Abstract
Background:
Common mental health disorders (CMDs) significantly contribute to the global burden of disease among adolescent girls and young women (AGYW). In South Africa, the high proportion of AGYW experiencing CMDs in part reflects adversity in early life and resulting chronic stress, particularly through exposure to poor economic conditions such as frequent household economic shocks. Yet, longitudinal evidence for the relationship between household economic shocks, CMDs, and chronic stress amongst AGYW is limited.
Methods:
Data are from the HIV Prevention Trials Network (HPTN) 068 study in rural South Africa (2012–2019). AGYW, 13–20 at baseline, were followed annually for up to 6 years. Exposures included experience of any shock, type of shock (illness/death, agricultural, wealth), and number of shocks (0, 1, ≥2). We calculated risk ratios for the association between exposures and depressive symptoms (CESD–10 > 16)), stratified by grant receipt, and odds ratios for the association between exposures and stress-responsive biomarkers (C-reactive protein (CRP), cytomegalovirus (CMV), herpes simplex virus type-1 (HSV-1)) and HIV infection.
Results:
Among 1892 AGYW, unadjusted analyses suggested a relationship between experiencing any shock (RR: 1.10, 95% CI: 1.00–1.22), or increased shocks (RR: 1.05, 95% CI: 1.00–1.11), and depressive symptoms. In adjusted analyses, experiencing any shock (OR: 1.21, 95% CI: 1.04–1.34), increased shocks (OR: 1.19, 95% CI: 1.03–1.38), and wealth shocks (OR: 1.20, 95% CI: 1.03–1.29) were associated with increased CRP. We found no relationship between shocks and HIV acquisition.
Conclusion:
AGYW experiencing economic adversity in early adolescence are at increased risk for depressive symptoms and elevated stress-biomarker levels during the transition to adulthood.
Keywords: stress, biomarkers, depression, adolescents, cash transfers
INTRODUCTION
Common mental health disorders (CMDs), including depression, are the largest contributor to the burden of disease among adolescent girls and young women (AGYW) ages 15–24, globally and in east and Southern Africa.1 In South Africa, the prevalence of adolescent depression ranges from 6% - 41% and is consistently higher among females than males.2–12 This gender gap in depression has been attributed to differential exposure to stressors and heightened biological susceptibility among AGYW.13–17 Differential exposure to stressors is reflective of social and economic marginalization and AGYW growing up in households and communities impacted by high levels of poverty and frequent household economic shocks.18–20 These negative economic conditions are suggested to adversely impact AGYW mental health through declining caregiver wellbeing, community social cohesion, protection from violence, economic opportunities, and resources for food, school, and medicine.21–26 Prolonged exposure to negative economic conditions can also result in chronic stress, which can be particularly harmful during the critical developmental period of adolescence and has been linked to depression and other CMDs.27–29 Yet, little work has been done to assess the association between household economic shocks, depression, and chronic stress in this context.
Among adults in sub-Saharan Africa, there is largely cross-sectional evidence that household economic shocks are associated with increased depression and anxiety. Death of a family member, loss of agriculture yield, and income loss are household economic shocks that have been shown to have the largest negative effects on CMDs. Existing studies amongst adults in the region also all emphasize that poverty, household economic shocks, and depression have the potential to be endogenous.20,30–32 Some individuals might be experiencing poverty due to household economic shocks or depression, and some individuals maybe be more impacted by household economic shocks because they are already experiencing depression. Experiencing a shock, or frequent shocks, may also manifest as chronic stress which as been measured using biomarkers such as (C-reactive protein (CRP), cytomegalovirus (CMV), herpes simplex virus type-1 (HSV-1)).33–35 Acknowledging the potential for endogeneity between these concepts is important as it has implications for the types of interventions that might buffer the impacts of poverty and household economic shocks on mental health.
