Impact of antenatal antiretroviral drug exposure on the growth of children who are HIV-exposed uninfected: the national South African Prevention of Mother to Child Evaluation cohort study

BMC Infectious Diseases, Volume 22, No. 1, Year 2022

Interpretation

Background: The relationship between in-utero antiretroviral (ARV) drug exposure and child growth needs further study as current data provide mixed messages. We compared postnatal growth in the first 18-months of life between children who are HIV-exposed uninfected (CHEU) with fetal exposure to ARV drugs (prophylaxis or triple-drug therapy (ART)) and CHEU not exposed to ARVs. We also examined other independent predictors of postnatal growth. Methods: We analysed data from a national prospective cohort study of 2526 CHEU enrolled at 6-weeks and followed up 3-monthly till 18-months postpartum, between October 2012 and September 2014. Infant anthropometry was measured, and weight-for-age (WAZ) and length-for-age (LAZ) Z-scores calculated. Generalized estimation equation models were used to compare Z-scores between groups. Results: Among 2526 CHEU, 617 (24.4%) were exposed to ART since -pregnancy (pre-conception ART), 782 (31.0%) to ART commencing post-conception, 879 (34.8%) to maternal ARV prophylaxis (Azidothymidine (AZT)), and 248 (9.8%) had no ARV exposure. In unadjusted analyses, preterm birth rates were higher among CHEU with no ARV exposure than in other groups. Adjusting for infant age, the mean WAZ profile was lower among CHEU exposed to pre-conception ART [-0.13 (95% confidence interval − 0.26; − 0.01)] than the referent AZT prophylaxis group; no differences in mean WAZ profiles were observed for the post-conception ART (− 0.05 (− 0.16; 0.07)), None (− 0.05 (− 0.26; 0.16)) and newly-infected (− 0.18 (− 0.48; 0.13)) groups. Mean LAZ profiles were similar across all groups. In multivariable analyses, mean WAZ and LAZ profiles for the ARV exposure groups were completely aligned. Several non-ARV factors including child, maternal, and socio-demographic factors independently predicted mean WAZ. These include child male (0.45 (0.35; 0.56)) versus female, higher maternal education grade 7–12 (0.28 (0.14; 0.42) and 12 + (0.36 (0.06; 0.66)) versus ≤ grade7, employment (0.16 (0.04; 0.28) versus unemployment, and household food security (0.17 (0.03; 0.31). Similar predictors were observed for mean LAZ. Conclusion: Findings provide evidence for initiating all pregnant women living with HIV on ART as fetal exposure had no demonstrable adverse effects on postnatal growth. Several non-HIV-related maternal, child and socio-demographic factors were independently associated with growth, highlighting the need for multi-sectoral interventions. Longer-term monitoring of CHEU children is recommended. The 2012 South African Prevention of Mother to Child Transmission of HIV Evaluation (SA-PMTCT-E) was a nationally-representative health facility-based cross-sectional study that enrolled 6-week (range 4–8 weeks) old infants attending immunization clinics in 9 South African provinces, with the primary aim to measure national PMTCT programme’s early effectiveness [20, 21]. In brief, nurse data collectors sampled mother-infant pairs systematically in large facilities (where participants were recruited at selected fixed intervals based on the target sample) or consecutively in small facilities (where all eligible participants were recruited until target sample obtained) during 6-week immunization visits. Sick infants needing emergency care or hospitalization were excluded. Infant HIV exposure was used as a marker of maternal HIV status. This was established through antibody testing [Genscreen HIV1/2 Ab EIA (enzyme immunoassay) Bio-Rad and confirmatory Vironostika HIV Uni-form II plus O, bioMérieux, France] on infant dried blood spots. Infant HIV infection status was assessed using polymerase chain reaction (PCR) testing [COBAS AmpliPrep/COBAS TaqMan assay, Roche, New Jersey] on the same dried blood spots [2]. Infants whose mothers reported living with HIV or infants with a positive 6-week HIV antibody test regardless of maternal self-reported HIV status, were eligible for recruitment into a prospective cohort study, nested within a national cross-sectional survey, to measure vertical transmission risk until 18-months postpartum. Recruitment was from 29 October 2012 to 31 May 2013, with follow-up until September 2014. Detailed cohort methods are described elsewhere [2]. In brief, consenting mother-infant pairs were followed at 3, 6, 9, 12, 15 and 18-months during scheduled facility visits coinciding with routine childcare appointments. Infant blood specimens collected between 6-weeks and 15-months underwent afore-mentioned diagnostic tests. At the 18-month visit, study nurses documented results from routinely-administered HIV-1 rapid test (SD Bioline HIV 1/2 3.0 Titma Health, Pty) on the child. Our primary exposure of interest was fetal exposure to maternal ARV, based on self-reported maternal ARV drug use data obtained using a structured questionnaire. During the study period, the national PMTCT programme was implementing CD4 count criteria-based life-long maternal