Antiretroviral treatment coverage in a rural district in Tanzania – a modeling study using empirical data

In recognition of the positive impact of antiretroviral treatment (ART) on HIV-associated survival, the Tanzanian Government, in collaboration with international organizations and donor agencies, introduced a treatment program in October 2004 aimed at treating all eligible AIDS patient with antiretroviral drugs (ARV) [1-5]. ART scale-up in Tanzania started in 2004 with 96 care and treatment centers (CTC) which grew to about 1100 in 2010 [6]. The number of PLHIV enrolled on ART was 23,951 in December 2005, reached approximately 384,816 by 2010 [6,7]. AIDS-related mortality and new HIV infection decreased by 41% and 46% between 2005 and 2013 respectively [8]. Recent evidence shows in addition to treating AIDS patients, ART is potential for preventing further spread of HIV virus [9-11]. Slow progress in ART enrollment in many countries in resource-limited setting prompted a political declaration on HIV and AIDS in 2011 aimed at “Intensifying efforts to eliminate HIV and AIDS and reaffirming commitment towards the 2001 and 2006 goals of universal access to comprehensive prevention, treatment, care and support” [12,13]. Several global initiatives followed the UN declaration includes “Treatment 2015” aimed at reaching an estimated 15 million people with antiretroviral therapy by 2015 [14] and “UNAIDS 90-90-90” aimed to achieve the following by 2020; 90% of all PLHIV know their HIV status, 90% of all people with diagnosed HIV infection receive sustained antiretroviral therapy and 90% of all people receiving antiretroviral therapy have viral suppression [15].

ART treatment coverage is one of the key indicators used by program managers in assessing health system performance and community engagement in treating and preventing HIV. It is measured as PLHIV on combination treatment as a proportion of those in need of treatment [16]. The definition of the number of people in need of ART has varied over the years with changes in WHO guidelines (<200 CD4 in 2006, <350 in 2010 and <500 in 2013). Changes in the guidelines came as a result of emerging evidence from clinical trials of improved survival upon early ART initiation. In Tanzania, an eligibility criterion of CD4 count < 200 was adopted when the treatment program started in 2004 until 2012 when changes to adopt the 2010 World Health Organization (WHO) guideline of CD4 < 350 were made. An estimate from WHO showed adult coverage was 49% under CD4 < 200 in 2006. Under CD4 < 350 adult ART coverage was 32% in 2009, 40% in 2011 and 68% in 2012 [16].

Evaluation of the efficiency of ART programs through ART coverage has been hampered by a lack of reliable statistics on who is on treatment and who is eligible for treatment in most resource-limited countries. A substantial proportion of PLHIV are not aware of their HIV status and ART eligibility due to low HIV testing rates, therefore the ideal number of those in need of treatment cannot directly be obtained unless mathematical models are applied. The absence of a comprehensive information system to document exact number of PLHIV on treatment, ART adherence, drop-out and mortality makes the ART coverage estimation uncertain. UNAIDS developed the Spectrum model, a constantly updated, user-friendly software program which predicts the theoretical need for ART at national level by projecting distribution of expected CD4 counts and past HIV incidence in the population among other parameters [17,18]. However, some of the Spectrum’s model input parameters, such as the distribution of CD4 counts and HIV incidence, are rarely available at national and sub-national level and may lead to less reliable estimates of treatment coverage. This gap in valid program data, especially at sub-national level such as district, may compromise strategic priority and target settings in this era of power decentralization to the districts. So far, regional and district level data have not been well utilized in generating local level estimates of treatment coverage for district level planning. Recently, UNAIDS used all PLHIV as the denominator to calculate ART coverage. This is due to the fact that national ART initiation guidelines do not always match global guidelines, making country comparisons and time trend analysis of little value [19]. In 2013, UNAIDS estimated ART coverage for adults living with HIV regardless of CD4 count in Tanzania to be 41% [8]. A review of ART initiation criteria in the national guidelines for 94 countries (representing 86% of global HIV burden) showed that only seven countries from the developed world initiate ART irrespective of CD4 cell count [10]. Alternative methods to estimate the number of PLHIV in need of treatment for countries that have not adopted a “test and treat” policy are needed to evaluate whether programs have achieved their coverage target based on their recommended ART initiation policy.