One such intervention is cash transfers, which are cash payments made to poor and vulnerable families which can be either unconditional (provided without any obligations), or conditional, where receipt is tied to certain behaviors.36 Cash transfers are a central part of many countries’ social protection programs; it is estimated that over 40 million people globally receive cash transfers as social safety nets with the goal of alleviating the impacts of poverty.37 It is well established that cash transfers can improve household financial security, which in turn decreases stress and can improve mental health.19,36,38–42 While there is evidence for these relationships amongst adults, there is less evidence amongst adolescents, particularly AGYW. 36,43–45
The objectives of our study are to 1) examine the relationship between household economic shocks (i.e., experience of any shock, type of shock, and number of shocks) and depression over time in AGYW living in a rural, low resource area of South Africa; 2) examine the relationships between economic shocks and stress-related biomarkers (C-reactive protein (CRP), cytomegalovirus (CMV), herpes simplex virus type-1 (HSV-1)), and human immunodeficiency virus (HIV); and 3) determine whether these associations varied by household receipt of cash transfers. Our study provides novel evidence on the temporal relationship between economic shocks and mental health that can be used to better inform household interventions to promote mental health and wellbeing for AGYW.
METHODS
HPTN 068 Overview
Data are from the HIV Prevention Trials Network 068 study (HPTN 068), a phase III randomized controlled trial which aimed to measure the effect of providing cash transfers, conditional on school attendance, on risk of HIV acquisition among AGYW. The study enrolled 2,533 adolescent girls and young women aged 13 to 20 years who were attending high school grades 8 to 11 in the Agincourt Health and Socio-Demographic Surveillance System (HDSS) site in Bushbuckridge sub-district in Mpumalanga province, South Africa.46,47 Individuals who were pregnant or married at the time of recruitment, unable to read, intending to leave the study area before study completion, or without a parent or guardian in the house, were ineligible to participate in the study. Young women were identified from the HDSS and visited at their homes to assess their eligibility to participate. Enrollment was limited to one young woman from each household and mean age at enrollment was age 15.
AGYW randomized to the control group received no cash payments. AGYW randomized to the intervention group received 100 rands (R; about US$10 in 2012), and their parent or guardian received R200 (about $20) every month, conditional on the young woman attending 80% of school days per month. The cash was directly deposited into separate bank accounts for the AGYW or guardian. The decision to provide a total of R300 aligned with South African Government’s social protection grant, the Child Support Grant, which in 2012 provided R280 per month per child younger than 18 years; by 2015 the amount provided had increased to R330. 47
HPTN 068 enrollment visits began in 2011, and follow-up visits occurred for every subsequent year that the participant was in high school (up to grade 12). Follow-up visits for the original trial concluded in 2015. After the trial ended in 2015, two post-intervention visits were conducted in 2016/2017 and 2018/2019. AGYW who completed these visits could have up to 8 years of follow-up. Each study visit included a behavioral questionnaire with questions about sociodemographic information, household economics, sexual behaviors, and mental health, and testing for HIV. At the finial visit in 2019, testing was also done for stress-responsive biomarkers (herpes simplex virus type 1 (HSV-1), cytomegalovirus (CMV) and C-reactive protein (CRP)).
This analysis included AGYW with information on household shocks, depression, and stress-responsive biomarkers to examine how household shocks affect depression over time and biological stress at the last visit in 2019. For the stress outcomes, we included only AGYW who completed the final post-intervention visit in 2018/2019 and who had biomarker data available.
Additional details on recruitment, data collection, and laboratory procedures are available in the parent publication of the trial.47 For the present analyses, we only include AGYW who were HIV negative at baseline so we could examine whether experiencing household economic shocks or depression in early life was associated with HIV acquisition.
Measures
The main exposure of any household shock was a time-varying, binary variable defined as a household having experienced one of 10 negative events in the previous 12 months. (0=No, 1=Yes). Possible negative events included: 1) the death, injury, or serious illness of a household member; 2) the death of someone upon whom the household depended for financial assistance; 3) death/disease/theft of livestock; 4) crop failure; 5) job loss; 6) a decrease in remittances to the household; 7) a decrease in government grants; 8) destruction of household property; 9) inherited debt; 10) or another specified negative event. During the main study period from 2011–2015, the parent guardian of the AGYW was asked to mark all that applied for their household. AGYW completed this question themselves at the post-intervention visit in 2017. We chose to dichotomize household shocks as the main exposure because there were few girls who lived in a household with two or more negative household events. However, we also explored number of events (0–9) and type of event (death, agricultural, wealth) as secondary exposures.
