When examining differences in total predicted values for both countries, PDLs were much lower in South Africa than in Zambia for nearly all subgroups (Table 2). Of the four groups formed based on HIV and labour force status, HIV-positive ILF had highest PDLs at 1.88 (95% CI 1.57–2.19) and 0.32 (95% CI 0.23–0.41) in Zambia and South Africa, respectively, followed by HIV-positive NILF at 1.54 (95% CI 1.28—1.80) and 0.26 (95% CI 0.14–0.37), HIV-negative ILF at 1.05 (95% CI 0.94–1.17) and 0.18 (95% CI 0.15–0.21), and HIV-negative NILF with lowest PDLs at 0.86 (95% CI 0.78–0.95) and 0.15 (95% CI 0.10–0.20). Predicted PDLs increased with age. Among HIV-positive individuals, 35–44-year olds who had been on ART for less than 1 year had highest PDLs at 3.38 days (95% CI 1.94–4.81) in Zambia and 2.24 days (95% CI 0.19–4.28) in South Africa, whereas under 25-year olds who were not on ART had lowest PDLs at 1.24 days (95% CI 0.97–1.51) in Zambia, and 0.16 days (95% CI 0.09–0.23) in South Africa.
There was no substantial sex difference in predicted PDLs among HIV-positive and HIV-negative individuals in both countries. HIV-negative and HIV-positive individuals across all categories who had completed secondary school had lower PDLs than those with primary school and higher education in Zambia. In South Africa, those with higher education had fewest PDLs, followed by those with secondary education and those with primary education. Predicted PDLs also differed across regions in Zambia, but there was little variation across regions in South Africa. Likelihood ratio tests for comparison with the Poisson model rejected the null of no over-dispersion (Table 3), confirming that NegBin provided a better fit (A6, http://links.lww.com/QAD/B450).
This is the first study of productive days lost to illness or accessing healthcare among HIV-positive and HIV-negative individuals in a random sample of adults in sub-Saharan Africa. It offers a rare insight into PDLs for the large majority of the population that is informally employed, self-employed, unemployed or not part of the labour force. The study further provides estimates of the PDLs of PLWH at different stages of engagement with HIV care, including those not on treatment, and those who were unaware of their status (44% in Zambia and 53% in South Africa) . We undertook a direct comparison of PDLs between HIV-positive and HIV-negative individuals based on laboratory-confirmed HIV status. HIV-negative individuals provided an important benchmark that allowed us to analyse the association between HIV and PDLs, which is a crucial information in countries with competing risks that impede productivity, most notably other diseases. We analysed PDLs, which were lost to both sickness and accessing healthcare. Travel and waiting times at facilities have been identified as important barriers to accessing and remaining in HIV care . We performed analyses separately for Zambia and South Africa because of substantial differences in labour markets, social security and healthcare systems.
In Zambia, 21% of the sample were HIV-positive and had 0.74 more PDLs than HIV-negative individuals over 3 months, whereas in South Africa, the 22% PLWHs had only 0.13 more PDLs. Our estimates are markedly lower than those from previous studies [5,11–17]; the median excess PDLs across eight previous studies was 5.1 days over 3 months, with high SD of 9.55 and estimates ranging between zero and over 33 excess PDLs for HIV-positive workers in their final year of life. Previous studies analysed PLWH in formal employment who were not representative of the population of PLWH, which may explain some of the divergence. Most formal sector workers enjoy statutory paid sick leave and have, therefore, lower opportunity costs of work absenteeism. Most respondents in our sample were informal sector workers, or unemployed workers with informal jobs and less able to afford a day of lost pay. This may explain why our estimates are lower than those of previous studies. Moreover, our disaggregated results for Zambia indicate that the two HIV-positive fractions with the lowest excess PDLs, that is, those not on ART and those on ART for 3 years or more together, make up 76% of the HIV-positive population. It seems reasonable that these groups lose fewer days than those more recently started on ART, because the former are in the earlier stages of the disease (and therefore, not yet on ART), and the latter are virally suppressed because they have been on ART long-term. Our comparison of community-level variations in excess PDLs showed significant differences within Zambia, but less so across communities in South Africa. These differences may be driven by a range of unobservable factors that are not captured in the model, including variations in economic conditions across regions, health system differences and social norms. The larger variations observed in Zambia are most likely because the study communities are spread across the country, reflecting the heterogeneity across regions, whereas in South Africa, the communities are all located in the Western Cape Province, and thus more likely to be similar in unobservable characteristics.
