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HTLV-1 and HIV-2 Infection Are Associated with Increased Mortality in a Rural West African Community
research-article
Author(s):
Carla van Tienen
1 ,
Maarten Schim van der Loeff
2
,
* ,
Ingrid Peterson
1 ,
Matthew Cotten
1 ,
Sören Andersson
3 ,
Birgitta Holmgren
4 ,
Tim Vincent
1 ,
Thushan de Silva
1
,
5 ,
Sarah Rowland-Jones
6 ,
Peter Aaby
7 ,
Hilton Whittle
1
Publication date (Electronic):
14 December 2011
Journal:
PLoS ONE
Publisher:
Public Library of Science
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Background
Survival of people with HIV-2 and HTLV-1 infection is better than that of HIV-1 infected people, but long-term follow-up data are rare. We compared mortality rates of HIV-1, HIV-2, and HTLV-1 infected subjects with those of retrovirus-uninfected people in a rural community in Guinea-Bissau.
Methods
In 1990, 1997 and 2007, adult residents (aged ≥15 years) were interviewed, a blood sample was drawn and retroviral status was determined. An annual census was used to ascertain the vital status of all subjects. Cox regression analysis was used to estimate mortality hazard ratios (HR), comparing retrovirus-infected versus uninfected people.
Results
A total of 5376 subjects were included; 197 with HIV-1, 424 with HIV-2 and 325 with HTLV-1 infection. The median follow-up time was 10.9 years (range 0.0–20.3). The crude mortality rates were 9.6 per 100 person-years of observation (95% confidence interval 7.1-12.9) for HIV-1, 4.1 (3.4–5.0) for HIV-2, 3.6 (2.9–4.6) for HTLV-1, and 1.6 (1.5–1.8) for retrovirus-negative subjects. The HR comparing the mortality rate of infected to that of uninfected subjects varied significantly with age. The adjusted HR for HIV-1 infection varied from 4.0 in the oldest age group (≥60 years) to 12.7 in the youngest (15–29 years). The HR for HIV-2 infection varied from 1.2 (oldest) to 9.1 (youngest), and for HTLV-1 infection from 1.2 (oldest) to 3.8 (youngest).
Related collections
Most cited references55
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The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies
Erik von Elm, Douglas G. Altman, Matthias Egger … (2008)
Introduction Many questions in medical research are investigated in observational studies [1]. Much of the research into the cause of diseases relies on cohort, case-control, or cross-sectional studies. Observational studies also have a role in research into the benefits and harms of medical interventions [2]. Randomised trials cannot answer all important questions about a given intervention. For example, observational studies are more suitable to detect rare or late adverse effects of treatments, and are more likely to provide an indication of what is achieved in daily medical practice [3]. Research should be reported transparently so that readers can follow what was planned, what was done, what was found, and what conclusions were drawn. The credibility of research depends on a critical assessment by others of the strengths and weaknesses in study design, conduct, and analysis. Transparent reporting is also needed to judge whether and how results can be included in systematic reviews [4,5]. However, in published observational research important information is often missing or unclear. An analysis of epidemiological studies published in general medical and specialist journals found that the rationale behind the choice of potential confounding variables was often not reported [6]. Only few reports of case-control studies in psychiatry explained the methods used to identify cases and controls [7]. In a survey of longitudinal studies in stroke research, 17 of 49 articles (35%) did not specify the eligibility criteria [8]. Others have argued that without sufficient clarity of reporting, the benefits of research might be achieved more slowly [9], and that there is a need for guidance in reporting observational studies [10,11]. Recommendations on the reporting of research can improve reporting quality. The Consolidated Standards of Reporting Trials (CONSORT) Statement was developed in 1996 and revised 5 years later [12]. Many medical journals supported this initiative [13], which has helped to improve the quality of reports of randomised trials [14,15]. Similar initiatives have followed for other research areas—e.g., for the reporting of meta-analyses of randomised trials [16] or diagnostic studies [17]. We established a network of methodologists, researchers, and journal editors to develop recommendations for the reporting of observational research: the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement. Aims and Use of the STROBE Statement The STROBE Statement is a checklist of items that should be addressed in articles reporting on the 3 main study designs of analytical epidemiology: cohort, case-control, and cross-sectional studies. The intention is solely to provide guidance on how to report observational research well: these recommendations are not prescriptions for designing or conducting studies. Also, while clarity of reporting is a prerequisite to evaluation, the checklist is not an instrument to evaluate the quality of observational research. Here we present the STROBE Statement and explain how it was developed. In a detailed companion paper, the Explanation and Elaboration article [18–20], we justify the inclusion of the different checklist items and give methodological background and published examples of what we consider transparent reporting. We strongly recommend using the STROBE checklist in conjunction with the explanatory article, which is available freely on the Web sites of PLoS Medicine (http://www.plosmedicine.org/), Annals of Internal Medicine (http://www.annals.org/), and Epidemiology (http://www.epidem.com/). Development of the STROBE Statement We established the STROBE Initiative in 2004, obtained funding for a workshop and set up a Web site (http://www.strobe-statement.org/). We searched textbooks, bibliographic databases, reference lists, and personal files for relevant material, including previous recommendations, empirical studies of reporting and articles describing relevant methodological research. Because observational research makes use of many different study designs, we felt that the scope of STROBE had to be clearly defined early on. We decided to focus on the 3 study designs that are used most widely in analytical observational research: cohort, case-control, and cross-sectional studies. We organised a 2-day workshop in Bristol, UK, in September 2004. 