la participation des amis, l'état de santé perçu et les problèmes de santé anticipés sont apparus comme les principaux facteurs de prédiction de l'inactivité physique. Les résultats aident à mieux com- prendre les facteurs qui encouragent la participation à des activités physiques.



INACTIVITY

protocol used in the Canada Fitness Survey.8,14 Physical Activity Index consists of the energy expenditure of combined activities. Respondents were classified as Active, Moderate, or Inactive. An Active level included those who averaged 3.0+ kcal/kg/day of energy expenditure (com- patible with the amount needed for cardio- vascular health benefits). A Moderate level included those who averaged 1.5-2.9 kcal/kg/day (a level which may produce some health benefits, but possibly not car- diovascular). The Inactive level included those with an energy expenditure below 1.5 kcal/kg/day.14 The set of predictor variables used in the

analysis consist of the following: age group, gender, level of household income, major activity, household type, number of health problems, visits to a health professional, perceived health status, body mass index zones, smoking type, future health problems as a result of present physical activity levels, and friends' participation. The coding categories for these variables appear in the OHS documentation.12

ANALYSIS

Following bivariate analysis of the rela- tionship between Physical Activity Index and the predictor variables, logistic regression analysis was used to examine the multivariate relationships between physical activity and sets of the independent (predictor) variables. Logistic regression allows one to directly estimate the probability of an event or condition occurring (active versus inactive status), using the maximum likelihood method. 1 5

For the logistic regression analysis, Physical Activity Index was recoded into a dichotomous dependent variable with Actives and Moderate Actives coded as Actives (32.6% of respondents), and Inactives (67.4% of respondents) remain- ing as an intact category. Indicator coding was used for categorical independent variables with the reference categories selected on the basis of their activity status. Thus, the estimated odds of inactivity for categories of the independent variables were in comparison to the most active category determined in the earlier bivariate analysis. The Wald statistic was used to estimate the

TABLE I Physical Activity Index by Selected Predictor Variables

Ontario, Age 1 2 and Over, 1 990 Variable Moderate

Active Active Inactive Number Assoc. Chi Square Age (years) Ga=.364 2084.38* 12 to 15 55.1 21.1 23.7 2733 16 to 19 36.6 20.5 42.8 3188 20 to 24 20.7 21.1 58.1 3646 25 to 29 13.7 18.1 68.2 4187 30 to 34 12.0 17.2 70.8 4523 35 to 39 11.3 15.1 73.6 4227 40 to 44 9.1 13.8 77.1 3936 45 to 49 9.6 14.6 75.8 2904 50 to 54 6.1 13.5 80.5 2450 55 to 59 7.4 14.6 78.1 2087 60 to 64 7.5 16.4 76.1 1863 65 to 69 7.5 15.4 77.1 1900 70 to 74 5Z 14.9 79.4 1108 75 to 79 - 14.4 80.6 803 80+ - 8.4 89.5 513 Gender Phi=.143 279.67* Male 20.4 18.5 61.0 19,757 Female 11.4 15.0 73.6 20,309 Household Income Ga=-.145 83.49* $50,000 or higher 18.2 17.9 64.0 17,549 Less than $50,000 13.7 16.1 70.2 13,167 Low¥ 11.3 15.0 73.7 4003 Major Activity Phi=.345 1 626.44* Going to school 40.7 21.0 38.3 7068 Looking for work 17.7 19.4 62.9 518 Working 12.0 16.3 71.8 23,191 Housekeeping 6.0 12.4 81.7 4248 Retired 6.9 16.5 76.5 4293 Other M HZ 80.5 623 Household Type Phi=.141 272.80* Single parents, children 25.0 18.7 56.3 1453 Head, spouse, depch 20.1 16.6 63.3 16,572 Head, spouse, nodep 12.9 16.4 70.7 5604 Unattached ind. alone 12.2 18.9 68.9 3238 Unattached ind. tog. 19.7 21.9 58.4 1371 Head, spouse, deps. 16.2 13.5 70.3 1213 Head and spouse 9.3 15.6 75.2 8383 Other family types 13.5 16.1 70.3 1959 Single parent variation 14.9 21.0 64.1 274 Friends' Participation Ga=.488 1811.08* All of them 53.7 21.0 25.3 1291 Most of them 34.8 24.5 40.7 5975 Half of them 17.3 22.4 60.3 6956 A few of them 10.1 14.5 75.3 19,861 None of them 5.7 8.5 85.8 4971 Future Health Problems Ga=-.355 750.17* Very unlikely 24.7 20.7 54.6 14,253 Somewhat unlikely 14.0 17.6 68.3 13,042 Somewhat likely 7.3 11.9 80.7 9408 Very likely 9.9 9.8 80.2 2549 Perceivea Health Ga=.289 512.41* Excellent 25.1 20.3 54.6 8953 Very good 16.4 17.8 65.8 15,936 Good 11.0 14.4 74.6 11,332 Fair 6.7 11.0 82.2 2942 Poor - 9^9 85.3 737 BMI Category Ga=.194 290.99* Zone A 26.9 17.1 55.9 4995 ZoneB 16.2 17.3 66.5 18,188 Zone C 14.4 19.2 66.4 5904 Zone D 9.9 14.2 75.9 9024 Number of Health Problems Ga=.1 32 1 33.04* None 19.5 17.9 62.6 12,928 One 16.2 16.5 67.3 11,960 Two 13.2 17.2 69.6 7331 Three 13.1 16.2 70.7 4044 Four 11.8 14.4 73.8 1940 Five or more 9.6 11.2 79.2 1864 Type Smoker Ga=.151 189.03* Never smoked 19.7 16.8 63.5 18,912 Former smoker 11.5 17.5 71.0 8317 Occasional smoker 17.7 21.1 61.2 2028 Daily smoker 11.6 14.7 73.7 9232 * p<0.0001 - Not releasable due to high sampling variability. Underlined values are qualified, interpret with caution. Cases are weighted by DYV; Statistical analysis is based on the effective sample size to adjust for the design effect. Number of missing observations range from 9097 to 14,444. ¥ The designation low income was based on household income, family size, and area of residence.

