The impact of cataract surgery on vision-related quality of life for bilateral cataract patients in Ho Chi Minh City, Vietnam : a prospective study

Background To determine the impact of cataract surgery on vision-related quality of life (VRQOL) and examine the association between objective visual measures and change in VRQOL after surgery among bilateral cataract patients in Ho Chi Minh City, Vietnam. Methods A cohort of older patients with bilateral cataract was assessed one week before and one to three months after first eye or both eye cataract surgery. Visual measures including visual acuity, contrast sensitivity and stereopsis were obtained. Vision-related quality of life was assessed using the NEI VFQ-25. Descriptive analyses and a generalized linear estimating equation (GEE) analysis were undertaken to measure change in VRQOL after surgery. Results Four hundred and thirteen patients were assessed before cataract surgery and 247 completed the follow-up assessment one to three months after first or both eye cataract surgery. Overall, VRQOL significantly improved after cataract surgery (p < 0.001) particularly after both eye surgeries. Binocular contrast sensitivity (p < 0.001) and stereopsis (p < 0.001) were also associated with change in VRQOL after cataract surgery. Visual acuity was not associated with VRQOL. Conclusions Cataract surgery significantly improved VRQOL among bilateral cataract patients in Vietnam. Contrast sensitivity as well as stereopsis, rather than visual acuity significantly affected VRQOL after cataract surgery.

Prevalence and visual risk factors for falls in bilateral cataract patients in Ho Chi Minh City, Vietnam

Purpose To determine the prevalence of falls in the 12 months prior to cataract surgery and examine the associations between visual and other risk factors and falls among older bilateral cataract patients in Vietnam. Methods Data collected from 413 patients in the week before scheduled cataract surgery included a questionnaire and three objective visual tests. Results The outcome of interest was self-reported falls in the previous 12 months. A total of 13% (n = 53) of bilateral cataract patients reported 60 falls within the previous 12 months. After adjusting for age, sex, race, employment status, comorbidities, medication usage, refractive management, living status and the three objective visual tests in the worse eye, women (odds ratio, OR, 4.64, 95% confidence interval, CI, 1.85–11.66), and those who lived alone (OR 4.51, 95% CI 1.44–14.14) were at increased risk of a fall. Those who reported a comorbidity were at decreased risk of a fall (OR 0.43, 95% CI 0.19–0.95). Contrast sensitivity (OR 0.31, 95% CI 0.10–0.95) was the only significant visual test associated with a fall. These results were similar for the better eye, except the presence of a comorbidity was not significant (OR 0.45, 95% CI 0.20–1.02). Again, contrast sensitivity was the only significant visual factor associated with a fall (OR 0.15, 95% CI 0.04–0.53). Conclusion Bilateral cataract patients in Vietnam are potentially at high risk of falls and in need of falls prevention interventions. It may also be important for ophthalmologists and health professionals to consider contrast sensitivity measures when prioritizing cataract patients for surgery and assessing their risk of falls.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.