The outcome of depression was defined as a Center for Epidemiological Studies Depression −10 (CESD - 10) score greater than or equal to 16. CESD-10 scores were measured at all visits after enrollment including at the last visit.
Stress-responsive biomarker outcomes were defined using elevated levels of CRP, CMV, and HSV-1 based on prior analyses of these study data.34 CMV and HSV-1 are herpesviruses with a latent phase that develops after primary infection.48–50 Changes in immunity and stress hormone levels can cause these herpesviruses to reactivate and start replicating to cause disease, which is why these measures are used as a proxy for the stress response and its effect on health.50 CRP is a measure of systematic inflammation which can indicate biological stress or other biologic processes.51 Stress-responsive biomarkers were only collected at the final cohort visit in 2018/2019 visit via dried blood spots (DBS) and were processed using enzyme-linked immunosorbent assays (ELISAs). CRP, HSV-1, and CMV have been shown to be valid and reliable when collected from DBS samples and have all been associated with social determinants of health like poverty and violence in prior studies.19,20,27,42,46,48–50,52–67 CRP levels were operationalized categorically by dividing the distribution into quartiles to enhance interpretability and clinical relevance. CMV and HSV-1 optical density levels were measured among participants using Euroimmun Anti-CMV and Anti-HSV-1 ELISA IgG assays, respectively. Because of the high prevalence of both CMV and HSV-1 in the study population, ordinal CMV and HSV-1 variables were created with four levels: seronegative participants (0), followed by seropositive optical density levels divided into terciles (1=low, 2=medium, 3=high).
The outcome of incident HIV infection was defined as new cases of HIV following study enrollment. HIV testing included two HIV rapid tests performed in parallel followed by a confirmatory test, if one or both rapid results were HIV positive. Rapid tests included the Determine HIV-1/2 test (Alere Medical Co., Ltd, Matsudo-shi, Chiba, Japan) and the FDA-cleared Uni-gold Recombigen HIV test (Trinity Biotech plc, Bray, Co. Wicklow, Ireland). Confirmatory HIV testing was performed using the FDA-cleared GS HIV-1 WESTERN BLOT assay (Bio-Rad Laboratories, Inc. Redmond, Washington, USA) at the HPTN Laboratory Center. We created a binary variable indicating whether the participant had experienced a new HIV infection for each study visit.
Baseline covariates included age, relative socioeconomic status (measured by household consumption), orphanhood, receipt of child support grant (CSG), and receipt of conditional cash transfer (CCT) intervention. South Africa’s CSG is one of the largest unconditional CTs in SSA. Primary caregivers of children from low-income families are eligible to apply for the CSG of R420 (~US$30) which is paid monthly for each child from birth until the age of 18.41,68,69 Covariates were selected a priori based on prior literature and analyses from the HPTN 068 study. Most variables had no or limited missing data (<10% missing).
Statistical Analysis
First, we described demographics, economic shocks, and health outcomes of the analytical sample at the baseline visit and endline visit. To examine the longitudinal relationship between economic shocks and depression at the next visit, we used unadjusted and adjusted log-binomial models. Generalized estimating equations (GEE) with exchangeable correlation matrices were used to account for repeated measures over the study period. To examine the relationship between economic shocks and stress biomarkers we used unadjusted and adjusted ordinal logistic regression models with biomarker levels as the outcomes. We estimated associations between economic shocks at each of the five waves and biomarker outcomes in 2018/2019. Follow-up time for each shock and biomarker comparison was as follows: Wave 1 to 6 (7–8 years), Wave 2 to 6 (6–7 years), Wave 3 to 6 (4–5 years), Wave 4 to 6 (3–4 years), Wave 5 to 6 (1–2 years). To determine if the relationships between economic shocks and depression, and economic shocks and stress biomarkers, were modified by receipt of a child support grant or the conditional cash transfer intervention we examined stratified estimates from models with an interaction term between the exposure variable and randomization to the CCT. A likelihood ratio test was also used to compare the fit of the model with and without an interaction term at an alpha of 0.05. Adjusted models controlled for age, HPTN 068 study intervention arm assignment (CCT), and household expenditures. In addition, listwise deletion was used to handle missingness when adjusted models featured units with missing data for at least one of the control variables. All analyses were conducted using R, version 4.1.1.