23 individuals attended this meeting, including editorial staff from Annals of Internal Medicine, BMJ, Bulletin of the World Health Organization, International Journal of Epidemiology, JAMA, Preventive Medicine, and The Lancet, as well as epidemiologists, methodologists, statisticians, and practitioners from Europe and North America. Written contributions were sought from 10 other individuals who declared an interest in contributing to STROBE, but could not attend. Three working groups identified items deemed to be important to include in checklists for each type of study. A provisional list of items prepared in advance (available from our Web site) was used to facilitate discussions. The 3 draft checklists were then discussed by all participants and, where possible, items were revised to make them applicable to all three study designs. In a final plenary session, the group decided on the strategy for finalizing and disseminating the STROBE Statement. After the workshop we drafted a combined checklist including all three designs and made it available on our Web site. We invited participants and additional scientists and editors to comment on this draft checklist. We subsequently published 3 revisions on the Web site, and 2 summaries of comments received and changes made. During this process the coordinating group (i.e., the authors of the present paper) met on eight occasions for 1 or 2 days and held several telephone conferences to revise the checklist and to prepare the present paper and the Explanation and Elaboration paper [18–20]. The coordinating group invited 3 additional co-authors with methodological and editorial expertise to help write the Explanation and Elaboration paper, and sought feedback from more than 30 people, who are listed at the end of this paper. We allowed several weeks for comments on subsequent drafts of the paper and reminded collaborators about deadlines by e-mail. STROBE Components The STROBE Statement is a checklist of 22 items that we consider essential for good reporting of observational studies (Table 1). These items relate to the article's title and abstract (item 1), the introduction (items 2 and 3), methods (items 4–12), results (items 13–17) and discussion sections (items 18–21), and other information (item 22 on funding). 18 items are common to all three designs, while four (items 6, 12, 14, and 15) are design-specific, with different versions for all or part of the item. For some items (indicated by asterisks), information should be given separately for cases and controls in case-control studies, or exposed and unexposed groups in cohort and cross-sectional studies. Although presented here as a single checklist, separate checklists are available for each of the 3 study designs on the STROBE Web site. Table 1 The STROBE Statement—Checklist of Items That Should Be Addressed in Reports of Observational Studies Implications and Limitations The STROBE Statement was developed to assist authors when writing up analytical observational studies, to support editors and reviewers when considering such articles for publication, and to help readers when critically appraising published articles. We developed the checklist through an open process, taking into account the experience gained with previous initiatives, in particular CONSORT. We reviewed the relevant empirical evidence as well as methodological work, and subjected consecutive drafts to an extensive iterative process of consultation. The checklist presented here is thus based on input from a large number of individuals with diverse backgrounds and perspectives. The comprehensive explanatory article [18–20], which is intended for use alongside the checklist, also benefited greatly from this consultation process. Observational studies serve a wide range of purposes, on a continuum from the discovery of new findings to the confirmation or refutation of previous findings [18–20]. Some studies are essentially exploratory and raise interesting hypotheses. Others pursue clearly defined hypotheses in available data. In yet another type of studies, the collection of new data is planned carefully on the basis of an existing hypothesis. We believe the present checklist can be useful for all these studies, since the readers always need to know what was planned (and what was not), what was done, what was found, and what the results mean. We acknowledge that STROBE is currently limited to three main observational study designs. We would welcome extensions that adapt the checklist to other designs—e.g., case-crossover studies or ecological studies—and also to specific topic areas. Four extensions are now available for the CONSORT statement [21–24]. A first extension to STROBE is underway for gene-disease association studies: the STROBE Extension to Genetic Association studies (STREGA) initiative [25]. We ask those who aim to develop extensions of the STROBE Statement to contact the coordinating group first to avoid duplication of effort. The STROBE Statement should not be interpreted as an attempt to prescribe the reporting of observational research in a rigid format. The checklist items should be addressed in sufficient detail and with clarity somewhere in an article, but the order and format for presenting information depends on author preferences, journal style, and the traditions of the research field. For instance, we discuss the reporting of results under a number of separate items, while recognizing that authors might address several items within a single section of text or in a table. Also, item 22, on the source of funding and the role of funders, could be addressed in an appendix or in the methods section of the article. We do not aim at standardising reporting. Authors of randomised clinical trials were asked by an editor of a specialist medical journal to “CONSORT” their manuscripts on submission [26]. We believe that manuscripts should not be “STROBEd”, in the sense of regulating style or terminology. We encourage authors to use narrative elements, including the description of illustrative cases, to complement the essential information about their study, and to make their articles an interesting read [27]. We emphasise that the STROBE Statement was not developed as a tool for assessing the quality of published observational research. Such instruments have been developed by other groups and were the subject of a recent systematic review [28]. In the Explanation and Elaboration paper, we used several examples of good reporting from studies whose results were not confirmed in further research – the important feature was the good reporting, not whether the research was of good quality. However, if STROBE is adopted by authors and journals, issues such as confounding, bias, and generalisability could become more transparent, which might help temper the over-enthusiastic reporting of new findings in the scientific community and popular media [29], and improve the methodology of studies in the long term. Better reporting may also help to have more informed decisions about when new studies are needed, and what they should address. We did not undertake a comprehensive systematic review for each of the checklist items and sub-items, or do our own research to fill gaps in the evidence base. Further, although no one was excluded from the process, the composition of the group of contributors was influenced by existing networks and was not representative in terms of geography (it was dominated by contributors from Europe and North America) and probably was not representative in terms of research interests and disciplines. We stress that STROBE and other recommendations on the reporting of research should be seen as evolving documents that require continual assessment, refinement, and, if necessary, change. We welcome suggestions for the further dissemination of STROBE—e.g., by re-publication of the present article in specialist journals and in journals published in other languages. Groups or individuals who intend to translate the checklist to other languages should consult the coordinating group beforehand. We will revise the checklist in the future, taking into account comments, criticism, new evidence, and experience from its use. We invite readers to submit their comments via the STROBE Web site (http://www.strobe-statement.org/).
