TY - JOUR
T1 - The unknown denominator problem in population studies of disease frequency
AU - Morrison, Christopher N.
AU - Rundle, Andrew G.
AU - Branas, Charles C.
AU - Chihuri, Stanford
AU - Mehranbod, Christina
AU - Li, Guohua
PY - 2020/11
Y1 - 2020/11
N2 - Problems related to unknown or imprecisely measured populations at risk are common in epidemiologic studies of disease frequency. The size of the population at risk is typically conceptualized as a denominator to be used in combination with a count of disease cases (a numerator) to calculate incidence or prevalence. However, the size of the population at risk can take other epidemiologic properties in relation to an exposure of interest and the count outcome, including confounding, modification, and mediation. Using spatial ecological studies of injury incidence as an example, we identify and evaluate five approaches that researchers have used to address “unknown denominator problems”: ignoring, controlling for a proxy, approximating, controlling by study design, and measuring the population at risk. We present a case example and recommendations for selecting a solution given the data and the hypothesized relationship between an exposure of interest, a count outcome, and the population at risk.
AB - Problems related to unknown or imprecisely measured populations at risk are common in epidemiologic studies of disease frequency. The size of the population at risk is typically conceptualized as a denominator to be used in combination with a count of disease cases (a numerator) to calculate incidence or prevalence. However, the size of the population at risk can take other epidemiologic properties in relation to an exposure of interest and the count outcome, including confounding, modification, and mediation. Using spatial ecological studies of injury incidence as an example, we identify and evaluate five approaches that researchers have used to address “unknown denominator problems”: ignoring, controlling for a proxy, approximating, controlling by study design, and measuring the population at risk. We present a case example and recommendations for selecting a solution given the data and the hypothesized relationship between an exposure of interest, a count outcome, and the population at risk.
KW - Acute
KW - Injury
KW - Methodology
KW - Space-time
KW - Spatial
UR - http://www.scopus.com/inward/record.url?scp=85088800152&partnerID=8YFLogxK
U2 - 10.1016/j.sste.2020.100361
DO - 10.1016/j.sste.2020.100361
M3 - Review Article
C2 - 33138954
AN - SCOPUS:85088800152
SN - 1877-5845
VL - 35
JO - Spatial and Spatio-temporal Epidemiology
JF - Spatial and Spatio-temporal Epidemiology
M1 - 100361
ER -