Ethical Approval
Institutional Review Board approval for this study was obtained from the University of North Carolina at Chapel Hill and the University of the Witwatersrand Human Research Ethics Committee. Written assent and parent/guardian consent was obtained for AGYW younger than 18 years’ AGYW 18 or older provided written informed consent to participate. Consent and assent forms were available in English and Shangaan.
RESULTS
Participant Characteristics
Our baseline sample included 2553 AGYW who were HIV negative with a median age of 15 (IQR 14–17) (Table 1). The majority lived in a household that received a child support grant (79%, n = 2005) and roughly a third reported food insecurity (34%, n = 862) or experiencing any type of household economic shock (27%, n = 673).
Table 1.
Participant Characteristics at Baseline and Endline
| All Participants at Baseline Visit (N = 2553)* | Eligible Participants at Endline Visit (N = 1279)* | |||
|---|---|---|---|---|
|
| ||||
| N or Median | % or IQR | N or Median | % or IQR | |
| Demographics | ||||
| Age | 15 | 14–17 | 23 | 22–24 |
| Any Orphanhood | 683 | 27% | 456 | 36% |
| Assets | ||||
| Lowest Quartile | 645 | 25% | 88 | 7% |
| 2nd Quartile | 672 | 26% | 204 | 16% |
| 3rd Quartile | 616 | 24% | 351 | 27% |
| Highest Quartile | 595 | 23% | 599 | 47% |
| CCT Intervention Arm | 1261 | 50% | 663 | 52% |
| Child Support Grant | 2005 | 79% | 1306 | 65% |
| Food Insecurity in Past Year | 862 | 34% | 128 | 10% |
| Shocks | ||||
| Any Shock | 673 | 27% | -- | -- |
| Number of Shocks (Median) | 0 | 0–1 | -- | -- |
| Type of Shock | ||||
| Any Family Death or Illness Shocks | 411 | 16% | -- | -- |
| Any Agricultural Shocks | 207 | 8% | -- | -- |
| Any Wealth Shocks | 274 | 11% | -- | -- |
| Health Outcomes | ||||
| Depression (CESD-10** > 16)) | -- | -- | 141 | 11% |
| Human Immunodeficiency Virus (HIV) Positive | -- | -- | 126 | 10% |
| Herpes Simplex Virus Type-1 (HSV-1) Positive | -- | -- | 476 | 37% |
| Cytomegalovirus (CMV) Positive | -- | -- | 1027 | 80% |
| C-Reactive Protein (CRP) | ||||
| Low | -- | -- | 700 | 55% |
| Average | -- | -- | 429 | 34% |
| High | -- | -- | 150 | 12% |
Totals do not sum to 100% due to missing data; -- data not collected at time point; eligible participants at endline include those with biomarker outcomes;
Center of Epidemiologic Studies Depression Scale - 10
Our endline sample included 1279 AGYW with a median age of 23 (IQR 22–24) (Table 1). The majority still lived in households that received a child support grant (65%, n = 1306). The proportion of AGYW reporting food insecurity decreased from baseline to 10% (n = 128). At endline, 11% (n = 141) of AGYW were experiencing depressive symptoms, 10% (n = 126) were living with HIV, 37% (n = 476) were positive for HSV-1, 80% (n = 1207) were positive for CMV, and 12% (n= 150) were in the highest quartile of CRP.
Household Shocks and Depression
At the first depression measurement (Wave 2) 27.8% (n = 201) of participants who were experiencing depressive symptoms had experienced a previous shock. At endline, 24.8% (n = 35) of participants who were experiencing depressive symptoms had experienced a previous shock. Experiencing any type of household shock (RR: 1.10, 95% 1.00, 1.22) and an increased number of shocks (RR 1.05, 95% CI: 1.00, 1.11) were positively associated with depressive symptoms, though the associations were weak (Table 2). However, these associations with depressive symptoms were attenuated once covariates were added into the model for both experiencing any type of household shock (aRR: 1.06, 95% 0.95, 1.18) and an increased number of household shocks (aRR: 1.04, 95% 0.98, 1.10). We found no association between specific types of shocks (e.g., wealth, agricultural or family death or illness) and depressive symptoms (Table 2).