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Mortality of HIV-Infected Patients Starting Antiretroviral Therapy in Sub-Saharan Africa: Comparison with HIV-Unrelated Mortality
Martin Brinkhof, Andrew Boulle, Ralf Weigel … (2009)
Introduction The widespread use since 1996 of highly active antiretroviral therapy (ART) has substantially improved the prognosis of HIV-infected patients both in industrialised and low-income settings [1]. Recent studies from industrialised countries have suggested that all-cause mortality in patients successfully treated with ART might approach that of the general population, and that in many patients mortality rates are comparable to those associated with other chronic conditions, such as diabetes [2]–[6]. Such comparisons are important to gain a better understanding of the treated history of HIV infection, to monitor and predict the progress of the HIV/AIDS epidemic, and to plan health services in the era of potent ART. As a result of scaling up of ART in low- and middle-income countries, 2.99 million people living with HIV/AIDS were estimated to be receiving treatment at the end of 2007, representing 31% of the estimated 9.6 million people in urgent need of treatment in these countries [7]. In sub-Saharan Africa, the number of patients on combination ART increased from 1.38 million to 2.12 million from 2006 to 2007. Although the immunological and virological responses to treatment in resource-limited countries can equal that in high-income settings [1],[8]–[10], mortality of patients starting ART has been substantially higher than in industrialised countries, particularly in the first few months of treatment [1],[10],[11]. To our knowledge, no studies have compared mortality among HIV-infected people starting ART in sub-Saharan Africa with the non-HIV–related background mortality. We analysed data from a network of treatment programmes in sub-Saharan Africa to compare mortality rates observed in HIV-1–infected patients starting ART with non-HIV–related mortality in four countries in sub-Saharan Africa. Methods The International Epidemiological Databases to Evaluate AIDS Analyses were based on cohorts participating in the West African and Southern African regions of the International epidemiological Databases to Evaluate AIDS (IeDEA) [12]. The databases are regularly updated; the November 2007 version was used for the present analysis. We restricted analyses to five large treatment programmes in four sub-Saharan African countries, including two treatment programmes in townships in the greater Cape Town metropolitan area, Khayelitsha [9] and Gugulethu [13], South Africa; the Lighthouse clinic in Lilongwe, Malawi [14]; the Centre de Prise en Charge de Recherches et de Formation (CEPREF)/Agence National de Recherches sur le Sida (ANRS) 1203 cohort from Abidjan, Côte d'Ivoire [15]; and the Connaught Clinic in Harare, Zimbabwe [16]. All patients aged 16 y or older who were ART-naïve at the start of ART were included. ART was defined as any combination of three antiretroviral drugs. Loss to follow-up was assumed in patients who were not known to have died and who were not seen for at least 1 y before closing the database for the present analysis. The local Ethics Committees of all clinics approved participation in IeDEA, which was also approved by the Cantonal Ethics Committee in Bern, Switzerland. Estimates of HIV-Free Background Mortality Country-specific rates of all-cause mortality and HIV-free mortality by sex and 5-y age groups were obtained from the World Health Organization (WHO) Global Burden of Disease project [17]. Beginning with the year 1999, WHO has been producing annual life tables for all member states. A key use of these tables is the calculation of healthy life expectancy (HALE), the basic indicator of population health published each year in the World Health Report [18]. The methods used to estimate all-cause and cause-specific mortality have been described in detail elsewhere [19],[20]. Briefly, life tables based on vital registration data, corrected for under registration of deaths using demographic techniques, were used to estimate all-cause mortality in South Africa and Zimbabwe. In Côte d'Ivoire and Malawi data from other sources, such as census and surveys, were applied to a modified logit life-table model, using a global standard [20],[21]. For all four countries Joint United Nations Programme on HIVAIDS (UNAIDS) estimates of HIV/AIDS mortality were used, on the basis of epidemiological models and sentinel surveillance data on HIV seroprevalence [22]. Multiple Imputation of Missing Individual Patient Data Information on the CD4 count, clinical stage at the start of ART, and vital status at the last contact date was missing in some patients. Vital status was considered missing if the patient was not known to have died and the last date of information was less than 2 y after starting ART or before the administrative closure date of the cohort, whatever came first. We used multiple imputation by chained equation methods to impute missing information [23]. Multiple imputation included the outcome, i.e., whether or not a patient had died. Baseline CD4 cell count, clinical stage of disease, and survival time after censoring were imputed conditional on each other as well as on age and sex. All prediction equations included cohort, log age at start of ART, and sex. To optimise the imputation procedure we further included available clinical information on baseline viral load, total lymphocyte count, and haemoglobin; since females had lower haemoglobin levels the