SEPTEMBER - OCTOBER 1996 CANADIAN JOURNAL OF PUBLIC HEALTH 355



INACTIVITY

TABLE II Odds of Inactive Status for Selected Categories of the Predictor Variables

Ontario, Age 1 2 and Over, 1 990 Variable Wald Odds 95% CI Age (years) 145.86* 1 2 to 15 1 .0 Reference 16 to 19 1.8 1.4,2.4 20 to 24 2.5 1.9,3.4 25 to 29 3.7 2.7,5.2 30 to 34 4.0 2.8,5.5 35 to 39 4.6 3.3, 6.4 40 to 44 5.3 3.7, 7.4 45 to 49 4.8 3.3, 6.8 50 to 54 5.9 4.1,8.6 55 to 59 6.1 4.1,9.0 60 to 64 6.4 4.2, 9.8 65 to 69 7.0 4.5,11.1 70 to 74 8.2 4.9,13.8 75 to 79 9.5 5.3,17.0 80+ 16.0 7.6,34.0 Gender 118.86* Male 1.0 Reference Female 1.8 1.6,2.0 Household Income 24.40* $50,000 or more 1 .0 Reference Less than $50,000 1.2 1.1,1.4 Low ¥ 1.5 1.3,1 .8 Major Activity 1 8.66$ School 1 .0 Reference Looking for work 1.1 0.7, 1.7 Working 1.3 1.1,1.6 Housekeeping 1 .3 1 .0, 1 .7 Retired 0.8 0.6,1.2 Other 1 .5 0.9, 2.4 Household Type 38.80* Single parent, children 1.0 Reference Head, spouse, depch 1 .5 1 .2, 2.0 Head, spouse, nodepch 1 .5 1.1, 2.0 Unattached ind. alone . 1.0 0.8,1.4 Head, spouse, deps. 2.1 1.4, 3.1 Head and spouse 1 .5 1 .1 , 2.0 Other family types 1.3 0.9,1.8 Single parent variation 1 .5 0.8, 2.8 Friends' Participation 431 .84* All of them 1.0 Reference Most of them 1 .4 1 .0, 2.0 Half of them 2.5 1.8,3.4 A few of them 4.3 3.1,5.8 None of them 7.4 5.3,10.5 Future Health Problems 21 5.79* Very unlikely 1.0 Reference Somewhat unlikely 1.5 1.4,1.7 Somewhat likely 2.7 2.1,3.3 Very likely 2.6 2.0, 3.2 Perceived Health 63.51* Excellent 1.0 Reference Very good 1 .3 1 .2, 1 .5 Good 1.7 1.5,1.9 Fair 1.9 1.7,2.2 Poor 2.3 1.5,3.6 BMI Category 10.82§ Zone A 1 .0 Reference Zone B 1 .0 0.6, 1 .5 Zone C 0.8 0.6, 0.9 Zone D 1.0 0.8,1.1 Number Health Problems 1 1 .25§ None 1 .0 Reference One 1.0 0.9,1.1 Two 0.9 0.8, 1 .0 Three 0.8 0.7, 0.9 Four 0.7 0.6, 0.9 Five or more 0.9 0.7, 1 .1 Type Smoker 13.05$ Never smoked 1 .0 Reference Former smoker 0.8 0.7, 1 .0 Occasional smoker 0.8 0.6, 0.9 Daily smoker 1.0 0.9,1.1 Visits to Health Professional H 6.26§ * pcO.0001, t p<0.001, t pcO.OI, § p<0.05 Number of cases included in the analysis = 29,232 Cases are weighted by DW; Statistical analysis is based on the effective sample size to adjust for the design effect. ¥ Tne designation low income was based on household income, family size, and area of residence. H Treated as a continuous variable, with no categories.