Table 2.
Longitudinal Association Between Shocks and Depressive Symptoms
| Unadjusted | Adjusted* | |||
|---|---|---|---|---|
|
| ||||
| RR | 95 % CI | RR | 95 % CI | |
| Any Shock | 1.10 | (1.00, 1.22) | 1.06 | (0.95, 1.18) |
| Number of Shocks | 1.05 | (1.00, 1.11) | 1.04 | (0.98, 1.10) |
| Type of Shock | ||||
| Any Family Death or Illness Shocks | 1.07 | (0.94, 1.21) | 1.04 | (0.91, 1.19) |
| Any Agricultural Shocks | 1.01 | (0.86, 1.19) | 1.02 | (0.86, 1.22) |
| Any Wealth Shocks | 1.14 | (0.98, 1.32) | 1.10 | (0.94, 1.29) |
Adjusted for age, grade, household expenditure, orphanhood, Child Support Grant, and Conditional Cash Transfer
At the first depression measurement (Wave 2) 90.4% (n = 653) of participants who were experiencing depressive symptoms reported receiving the CSG or CCT at the baseline wave. At endline, 92.9% (n = 131) of participants who were experiencing depressive symptoms had indicated received the CSG or CCT from a previous wave. We didn’t find the association between experiencing any type of household shock and depressive symptoms to significantly vary by receipt of the CSG or receipt of the CCT However, in further probing of these non-significant results we observed that AGYW who experienced any household shock and received the CSG (aRR: 0.86, 95% CI: 0.64, 1.17) and CCT (aRR: 0.97, 95% CI: 0.84. 1.18) were less likely to have depressive symptoms compared to AGYW who experienced any household shock and received no CSG (aRR: 1.12, 95% CI: 0.91, 1.39) or CCT (aRR: 1.13, 95% CI: 0.95, 1.34), though these findings are just directional and failed to reach statistical significance (Table 3).
Table 3.
Longitudinal Association Between Any Shock and Depressive Symptoms Stratified by Grants
| Unadjusted | Adjusted | |||
|---|---|---|---|---|
|
| ||||
| RR | 95 % CI | RR | 95 % CI | |
| Child Support Grant | ||||
| No | 1.26 | (0.99, 1.60) | 1.12 | (0.91, 1.39) |
| Yes | 0.85 | (0.72, 1.13) | 0.86 | (0.64, 1.17) |
| Conditional Cash Transfer | ||||
| No | 1.16 | (0.99, 1.34) | 1.13 | (0.95, 1.34) |
| Yes | 0.84 | (0.71, 1.00) | 0.97 | (0.84, 1.18) |
Adjusted for age, grade, household expenditure, and orphanhood
Shocks and Stress Biomarkers and HIV Infection
There were no significant associations between any of the shock exposures at visits 1–5 and HIV, HSV-1, or CMV outcomes at visit 6, except for the relationship between experiencing a wealth shock at visit 1 and HSV-1 at visit 6 (aOR: 2.87, 95% CI: 1.23, 6.68) (Table 4). However, we did find that experiencing any type of household shock at visit 5 (aOR: 1.21, 95% CI: 1.04, 1.34), an increased number of household shocks at visit 5 (aOR: 1.19, 95% CI 1.03, 1.38), and wealth shocks at visit 5 (aOR; 1.20, 95% CI; 1.03, 1.39) were associated with increased CRP levels at visit 6.
Table 4.