interaction between haemoglobin and sex was also fitted. Continuous variables were normalised prior to imputation modelling if needed, using log-transformation for age at start of ART and survival time, and square-root transformation for the baseline CD4. Interval censoring was used for baseline CD4 and survival time to ensure imputation values within the appropriate range. To impute survival time we used the complete follow-up history of all patients and used a log distribution to sample survival time after censoring in patients for which no death was recorded. The imputation of log survival time involved left-censoring at the date of last information, but no right-censoring. In the analysis we right-censored survival time at 2 y or at the closure date of the cohort. We created 20 imputed datasets in total. We analyzed imputed datasets using Poisson regression models (see below) to examine the association of time on treatment (months 1–3, 4–6, 7–12, and 13–24 after start of ART) and patient characteristics at baseline as risk factors of relative survival. Estimates of coefficients were derived by averaging, and appropriate standard errors were calculated using the within and between imputation standard errors of the estimates using Rubin's rules [23]. Modelling of Standardised Mortality Ratios and Excess Mortality We quantified mortality of HIV-infected patients on ART relative to the mortality in the general population using excess mortality and standardised mortality ratios (SMRs). The excess mortality risk is derived using an additive model, by subtracting age- and sex-specific HIV-unrelated mortality rates in the reference population from mortality in HIV-infected patients. SMRs are based on a multiplicative model and calculated as the ratio of the number of observed deaths over the expected deaths, using age- and sex-specific rates of HIV-unrelated mortality from the reference population. The SMR thus quantifies how much higher mortality is in HIV-infected patients compared to the reference population, but gives no indication of the excess mortality in absolute terms. Excess mortality and SMRs with 95% confidence intervals (CIs) were obtained from generalised linear models with a Poisson error structure, as described by Dickman and colleagues [24]. The expected number of deaths due to causes other than HIV d* j for observation j was calculated by multiplying the person-time at risk y j by the corresponding sex, age- (in 5-y age groups), and country-specific rates of HIV-free mortality. The excess mortality model assumes piecewise constant hazards λj, implying a Poisson process for the number of deaths d j in each interval. The generalised linear model with Poisson error structure for outcome d j involves offset ln (y j ) and the user-defined link function ln (μ j − d* j ), where μ j = λj y j. In SMR modelling d j is modelled with offset ln (d* j ). Robust standard errors were used to account for the clustering of data on treatment programme. Significance testing was by Wald tests. Multivariable models were calculated for excess mortality on the 20 imputed datasets. The interpretation of the excess hazard ratios (eHRs) from these models is similar to that of the hazard ratio in the familiar Cox model. For example, an eHR of 0.80 for females relative to males would indicate that females have a 20% lower risk of death as compared to males, after controlling for the variation in background mortality. The following variables were included: age, sex, ART regimen, baseline CD4 cell count, clinical stage of disease, and calendar period of starting ART. Time periods considered were months 1–3, 4–6, 7–12, and 13–24 after start of ART. ART regimen was defined as non-nucleoside reverse transcriptase inhibitor (NNRTI)-based, protease inhibitor (PI)-based, and other. Baseline CD4 count was analysed in five categories (0–24, 25–49, 50–99, 100–199, and 200 or more cells/µl). Clinical stage of disease was defined as less advanced (WHO stage I or stage II) or advanced (WHO stage III or stage IV). In a sensitivity analysis we excluded two sites with high loss to follow-up. All analyses were done in Stata version 10.0 (Stata Corporation), using the “ice” routine for imputation of missing values. Results Treatment Programmes and Patient Characteristics The combined dataset included 13,249 patients. Table 1 describes the five treatment programmes from four sub-Saharan African countries. Patient numbers ranged from 857 patients (Connaught clinic, Zimbabwe) to 4,710 patients (Lighthouse clinic, Malawi). The majority of patients in each of the treatment programmes were women, the median age ranged from 32 to 37 y. The median baseline CD4 cell count ranged from 87 cells/µl in Khayelitsha, South Africa to 131 cells/µl in Abidjan, Côte d'Ivoire, and the proportion with advanced clinical stage of disease (WHO stage III/IV) from 68% (Connaught) to 90% (Khayelitsha). A total of 1,177 deaths were recorded during 14,695 person-years of follow-up. Crude estimates of cumulative mortality at 2 y on ART ranged from 7.4% to 12.3%, and loss to follow-up from 7.1% to 31.7% across programmes. 