Chapter 7 Population health approach
7.1 Introduction
In public health we see community health through a socioecological lens focused on complex adaptive systems and life course social networks. We are called upon to lead teams and organizations in making high stakes decisions in the setting of complex environments, limited information, multiple objectives, competing trade-offs, uncertainty, and time constraints. The emerging field of population health data science provides an integrative, dynamic approach to “data-driven decision making” that applies to community and health care systems. Our approach to population health has been heavily influenced by public health epidemiology.
First we start with some key definitions:
- Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity [18].
- Public health is what we, as a society, do collectively to assure the conditions in which people can be healthy [19].
- Population health is a systems framework for studying and improving the health of populations through collective action and learning [20].
- Data science is the art and science of transforming data into actionable knowledge [20].
- Population health data science is the art and science of transforming data into actionable knowledge to improve health [20].
Our definition of population health differs from the mainstream definition of population health as “the health outcomes of a group of individuals, including the distribution of such outcomes within the group” [21]. Our definition of population health emphasizes the following:
- complex adaptive socioecological systems
- life course health development
- inter-generational transmission of risks (social, epigenetic)
Our definition supports modern approaches to community health and health systems transformation [22]. In general, in public health we have four population health goals.
- Protecting and promoting health and equity.
- Transforming people and place
- Ensuring a healthy planet
- Achieving health equity
To make progress on these goals we focus on changing
- Policies: economic, social, environmental, and health
- Systems: economic, social, environmental, and health
- Enviroments: social, physical, and environmental
The basis for change is decision making; hence, the need for transforming data into actionable knowledge. We start by review data from an epidemiologic perspective.
7.2 Epidemiologic approach
Epidemiology is a basic science of public health, and is commonly defined as
The study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems [23].
Operationally, the epidemiologic approach, from problem definition to public health action is summarized here:
- Epidemiologic measures
- Public health surveillance
- Field investigations (e.g., outbreaks)
- Descriptive and analytic epidemiology
- Program and project evaluation
- Causal and statistical inference
- Evidence-based public health action
7.3 Epidemiologic analyses for 2-by-2 tables
7.3.1 Cohort studies with risk data or prevalence data
| Exposed | Unexposed | Total | |
|---|---|---|---|
| Cases | \(a\) | \(b\) | \(M_{1}\) |
| Noncases | \(c\) | \(d\) | \(M_{0}\) |
| Total at risk | \(N_{1}\) | \(N_{0}\) | \(T_i\) |
7.3.1.1 Risk difference
7.3.1.2 Risk ratio
7.3.1.3 Odds ratio
7.4 Epidemiologic analyses for stratified 2-by-2 tables
7.4.1 Cohort studies with binomial (risk or prevalence) data
| Exposed | Unexposed | Total | |
|---|---|---|---|
| Cases | \(a_i\) | \(b_i\) | \(M_{1i}\) |
| Noncases | \(c_i\) | \(d_i\) | \(M_{0i}\) |
| Total at risk | \(N_{1i}\) | \(N_{0i}\) | \(T_i\) |
7.4.1.1 Pooled risk difference with confidence limits
\[ RD_{MH}=\frac{\sum_i {\frac{a_i N_{0i}-b_i N_{1i}}{T_i}}} {\sum_i {\frac{N_{1i} N_{0i}}{T_i}}} \]
\[ \mathrm{Var}(RD_{MH}) = \frac{\sum_i \left(\frac{N_{1i} N_{0i}}{T_i}\right)^2 \left[ \frac{a_i c_i}{N_{1i}^2 (N_{1i}-1)} + \frac{b_i d_i}{N_{0i}^2 (N_{0i}-1)} \right]} {\left( \sum_i \frac{N_{1i} N_{0i}}{T_i} \right)^2} \]
rd.mh <- function(x, conf.level = 0.95) {
ai <- x[1, 1, ] # exposed cases
bi <- x[1, 2, ] # unexposed cases
ci <- x[2, 1, ] # exposed noncases
di <- x[2, 2, ] # unexposed noncases
N0i <- bi + di # total exposed (at risk)
N1i <- ai + ci # total unexposed
M0i <- ci + di # total cases
M1i <- ai + bi # total noncases
Ti <- ai + bi + ci + di # stratum total
RDmh <- (sum((ai * N0i - bi * N1i)/(Ti)))/(sum((N1i * N0i)/(Ti)))
numer <- ((N1i * N0i)/(Ti))^2 * (((ai * ci)/(N1i^2 * (N1i - 1))) +
((bi * di)/(N0i^2 * (N0i - 1))))
denom <- (sum((N1i * N0i)/(Ti)))^2
Z <- qnorm((1 + conf.level)/2)
SE.RDmh <- sqrt(numer/denom)
LL <- RDmh - Z * SE.RDmh
UL <- RDmh + Z * SE.RDmh
list(data = x, risk.diff = RDmh, conf.int = c(LL, UL))
}7.4.1.2 Pooled risk ratio with confidence limits
\[ RR_{MH}=\frac{\sum_i {\frac{a_i N_{0i}}{T_i}}} {\sum_i {\frac{b_i N_{1i}}{T_i}}} \]
\[ \mathrm{Var}[\log(RR_{MH})]= \frac{\sum_i \left(\frac{M_{1i} N_{1i} N_{0i}}{T_i^2} - \frac{a_i b_i}{T_i}\right)} {\left( \sum_i \frac{a_i N_{0i}}{T_i}\right) \left( \sum_i \frac{b_i N_{1i}}{T_i}\right)} \]
rr.mh <- function(x, conf.level = 0.95) {
ai <- x[1, 1, ] # exposed cases
bi <- x[1, 2, ] # unexposed cases
ci <- x[2, 1, ] # exposed noncases
di <- x[2, 2, ] # unexposed noncases
N0i <- bi + di # total exposed (at risk)
N1i <- ai + ci # total unexposed
M0i <- ci + di # total cases
M1i <- ai + bi # total noncases
Ti <- ai + bi + ci + di # stratum total
RRmh <- sum(ai * N0i/Ti)/sum(bi * N1i/Ti)
numer <- sum(M1i * N1i * N0i/Ti^2 - ai * bi/Ti)
denom <- sum(ai * N0i/Ti) * sum(bi * N1i/Ti)
Z <- qnorm((1 + conf.level)/2)
SE.logRRmh <- sqrt(numer/denom)
LL <- exp(log(RRmh) - Z * SE.logRRmh)
UL <- exp(log(RRmh) + Z * SE.logRRmh)
list(data = x, risk.ratio = RRmh, conf.int = c(LL, UL))
}7.4.1.3 Odds ratio
References
18. World Health Organization. WHO definition of health. World Health Organization; World Health Organization; 1948.
19. Institute of Medicine. The future of public health [Internet]. Washington, DC: The National Academies Press; 1988. Available from: https://www.nap.edu/catalog/1091/the-future-of-public-health
20. Aragón TJ, Colfax G. We will be the best at getting better! A playbook for population health improvement. UC Berkeley eScholarship [Internet]. 2019; Available from: https://escholarship.org/uc/item/9xg5t30s
21. Kindig D, Stoddart G. What is population health? Am J Public Health. 2003 Mar;93(3):380–3.
22. Halfon N, Larson K, Lu M, Tullis E, Russ S. Lifecourse health development: Past, present and future. Matern Child Health J. 2014 Feb;18(2):344–65.
23. Last J. A dictionary of epidemiology. 4th ed. New York: Oxford University Press; 2000.