Associations Between Shocks, Stress Biomarkers, and HIV Infection
| HIV | HSV-1 | CMV | CRP | ||||||
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| Adjusted* | Adjusted* | Adjusted* | Adjusted* | ||||||
|
| |||||||||
| OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | ||
| Any Shock | |||||||||
| (201) | Wave 1 | 0.79 | (0.4, 1.57) | 1.07 | (0.71, 1.62) | 0.87 | (0.58, 1.3) | 0.86 | (0.59, 1.26) |
| (301) | Wave 2 | 0.84 | (0.43, 1.66) | 0.72 | (0.46, 1.12) | 1.16 | (0.73, 1.82) | 0.80 | (0.52, 1.24) |
| (401) | Wave 3 | 0.91 | (0.43, 1.92) | 1.09 | (0.69, 1.72) | 1.13 | (0.72, 1.78) | 1.48 | (0.96, 2.28) |
| (501) | Wave 4 | 0.76 | (0.35, 1.64) | 0.72 | (0.45, 1.17) | 0.61 | (0.38, 0.98) | 0.92 | (0.59, 1.44) |
| (701) | Wave 5 | 0.64 | (0.28, 1.48) | 1.07 | (0.92, 1.24) | 1.03 | (0.88, 1.21) | 1.21 | (1.04, 1.34) |
| Each Additional Shocks | |||||||||
| (201) | Wave 1 | 0.80 | (0.54, 1.19) | 1.20 | (0.95, 1.51) | 0.96 | (0.76, 1.2) | 0.89 | (0.72, 1.09) |
| (301) | Wave 2 | 0.94 | (0.65, 1.36) | 0.85 | (0.64, 1.12) | 1.07 | (0.79, 1.46) | 0.91 | (0.67, 1.23) |
| (401) | Wave 3 | 1.00 | (0.62, 1.62) | 1.05 | (0.77, 1.43) | 1.18 | (0.85, 1.63) | 1.42 | (1.04, 1.95) |
| (501) | Wave 4 | 0.73 | (0.4, 1.34) | 0.82 | (0.62, 1.1) | 0.72 | (0.54, 0.95) | 1.02 | (0.8, 1.29) |
| (701) | Wave 5 | 0.77 | (0.45, 1.32) | 1.08 | (0.93, 1.26) | 1.03 | (0.88, 1.21) | 1.19 | (1.03, 1.38) |
| Any Family Death or Illness Shocks | |||||||||
| (201) | Wave 1 | 0.85 | (0.35, 2.07) | 1.00 | (0.59, 1.72) | 1.52 | (0.9, 2.57) | 0.87 | (0.52, 1.44) |
| (301) | Wave 2 | 0.95 | (0.38, 2.38) | 0.63 | (0.33, 1.19) | 0.57 | (0.29, 1.11) | 0.91 | (0.47, 1.77) |
| (401) | Wave 3 | 0.50 | (0.14, 1.76) | 0.80 | (0.44, 1.47) | 1.47 | (0.79, 2.73) | 1.37 | (0.78, 2.41) |
| (501) | Wave 4 | 1.35 | (0.55, 3.33) | 0.54 | (0.28, 1.06) | 0.76 | (0.4, 1.44) | 1.15 | (0.64, 2.08) |
| (701) | Wave 5 | 0.52 | (0.15, 1.79) | 0.78 | (0.37, 1.63) | 0.93 | (0.45, 1.95) | 1.49 | (0.76, 2.93) |
| Any Agricultural Shocks | |||||||||
| (201) | Wave 1 | 0.25 | (0.03, 1.92) | 1.15 | (0.67, 1.98) | 0.84 | (0.49, 1.45) | 0.68 | (0.4, 1.15) |
| (301) | Wave 2 | 0.97 | (0.38, 2.47) | 0.95 | (0.48, 1.87) | 1.79 | (0.88, 3.65) | 0.78 | (0.4, 1.52) |
| (401) | Wave 3 | 2.37 | (0.91, 6.13) | 0.82 | (0.32, 2.09) | 0.60 | (0.24, 1.53) | 1.74 | (0.62, 4.88) |
| (501) | Wave 4 | 0.00 | -- | 0.66 | (0.3, 1.44) | 0.66 | (0.31, 1.4) | 0.64 | (0.31, 1.31) |
| (701) | Wave 5 | 0.92 | (0.26, 3.28) | 0.89 | (0.47, 1.67) | 1.54 | (0.83, 2.83) | 0.89 | (0.5, 1.61) |
| Any Wealth Shocks | |||||||||
| (201) | Wave 1 | 0.88 | (0.31, 2.48) | 2.87 | (1.23, 6.68) | 0.54 | (0.26, 1.11) | 1.11 | (0.58, 2.1) |
| (301) | Wave 2 | 1.18 | (0.37, 3.8) | 0.93 | (0.43, 2.03) | 1.15 | (0.52, 2.53) | 0.88 | (0.43, 1.82) |
| (401) | Wave 3 | 0.48 | (0.1, 2.29) | 1.89 | (0.96, 3.72) | 0.88 | (0.45, 1.73) | 1.36 | (0.7, 2.63) |
| (501) | Wave 4 | 0.66 | (0.19, 2.33) | 1.15 | (0.5, 2.66) | 0.58 | (0.25, 1.32) | 0.97 | (0.45, 2.11) |
| (701) | Wave 5 | 1.15 | (0.32, 4.17) | 1.08 | (0.92, 1.26) | 1.07 | (0.91, 1.25) | 1.20 | (1.03, 1.39) |