10.1371/journal.pmed.1000066.t001 Table 1 Description of treatment programmes included in analyses. Programme Country Number of Patients Median Age (IQR) Number of Women (%) Median (IQR) Baseline CD4 (Cells/µl) Number in WHO stage III/IV at Baseline (%a) Number of Patients Lost to Follow-up at 2 y Cumulative Loss to Follow-up (95% CI) at 2 y (%)b Cumulative Mortality (95% CI) at 2-y (%) Crude Following Multiple Imputationc CEPREF Côte d'Ivoire 2,400 35 (30–42) 1,770 (74) 131 (51–217) 1,939 (82) 218 13.7 (12.1–15.6) 10.6 (9.3–12.1) 11.2 (9.7–12.8) Connaught Zimbabwe 857 37 (32–44) 585 (68) 102 (51–159) 263 (68) 33 7.1 (5.1–9.8) 7.4 (5.7–9.6) 7.5 (5.6–9.6) Gugulethu South Africa 1,916 33 (29–39) 1,310 (68) 103 (50–160) 1,528 (80) 62 7.6 (5.9–9.7) 11.1 (9.5–13.1) 11.1 (9.2–12.8) Khayelitsha South Africa 3,366 32 (28–38) 2,353 (70) 87 (35–146) 3,018 (90) 148 7.1 (6.1–8.4) 11.2 (10.1–12.4) 11.3 (10.2–12.5) Lighthouse Malawi 4,710 36 (30–42) 2,813 (60) 126 (54–211) 4,063 (86) 829 31.7 (29.9–33.6) 12.3 (11.1–13.6) 13.2 (11.9–14.4) Combined — 13,249 34 (29–41) 8,831 (67) 107 (46–175) 10,811 (85) 1,290 16.2 (15.4–17.1) 11.1 (10.5–11.8) 11.7 (11.1–12.3) Number of patients (%) unless otherwise indicated. a Percent of patients with known clinical stage at baseline. b Estimated for patients with at least one additional potential year of follow-up until administrative censoring date of the database of their programme. c Outcomes imputed in patients lost to follow-up. CEPREF, Centre de Prise en Charge de Recherches et de Formation/Agence National de Recherches sur le Sida (ANRS) 1203 cohort. Information on the CD4 count and clinical stage at the start of ART was missing for 2,535 patients (19.1%) and 529 patients (4.0%), respectively. Total follow-up time after imputation increased to 17,480 y, and the number of deaths to 1,338. Mortality estimates at 2 y were somewhat higher after imputation for the Centre de Prise en Charge de Recherches et de Formation (CEPREF) and Lighthouse cohorts, but similar to the crude estimates in the other cohorts (Table 1). Patient characteristics at baseline and the effect of multiple imputation of missing information on the distribution of CD4 cell count and clinical stage of disease at baseline are shown in Table 2. At 6 mo, the median CD4 cell count had increased to 245 cells/µl (interquartile range [IQR] 167–347), varying between 220 and 272 cells/µl across programmes. At 12 mo, the median CD4 count was 285 cells/µl (IQR 197–393), ranging from 253 to 307 cells/µl. 10.1371/journal.pmed.1000066.t002 Table 2 Baseline characteristics and mortality over the first 2 y of ART. Category Subcategory Original Data Following Multiple Imputationa n (%) Person-Years n Deaths (%) n (%) Person-Years n Deaths (%) Overall — 13,249 (100) 14,695 1,177 (100) 13,249 (100) 17,480 1,338 (100) Age (y) 16–29 3,436 (26) 3,856 276 (23) 3,436 (26) 4,564 309 (23) — 30–39 5,875 (44) 6,567 521 (44) 5,875 (44) 7,789 594 (44) — 40–49 2,919 (22) 3,232 266 (23) 2,919 (22) 3,851 308 (23) — ≥50 1,019 (8) 1,041 114 (10) 1,019 (8) 1,276 127 (10) Sex Female 8,831 (67) 10,047 701 (60) 8,831 (67) 11,796 789 (59) — Male 4,418 (33) 4,648 476 (40) 4,418 (33) 5,684 549 (41) Initial ART regimen NNRTI-based 11,325 (85) 12,616 1,027 (87) 11,325 (85) 14,969 1162 (87) — PI-based 94 (1) 124 8 (1) 94 (1) 148 9 (1) — Unknown or other 1,830 (14) 1,955 142 (12) 1,830 (14) 2,363 167 (12) Baseline CD4 count (cells/µl) 0.48). The association between the baseline CD4 count and excess mortality became weaker with time on treatment and the interaction was included in estimating excess mortality and SMRs. Excess Mortality Excess mortality declined with time on treatment and increasing baseline CD4 cell count. It was lower in women as compared to men, and higher in patients starting ART with advanced stage of disease (Table 4). Overall excess mortality per 100 person-years was 6.95 (5.95–8.13), varying between 17.51 (14.50–21.14) and 1.00 (0.55–1.81) for patients starting with worst prognosis (CD4 cell count <25 cells/µl and advanced stage of disease) and best prognosis (CD4 cell count ≥200 cells/µl and less advanced stage of disease), respectively. In the second year on ART excess mortality in the patients group with best prognosis was 0.27 (0.08–0.94) per 100 person-years. Figure 2 shows the distribution of estimated excess mortality rates over the first 2 y of ART, taking into account baseline CD4 count, clinical stage, age, and sex. 34% of patients were exposed to excess mortality rates between four and six additional deaths per 100 person-years, 25% to rates below four per 100 person-years, and 41% to rates above six per 100 person-years. Table S2 gives excess mortality rates over 2 y by baseline CD4 count, clinical stage, and by sex and age group. 10.1371/journal.pmed.1000066.g002 Figure 2 Distribution of excess mortality over the first 2 y of ART in patients starting ART in five treatment programmes in sub-Saharan Africa. Standardised Mortality 10.1371/journal.pmed.1000066.t004 Table 4 Excess mortality per 100 person-years by time period on ART, baseline CD4 count, and clinical stage of disease. CD4 Count (Cells/µl) Clinical Stage Time after Starting ART (mo) 1–3 4–6 7–12 13–24 Overall (1–24) <25 Advanced 63.79 (44.67–91.10) 15.87 (8.62–29.22) 7.99 (4.87–13.09) 4.94 (3.39–7.21) 17.5 (14.5–21.1) — Less advanced 18.25 (9.15–36.41) 4.54 (1.91–10.79) 2.29 (0.96–5.43) 1.41 (0.75–2.67) 4.87 (2.64–9.00) 25–49 Advanced 38.32 (25.15–58.38) 14.36 (8.49–24.28) 7.87 (3.98–15.57) 2.70 (1.18–6.16) 12.1 (9.09–16.0) — Less advanced 10.96 (5.34–22.50) 4.11 (1.81–9.35) 2.25 (0.83–6.11) 0.77 (0.26–2.28) 3.36 (1.74–6.49) 50–99 Advanced 21.86 (11.39–41.97) 8.80 (4.46–17.36) 4.55 (2.22–9.33) 3.18 (1.65–6.12) 7.38 (4.98–10.95) — Less advanced 6.25 (2.49–15.70) 2.52 (0.98–6.44) 1.30 (0.45–3.77) 0.91 (0.36–2.29) 2.05 (0.98–4.31) 100–199 Advanced 13.73 (7.20–26.19) 7.52 (4.65–12.18) 2.71 (1.54–4.79) 1.81 (0.94–3.50) 4.83 (3.56–6.56) — Less advanced 3.93 (1.57–9.84) 2.15 (0.95–4.87) 0.78 (0.34–1.80) 0.52 (0.23–1.17) 1.35 (0.70–2.59) ≥200 Advanced 10.20 (7.55–13.77) 3.50 (1.90–6.45) 3.12 (0.19–5.01) 0.96 (0.37–2.49) 3.59 (2.82–4.56) — Less advanced 2.92 (1.53–5.58) 1.00 (0.43–2.35) 0.89 (0.48–1.66) 0.27 (0.08–0.94) 1.00 (0.55–1.81) Overall Overall 21.20 (19.21–23.38) 7.58 (6.48–8.86) 3.79 (2.93–4.90) 2.15 (1.79–2.58) 6.95 (5.95–8.13) Results from Poisson model that included all variables listed and allowed for interaction between baseline CD4 cell count and time after starting ART. SMRs, overall and stratified by time period on ART, baseline CD4 cell count, and clinical stage of disease are shown in Table 5. The overall SMR over the first 2 y was 18.7 (17.7–19.8), declining from 130.0 (110.9–152.4) to 4.0 (3.3–5.0) over months 1–3 to months 13–24, respectively. Over the first 3 mo, SMRs varied between 552.7 (400.1–763.5) for patients starting ART with worst prognosis to 30.2 (15.7–58.0) among patients starting with best prognosis. In the second year on ART, SMRs for these two patients groups were 11.5 (7.95–16.7) and 1.14 (0.47–2.77), respectively. Over the full first 2 y and depending on CD4 count and clinical stage of disease, SMRs varied between 47.1 (39.1–56.6) and 3.4 (1.9–6.2). Table S3 gives SMRs over 2 y by baseline CD4 count, clinical stage, and by sex and age group. 10.1371/journal.pmed.1000066.t005 Table 5 SMRs by time period on ART, baseline CD4 count, and clinical stage of disease. CD4 Count (Cells/µl) Clinical Stage Time Period (mo) 1–3 4–6 7–12 13–24 Overall (1–24) <25 Advanced 552.7 (400.1–763.5) 142.7 (85.3–238.7) 37.2 (22.3–62.0) 11.5 (7.95–16.7) 47.1 (39.1–56.6) Less advanced 186.3 (99.3–349.2) 48.1 (22.7–102.0) 12.5 (5.52–28.4) 3.88 (2.10–7.17) 15.8 (8.99–27.9) 25–49 Advanced 333.1 (233.3–475.5) 130.4 (79.9–212.6) 37.2 (20.1–68.9) 7.01 (3.51–14.0) 31.4 (26.1–37.7) Less advanced 112.3 (59.9–210.4) 43.9 (20.7–93.1) 12.5 (5.17–30.3) 2.36 (0.95–5.85) 10.6 (6.08–18.4) 50–99 Advanced 192.2 (108.5–340.5) 80.4 (44.5–145.1) 22.6 (11.7–43.6) 8.04 (4.73–13.7) 19.6 (15.1–25.5) Less advanced 64.8 (28.9–145.0) 27.1 (11.9–61.7) 7.61 (2.95–19.6) 2.71 (1.24–5.90) 6.59 (3.58–12.1) 100–199 Advanced 123.0 (70.6–214.4) 70.6 (46.7–106.8) 14.5 (8.67–24.1) 5.34 (3.46–8.23) 13.6 (11.5–16.1) Less advanced 41.5 (18.7–91.9) 23.8 (11.8–48.1) 4.87 (2.29–10.4) 1.80 (0.98–3.31) 4.57 (2.67–7.84) ≥200 Advanced 89.5 (62.1–129.0) 34.3 (18.4–63.8) 16.1 (11.2–23.1) 3.39 (1.79–6.40) 10.2 (7.63–13.7) Less advanced 30.2 (15.7–58.0) 11.5 (4.98–26.8) 5.43 (3.13–9.43) 1.14 (0.47–2.77) 3.44 (1.91–6.17) Overall Overall 130.0 (110.9–152.4) 49.6 (42.2–58.3) 13.4 (10.4–17.3) 4.05 (3.25–5.04) 18.7 (17.7–19.8) Sensitivity Analyses When restricting the analysis to the three treatment programmes with rates of loss to follow-up below 10% (Khayelitsha, Gugulethu, Connaught), estimates of excess mortality and SMRs were somewhat lower, but the variation with time period, baseline CD4, and clinical stage was similar to that observed using all data (Tables S4 and S5). For example, in the second year on ART excess mortality in the patients group with the best prognosis was 0.15 (0.015–1.50) per 100 person-years and the SMR was 0.76 (0.18–3.10). Discussion In this collaborative study of five treatment programmes in four countries in sub-Saharan Africa, the mortality of HIV-infected patients starting ART could be compared with that estimated for the corresponding non-HIV–infected general populations. In these countries, a large proportion of deaths among young and middle-aged adults are HIV-related. We found that mortality during the first 2 y of ART was more than 18 times higher than in the general population not infected by HIV. However, there was large variability between prognostic groups and over time: in patients with very low CD4 counts and advanced clinical disease, mortality was increased over 300 times in the first 3 mo of treatment, whereas in the second year of ART, patients who started with high CD4 counts and less advanced disease had mortality rates that were comparable to those estimated for non-HIV–infected individuals. We used excess mortality rates as well as SMRs and thus took the background mortality in the general population into account. The calculation of expected numbers of deaths was restricted to people not infected with HIV, which is crucial when the prevalence of the exposure (HIV infection) in the general population is high and SMRs are large [25]. The mortality of over 13,000 patients was analyzed, including women and men, teenagers and middle-aged people, and patients with severe and less pronounced immunodeficiency. Our results should therefore be applicable to many other patients receiving ART in sub-Saharan Africa. We used estimates of non-HIV–related mortality from the WHO Global Burden of Disease project [17]. Beginning with the year 1999, WHO has been producing annual life tables for all member states. A key use of these tables is the calculation of healthy life expectancy, the basic indicator of population health published each year in the World Health Report [18]. One limitation of our study is that the reference rates for HIV-unrelated mortality are unlikely to be completely accurate for the source populations from which the HIV-infected patients originate, and that errors in the calculation of expected number of deaths are not reflected in the confidence limits of SMRs and excess mortality rates [26]. The five ART programmes included in this study are public sector scale-up programmes, which serve disadvantaged urban populations. Data from the 1970s and early 1980s suggest that adult mortality is lower in urban Africa than in rural