The ALPHA network (The Analyzing Longitudinal Population-based HIV/AIDS data on Africa) has developed an alternative model which requires only age-specific mortality patterns of PLHIV to predict ART need for different CD4 initiation criteria [20].

The model estimates the proportion in need of ART, by sex and age group, among PLHIV who would be expected to die within a pre-specified period in the absence of ART. The pre-specified period is equivalent to CD4 count cut-offs; thus the model can be adopted for different countries or local policy guidelines of ART eligibility. The optimal period from ART initiation to death is an important parameter in deciding when to start ART in most policy guidelines. The model takes into account age-related disease progression and cumulative estimated treatment need due to the improved survival of those accessing treatment [21]. Model estimates can then be applied on national or sub-national populations of PLHIV to estimate the number in need of ART. Estimates of the number of HIV-positive individuals can be derived from representative HIV prevalence surveys available in many resource-limited countries. The additional advantage of the ALPHA model is its capability to estimate the proportion of HIV individuals in need of pre-ART care. Pre-ART care coverage is the ratio of PLHIV who are not eligible for ART (CD4 count > policy recommendation), over all PLHIV in the population. People on pre-ART care are enrolled for CD4 count monitoring, prophylaxis, nutritional advice and counseling. Early enrolment to pre-ART care is a gateway to appropriate timing of ART initiation, therefore pre-ART care coverage is an important indicator of the future success of national and regional ART program implementation [22,23].

We aimed to estimate adult pre-ART care and ART coverage by age and sex in the rural Rufiji district in Tanzania in line with the WHO guideline of 2006 and 2010 and with PLHIV using routinely aggregated patient-level cohort data from 2006–2010.

Age- and sex-specific ART coverage are essential in evaluating progress and program implementation in the district. ART is not only relevant in treating AIDS patient but implemented as prevention strategy (TasP). We utilized district care and treatment data to estimate pre-ART care and ART coverage in a rural district of Tanzania. We provided age- and sex-disaggregated estimates of ART coverage to give district program managers a broader understanding of the coverage gap so that they could make informed decisions on resource allocation. National estimates of ART coverage are routinely reported separately for children (<15 years) and adults (15 years and above) which may be slightly less comparable to the coverage reported in this analysis (15–49 years). The district and national comparisons in this research may provide insights into trends and coverage levels and must be interpreted with caution. Our annual analysis of ART coverage in the rural district of Rufiji in Tanzania between 2006 and 2010 showed slow progress in scaling up and in reaching PLHIV with ART. Overall ART coverage among adult PLHIV increased from 3 to 18% while coverage based on CD4 criteria of < 350 increased from 6 to 30% between 2006 and 2010. Using ART eligibility that was operational during the study period (CD4 < 200), coverage increased from 10 to 38% between 2006 to 2010. The national estimate reported by WHO in 2009 was 32%, slightly higher than the estimated 25% in the district in the same year. Using the 2010 WHO guideline, the ART coverage forecast in the district reached 48 and 54% where the national estimate was 40 and 68% in 2011 and 2012 respectively. In 2013, WHO estimated ART coverage at 41% for adults PLHIV in Tanzania while projected district ART coverage for PLHIV was 32%, substantially lower than the national estimate. The findings from the analysis provide evidence of low ART coverage in the district, under the operational target of reaching PLHIV with a CD4 count < 200 and universal coverage.

Disaggregated analysis of ART coverage provides evidence that treatment-seeking behavior is driven by aging and sex. Results suggest that more than twice as many women compared to men were enrolled in ART. Women are generally diagnosed earlier with HIV because of HIV testing services offered during antenatal care visits [33]. ART coverage was consistently lower for males and young people over the study period. Remarkably low coverage among young people aged 15 to 24 and men in particular were recorded in this study. While the overall ART coverage was 18% in 2010, coverage among females aged 35–44 was above 30% and below 4% among young males under the age of 25. This is a cause for concern, given that men and women aged 15–24 years accounted for 22% of all new adult HIV infections in 2011, and this is the group least likely to know their HIV status and most likely to be sexually active, have multiple partners and reproduce [34,35]. An HIV testing study conducted in 2011 for sub-Saharan Africa showed that only 15% and 10% of young women and men respectively aged 15–24 years had been tested and had their HIV status confirmed [36]. In 2012, the HIV prevalence was estimated at 3.4% among women and 1.4% among men aged 15–24 years in Tanzania [32,37].

