The health sector is one of the many sectors where performance measures are very crucial. The importance of measuring performance is that it enables customers to make good choices. Despite the positives of the performance indicators, they are always bound by some degrees of uncertainty. Statistically, no measurement is 100% correct, meaning that there must be some margin for error (Gerzoff & Williamson, 2001). However, most studies tend to ignore the importance of the uncertainty aspect in their rankings. The uncertainties lead to random variations in the performance measures or any quantitative estimates. These random variations can be corrected by incorporating confidence intervals (CIs) in the statistical estimates, but this step is often ignored in many health care research papers (Gerzoff & Williamson, 2001). The omission of CIs may lead to severe consequences in that the uncertainties can cause unfairness in resource allocation, misappropriation of funds, inequality, and so on. In turn, the negative consequences lead to variations in health outcomes.
Factors that Influence Variability
Three major factors might influence variability in health outcomes. The first major source of variability in health outcomes is age adjustment (Gerzoff & Williamson, 2001). Age adjustments are made when comparing the epidemiological rates for regions or populations with different age structures. In this case, the disease, mortality, or morbidity rates are adjusted to cater for the different age compositions in a study population (Gerzoff & Williamson, 2001). The rates are adjusted to make valid comparisons between different populations over time. Therefore, a particular population, either drawn from a certain year or locality, is taken to be the basis of comparison. The age distribution within the chosen population becomes the standard population. However, age adjustments add to uncertainty leading to variations in health outcomes because it does not use a single estimate for an entire population (Gerzoff & Williamson, 2001). It uses multiple estimates, one for every age subgroup, meaning the overall result is even more uncertain.
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The second source of variability in health outcomes is the use of census reports. Even though the Census Bureau provides the relative standard errors (SEs) and the 95% CIs for most of the population estimates, they do not provide the SEs and 95% CIs for intercensal estimates (Gerzoff & Williamson, 2001). Therefore, on most occasions, the SEs and the 95% CIs are assumed to be larger than those provided during the main census. For this reason, most of the estimates used in scholarly papers at times use the wrong SEs and 95% CIs, meaning that the levels of uncertainties accounted for in most studies might be wrong.
The use of survey data is the third source of variability in health outcomes. Some health data, such as the prevalence of obesity in a given area, are collected using surveys. However, this data collection method has uncertainties associated with the sampling process used (Gerzoff & Williamson, 2001). Furthermore, since most survey reports provide the 95% CIs and/or SEs along with the data, the CI and SE values may change over time. Thus, if a study uses survey data, it may lead to uncertainties associated with time.
Social Determinants of Health
The three factors of variability in health outcomes can greatly influence the social determinants of health (SDH). Health outcomes such as mortality, morbidity, and life expectancy levels vary according to the SDHs. Thus, age adjustment can lead to variability since the health outcomes for the different populations or localities vary according to the specific SDHs. For example, the mortality rates for an insecure low-income neighborhood are likely to be higher than for an affluent one. Similarly, the lack of SEs and 95% CIs in intercensal reports can influence the SDHs. For example, it may be impossible to obtain accurate estimates of employment, income, or educational attainment for a given population. Lastly, the use of improper sampling techniques can lead to incorrect information on some of the SDHs, such as access to health coverage.
Reference
Gerzoff, R. B., & Williamson, G. D. (2001). Who’s number one? The impact of variability on rankings based on public health indicators. Public Health Reports, 116 (2), 158-164. https://doi.org/10.1093/phr/116.2.158