Africa [27]. The generally lower mortality rates observed in urban settings may, however, conceal pockets of poverty and high mortality among urban dwellers [27]. Nevertheless, the use of national rates may have lead to estimates of the expected number of HIV-unrelated deaths that are too high, and SMRs and excess mortality rates that are too low. Given that reliable local mortality data are not available, we believe that the data from the Global Burden of Disease project are the best reference data available. Of note, the estimates used in this study for South Africa are in line with those from other analyses. For example, a recent modelling study of the demographic impact of HIV/AIDS in South Africa by the University of Cape Town and the South African Medical Research Council estimated that in 2006, 71% of deaths in the 15–49 y age group were due to HIV infection [28]. Similarly, a study of AIDS-related mortality in rural KwaZulu-Natal estimated that 127 of 186 deaths (68%) were attributable to AIDS in 2004 [29]. A demographic surveillance study using verbal autopsy in the Agincourt subdistrict, rural South Africa, also found that HIV and tuberculosis were the leading causes of death in people aged 15–49 y [30]. Our study has other limitations. Complete ascertainment of risk factors and deaths and complete follow-up of patients is difficult to achieve in treatment programmes in low-income countries [31],[32]. Loss to follow-up was particularly high in one programme in Malawi, however, this is probably due to a higher rate of transfer out of patients in this programme. At present we cannot distinguish between loss to follow-up and transfer to another programme; this will be remedied in the next update of the database. We used multiple imputation to deal with missing baseline CD4 cell counts and loss to follow-up. This method assumes that missing values can accurately be predicted using the available data. In other words, the probability of missing no longer depends on the missing value after taking the available data into account (“missing at random” in Rubin's terminology [33]). The plausibility of this assumption is unverifiable, but it is clear that mortality is increased in patients lost to follow-up [34]–[36], and unlikely that this can fully be captured by the clinical stage and CD4 cell count at baseline. Of note, sensitivity analyses excluding the sites with high rates of loss to follow-up from Malawi and Côte d'Ivoire gave similar results. Follow-up was limited to 2 y in the present analyses, reflecting the relatively recent scale up of ART in sub-Saharan Africa, and it is possible that mortality will increase again in HIV-infected patients with longer duration of treatment. The short follow-up also meant that life expectancy of patients starting ART could not be examined. The ART Cohort Collaboration of HIV cohorts in Europe and North America recently estimated that life expectancy at age 35 y among patients on ART not infected through injecting drug use was 33 y [37]. These questions will be addressed in future analyses of the IeDEA databases. Finally, our analysis did not consider differences between the HIV-infected and non-HIV–infected populations other than gender and age. In industrialised countries, there are important differences in the prevalence of risk factors, for example smoking, between infected and noninfected populations. In sub-Saharan Africa, where the epidemic is generalised and transmission by heterosexual contacts, differences in lifestyle factors are unlikely to be a major source of bias. How do these SMRs compare with other population groups at increased risk of death due to unhealthy lifestyles, occupational exposures, or chronic conditions other than HIV infection? Few data are available for sub-Saharan Africa. White South African gold miners, compared to the white male population, had an SMR of 1.3, because of excess mortality due to lung cancer, chronic obstructive lung disease, and liver cirrhosis [38]. Among male British doctors born in the 1920s, the probability of dying from any cause in middle age was three times higher in smokers than lifelong nonsmokers [39]. Similarly, an analysis of the National Alcohol Survey in the US showed that regular, heavy drinkers had mortality rates from all causes that were 2.2 times higher than those observed in lifetime abstainers [40]. The mortality of people with a body mass index (BMI) over 35 kg/m2 is increased by factor 1.5 to 2.5, compared to those with a BMI between 20 and 25 kg/m2, and a similar increase in all-cause mortality is found in physically inactive people compared to physically active individuals [41]. In a population-based study in Turin, Northern Italy, women with type 1 diabetes had an SMR for all causes of 3.4 and men an SMR of 2.0 [42]. The SMRs found in these patients and populations exposed to risk factors are thus quite comparable to those found in some of the patient groups included in our analysis. Excess mortality was greater among men than among women. A recent analysis from the ART in Lower Income Countries (ART-LINC) collaboration found that although women are more vulnerable than men to becoming infected with HIV, they were equally or more likely than men to start ART [43]. Women were younger and started treatment at a less advanced clinical stage, which could partly explain their lower excess mortality. Gender inequities in health may affect men as well as women: traditional masculine roles cast men as taking risks, being unconcerned about their health, and not needing help or healthcare [44]. Conventional