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

VL - 35

JO - Spatial and Spatio-temporal Epidemiology

JF - Spatial and Spatio-temporal Epidemiology

SN - 1877-5845

M1 - 100361

ER -