statistical significance of predictor variables. Weighted data were used in the analysis for this paper.12 In order to account for the design effect of the Ontario Health Survey, the effective sample size was used as the basis for statistical tests.

RESULTS

Table I shows a summary of the bivariate relationships between Physical Activity Index and the predictor variables included in the analysis. All of these were statistical- ly significant, although there was consider- able variation in the relative strength and direction of the relationships.

For the logistic regression analysis, variables were entered in the following blocks: background (age group, gender); socioeco- nomic status (household income, major activity); situational (household type); social (friends' participation); and health- related (BMI category, smoking status, number of health problems, perceived health status, future health problems, visits to a health professional). The findings reported here represent the final model of the logistic regression analysis, with all of the blocks of variables entered.

The analysis confirmed the major role of age as a predictor of activity status. The odds of inactivity increase with age, with the greatest odds of inactive status in the oldest age groups (Table II). Thus, one of the central characteristics of the Inactives is their increased age. Also, the odds of being inactive are 1.8 times greater for females than for males (Table II).

Those in low income categories have a greater probability (odds 1.5) of being inactive than those with family incomes over $50,000 (Table II). In terms of major activity, those currently working or housekeeping had somewhat greater odds of being inactive than students (the reference category).

Household type was also shown to be related to inactive status. Compared to households comprised of a single parent with child(ren), the odds of inactivity were greater among respondents living in households with a head and spouse with dependent children, a head and spouse without dependent children, a head and spouse




INACTIVITY

with other dependents, and a head and spouse (alone) (Table II).

Those who have few or no friends participating have greater odds of inactivity. The greatest odds of inactive status (7.4) are among those with no friends participating, but the odds of inactivity are also great for those with a few (odds 4.3) or half (odds 2.5) of them participating (Table II).

Some of the health-related factors (body mass index categories, smoking status, and number of health problems) had little effect on increasing the odds of inactive status when taking into account the other variables in the model. However, for other health-related variables, there were important increases in the odds of inactivity for certain categories. The odds of inactive status were higher for those who believed that it was very likely (odds 2.6) or somewhat likely (odds 2.7) that they would develop health problems later in life as a result of their current activity level, compared to those who believed this to be very unlikely (Table II). The odds of being inactive were also higher for those perceiving themselves to be in poor (odds 2.3), fair (odds 1.9), or even good (odds 1.7) health, compared to those believing themselves to be in excellent health (Table II).

DISCUSSION AND IMPLICATIONS

The findings suggest that background characteristics, social context, social structure, and health-related perceptions all influence physical activity participation status. Age differences in physical activity are one of the clearest and most important findings. The increase in the proportion of Inactives associated with age is at least partly explainable by physical and biological changes associated with ageing,16 but some of the increase in the proportion of Inactives associated with age can be attrib- utable to changes in life circumstances over time. Since the health benefits of regular physical activity are now more widely accepted, the promotion of activity across the life span is of particular importance.

The greater odds of inactivity for females may be partly due to additional barriers to physical activity, attached to their multiple time and role demands in the workplace and at home (for some), or to fewer oppor-

tunities for women to engage in physical activity in school or the general communi- ty.17 The need to increase opportunities for women to engage in physical activity needs to be addressed in programs and policies.

The greater odds of being inactive among those with lower incomes may be partly due to less discretionary time or a lack of financial resources to devote to leisure pursuits. Underlying structural factors in society may make physical activity and other health-related behaviours diffi- cult or impossible to perform. Financial hardship, occupational frustration and alienation, and uncertainty are all affected by social structure. In a sense inactivity by certain individuals or groups is under- standable, given the concrete conditions of their lives.18 Policies and programs to promote physical activity need to recognize and address the barriers to increased physical activity.

