Detection and Management of Confounding Variables in Prevention of Blindness Research: A Methodological Approach with Age and Sex as Models

Research Article

Austin J Clin Ophthalmol. 2014;1(2): 1006.

Detection and Management of Confounding Variables in Prevention of Blindness Research: A Methodological Approach with Age and Sex as Models

Ahmed Mousa*

Department of Ophthalmology, College of Medicine, King Saud University, KSA

*Corresponding author: Ahmed Mousa, Department of Ophthalmology, College of Medicine, King Saud University, King Abdul-Aziz University Hospital, KSA

Received: January 20, 2013; Accepted: February 24, 2014; Published: March 03, 2014


Introduction: Confounding is a common type of bias in which a third variable may distort the assessment of a potential risk factor on the outcome of interest. Confounding is further classified into positive, negative and extreme negative confounding. Several methods are quite common in the management of confounding, of which: stratification using Mantel Haenszel common OR\ (MH) and binary logistic regression. The prevalence of low vision and blindness remains relatively high in developing countries, despite global effortsfor prevention and intervention. In order to make the best use of the limited vailable resources for prevention and elimination in these poorer countries, it is essential to accurately detect the association between avoidable causes and blindness. There is a need to explore the accuracy, utilization and variation between both methods.

Methods: Data from two different blindness surveys (Menoufiya 1999, and Menia 2002) were abstracted and managed. Crude Odds ratios were calculated for the prevalence of blindness in both surveys. Two major causes of blindness (cataract and trachomatous corneal opacity) were implied in the analysis as potential risk factors. The estimated prevalence was adjusted for age and sex using Mantel Haenszel stratification and binary logistic regression methods separately. Adjusted odds ratios were compared to evaluate the variation.

Results: In Menia data, the unadjusted prevalence of blindness using crude odds ratio was 7.54, which was reduced to 3.63 with Mantel Haenszel adjustment and to 3.93 using logistic regression adjustment for age. Adjusting for sex, the OR was not much reduced using both methods (7.5 and 7.48), respectively while adjusting for both age and sex it was compromised to 3.83. Moreover, using Menoufiya data, the crude OR was 10.09, which was reduced adjusting for age with MH method to 7.53 and to 7.64 using regression analysis. In terms of sex, the OR was not much changed in both methods (OR: 10.16) for both. Adjusting for both confounders, the OR was compromised to 7.62.

Conclusion: Prevention of blindness requires accurate assessment of the magnitude and the associated risk factors. Confounders may distort this assessment and hence, yield biased results. Accounting for confounding is quite crucial to avoid resource wasting. Mantel Haenzel method can be used to manage the effect of a single variable, while regression models are more preferred in case of multiple confounders.


Confounding is a situation in which a non–causal association between a given exposure and an outcome is observed as a result of the influence of a third variable (the confounder) [1]. Such cofounder must be related to both the putative risk factor(s) and the outcome of interest. Meanwhile, it should not be in the exposure—outcome pathway. Consequently, the association between the exposure and the outcome can be: induced, strengthened, weakened, or eliminated via the confounder’s effect which may differ between the exposed and unexposed groups. Moreover, confounding is more likely to occur in observational studies than in experimental studies where the latter is one of the confounding–control approaches. According to its effect, confounding is further classified into: 1) Positive confounding; in which the confounder exaggerates the association (usually occurs in direct relationships); 2) Negative confounding; in which the confounder results in attenuation of the association (usually occurs in inverse relationships); 3) Extreme negative confounding; in which, the confounder over attenuates the association (resulting in totally reversed direction of the association).

A potential confounder is often suspected and detected through previous knowledge (experience ⁄ literature) and then confirmed through statistical testing. Therefore, when both crude and adjusted analyses are markedly varied, then adjustment for potential confounders is quite necessary. Although there is a debate about the threshold of such variation, it is highly recommended that adjustment is necessary when there is a more than 10 % difference [2].

Several methods of adjustment for confounding are available and applicable during different study phases. For example: at the design phase, some precautions may include: (1) increasing the sample size; (2) restricting the study population to those who are unexposed to the targeted confounder; (3) matching between cases and controls; and (4) randomization. Alternatively, the control for confounders in the analysis phase is usually conducted when the potential confounders were not controlled for, or couldn’t be accounted for during the design phase. Meanwhile, controlling for potential confounders in the analysis phase is also dependent on the measure of association. For example, in cross sectional surveys where odds ratios or prevalence ratios can be alternatively used, the adjustment method would also vary [3]. At this stage, a potential confounder is usually tested to estimate the value of its induced bias in the study results. In this scenario, two main mathematical approaches can be implemented to control for the identified confounder; stratified analysis method and statistical modeling (multivariate analysis) [4–5].

Researchers are quite often not aware of the need to control for confounders. Meanwhile, the process of detection and management of confounders may require specific knowledge and skills [6]. In a study in 2002, Mullner M et al. reviewed 537 original articles published in 34 different medical journals in January 1998, and found out that only 169 (31.5%) articles controlled for confounders while very few of them mentioned the methods they used. Thus, only a few authors have provided adequate evidence of correct controlling, although most of those authors were affiliated to reputable statistics, epidemiology, or public health departments [7].