Adjusted for age, grade, household expenditure, orphanhood, CSG, and CCT; -- non-convergence due to small sample size
DISCUSSION
In this manuscript we explored the longitudinal association between household economic shocks, depression, and stress-related biomarkers amongst AGYW living in rural South Africa. Experiencing any type of household economic shock, and a greater number of household economic shocks, marginally increased AGYW’s risk of depressive symptoms. Furthermore, experiencing any type of household economic shock, a greater number of shocks, and wealth-specific shocks approximately a year prior, was associated with increased CRP levels but no other stress-related biomarkers or HIV. Taken together, these findings provide preliminary evidence for the potential impact of household economic shocks on depressive symptoms and stress amongst AGYW and underscore the importance of both economic strengthening and mental health interventions for this population.
Though we found a bivariate association between experiencing household economic shocks and depressive symptoms, the association became non-significant in our multivariate models that included our covariates of age, orphanhood, household consumption, and receipt of cash grants. These results might reflect the existence of other associations that can affect depressive symptoms and highlight the value of future analyses to examine the pathways between experiencing household economic shocks and poor mental health outcomes. Existing studies in sub-Saharan Africa have hypothesized and identified numerous individual, interpersonal, structural, and environmental mechanisms as mediators.20,21,32,70–74 However, much of this existing evidence is cross-sectional, qualitative, and focused on childhood or adulthood. Exploring pathways using longitudinal data is imperative for several reasons including accounting for the magnitude of changes that occur during the transition to adulthood for this population.
Elevated CRP levels were only associated with experiencing household economic shocks in the previous year. This finding potentially aligns with existing literature on the differential association of threat (unexpected inputs that threaten wellbeing) and deprivation (absence of social, cognitive, and emotional inputs that impact development) with stress and psychopathology, where threat has had stronger associations.75–78 Experiencing a recent household economic shock (threat) may result in detectable stress or depressive symptoms as compared to household economic shocks that occurred earlier in adolescence or are less acute. Other studies suggest that the biological and mental health effects of stress exposure can vary in duration and may also explain the directionality and differential presentation of acute stress and long term stress.78,79 Furthermore, participants in our sample were well into young adulthood when the stress-related biomarkers were collected. Future research where stress-biomarkers are collected annually from early adolescence to young adulthood would be beneficial in untangling the impact and timing of household economic shock related threat and deprivation on stress and psychopathology amongst AGYW.