One of the strongest predictors of physical activity status in the OHS was friends' participation. The social element of participation, while operational for all age groups and both sexes, has a particularly strong predictive effect with girls and women. These findings offer some empirical support for programs and policies emphasizing social influences and social support.19'21 However, as one of the anony- mous reviewers of this paper points out, social influences do not necessarily prompt decisions regarding physical activity. It may be that like-minded individuals (those not engaging in physical activity) tend to become friends.

Perceived susceptibility, as measured by the likelihood of future health problems as a result of present levels of physical activity, is another strong predictor of physical activity status. Believing that one will develop health problems in the future and present inactivity are related. This indicates that many individuals aware of the health implications of inactivity are not acting on this knowledge.

Physical inactivity was associated with poor health status among respondents. However, the causal direction of this rela- tionship is unspecified. Those believing they are in poor health may be unlikely to engage in physical activity, for many rea- sons (illness, physical limitations).

Conversely, those who are inactive may be unlikely to feel they are in good health -

they may feel poorly, partly due to a lack of regular activity.

The cross-sectional design of the Ontario Health Survey presents limitations as to the interpretation of results. Also, the results need to be interpreted with some caution, since item non-response for the underlying question on physical activity participation was 18.5%. Non-response for this item was higher among older individuals and females. Thus, it is possible that the extent of inactivity was underestimated in those groups. Finally, the analysis did not include non-leisure physical activity. H owever, the inclusion of usual daily activity (sitting, standing/walking, light loads, heavy work) into the logistic regression does not diminish the effects of the original predictor variables.

Additional studies of physical activity among specific sub-groups in the population (adolescents, women, older adults) are needed for more in-depth investigations of physical activity and the factors influencing it. In addition to cross-sectional designs, longitudinal studies, experimental studies, and qualitative studies of the physical activity patterns and life experiences of these groups are needed. Information derived from these studies will further develop the knowledge base concerning physical activity participation and its promotion.

ACKNOWLEDGEMENTS

Thanks to Dr. Joan Hurlock, formerly of the Population Health Service, Public Health Branch, Ontario Ministry of Health, and Dr. Ed Adlaf of the Addiction Research Foundation (Ontario).

REFERENCES 1. Blair S, Kohl H, Paffenbarger R, et al. Physical

fitness and all-cause mortality: A prospective study of healthy men and women. J Am Med Assoc 1989;262:2395-401.

2. Powell K, Thompson P, Casperson C, Kendrick J. Physical activity and the incidence of coronary heart disease. Annu Rev Public Health 1987;8:253-87.

3. Shephard R. Aerobic Fitness and Health. Champaign: Human Kinetics, 1994.

4. McCauley L, Courneya K. belt efhcacy relationships with affective and exertion responses to exercise. J Appi Soc Psychol 1992;22:312-26.

SEPTEMBER - OCTOBER 1996 CANADIAN JOURNAL OF PUBLIC HEALTH 357



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5. Stephens T. Physical activity and mental health in the United States and Canada: Evidence from four population surveys. Prev Med 1 988; 17:35- 47.

6. Leith L. Foundations of Exercise and Mental Health. Morgantown, W.V.: Fitness Information Technology, 1994.

7. Haskell W. Physical/physiological/biological outcomes of physical activity, in: Quinney H, Gauvin L, Wall E. (Eds.), Toward Active Living: Proceedings of the International Conference on Physical Activity, Fitness , and Health. Champaign: Human Kinetics, 1994.

8. Fitness and Lifestyle in Canada. Ottawa: Canada Fitness (distributed by the Canadian Fitness and Lifestyle Research Institute), 1983.

9. Stephens T, Craig C. The Well-Being of Canadians: Highlights of the 1988 Campbell's Survey. Ottawa: Canadian Fitness and Lifestyle Research Institute, 1990.

10. Health and Welfare Canada. Stephens T, Fowler G. (Eds.), Canada's Health Promotion Survey, 1990: Technical Report. Ottawa: Minister of Supplies and Services, 1993.

1 1 . Allison K. Physical Activity in Ontario: A Report on the Ontario Health Survey. Ontario Ministry of Health and Premier's Council on Health, Well-Being, and Social Justice, Ontario Health Survey 1990 Working Paper No. 9, 1995.

12. Ontario Ministry of Health. Ontario Health Survey 1990: Users Guide Vol. 1 Documentation, and Vol. 2 Microdata Manual, 1992.

13. Taylor H, Jacobs D, Schucker B, et al. A questionnaire for the assessment of leisure time physical activities./ Chron Dis 1978;31:741-55.

