The economic burden of HIV/AIDS on individuals and households in Nepal: a quantitative study
Average total direct costs to the HIV-affected household (NRs 1512 or US$20.4 per visit) for HIV/AIDS treatment in our study were higher than other studies on HIV/AIDS conducted in Nepal [28, 29]. Higher direct costs in our study were due to inclusion of all costs components and various geographical locations, availability of better but expensive diagnostic and treatment facilities in the private and government hospitals in recent years, and higher awareness levels among PLHIV than before. The average total direct costs for HIV treatment in our study were higher than reported for tuberculosis, water borne diseases and malaria treatment in Nepal [31–33]. However, we cannot compare other studies in Nepal with costs of kala-azar (visceral leishmaniosis or VL) [8, 34], and hepatitis E treatment [35] because of methodological differences in calculation of the costs.
Our study findings show that the highest proportion of direct costs was accounted for by diagnostic tests (32.2%). There is difference between costs of treatment (i.e. doctor’s fee, diagnostic tests, medicines etc.) in government hospitals and private clinics or hospital. The treatment costs increase significantly if the PLHIV visits to the private clinics or hospitals. Although the PLHIV suspect that they might have HIV, they do not visit the hospital or clinic until they are very ill because of possible discrimination from families, relatives and society. This further helps to increase the costs of treatment. Access costs accounted for the second highest proportion (29.4%) of direct costs. There are no HIV/AIDS treatment and care services in every district of Nepal, meaning some PLHIV need to travel farther resulting into higher access costs. A previous study also mentioned the distance from health care facilities as a main problem in getting HIV/AIDS treatment services in Nepal [27]. Moreover, there are no insurance facilities for PLWA in Nepal, which forces individuals to pay out-of-pocket for their treatment. This puts the majority of HIV-affected families at risk of falling into ‘the poverty trap’.
In our study, HIV-affected households spent more than a five times higher proportion of household income for HIV/AIDS (19.3%) than reported by the government of Nepal for general health care (3.3% of average household income) [36], even though nearly half of the sample households (47.2%) were living in poverty, compared to the general population figure (25%). This evidence shows that HIV-affected households pay a considerably higher proportion of their income on healthcare compared to the general population.
While comparing our study findings with similar studies conducted in other countries, the average total direct costs of HIV/AIDS treatment in our study is similar to the total median costs in South India [4] and comparable to Vietnam [20] and Malaysia [37]. However, a study in Chad reported the average total costs more than four times higher than those found in our study [13]. The lower average direct costs in our study compared with the findings in Chad were mainly due to inclusion of both AIDS and non-AIDS respondents. However, a study conducted in Benin reported almost half the costs to access the package of care for ART therapy, than those from our own study findings [38]. The higher direct costs in our study than the study in Benin may be due to methodological differences, as they assessed the costs only to get ART medicine, unlike in our study.
Average total productivity losses (absenteeism and presenteeism) due to HIV/AIDS before adjustment for coping strategies in Nepal was found to be very high in a monthly period (5.05 days). Per month absenteeism (days completely unable to carry out normal daily activities) was 3.15 days and presenteeism (days lost due to reduced working efficiency because of poor health) was 1.9 days. Productivity costs before adjustment for coping strategies by using per capita GDP for valuation was NRs 721 (US$9.7) per month, which was 9.2% of the average monthly household income. The proportion of productivity costs to total costs (sum of direct costs and productivity costs) before adjustment of coping strategies was 32.3%, when doing a valuation using per capita GDP (NRs 721 of NRs 2233).
There have been no studies conducted in Nepal, which measured productivity losses due to HIV/AIDS. The findings of the studies on kala-azar (VL) [34, 39], hepatitis E [35] and malaria [33] could not be compared because of methodological differences in calculating days lost, since these studies calculated costs on a per episode basis.
We note that studies related to productivity losses due to HIV/AIDS in other countries, studies in India [4] and Malaysia [40] reported lower productivity losses than were found in our study. The productivity losses among AIDS patients reported in Chad [13] is comparable to the productivity losses among PLHIV with a self-reported poor health status in our study. The higher productivity losses in our study might be for two reasons. Firstly, PLHIV in Nepal do not have access to a balanced diet required to keep them healthy, due to their chronic poverty. This may weaken their immune system, making them more vulnerable to opportunistic infections. Secondly, we also included presenteeism in productivity losses unlike other studies [4, 40].
Average total costs (sum of average total direct costs and average productivity costs before adjustment of coping strategies, by using per capita GDP for valuation) due to HIV/AIDS in Nepal was NRs 2233 (US$ 30.2), which was 28.5% of an average household monthly income. There were no studies in Nepal assessing the per month average total cost of HIV/AIDS to compare to our study. The average total costs due to HIV/AIDS in our study are considerably higher compared to other diseases in Nepal, because of its chronic nature. Other diseases like tuberculosis, kala-azar (VL), hepatitis E, and malaria can be healed through treatment; therefore, HIV/AIDS causes a higher and long-term economic burden on the HIV-affected households than is caused by the above-mentioned diseases.