ART initiation (CD4 cell counts ≤ 350 cells/mm3) and infant prophylaxis (World Health Organization (WHO) PMTCT policy Option A: 1 April 2010- 31 March 2013). Women with CD4 cell counts > 350 cells/mm3 were given Azidothymidine (AZT) prophylaxis from 14 weeks gestation. In April 2013, the PMTCT programme transitioned to lifelong ART for all pregnant and lactating WLHIV (WHO PMTCT Option B+) [22, 23]. ART regimens generally consisted of Nevirapine, Tenofovir, and Lamivudine or Emtricitabine [24]. We classified reported maternal ARV exposure as follows in analyses: (1) “pre-conception ART” when ART was started before pregnancy, (2) “post-conception ART” when ART was started during pregnancy, (3) “AZT only” when only AZT-based prophylaxis was given, (4) “None” when a known WLHIV took no ARV drugs during pregnancy, and (5) “Newly-infected” when women reported to have been HIV-negative during pregnancy but their children’s 6-week HIV antibody test result was positive. Study questionnaires also included questions on self-reported 24-h and 1-week child feeding practices, maternal (Tuberculosis (TB), HIV, CD4 count, syphilis) and child (coughing, diarrhea) morbidity and treatment, maternal obstetric history, socio-demographics characteristics, and peripartum community social support at each time point. Trained nurse data collectors collected anthropometric data using standardised procedures based on WHO guidelines [25]. Child weight was measured using calibrated A&D personal precision weight scales (UC-321) and length using SECA portable baby length boards (SCA417BLM). Measurements were recorded in kilograms or centimeters to two decimal places. Birthweight, birth length, and gestational age were extracted from participant-held health booklets. We defined low birthweight (LBW) as birthweight < 2.5 kg; preterm birth (PTB) as birth before 37 completed weeks gestation; and small for gestational age (SGA) as birthweight for gestational age Z-score below − 1.28 [26]. We estimated birthweight and length–for-gestational-age Z-scores using Intergrowth international standards for assessing newborn size for term and pre-term infants [27] and LMSgrowth [28], and excluded gestational ages outside of range for these standards (20 to 24 weeks). We estimated weight-for-age (WAZ), weight-for-length (WLZ), and length-for-age (LAZ) Z-scores at each postnatal timepoint using WHO growth standards [29]. We considered infants as underweight and stunted if their WAZ and LAZ were below -2 standard deviations respectively [25]. Anthropometric measurements and Z-scores were flagged based on criteria (Additional file 1: Box 1), and set to missing if no plausible explanation was established. We performed statistical analyses using STATA standard edition version 15. We calculated frequencies for categorical variables and means (standard deviations) or medians (inter quartile range) for continuous variables. Proportions were compared using Pearson chi-squared test while F-test was used for comparing means. Generalized estimation equations, with a gaussian distribution, were used for univariable and multivariable regression analyses to account for correlations between repeated anthropometric measurements within the same participant. Model covariates were selected based on literature (Additional file 1: Fig. S1) [30] and quasi-likelihood under an independence model criterion. These criteria were also used to inform the best fitting final multivariable models, and to select the independent correlation structure [31]. Mean WAZ and LAZ over the study period were modelled on exposure group, infant age (in months) and important covariates including maternal age, education, employment status, syphilis and TB status, mode and place of delivery, birth attendant, household food security, housing type (brick or non-brick), access to flush toilet and electricity, infant breastfeeding, sex and geographic location. These covariates were treated as potential confounders in the adjusted models assessing the association between foetal ARV exposure and postnatal growth. They were also included as predictors in the multivariable predictive models assessing factors independently associated with study outcomes. Presence of effect measure modification was also explored. We excluded variables that were affected by exposures of interest and shared common causes with outcomes (i.e., LBW, SGA and PTB) from models to minimize bias introduced by adjustment of potential mediators in the presence of unmeasured common causes [32, 33]. We did not include survey sampling weights in final analyses because this adjustment (1) did not change the findings and (2) generally increases standard errors. Instead, we added province in models to adjust for the survey structure. Point estimates were calculated with 95% confidence intervals. Analyses only included CHEU and children were censored if they tested HIV PCR positive at last HIV negative PCR result or died (censored at time point when death was reported). Although statistical testing was performed at the 5% statistical significance level, results were interpreted primarily based on the precision of the estimates.