Cash transfers provide an obvious opportunity to decrease the impact of household economic shocks on health and wellbeing. However, in our sample, we found that the association between experiencing household economic shocks and depressive symptoms and stress did not vary by receipt of the CSG or the CCT. Though, we did find the direction of the relationship to be protective, even if non-significant. Specifically, AGYW who experienced a household economic shock and received the CSG or CCT were potentially less at risk for depressive symptoms compared to AGYW who experienced a household economic shock and didn’t receive either cash grant. Our findings align with recent studies and reviews of cash transfers and mental health among adults in SSA which suggest that cash alone may not be sufficient to overcome many challenges related to poverty, and cash needs to be paired with counseling or psychosocial support to effectively improve mental health.80,81 Studies that have examined the impact of cash plus care amongst adolescents in SSA have largely reported improvements in self-efficacy, self-concept, and hopefulness.82 Of the three studies reporting reductions in depressive symptoms, two included family strengthening and savings interventions in Uganda and the other included cash plus complementary programming in Tanzania.83–85 There is also limited evidence on whether there is a difference in effects between CCTs and CSGs. To date, no studies have evaluated the combination of cash plus brief psychological counseling which may present another opportunity to buffer the effects of poor household economic conditions and improve mental wellbeing amongst adolescents.
We consistently found experiencing any type of household economic shock, and a greater number of shocks, to be associated with our outcomes, in comparison to a specific type of shock. The exception being the association between experiencing a wealth shock and increased CRP levels. Some studies examining the relationship between early life adversity, mental health, and stress have reported that the form of adversity doesn’t matter as much as adversity occurring at all and a dose-response relationship between cumulative adversity exposure and mental health outcomes.19,72,78,86 Other studies have found that the form of adversity, or certain combinations of adversities, may be important than the number of adversities experienced.87–89 The findings reinforce the need for accessible and culturally appropriate mental health care services for AGYW, particularly those who experience adversity during childhood and the transition to adulthood.
Despite the strengths of using longitudinal data, a large sample size of AGYW, and precise biomarkers, there are several limitations that warrant discussion. First, AGYW who were not currently enrolled in school were not eligible to participate in the original trial. As such, our sample likely excludes AGYW who experienced the type of household economic shocks that prevented school enrollment or consistent attendance. There was also considerable attrition between baseline data collection and endline data collection. It is possible that AGYW retained in the study were less likely to experiences stressors or were different from those who were lost to follow up53. Thus, our results may not be completely generalizable to the original study cohort or other rural settings in SSA. Second, we utilized the CESD-10 as our measure of depressive symptoms. These types of brief screeners are useful in research settings but limited our ability to capture clinical diagnosis of depression. Third, our results are limited by unmeasured confounding which reflects our inability to account for all stressors that AGYW could encounter as well as other factors that could impact AGYW stress and mental health. Finally, CRP is a generic measure of inflammation and HSV-1 and CMV are related to viral infection. These factors, combined with only a single time point of biomarker data, made it hard to pinpoint causality.
CONCLUSION
AGYW who are experience household economic shocks in early adolescence are potentially at an increased risk for depressive symptoms and elevated stress during the transition to adulthood. Though cash transfers provide an opportunity to decrease the impact of household economic shocks on stress and mental health, we did not find them to be significantly protective in our sample. These results suggest that cash transfers alone may not be sufficient to overcome many challenges related to household economic shocks, and cash transfers may need to be paired with counseling or psychosocial support to effectively improve mental health for AGYW.
HIGHLIGHTS.
Household economic shocks in adolescence impact mental health in young adulthood
Experiencing household economic shocks increases risk for depressive symptoms
Experiencing household economic shocks elevates stress-responsive biomarkers
Experiencing household economic shocks does not increase HIV acquisition
Cash transfers alone are insufficient to buffer impact of shocks on mental health
ACKNOWLEDGEMENTS:
We thank the HPTN 068 study team and all trial participants. The MRC/Wits Rural Public Health and Health Transitions Research Unit and Agincourt Health and Socio-Demographic Surveillance System have been supported by the University of the Witwatersrand, the Medical Research Council, South Africa, and the Wellcome Trust, UK (grants 058893/Z/99/A; 069683/Z/02/Z; 085477/Z/08/Z; 085477/B/08/Z).
SOURCES OF FUNDING:
This study was funded by the National Institutes of Health (R21HD106583, R01MH135098, R01MH110186, R01MH087118, T32AI070114, R34MH130232) and by award numbers UM1AI068619 (HPTN Leadership and Operations Center), UM1AI068617 (HPTN Statistical and Data Management Center), and UM1AI068613 (HPTN Laboratory Center). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
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CONFLICT OF INTEREST:
All authors declare that they have no conflict of interest.
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