14. Stephens T, Craig C, Ferris B. Adult physical activity in Canada: Findings from the Canada Fitness Survey. Can J Public Health 1986;77:285-90.

15. Tabachnick B, Fidell L. Using Multivariate Statistics, Second Edition. New York: HarperCollins, 1989.

16. Orban W. Active living for older adults: A model for optimal active living, in: Quinney H, Gauvin L, Wall E. (Eds.), Toward Active Living. Champaign: Human Kinetics, 1994.

17. Yoshiďa K, Allison K, Osborn R. Social factors influencing perceived barriers to physical exercise among women. Can J Public Health 1988;79:104-8.

18. Allison K. Theoretical issues concerning the rela- tionship between perceived control and preven- tive health behaviour. Health Education Research: Theory and Practice 1 99 1 ;6: 1 4 1 -5 1 .

19. Botvin G, Eng A. A comprehensive school-based smoking prevention program. J School Health 1980;50:209-13.

20. Norem-Habeisen A, Hedin D. Influences on adolescent problem behavior: Causes, connec- tions, and contexts, In: Adolescent Peer Pressure: Theory, Correlates, and Program Implications for Drug Abuse Prevention , National Institute on Drug Abuse DDHS Publication No. (ADM) 83- 11252, Washington D.C.: U.S. Government Printing Office, 1988.

21. Fishbein M, Ajzen I. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, Mass.: Addison-Wesley, 1975.

Received: February 15, 1996 Accepted: July 2, 1996

BOOK REVIEWS / RECENSION

CJPH Book Review Editor passes baton

The CPHA Board of Directors and the editorial staff of the Canadian Journal of Public Health , on behalf of the readership of the Journal, would like to express our sincere thanks and great appreciation to Dr. Donald T. Wigle who has recently stepped down from his voluntary position as CJPH Associate Editor (Book Reviews) after 1 1 years of dedicated service. Thank you, Don, for all of your work and the time you have given to the Journal.

We welcome Dr. William D. Osei to the position of CJPH Associate Editor (Book Reviews). Dr. Osei is Provincial Epidemiologist with the Saskatchewan Department of Health and is responsible for analyzing the determinants of health of the Saskatchewan population, including surveillance of health and disease patterns in the province, and rec- ommending strategies and policies that focus efforts on outcomes and a wellness-oriented health system. Dr. Osei has a Master of Public Health in General Epidemiology from the University of Michigan, USA, and graduated from medical school at the University of Ghana in 1974.

We look forward to working with Dr. Osei and wish him well in his new responsibilities with the Canadian Journal of Public Health.

By way of introduction, Dr. Osei has provided a book review of "The Coming Plague: Newly Emerging Diseases in a World Out of Balance", which can be found on page 359.

Le rédacteur associé (recension)

passe la main

Au nom de tous les lecteurs de la Revue canadienne de santé publique , le Conseil d'admi- nistration de l'ACSP et le personnel de la rédaction de la Revue tiennent à exprimer leur sincère appréciation et tous leurs remerciements au docteur Donald T. Wigle au moment où il quitte ses fonctions bénévoles de rédacteur associé (recension) de la RCSP après onze années de consciencieux services. Un grand merci à vous, Don, pour tout le travail et tout le temps que vous avez consacrés à la Revue.

Nous souhaitons la bienvenue au docteur William D. Osei qui occupe désormais le poste de rédacteur associé (recension) de la RCSP. Le docteur Osei travaille comme épidémiologiste provincial au ministère de la Santé de la Saskatchewan; il est chargé d'une part de l'analyse des facteurs déterminants de la santé de la population en Saskatchewan, notamment la surveillance de l'évolution de la santé et des maladies dans la province et, d'autre part, de recommander des stratégies et des politiques dont les efforts soient axés sur les résultats et sur un système de santé orienté sur le mieux-être. Outre une maîtrise de santé publique en épidémiologie générale de l'Université du Michigan aux É.-U., le docteur Osei possède un diplôme de la faculté de médecine de l'Université du Ghana décroché en 1974.

Nous nous réjouissons à l'idée de travailler avec le docteur Osei et lui souhaitons le meilleur dans ses nouvelles fonctions pour la Revue canadienne de santé publique.

À titre d'introduction, le docteur Osei nous présente un compte rendu du livre "The Coming Plague: Newly Emerging Diseases in a World Out of Balance" que l'on peut lire à la page 359.




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Predictors of Inactivity: An Analysis of the Ontario Health Survey