Study setting
We carried out this study at the Centre for Geography Medicine Research-Coast at the Kenya Medical Research Institute (CGMRC-KEMRI) located in Kilifi County, Coastal Kenya. It was part of a baseline phase in an on-going longitudinal larger Adolescent Health Outcome Study (AHOS) between January and July 2018.
Kilifi County had been estimated to have a population of 1.45 million by 2019, of whom, 61% are rural dwellers, and 22% are aged between 10 and 20 years [27, 28]. Kilifi is termed as a “moderate HIV county" with 4% HIV prevalence, and 6000 (19%) of people living with HIV are adolescents and youth aged 15-24 years [29]. About a third of Kilifi County is covered by Kilifi Health and Demographic Surveillance System (KHDSS) nested in the KEMRI-Wellcome Trust Research Programme. The economic activity within the KHDSS residence is mainly subsistence farming.
Study design
This was a cross-sectional study nested in a larger adolescents’ health outcome study (AHOS). AHOS is an ongoing longitudinal study aimed at examining neurocognitive and mental health outcomes of 12-17 years old adolescents in the context of HIV. In this sub-study, we recruited primary caregivers accompanying PHI adolescents participating in the parent study. PHI caregivers in the parent study were recruited through consecutive sequential sampling from all families that attended HIV clinic days at eight HIV treatment and care clinics in Kilifi County until the targeted number was achieved. The selection of the eight clinics was done purposively based on distribution and client capacity of HIV specialized clinics within the KHDSS.
Primary caregivers of the adolescents attending a HIV clinic day were approached and briefed about the study during their waiting time at the HIV clinics. Informed consent was obtained from all the individual participants sampled in this sub-study. This included obtaining written parental or guardian consent as well as adolescents’ assent. Recruitment was done by a trained research assistant in liaison with local field worker and community health worker at the HIV treatment facilities. An adolescent living with HIV using anti-retroviral drugs (ARVs) has regular visits to the HIV specialized clinic for comprehensive care including anti-retroviral therapy (ART) refill, treatment for routine medical check-up, counselling services or nutritional advice (when recommended). Ethical approval to conduct this study was obtained from the Kenya Medical Research Institute Scientific and Ethics Review Unit (KEMRI/SERU/CGMR-C/084/3454) and permission from county government of Kilifi (HP/KCHS/VOL-VII/209).
Data collection
We collected data using survey cost questionnaire/tool developed by adapting costing items from different existing tools [30, 31]. The items included information on direct and indirect costs incurred and coping cost strategies for PHI caregivers. The survey cost questionnaire was translated from English to Swahili and then back translated to ensure accuracy and coherence. We piloted the tool with 12 participants from an HIV specialized clinic in Kilifi prior to roll-out in this study. We further revised the questionnaire based on feedback and results from the pilot phase. Data from the pilot phase were not analysed in the sub-study. The final version of the tool was later designed in a tablet platform using Research Electronic Data Capture (REDCap) [32] and administered to primary caregivers of PHI adolescents.
Sample size calculation
At the time of setting up the study, there was no integrated medical/health system data on exact number of adolescents in active follow-up. Since the parent study did not aim to do a population-level research on adolescents with HIV, we did not collect the data on number of adolescents attending the clinics. However, informally we had been advised that there were approximately 300 PHI adolescents in active follow-up across different clinics in Kilifi.
Sample size was calculated using Yamane’s formula [33], n=N/(1+Ne2), as applied in previous similar costing study approaches [34]. In this formula, n is minimal required number of participants, N is the population size and e is the desired level of precision. We used population size of about 300 caregivers of perinatally HIV adolescents who are on active follow-up at HIV care centres in Kilifi and 7% precision level to compute a minimum sample size as follows.
[Please see supplementary files for equation.]
Assuming a 97% response rate, we estimated a sample size of 124 caregivers.
Economic burden measures
We applied a cost of illness approach and measured direct costs, indirect costs and coping strategies from the primary caregivers’ perspective, i.e. we sought to elicit the economic costs incurred and paid for by the caregivers.
Direct cost: Average direct costs were measured by combining all the average out-of-pocket medical and non-medical costs for the PHI adolescent, caregiver and person accompanying PHI adolescent (other than the caregiver). These included administrative costs (registration and consultation); cost for diagnostic tests, medicine, other medical costs; food, accommodation/bed charges, and travel at the time of treatment visit. Besides the regular visits to the HIV specialized clinics, individuals living with HIV sometimes visit other healthcare providers for general treatment. In this study, the direct costs from regular visits and other healthcare provider visits were analysed. The average costs from these visits within the month of the interview was measured and assumed to be equivalent to the direct monthly costs due to HIV/AIDS in this study.
Indirect costs (productivity loss): We examined the loss of productive working time by the caregiver due to illness of PHI adolescent. Productivity cost was defined as the inability to carry out normal daily activities (paid and unpaid), and their valuation in United States Dollar (USD). Normal activities were defined as formal and informal work carried out by the caregiver. To calculate productivity losses, the inability to work, were divided into absenteeism and presenteeism. Absenteeism was defined when the caregiver was unable to carry out normal daily activities at all due to adolescent illness. On the other hand, ‘presenteeism’ was defined when the caregiver reduced efficiency in work where he/she could work some hours, but not the whole day [35]. The summation of absenteeism and presenteeism was termed as ‘productivity loss' and their monetary valuation was termed as ‘productivity cost' in this study. Two aspects were considered: i) the days which losses were experienced; ii) the extent to which work efficiencies were affected in hours. Valuation of productivity losses in this study is presented as Gross Domestic Product (GDP) per capita of Kenya (USD 4.11) in a day, according to the 2017 World Bank report [36]. Valuation using per capita is preferred because this approach values time loss for the rich and the poor people by an average of the whole society [30].
Mental health assessment
The 9-item Patient Health Questionnaire (PHQ-9) [37] was also administered to the caregivers to assess their mental health functioning. The PHQ-9 is a screener and provides an indicator of depressive symptoms severity. The measure is scored on a 4-point Likert scale from ‘0’ (not at all) to ‘3’ (nearly every day) with total scores ranging from 0 to 27. As a measure of severity of depressive symptoms, minimal and mild symptoms of depression are interpreted as PHQ-9 total score of 0-4 and 5-9, minor and moderate depressive symptoms equate to PHQ-9 total score of 10-14 and 15-19 respectively, and severe depressive symptoms equate to PHQ-9 scores of 20 or more. In this study, we used a cut off of ≥10 to indicate a positive screen for depressive symptoms. This cut-off maximizes the sensitivity and specificity in a study conducted in SSA [38]. PHQ-9 has proved to be reliable on primary healthcare setting with a Cronbach alpha of 0.86 from its original validation [39]. Data from the PHQ-9 and socio-demographic data were collected from the parent study.
Statistical analysis
Descriptive statistics were used to present caregiver’s socio-demographic characteristics, caregiving costs, coping strategies and mental health. Means and median on continuous data and proportions on categorical data are summarized. Mean (average) costs were mainly reported because total cost, which is relevant and vital to society, can be directly estimated from mean cost [40-42]. However, we also used the Shapiro-Wilk test to test for normality. Median values were also reported, and Mann-Whitney U test was used where applicable. Data were analysed using R statistical software package, version 3.4.3 [43].