Five of nine previous studies were conducted before 2010 when ART was less accessible, or they focused on the (nowadays) small and nonrepresentative subgroup of PLWH in their final year of life, or with AIDS [11,15–17], and it is likely that they had higher PDLs than the population of PLWH today. Longitudinal studies among infected agricultural and mining workers are consistent with our findings. They have demonstrated a V-shaped pattern for labour force participation and productivity over the course of HIV disease, declining sharply as symptoms worsen in the months before ART initiation and rebounding within a few months to levels close to those experienced prior to becoming symptomatic [12,13,25–27]. Across all CD4+ cell count ranges except less than 50 μl, PLWH receiving ART are less absent than those not receiving treatment .
Estimates of PDL are higher for Zambia than for South Africa. It is possible that PDLs are affected by the time lost accessing healthcare, rather than inability to work because of sickness. As guidelines for both countries stipulate quarterly clinic visits for PLWH, the differences are likely explained by variations in travel and clinic waiting timings between the two countries. However, as the proportion of PLWH not on ART is only slightly higher in South Africa, it is unlikely that barriers to access can explain all differences. We could not find comparable empirical estimates of waiting times for the two countries; an evidence gap that requires further research. If PDLs were mainly explained by inefficiencies in accessing care, then they could possibly be reduced by supply-side interventions. Differentiated models of care policies, such as community pick-up points and adherence clubs, are being rolled out in both countries. They aim to shorten the time required to pick up drugs, and promise to remove or lower existing access barriers with possibly positive effects for PDLs .
This study has limitations. First, PDLs are based on self-reports and did not account for reduced productivity on working days, possibly underestimating productivity losses. Most previous studies have used employment records, but these are not available for informal sector workers and individuals not in the labour force. It is also difficult to measure reduced productivity while working. Second, we had no information on individuals’ clinical disease stage, and so stratified PDLs for PLWH by self-reported time on ART, which could have been affected by recall bias. This would not affect our overall estimates, but potentially those by treatment stage. However, mean CD4+ cell count at ART initiation has remained at about 152/μl in the past decade in sub-Saharan Africa . Our results for Zambia suggest that after 2 years on ART, PDLs recover almost to those of individuals in earlier disease stages, a finding corroborated by previous studies on HIV-positive workers [12,13,25–27]. We also had to rely on self-reports of ART initiation amongst those self-reporting being HIV-positive, which may have resulted in some over-classification of individuals into the ‘not being on ART category.’ Third, we could not control for all covariates that may affect PDLs, for example, the presence of other working age individuals in the household, something not assessed in our sample. Moreover, women are overrepresented in our sample, which may bias our findings. However, we control for sex in all models and the predicted PDLs for men and women are very similar in Zambia, and not statistically different in South Africa. Finally, our data comes from communities in urban and periurban areas with comparably high HIV prevalence, and are therefore, not necessarily representative of other communities in the two countries.
We have calculated the days of work and home productivity lost to illness of all individuals irrespective of whether they were in the labour force, overcoming ethical issues that arise when comparing the benefits of interventions between individuals who are working and those who are not, even if they make positive contributions to society. These estimates could be used to calculate the opportunity costs of HIV in monetary terms, for example, by multiplying the estimates with gross domestic product (GDP) per capita or minimum wage rates. However, micro estimates of productivity, such as ours are incorrect estimates of future financial gains resulting from prevention or treatment interventions; they may underestimate or overestimate the aggregate productivity benefits from improved health . Projection of the future macroeconomic impact requires more complex general equilibrium modelling, which considers additional factors, such as the degree to which infections are concentrated in hard-to-replace skilled workers, levels of unemployment, the impact of interventions on life expectancy, education, migration and changes in public and private savings or investments [7,31].