AI Digest

Study Justification:

The study aimed to investigate the impact of antenatal antiretroviral (ARV) drug exposure on the growth of children who are HIV-exposed uninfected (CHEU). This research was necessary because existing data on this topic have provided mixed messages, and further study was needed to clarify the relationship between ARV drug exposure and child growth.

Highlights:

1. The study analyzed data from a national prospective cohort study of 2526 CHEU enrolled at 6 weeks and followed up until 18 months postpartum.
2. The study compared postnatal growth in the first 18 months of life between CHEU with fetal exposure to ARV drugs (prophylaxis or triple-drug therapy) and CHEU not exposed to ARVs.
3. The study found that fetal exposure to ARV drugs had no demonstrable adverse effects on postnatal growth.
4. Several non-HIV-related factors, including child, maternal, and socio-demographic factors, were independently associated with growth, highlighting the need for multi-sectoral interventions.
5. The findings provide evidence for initiating all pregnant women living with HIV on ARV therapy.

Recommendations:

1. Initiate all pregnant women living with HIV on ARV therapy to ensure the health and growth of their children.
2. Implement multi-sectoral interventions targeting non-HIV-related factors that influence child growth, such as child male gender, higher maternal education, employment, and household food security.
3. Conduct longer-term monitoring of children who are HIV-exposed uninfected to assess their growth and development.

Key Role Players:

1. Healthcare providers: Responsible for implementing ARV therapy for pregnant women living with HIV and monitoring the growth of children who are HIV-exposed uninfected.
2. Policy makers: Responsible for developing and implementing policies that support the initiation of ARV therapy for pregnant women living with HIV and multi-sectoral interventions to address non-HIV-related factors affecting child growth.
3. Researchers: Responsible for conducting further studies to monitor the long-term growth and development of children who are HIV-exposed uninfected and evaluate the effectiveness of interventions.

Cost Items for Planning Recommendations:

1. ARV drugs: Budget for the provision of ARV drugs to pregnant women living with HIV.
2. Healthcare services: Budget for healthcare services, including antenatal care, delivery, and postnatal care, to support the initiation of ARV therapy and monitor child growth.
3. Multi-sectoral interventions: Budget for implementing interventions targeting non-HIV-related factors, such as education programs, employment support, and food security initiatives.
4. Research funding: Budget for conducting further studies to monitor the long-term growth and development of children who are HIV-exposed uninfected and evaluate the effectiveness of interventions.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study design is a national prospective cohort study, which provides a robust framework for data collection and analysis. The sample size is large, with 2526 children included in the study. The study examines the impact of antenatal antiretroviral drug exposure on the growth of children who are HIV-exposed uninfected, which is an important topic. The study uses appropriate statistical methods to analyze the data. However, there are a few areas where the evidence could be strengthened. First, the abstract does not provide information on the representativeness of the sample, which could affect the generalizability of the findings. Second, the abstract does not mention any potential limitations of the study, such as confounding factors or biases. Including this information would help readers interpret the results more accurately. Finally, the abstract does not provide any specific recommendations for action based on the findings. Providing actionable steps for healthcare providers or policymakers would enhance the practical relevance of the study.