Early enrolment to pre-ART care is a gateway to appropriate timing of ART initiation and pre-ART care coverage is therefore an important indicator of the future success of national and regional ART program implementation. The fact that the majority who start on ART in Tanzania do so at advanced stage of AIDS is likely to be the primary contributor to high attrition and mortality rates in the first year of the ART program [38]. Loss to follow-up (up to 36%) and high mortality (up to 15%) have been observed in the past three years following ART initiation in Tanzania [38].

Slow progress was made in the proportion of HIV-positive individuals in pre-ART care. Coverage was observed to increase from 5% in 2006 to 38% in 2010 under CD4 eligibility of < 350. The distribution in terms of age and sex was similar to ART coverage characterized by low coverage for young people and men in particular.

Scaling up ART coverage to 80% of PLHIV by 2015 entails increasing the number of people initiating ART every year while keeping those already started on treatment adherent to ART. Further, decentralizing service delivery to primary health care facilities and reaching the young population with counseling and testing services are key to expanding coverage. The low ART coverage in this rural Tanzanian district demonstrates the challenge in meeting global targets and calls for an adjustment of district pre-ART care and ART programs in order to fit local realities and priorities. The current financial context and severe lack of human resources are particularly challenging [37,39].

Coverage estimates depend considerably on an accurate estimation of the number of PLHIV in surveys and a comprehensive information system to keep track of those enrolled on ART. HIV prevalence surveys in Tanzania are often confined to individuals aged 15 to 49 years and therefore the numbers of PLHIV among children younger than 15 and adults older than 50 years are not known for certain. As a result, this study excludes coverage estimation for children and elderly. Due to a lack of reliable data on HIV prevalence in the district, we used national estimates of age- and sex-specific HIV prevalence to calculated the number of PLHIV, and thus run the risk of over-estimation if the national average is far from the true prevalence in the study area. Our basic model assumptions could bias the results. If a different survival assumption had been applied (or eligibility for treatment set at a higher CD4 count, which is currently indeed the case in global guidelines), the proportion in need of treatment would be higher than the current estimates. The second model assumption is that there is no sex difference in mortality. This is less likely to be violated based on existing empirical data, but a higher overall mortality level for one sex will lead to a higher treatment need across all age groups. To estimate the cumulative number in need of treatment year by year, the model assumes that treatment needs for prior years have been fully satisfied, thus estimating maximum HIV care and ART coverage. Based on this assumption, the real coverage may be slightly lower if substantial proportion of PLHIV is not enrolled every year. There is a reduced version of the model which estimates reduced needs (minimum need) taking into consideration program success in enrolling all HIV-positive individuals [21]. This study addresses priority areas that could assist policymakers and implementers to expand access to pre-ART care and ART beyond currently achieved targets in particular for young people aged 15–24 years and for men. This study has shown that behind low treatment coverage is very low pre-ART care coverage, a factor which is an important gateway to ART treatment and a better treatment outcome. A balanced approach should address both the recruitment of new eligible individuals to pre-ART care and ART as well as the successful retention of those already on ART in the program.

ART coverage in the Rufiji district in rural Tanzania is unevenly distributed and far from the universal coverage target of 80%, in particular among young men. Connected to the low ART coverage is the low pre-ART care coverage observed in this study. Taking the most recent WHO estimate of 2013 into consideration, there has been little progress in keeping up with ART needs since 2010. Low pre-ART care coverage may be highly associated with low testing rates and stigma towards individuals with AIDS as observed elsewhere in Tanzania. Although attrition analysis was not the aim of this study, we observed a substantial proportion of drop-out in both pre-ART care and ART programs which also may have contributed to the observed low coverage. To strive for universal coverage, both the recruitment of new eligible individuals to pre-ART and ART as well as the successful retention of those already on ART in the program need to be prioritized.