1. A Global Comparative Study of Religiosity and Health
Zachary Zimmer (Mount Saint Vincent University, Canada)
Invited lecture presented at the Expert Programme, Duke-NUS Medical School, Singapore, on August 3, 2018.
This research presented in this presentation was supported by a grant from the John Templeton Foundation (grant number 57521). Dr. Zimmer also acknowledges the support of the Social Sciences and Humanities Council of Canada through the Canada Research Chair program.

2. Religion is one of the oldest recognized social determinants of health
Thomas Malthus:
Emile Durkheim:

3. Many studies have looked at how religion and health are interrelated
Many studies have looked at how religion and health are interrelated. Review articles have summarized this research (e.g., Ellison & Levin, 1998; Harold G. Koenig, 2012; Krause, 2011; Larson, Swyers, & McCullough, 1998; Lavretsky, 2010; Levin & Chatters, 2008; Moreira‐Almeida, 2013; Seybold & Hill, 2001; Zimmer et al., 2016). The overwhelming conclusion is that religion is, on balance, salutary.

4. Mechanisms
Religion
Health

5. Linking spirituality and religiosity to life and health expectancy: A global comparative study

6. Overlooked issues
1. To what extent is the association between religion and health globally contextual?
Inglehart (2010) “Faith and freedom: traditional and modern ways to happiness”.
- Diversity (Choice)
- Tolerance (Restriction)
- Development (Range)
- Governance (Newness)
2. Which indicators of religiosity predict health?
- Action / behavior
- Belief / philosophy
- Contemplation

7. Research question
How does the religiosity health association differ across countries and indicators of religiosity?
Hypothesis: based on a majority of research, religion should be associated with good health
Participation is indicator
Research conducted in the United States

8. Data: World Values Survey
93 countries.
Covers 86% of global population.
Data from the most recent relevant wave ( )
N: 121, 770.

9. Measures Participation How often do you attend?
All indicators normalized to have a mean of zero and std dev of one.
Health
All in all, how would you describe your state of health these days:
Ordered response:
- Very good
- Good
- Fair
- Poor
Importance
How important is god in your life?
Meaning
How often do you ponder the meaning of life?

10. Analysis 1
93 country-specific ordered logit regressions LNθ(health < k) = intercept < k + religiosity + age + age2 + sex

11. Country-specific log odds: Participation*
5 countries negative and significant to p < .05
64 countries non-significant at p < .05
* Controlling for age, age-squared and sex. Country names are abbreviated.

12. Country-specific log odds: Importance*
64 countries non-significant at p < .05
17 countries negative and significant to p < .05
12 countries positive and significant to p < .05
* Controlling for age, age-squared and sex. Country names are abbreviated.

13. Country-specific log odds: Meaning*
21 countries negative and significant to p < .05
48 countries non-significant at p < .05
24 countries positive and significant to p < .05
* Controlling for age, age-squared and sex. Country names are abbreviated. Rwanda omitted because it is an outlier.

14. Log odds and confidence intervals in selected countries across indicators*
Point estimate
Lower CI
Upper CI
* Controlling for age, age-squared and sex. Country names are abbreviated.

15. Conclusion: There is a huge amount variation across countries and substantial variation across measures. Why? Analysis 2: Multi-level model

16. Introduce country-level measures
Operationalized
Variable range
Indicates
Diversity
Simpson’s Diversity Index
0 – least diversity
1 – most diversity
Religious choice.
Tolerance
Pew measure of government restrictions on religious practice
0 – least restricted
1 – most restricted
Restriction.
Development
HDI
0 – least developed
1 – most developed
Range of activities.
Governance
Current or former communist government
0 – non communist
1 - communist
Newness.

17. Multi-level model Combine data across countries 4 components:
Individual-level effects: Religiosity measure; Age; Age2, Sex
Country-level effects: Diversity, Restriction, HDI, Communism
Cross-level interaction: Religiosity X Country-level effects
4. Random effect: Intercept & Slope

18. Multi-level model tests whether…
the preponderance of the relationship between religiosity and health is positive
and
the magnitude of the relationship differs across contexts, i.e., countries

19. Summary of multilevel models predicting health
Religiosity indicator
Participation
Importance
Meaning
1. Religiosity indicator
n.s. +
2. Country effects
Diversity
Restrictions
Human development index + 
Communist governance
3. Cross-level interactions
Religiosity X Diversity
+  
Religiosity X Restrictions
n.s. 
Religiosity X HDI
Religiosity X Communism
4. Random slope
.006***
.020***
.014***
*Models control for age, age-squared and sex and include a random intercept.

20. Summary of multilevel models predicting health
Religiosity indicator
Participation
Importance
Meaning
1. Religiosity indicator
n.s. +
2. Country effects
Diversity
Restrictions
Human development index + 
Communist governance
3. Cross-level interactions
Religiosity X Diversity
+  
Religiosity X Restrictions
n.s. 
Religiosity X HDI
Religiosity X Communism
4. Random slope
.006***
.020***
.014***
*Models control for age, age-squared and sex and include a random intercept.

21. Summary of multilevel models predicting health
Religiosity indicator
Participation
Importance
Meaning
1. Religiosity indicator
n.s. +
2. Country effects
Diversity
Restrictions
Human development index + 
Communist governance
3. Cross-level interactions
Religiosity X Diversity
+  
Religiosity X Restrictions
n.s. 
Religiosity X HDI
Religiosity X Communism
4. Random slope
.006***
.020***
.014***
*Models control for age, age-squared and sex and include a random intercept.

22. Country-level measures for selected countries
Diversity
Restriction
HDI
Communism
China
.28
.91
.72 1
Taiwan
.80
.09
.89
Ethiopia
.52
.46
.37
Pakistan
.11
.64
.53
Singapore
.83
.66

23. Predicted probability of having very good health by low and high levels of religiosity in selected countries

24. Predicted probability of having very good health by low and high levels of religiosity in selected countries

25. Predicted probability of having very good health by low and high levels of religiosity in selected countries

26. Conclusion
There is wide variation in the association between religiosity and health.
The variation depends on context and indicator.

27. What might be going on?
1. Religious participation is good for you in places where there is a lot of religious diversity (e.g., Singapore). Where there is choice, we can choose what is best for us.
2. Importance of god is good for you in places with low levels of development (e.g., Ethiopia). Belief is important in the face of poverty.
3. Where there are restrictions on practice, and low levels of development, contemplation is most useful. It is better to express religiosity inwardly.
4. No expressions of religiosity are helpful in communist and post communist countries. Religion is new and
adherents may be those most in need of help.

28. Where is religion good for health?
Greater religious diversity
Participation
(outward action)
Non-communist
Lower economic development
Belief
(importance)
Greater restrictions
Meaning
(contemplation)
Lower economic development

29. Log odds and confidence intervals *
Point estimate
Lower CI
Upper CI
* Controlling for age, age-squared and sex. Country names are abbreviated.

30. Acknowledgements
This research was supported by a grant from the John Templeton Foundation (grant number 57521).
I acknowledge the support of the Social Sciences and Humanities Council of Canada through the Canada Research Chairs program.

31. Thank you