Abstract

The 2012 South African Prevention of Mother to Child Transmission of HIV Evaluation (SA-PMTCT-E) was a nationally-representative health facility-based cross-sectional study that enrolled 6-week (range 4–8 weeks) old infants attending immunization clinics in 9 South African provinces, with the primary aim to measure national PMTCT programme’s early effectiveness [20, 21]. In brief, nurse data collectors sampled mother-infant pairs systematically in large facilities (where participants were recruited at selected fixed intervals based on the target sample) or consecutively in small facilities (where all eligible participants were recruited until target sample obtained) during 6-week immunization visits. Sick infants needing emergency care or hospitalization were excluded. Infant HIV exposure was used as a marker of maternal HIV status. This was established through antibody testing [Genscreen HIV1/2 Ab EIA (enzyme immunoassay) Bio-Rad and confirmatory Vironostika HIV Uni-form II plus O, bioMérieux, France] on infant dried blood spots. Infant HIV infection status was assessed using polymerase chain reaction (PCR) testing [COBAS AmpliPrep/COBAS TaqMan assay, Roche, New Jersey] on the same dried blood spots [2]. Infants whose mothers reported living with HIV or infants with a positive 6-week HIV antibody test regardless of maternal self-reported HIV status, were eligible for recruitment into a prospective cohort study, nested within a national cross-sectional survey, to measure vertical transmission risk until 18-months postpartum. Recruitment was from 29 October 2012 to 31 May 2013, with follow-up until September 2014. Detailed cohort methods are described elsewhere [2]. In brief, consenting mother-infant pairs were followed at 3, 6, 9, 12, 15 and 18-months during scheduled facility visits coinciding with routine childcare appointments. Infant blood specimens collected between 6-weeks and 15-months underwent afore-mentioned diagnostic tests. At the 18-month visit, study nurses documented results from routinely-administered HIV-1 rapid test (SD Bioline HIV 1/2 3.0 Titma Health, Pty) on the child. Our primary exposure of interest was fetal exposure to maternal ARV, based on self-reported maternal ARV drug use data obtained using a structured questionnaire. During the study period, the national PMTCT programme was implementing CD4 count criteria-based life-long maternal ART initiation (CD4 cell counts ≤ 350 cells/mm3) and infant prophylaxis (World Health Organization (WHO) PMTCT policy Option A: 1 April 2010- 31 March 2013). Women with CD4 cell counts > 350 cells/mm3 were given Azidothymidine (AZT) prophylaxis from 14 weeks gestation. In April 2013, the PMTCT programme transitioned to lifelong ART for all pregnant and lactating WLHIV (WHO PMTCT Option B+) [22, 23]. ART regimens generally consisted of Nevirapine, Tenofovir, and Lamivudine or Emtricitabine [24]. We classified reported maternal ARV exposure as follows in analyses: (1) “pre-conception ART” when ART was started before pregnancy, (2) “post-conception ART” when ART was started during pregnancy, (3) “AZT only” when only AZT-based prophylaxis was given, (4) “None” when a known WLHIV took no ARV drugs during pregnancy, and (5) “Newly-infected” when women reported to have been HIV-negative during pregnancy but their children’s 6-week HIV antibody test result was positive. Study questionnaires also included questions on self-reported 24-h and 1-week child feeding practices, maternal (Tuberculosis (TB), HIV, CD4 count, syphilis) and child (coughing, diarrhea) morbidity and treatment, maternal obstetric history, socio-demographics characteristics, and peripartum community social support at each time point. Trained nurse data collectors collected anthropometric data using standardised procedures based on WHO guidelines [25]. Child weight was measured using calibrated A&D personal precision weight scales (UC-321) and length using SECA portable baby length boards (SCA417BLM). Measurements were recorded in kilograms or centimeters to two decimal places. Birthweight, birth length, and gestational age were extracted from participant-held health booklets. We defined low birthweight (LBW) as birthweight < 2.5 kg; preterm birth (PTB) as birth before 37 completed weeks gestation; and small for gestational age (SGA) as birthweight for gestational age Z-score below − 1.28 [26]. We estimated birthweight and length–for-gestational-age Z-scores using Intergrowth international standards for assessing newborn size for term and pre-term infants [27] and LMSgrowth [28], and excluded gestational ages outside of range for these standards (20 to 24 weeks). We estimated weight-for-age (WAZ), weight-for-length (WLZ), and length-for-age (LAZ) Z-scores at each postnatal timepoint using WHO growth standards [29]. We considered infants as underweight and stunted if their WAZ and LAZ were below -2 standard deviations respectively [25]. Anthropometric measurements and Z-scores were flagged based on criteria (Additional file 1: Box 1), and set to missing if no plausible explanation was established. We performed statistical analyses using STATA standard edition version 15. We calculated frequencies for categorical variables and means (standard deviations) or medians (inter quartile range) for continuous variables. Proportions were compared using Pearson chi-squared test while F-test was used for comparing means. Generalized estimation equations, with a gaussian distribution, were used for univariable and multivariable regression analyses to account for correlations between repeated anthropometric measurements within the same participant. Model covariates were selected based on literature (Additional file 1: Fig. S1) [30] and quasi-likelihood under an independence model criterion. These criteria were also used to inform the best fitting final multivariable models, and to select the independent correlation structure [31]. Mean WAZ and LAZ over the study period were modelled on exposure group, infant age (in months) and important covariates including maternal age, education, employment status, syphilis and TB status, mode and place of delivery, birth attendant, household food security, housing type (brick or non-brick), access to flush toilet and electricity, infant breastfeeding, sex and geographic location. These covariates were treated as potential confounders in the adjusted models assessing the association between foetal ARV exposure and postnatal growth. They were also included as predictors in the multivariable predictive models assessing factors independently associated with study outcomes. Presence of effect measure modification was also explored. We excluded variables that were affected by exposures of interest and shared common causes with outcomes (i.e., LBW, SGA and PTB) from models to minimize bias introduced by adjustment of potential mediators in the presence of unmeasured common causes [32, 33]. We did not include survey sampling weights in final analyses because this adjustment (1) did not change the findings and (2) generally increases standard errors. Instead, we added province in models to adjust for the survey structure. Point estimates were calculated with 95% confidence intervals. Analyses only included CHEU and children were censored if they tested HIV PCR positive at last HIV negative PCR result or died (censored at time point when death was reported). Although statistical testing was performed at the 5% statistical significance level, results were interpreted primarily based on the precision of the estimates.