Our results provide estimates of the burden of the HIV epidemic resulting from lost work and home productivity in Zambia and South Africa. These will be a crucial input for modelling studies that aim to calculate the number of days lost to sickness that could be averted through programs of enhanced HIV prevention and treatment, and to comprehensively assess the economic benefit of such programs. We generated predictions of PDLs in various subgroups so that our findings are useful for a wide range of future studies. UNAIDS policies directed at achieving the ambitious 90–90–90 targets , are partly motivated by estimates of improved work productivity generated by simulation studies [8,9]. Our findings help to assess the validity of the assumptions on which these studies were based. For example, our results showed that HIV-negative workers do not have a null absenteeism rate (previous studies assumed that they do), and that labour productivity of persons on ART for three or more years is very similar to asymptomatic HIV-infected adults (previous studies assumed that it is substantially less) [8,9].
As part of the United Nations’ Sustainable Development Goals, the world has pledged to end the AIDS epidemic as a public health threat by 2030. To reach this ambitious goal, UNAIDS estimates that domestic and international investments in HIV programs in LMICs need to increase by about one-third, from an estimated US$ 19.1 billion in 2016 to US$ 26.2 billion until 2020 . This represents a substantial allocation of resources that might otherwise be used for alternative worthwhile projects. At the country level, HIV interventions must compete against public investments into other interventions in the areas of health, education, infrastructure, housing, or agriculture. The benefits of these investments are commonly assessed on basis of their economic returns. It is difficult for policy makers to compare the benefits of the large investments needed to end the epidemic when their returns are only measured in terms of health outcomes, even if those are substantial. The findings from this study form an important contribution towards building a comprehensive and accurate investment case for HIV prevention and treatment interventions based upon their monetary benefits.
We are grateful to all members of the HPTN 071 (PopART) Study Team and to the study participants and their communities for their contributions to this research. We are grateful for comments from Ronelle Burger (Stellenbosch University, South Africa), Sam Griffith (FHI 360, USA), Gesine Meyer-Rath (Wits University, Johannesburg, South Africa & Boston University, USA), and Andrew Mirelman (University of York, United Kingdom) for comments on earlier versions of this article.
Funding: HPTN 071 is sponsored by the National Institute of Allergy and Infectious Diseases (NIAID) under Cooperative Agreements UM1-AI068619, UM1-AI068617, and UM1-AI068613, with funding from PEPFAR. Additional funding is provided by 3ie with support from the Bill & Melinda Gates Foundation, as well as by NIAID, the National Institute on Drug Abuse (NIDA), and the National Institute of Mental Health (NIMH), all part of NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAID, NIMH, NIDA, PEPFAR, 3ie, or the Bill & Melinda Gates Foundation. K.H. was also partly funded by the National Institute for Health Research Health Protection Research Unit in Modelling Methodology at Imperial College London in partnership with Public Health England, and by the Centre funding from the UK Medical Research Council and Department for International Development, MRC Centre for Global Infectious Disease Analysis, reference MR/R015600/1by.
Contributors: R.T. and K.H. both conceived and designed the work. R.T. conceived and led on the statistical analysis and contributed to drafting and revising the article. K.H. took the lead on writing and revising the article and contributed to analysis and interpretation of the data. R.F. and K.B. contributed to the analysis of the data and revising of the article. All other authors contributed to the conception or design of HPTN071 (PopART), interpretation of data for the work, acquisition of the data and revision of the article.
Conflicts of interest
K.H. and R.T. received personal fees from the international Decision Support Initiative for work unrelated to this study. K.H. also received personal fees from The Global Fund for work unrelated to this study.
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absenteeism; economics; HIV/AIDS; informal sector; labour productivity; sickness days
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