Low vision and blindness have a significant negative socioeconomic impact on both individual and community levels. According to the WHO guidelines, low vision is defined as: visual acuity (VA) < 6 ⁄ 18, severe low vision as: VA < 6⁄60 and blindness as: visual acuity < 3⁄60 in the better eye. Worldwide — as per the last formal WHO assessment – an estimated 160 (2.6 %) million people are visually impaired, of them 124 million (2 %) have low vision and 36 million (0.6 %) are blind [8]. These figures are recently estimated to be dramatically increased to 32.4 million blind and 191 million visually impaired [9–10]. Moreover, approximately 90 % of blindness occurs in developing countries, namely: Africa, Middle East, and Asia. The main documented causes of low vision and blindness are uncorrected refractive errors (43%), unoperated cataract (33%) and glaucoma [11]. Out of the total burden of blindness, 80 % is avoidable, either curative (cataract, glaucoma, and corneal opacity) or preventable (trachoma, and onchocerciasis). Of particular interest in this study are reports of the prevalence of low vision and blindness in the Eastern Mediterranean Region (EMR) of WHO classification (Morocco — Pakistan). This region has the highest prevalence of visual impairment and blindness. Epidemiological studies are considered as the first formative step to develop both preventive and intervention programs. Accurate estimates of the exact prevalence and effect size of different risk factors are quite crucial in program development. confounders may inadvertently reverse the arrangement of priorities and hence results in wasting of — the usually – very limited health resources.

Researchers in the prevention of blindness field, usually control for confounders during the analysis phase using the two mentioned common mathematical approaches (stratification and modeling), specifically; Mantel Haenszel common OR\, and binary logistic regression analysis. There is still uncertainty about assumptions, situations, variation, advantages and disadvantages of using either of these methods. There is also uncertainty about the consistency of results derived from both methods.

Two large community based surveys were conducted in Egypt by Al Noor Foundation in collaboration with Pfizer© pharmaceutical Inc., NY, USA and the International Trachoma Initiative (ITI), GA, USA in two different governorates (Menoufiya (Lower Egypt; 1999), and (Menia (Upper Egypt; 2002), with total sample sizes of 6000, and 4500 inhabitants, respectively. Both surveys were aiming to assess the prevalence and causes of blindness [12–13].


Data from Menoufiya (1999) and Menia (2002) surveys were extracted and stored in a new database specifically designed using Microsoft Access 2010®. A new coding system was applied to the original data sets of the two mentioned surveys to suit the analysis coping with purpose of the current study. A subset of the examined adults in the age 40 years and above with available visual acuity were abstracted which yielded 2325 and 2028 inhabitants in Menoufiya and Menia surveys respectively. A person was considered blind if his⁄her presenting visual acuity in the better eye was < 3⁄60. Two common causes of avoidable blindness were selected for the analysis, namely: cataract and trachomatous corneal opacity (TCO). To facilitate the analysis, a new variable was constructed to indicate the presence or absence of avoidable blindness. A person was considered to have avoidable blindness if he⁄she had bilateral cataract and ⁄ or bilateral trachomatous corneal opacity. The reason for using bilateral affection was to ensure that vision loss is attributed to the specifically selected major cause. Age was transformed from a continuous to a categorical variable (in decades) which are: (40 — 49), (50 — 59), (60 — 69), and (70 +). The latest category (70 +) was used as reference. The sex variable as entered as a dichotomous variable with men as the reference group.

Crude odds ratios were calculated using avoidable causes of blindness (cataract and ⁄ or TCO) as the main exposure and vision loss (identified as visual acuity in the better eye < 3⁄60) as an outcome. Mantel Haenszel common (pooled) odds ratios were calculated across categories of age and sex separately in each data set (controlling for age and sex) and then compared to the previously calculated crude odds ratios [14–15]. Calculation procedures were conducted as follows:

(A) Crude OR = (a * d) ⁄ (b * c).

(B) Mantel Haenszel common OR\ = Σk i=1 [(ai * di) ⁄ ni] ⁄ Σk i=1 [(bi * ci) ⁄ ni]

Where ni = ai + bi + ci + di, and confidence intervals = exp.(log odds ± zα * (S.E.).

Zα = 1.96 for 95 % CI. and S.E. = √ 1 ⁄ [(ai * di) ⁄ ni] + 1⁄ [(bi * ci) ⁄ ni].

(C) Binary logistic regression analysis was conducted including and then excluding age and sex, with OR as the measure of association in the presence and absence of age and sex as potential confounders.

In this analysis the binary logistic regression model was considered as follows:

Y = α + β1 X1+ β2 Z1 + β3 Z2 + Error;

where Y is the primary outcome (blindness as previously defined), Xi variable denotes the presence of avoidable causes of blindness (bilateral cataract and ⁄ or bilateral TCO), and Z1 & Z2 variables are the two targeted potential confounders (age and sex respectively). Adjusted odds ratios calculated by both methods were then compared to evaluate the difference in estimation.