Materials

https://efashare.b-cdn.net/materials/ace117e38ed939cd04f33c167e03347ed5353eacc0c2af4d47b48cf987721c3b/12879_2022_7847_MOESM1_ESM.docx

Innovations

The study you provided focuses on the impact of antenatal antiretroviral (ARV) drug exposure on the growth of children who are HIV-exposed uninfected. The findings suggest that fetal exposure to ARV drugs, such as prophylaxis or triple-drug therapy, did not have adverse effects on postnatal growth. Additionally, several non-HIV-related factors, including child, maternal, and socio-demographic factors, were found to independently predict growth.

Based on this study, here are some potential innovations that could improve access to maternal health:

1. Integrated HIV and maternal health services: Implementing integrated services that combine HIV testing, treatment, and prevention with routine maternal health care can ensure that pregnant women receive comprehensive care and support.

2. Early initiation of antiretroviral therapy (ART): Initiating ART as early as possible during pregnancy can help prevent mother-to-child transmission of HIV and improve maternal health outcomes.

3. Improved monitoring and follow-up: Implementing systems for regular monitoring and follow-up of pregnant women living with HIV can help ensure that they receive appropriate care throughout their pregnancy and postpartum period.

4. Multi-sectoral interventions: Recognizing that factors beyond HIV status can impact maternal and child health, implementing multi-sectoral interventions that address socio-demographic factors, maternal education, employment, and household food security can improve overall health outcomes.

5. Community-based interventions: Engaging communities and community health workers in maternal health programs can help increase access to care, provide education and support, and improve health outcomes for pregnant women.

6. Health information systems: Strengthening health information systems to collect and analyze data on maternal health, HIV, and other relevant factors can help identify gaps in care and inform targeted interventions.

7. Capacity building and training: Providing training and capacity building for healthcare providers on maternal health, HIV prevention, and treatment can improve the quality of care and ensure that pregnant women receive evidence-based interventions.

8. Peer support programs: Establishing peer support programs for pregnant women living with HIV can provide emotional support, share experiences, and promote adherence to treatment and care.

9. Mobile health (mHealth) interventions: Utilizing mobile technologies, such as text messaging and mobile applications, to deliver health information, reminders, and support to pregnant women can improve access to care and promote healthy behaviors.

