Otwombe Kennedy N, Petzold Max, Modisenyane Tebogo, Martinson Neil A, Chirwa Tobias
Perinatal HIV Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa;
Centre for Applied Biostatistics, Occupational and Environmental Medicine, University of Gothenburg, Gothenburg, Sweden.
Glob Health Action. 2014 Sep 29;7:25488. doi: 10.3402/gha.v7.25488. eCollection 2014.
Factors associated with mortality in HIV-infected people in sub-Saharan Africa are widely reported. However rural-urban disparities and their association with all-cause mortality remain unclear. Furthermore, commonly used classical Cox regression ignores unmeasured variables and frailty.
To incorporate frailty in assessing factors associated with mortality in HIV-infected people in rural and urban South Africa.
Using data from a prospective cohort following 6,690 HIV-infected participants from Soweto (urban) and Mpumalanga (rural) enrolled from 2003 to 2010; covariates of mortality were assessed by the integrated nested Laplace approximation method.
We enrolled 2,221 (33%) rural and 4,469 (67%) urban participants of whom 1,555 (70%) and 3,480 (78%) were females respectively. Median age (IQR) was 36.4 (31.0-44.1) in rural and 32.7 (28.2-38.1) in the urban participants. The mortality rate per 100 person-years was 11 (9.7-12.5) and 4 (3.6-4.5) in the rural and urban participants, respectively. Compared to those not on HAART, rural participants had a reduced risk of mortality if on HAART for 6-12 (HR: 0.20, 95% CI: 0.10-0.39) and >12 months (HR: 0.10, 95% CI: 0.05-0.18). Relative to those not on HAART, urban participants had a lower risk if on HAART >12 months (HR: 0.35, 95% CI: 0.27-0.46). The frailty variance was significant and >1 in rural participants indicating more heterogeneity. Similarly it was significant but <1 in the urban participants indicating less heterogeneity.
The frailty model findings suggest an elevated risk of mortality in rural participants relative to the urban participants potentially due to unmeasured variables that could be biological, socio-economic, or healthcare related. Use of robust methods that optimise data and account for unmeasured variables could be helpful in assessing the effect of unknown risk factors thus improving patient management and care in South Africa and elsewhere.
撒哈拉以南非洲地区艾滋病毒感染者的死亡相关因素已有广泛报道。然而，城乡差异及其与全因死亡率的关联仍不明确。此外，常用的经典Cox回归忽略了未测量的变量和脆弱性。
在评估南非城乡艾滋病毒感染者的死亡相关因素时纳入脆弱性因素。
利用2003年至2010年招募的来自索韦托（城市）和姆普马兰加（农村）的6690名艾滋病毒感染者的前瞻性队列数据；采用集成嵌套拉普拉斯近似法评估死亡的协变量。
我们招募了2221名（33%）农村参与者和4469名（67%）城市参与者，其中分别有1555名（70%）和3480名（78%）为女性。农村参与者的年龄中位数（四分位间距）为36.4（31.0 - 44.1）岁；城市参与者为32.7（28.2 - 38.1）岁。农村和城市参与者每100人年的死亡率分别为11（9.7 - 12.5）和4（3.6 - 4.5）。与未接受高效抗逆转录病毒治疗（HAART）的参与者相比，农村参与者接受HAART治疗6 - 12个月（风险比：0.20，95%置信区间：0.10 - 0.39）和超过12个月（风险比：0.10，95%置信区间：0.05 - 0.18）时，死亡风险降低。相对于未接受HAART的参与者，城市参与者接受HAART治疗超过12个月时风险较低（风险比：0.35，95%置信区间：0.27 - 0.46）。农村参与者的脆弱性方差显著且大于1，表明异质性更强。同样，城市参与者的脆弱性方差显著但小于1，表明异质性较弱。
脆弱性模型研究结果表明，农村参与者的死亡风险相对于城市参与者有所升高，这可能是由于未测量的变量所致，这些变量可能与生物学、社会经济或医疗保健相关。使用优化数据并考虑未测量变量的稳健方法，可能有助于评估未知风险因素的影响，从而改善南非及其他地区的患者管理和护理。