views of gender inequality might have made it easier for women than men in some settings to become engaged with HIV diagnosis and treatment services [43],[45],[46]. Clearly, continued efforts are needed to empower women and secure their rights to treatment and care for HIV infection. However, more attention needs to be paid to HIV-infected men. Although some HIV-infected patients starting ART in sub-Saharan Africa experienced mortality rates that were comparable with those experienced by other patients with a chronic condition, early mortality in adults starting ART continues to be high in sub-Saharan Africa [47]. Many patients start treatment late, with a history of AIDS defining illnesses and low CD4 cell counts. Leading causes of death include tuberculosis, acute sepsis, cryptococcal meningitis, malignancies, and wasting syndrome [47]. Of note, the Starting Antiretrovirals at three Points in Tuberculosis (SAPIT) trial recently showed that mortality among patients co-infected with tuberculosis and HIV can be reduced by 55% if ART is provided with TB treatment [48]. Although our study cannot determine the CD4 cell count when ART should be started in order to minimise mortality, much of the excess mortality observed in our study would probably be preventable with timely initiation of ART. Further expansion of public health strategies to increase access to ART in sub-Saharan Africa is therefore urgently needed. In collaboration with the Global Burden of Disease project, the IeDEA network will continue to monitor mortality of HIV-infected patients starting ART and compare their mortality to that of the general population not infected by HIV. Supporting Information Table S1 Age- and sex-specific HIV-unrelated mortality per 100 population in Côte d'Ivoire, Malawi, Zimbabwe, and South Africa, 2004. Data from the Global Burden of Disease study [17],[20]. (0.05 MB DOC) Click here for additional data file. Table S2 Excess mortality per 100 person-years for months 1–24 by baseline CD4 count and clinical stage of disease, and by sex and age group. (0.06 MB DOC) Click here for additional data file. Table S3 SMRs for months 1–24 by baseline CD4 count and clinical stage of disease, and by sex and age group. (0.06 MB DOC) Click here for additional data file. Table S4 Excess mortality per 100 person-years by time period on ART, baseline CD4 count, and clinical stage of disease in the three ART programmes with low rates of loss to follow-up (Connaught, Gugulethu, Khayelitsha). (0.04 MB DOC) Click here for additional data file. Table S5 SMRs by time period on ART, baseline CD4 count, and clinical stage of disease in the three ART programmes with low rates of loss to follow-up (Connaught, Gugulethu, Khayelitsha). (0.04 MB DOC) Click here for additional data file.
- Record: found
- Abstract: found
- Article: not found
Human T-lymphotropic virus 1: recent knowledge about an ancient infection.
Kristien Verdonck, Elsa González, Sonia Van Dooren … (2007)
Human T-lymphotropic virus 1 (HTLV-1) has infected human beings for thousands of years, but knowledge about the infection and its pathogenesis is only recently emerging. The virus can be transmitted from mother to child, through sexual contact, and through contaminated blood products. There are areas in Japan, sub-Saharan Africa, the Caribbean, and South America where more than 1% of the general population is infected. Although the majority of HTLV-1 carriers remain asymptomatic, the virus is associated with severe diseases that can be subdivided into three categories: neoplastic diseases (adult T-cell leukaemia/lymphoma), inflammatory syndromes (HTLV-1-associated myelopathy/tropical spastic paraparesis and uveitis among others), and opportunistic infections (including Strongyloides stercoralis hyperinfection and others). The understanding of the interaction between virus and host response has improved markedly, but there are still no clear surrogate markers for prognosis and there are few treatment options.
Author and article information
Contributors
: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
Title:
PLoS ONE
Publisher:
Public Library of Science
(San Francisco, USA
)
ISSN
(Electronic):
1932-6203
Publication date Collection: 2011
Publication date
(Electronic):
14
December
2011
Volume: 6
Issue: 12
Electronic Location Identifier: e29026
Affiliations
[5
]University College London Centre for Medical Molecular Virology, Division of Infection
and Immunity, University College London, London, United Kingdom
Author notes
* E-mail:
mschim@
123456ggd.amsterdam.nl
Article
Publisher ID: PONE-D-11-16657DOI: 10.1371/journal.pone.0029026
PMC ID: 3237577
PubMed ID: 22194980
SO-VID: 71c5bc43-a994-4dfb-8a42-793a2a06b8f7
Copyright © van Tienen et al. This is an open-access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original author and source are credited.
History
Date
received
: 3
August
2011
Date
accepted
: 18
November
2011
Page count
Pages: 8
Categories
Subject:
Research Article
Subject:
Biology
Subject:
Microbiology
Subject:
Virology
Subject:
Co-Infections
Subject:
Immunodeficiency Viruses
Subject:
Viral Transmission and Infection
Subject:
Medicine
Subject:
Clinical Research Design
Subject:
Survey Research
Subject:
Epidemiology
Subject:
Clinical Epidemiology
Subject:
Disease Informatics
Subject:
Infectious Disease Epidemiology
Subject:
Survey Methods
Subject:
Infectious Diseases
Subject:
Viral Diseases
Subject:
HIV
Subject:
HIV epidemiology
Subject:
Retrovirology and HIV immunopathogenesis
Subject:
Sexually Transmitted Diseases
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