10. Policy and advocacy: Advocating for policies that prioritize maternal health and HIV prevention and treatment can help ensure that resources are allocated appropriately and that pregnant women receive the care they need.

These innovations, along with ongoing research and collaboration between healthcare providers, policymakers, and communities, can contribute to improving access to maternal health and reducing the impact of HIV on maternal and child health outcomes.

AI Innovations Description

The study mentioned is titled “Impact of antenatal antiretroviral drug exposure on the growth of children who are HIV-exposed uninfected: the national South African Prevention of Mother to Child Evaluation cohort study.” The study aimed to compare the postnatal growth in the first 18 months of life between children who were HIV-exposed uninfected (CHEU) with fetal exposure to antiretroviral (ARV) drugs and CHEU not exposed to ARVs. The study also examined other independent predictors of postnatal growth.

The findings of the study provide evidence that initiating all pregnant women living with HIV on ARV therapy has no adverse effects on postnatal growth. The study also identified several non-HIV-related factors, including child, maternal, and socio-demographic factors, that independently predicted growth. These factors include child gender, maternal education, employment status, and household food security.

Based on these findings, the study recommends initiating ARV therapy for all pregnant women living with HIV to improve access to maternal health. Additionally, the study highlights the need for multi-sectoral interventions that address non-HIV-related factors to further improve maternal and child health outcomes. Longer-term monitoring of children who are HIV-exposed uninfected is also recommended.

In summary, the recommendation to improve access to maternal health is to initiate ARV therapy for all pregnant women living with HIV and implement multi-sectoral interventions that address non-HIV-related factors. Long-term monitoring of children who are HIV-exposed uninfected is also important.

AI Innovations Methodology

Based on the provided description, the study aims to investigate the impact of antenatal antiretroviral (ARV) drug exposure on the growth of children who are HIV-exposed uninfected. The methodology used in the study involves analyzing data from a national prospective cohort study of 2526 children who are HIV-exposed uninfected. The children were enrolled at 6 weeks and followed up every 3 months until 18 months postpartum. Infant anthropometry was measured, and weight-for-age (WAZ) and length-for-age (LAZ) Z-scores were calculated. Generalized estimation equation models were used to compare the Z-scores between different groups.

To simulate the impact of recommendations on improving access to maternal health, a methodology could involve the following steps:

1. Identify the recommendations: Based on the study findings and other relevant research, identify specific recommendations that can improve access to maternal health. These recommendations could include interventions to increase access to antenatal care, improve the quality of care, enhance health education and awareness, and strengthen healthcare infrastructure.

2. Define the simulation model: Develop a simulation model that represents the current state of maternal health access and outcomes. The model should include relevant variables such as the number of pregnant women, utilization of antenatal care services, maternal health indicators (e.g., maternal mortality rate, infant mortality rate), and other factors that influence access to maternal health.

3. Incorporate the recommendations: Modify the simulation model to incorporate the recommended interventions. This may involve adjusting variables such as the utilization rate of antenatal care services, the availability of healthcare facilities, and the quality of care provided. The model should reflect the potential impact of the recommendations on improving access to maternal health.

4. Run the simulation: Run the simulation model to simulate the impact of the recommendations on improving access to maternal health. This could involve running multiple scenarios to assess the potential effects of different combinations of interventions.

5. Analyze the results: Analyze the simulation results to evaluate the impact of the recommendations on access to maternal health. This could include comparing key indicators (e.g., maternal mortality rate, utilization of antenatal care) between the baseline scenario and the scenarios with the recommended interventions. Assess the magnitude of the improvements and identify any potential challenges or limitations.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and feedback from relevant stakeholders. Validate the model by comparing the simulated outcomes with real-world data, if available.

7. Communicate the findings: Present the findings of the simulation study in a clear and concise manner. Highlight the potential benefits of the recommended interventions in improving access to maternal health and inform decision-makers about the potential impact of implementing these recommendations.

By following this methodology, stakeholders can gain insights into the potential impact of specific recommendations on improving access to maternal health and make informed decisions regarding policy and programmatic interventions.

Author

Dima Health

Identifiers:

DOI: 10.1186/s12879-022-07847-9

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

South Africa

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female