Looking for studies similar to our own, we found few that had developed a complex methodology of measuring economic costs that could be compared with our results. One study in Chad reported the total cost for AIDS care to be considerably higher than our study finding. However, they studied only AIDS patients (a more serious stage of HIV/AIDS), and included funeral costs in their study. A study in Spain reported total costs of care for asymptomatic HIV, symptomatic HIV and AIDS patients to be relatively higher than the total costs found in our study [41].
In our study, treatment and access costs (in terms of direct costs) were significantly determined by the health status of respondents (CD4 level 200–400/mm3 – p??0.01, CD4 level 200/mm3 – p??0.001), household income (p??0.001), occupation of respondents (p??0.05), respondents accompanied by others (p??0.01) and study district (p??0.001).
As mentioned above, the CD4 level as a measure of the health status of respondents was found to be one of the significant predictor variables for the treatment and access costs. The respondents with lower CD4 level had to pay higher treatment and access costs compared to respondents with a higher CD4 level. This finding is supported by the studies conducted in in India [4], Italy [42] and Spain [41]. Another significant predictor variable for the treatment and access costs was household income. Respondents having a higher household income paid higher treatment and access costs. This finding was supported by a study in India [4]. Occupation was found to be another significant predictor variable for the treatment and access costs. The respondents with household work as their occupation had paid significantly lower treatment and access costs than the respondents with agriculture (farming) as their occupation. The descriptive analysis shows that respondents with an agriculture occupation paid higher access costs than respondents with household work as their occupation. This evidence suggests that respondents with an agriculture occupation travel further for their treatment, resulting in higher treatment and access costs. Study district and PLHIV accompanied by others were found to be other significant predicator variables for treatment and access costs, the reason being that respondents who need to go far for their treatment generally took an accompanying person. Likewise, respondents travel farther for the better treatment facilities which are not available in their local area. Therefore, longer travel distance and use of better treatment facilities increased the treatment and access costs for respondents who took an accompanying person them.
The health status of respondents was a significant predictor variable for productivity costs. This finding is also supported by studies from India and Switzerland [4, 43]. Ethnicity was found to be another significant predictor variable for the productivity costs. Dalit (lower class) respondents had higher productivity costs than Brahmin/Chhetri respondents. The reason may be their poor economic status, as they cannot afford healthy diet, or timely treatment, which require them to fight against infections. Sexual orientation was also found to be a significant predictor variable for productivity costs. Productivity costs were higher for LGBT (lesbian, gay, bisexual and transgender). The reason may be related to the risky behaviour (e.g. – injecting drugs) adopted by the LGBT respondents. It has been reported that substance abuse is seven times higher among LGBTs than heterosexuals [44]. Use of serious drugs may be one of the most important factors to increase productivity losses among LGBTs. This argument is indirectly supported by a study in Switzerland, which reported intravenous drug use as the important determinant of productivity costs among PLHIV [43]. Study district was another important predictor variable for productivity costs. The variation in productivity losses by study site has been supported by other studies [39, 45].
Surprisingly, income was not a significant predictor variable for the productivity costs. It was found that productivity losses between the poorest respondents (first income quintile) and the richest respondents (fifth income quintile) were almost the same (5.5 days vs 5 days). The poorest PLHIV may be sicker than the richest PLHIV due to the unaffordability of treatment in time and lack of a nutritious diet. However, they have to work every day for their own and their family’s livelihood, although they are sick [46]. Perhaps, the richest respondents may be less sick than the poorest respondents (due to affordability of treatment in time or better diet), or they can afford to take time off to be sick.
This is the first study of its kind in Nepal. The sample of the study is also representative of Nepal’s population. This is because it has a relatively large sample size (400), and was conducted in six representative districts of Nepal, which cover five development regions, east to west, and hilly and mountainous to Terai (the plains) regions of the country. In addition, the study included both rural and urban areas unlike previous studies, which were focussed only in urban areas. While calculating costs of illness, previous studies had excluded some important cost components like ‘costs for accompanying person’ in attempting to calculate direct costs, and cost of presenteeism in calculating productivity costs. Our study has included all components of the direct costs and productivity costs.
This study was conducted in government operated HIV/AIDS treatment centres. Therefore, it excluded those PLHIV who did not visit the treatment centre during the period of the study. Due to the limitations of time and resources, control groups (e.g. – HIV negative people, or people with other disease profiles) were not accessed. Cross-sectional studies only capture information from respondents at a certain point of time when data collection is carried out. Therefore, information collected in cross-sectional studies may not be as robust as information collected from longitudinal studies because variations in study subjects may be affected by weather, seasons and time of study. Most of the data in the study were derived from reporting of respondents who needed to recall their past. Therefore, there might be a possibility of recall bias by respondents about the given information and uncertainty about the robustness of the data. Moreover, the data in the study was collected in 2011. There might be changes in costs and burden to the HIV-affected households now due to changes in socioeconomic factors affecting the burden over the period.