Neighborhood-Based Socioeconomic Determinants of Cognitive Impairment in Zambian Children with HIV: A Quantitative Geographic Information Systems Approach

Journal of the Pediatric Infectious Diseases Society, Volume 10, No. 12, Year 2021

Interpretation

Background: Place-based inequalities, such as exposure to violence and access to nutritious food and clean water, may contribute to human immunodeficiency virus (HIV)-Associated cognitive impairment. In this study, we investigated neighborhood effects on cognition in children and adolescents with HIV in Lusaka, Zambia. Methods: We conducted a prospective cohort study of 208 children with perinatally acquired HIV (ages 8-17) and 208 HIV-exposed uninfected controls. Participants underwent neuropsychological testing and interviews assessing socioeconomic status. Geographic regions with clusters of participants with HIV and cognitive impairment were identified using quantitative geographic information systems (QGIS) and SaTScan. Associations between location of residence and cognitive function were evaluated in bivariable and multivariable regression models. Mediation analysis was performed to assess direct and indirect effects of location of the residence on cognitive impairment. Results: Residence in Chawama, one of the poorest neighborhoods in Lusaka, was significantly associated with cognitive impairment in participants with HIV (odds ratio 2.9; P=.005) and remained significant in a multivariable regression model controlling for potential confounders. Mediation analysis found that 46% of the cognitive effects of residence in Chawama were explained by higher rates of malnutrition, lower school attendance, and poorer self-reported health. Conclusions: Place-based socioeconomic inequality contributes to cognitive impairment in Zambian children and adolescents with HIV. Neighborhood effects may be mediated by concentrated poverty, malnutrition, limited access to education and health care, and other yet unknown environmental factors that may be potentially modifiable. HANDZ is a prospective cohort study that explores cognitive and psychiatric outcomes among children and adolescents living with HIV and HIV-exposed, uninfected (HEU) controls in Lusaka Province, Zambia [20]. HANDZ study is based in the Lusaka Province that contains the capital city and is 1 of the 10 Zambian provinces. The Lusaka Province contains 13 socioeconomically diverse constituencies, which are large neighborhoods with single-member representation in the National Assembly of Zambia [21]. Briefly, children and adolescents with perinatally acquired HIV (ages 8–17) were recruited from the Pediatric Center of Excellence (PCOE) in Lusaka, Zambia, a major outpatient pediatric HIV care referral center. Participants with HIV were included if treated with ART for longer than 1 year and excluded if they had a known history of CNS infection [20]. HEU controls were recruited from Lusaka neighborhoods by a community health worker using a stratified sampling method to ensure approximately equal age and sex distribution [20]. The HEU group provided a local normative sample for cognitive tests and served as a comparison group for rates of cognitive impairment. Each participant completed a demographic questionnaire, standardized interviews, and comprehensive neuropsychological testing. Participants were seen at baseline and subsequently every 3 months, with a median of 2 years of follow-up completed at the time of this analysis. Comprehensive neuropsychological testing was performed using a combination of the National Institutes of Health Toolbox—Cognition Battery and standard pencil-and-paper neuropsychological tests on a quarterly and annual basis, respectively [20]. Cognitive impairment was defined using a Global Deficit Score (GDS) approach [20]. Domain-specific deficit scores were calculated based on standard deviations below the mean performance of the control population, then domain-specific deficit scores were averaged to create the GDS. By convention, cognitive impairment was defined as a GDS score of greater than or equal to 0.5 [22]. SES was measured using an adaptation of the UNICEF Multiple Indicator Cluster Survey (MICS4) [23]. Individual SES variables of prespecified importance (maternal education, electricity, access to running water, presence of a flush toilet, food security, income, and possession index) were combined to form an SES index (SESI) ranging from 0 to 12. Negative life events (eg, hospitalization, exposure to violence or abuse, and illness or death of a family member) were measured using an instrument designed for the HANDZ study, the Negative Life Event Questionnaire (NLEQ), and summed into a Negative Life Event Index [20]. The components of the SES Index and Negative Life Event Index are listed in Supplementary Table 1. Each participant’s location of residence was approximated using Google Maps and OpenStreetMap. Estimated latitude/longitude coordinates and shapefiles of the Lusaka constituencies were overlayed in maps generated by quantitative GIS (QGIS) software (version 3.2.0) [24]. The geospatial relationship of prespecified socioeconomic factors was visualized. Distance between PCOE and the participants’ residence was calculated using the HubDistance tool in QGIS. To ensure the confidentiality and privacy of participants in the HANDZ study, participant points were enlarged, and constructed maps were zoomed out to view the entire city of Lusaka without specific landmarks. Geographic clustering analysis was performed with a spatial statistics software, SaTScan (version 9.6; https://www.satscan.org) using a Bernoulli model [25]. Maximum spatial cluster sizes were set at less than 50% of the population at risk within a circular window, default SaTScan parameters. Likelihood ratios were calculated for each cluster. Cluster analysis was not performed on the HEU sample, as these participants were recruited from specific constituencies, thus clustering detected in HEU participants could be an artifact of the location of recruitment. Additional statistical analyses were conducted using Stata 16.1 (College Station, TX). Chi-squared tests evaluated differences in dichotomous variables, t-test statistics for normally distributed continuous variables, and Kruskal-Wallis ranks for non-normally distributed continuous or ordinal variables. Constituencies identified as having clusters of participants with cognitive impairment using SaTScan with a significance of <=0.2 were evaluated with bivariable and multivariable logistic regression models. Two separate logistic regression models were fit; in the first, we adjusted for other SES variables and parental education in order to estimate the total causal effect of neighborhood of residence. In the second, we adjusted for confounding variables as well as all measured potential mediating variables in order to estimate the “direct” effect of neighborhood of residence. Using Dagitty (V. 3.0, http://www.dagitty.net), directed acyclic graphs (DAGs) were used to generate a causal model and select which variables to include in multivariable models (see Figure 1; Supplementary Table 2) [26, 27]. Mediation analysis using the “ldecomp” package in Stata was used to evaluate direct and indirect effects of neighborhood of residence on cognitive impairment [28]. Neighborhood of residence was used as the primary exposure variable, with each potential mediating variable chosen based on the DAG. P-values of <= .05 were considered significant in regression models. A directed acyclic graph (DAG) model of how socioeconomic status (SES) and neighborhood of residence influence cognitive impairment in Zambian children with HIV. This model implies that total effect of neighborhood of residence on cognition may be estimated by controlling for other SES variables and parental education. Testable implications of this model are that effects may be mediated through malnutrition, access to education, and exposure to violence and other negative life events. This study was approved by the institutional review boards of the University of Zambia (reference #004-08-17), the University of Rochester (protocol #00068985), and the National Health Research Authority of Zambia. Verbal and written parental permission were obtained from the parents of all participants who participated. Verbal and written assent was obtained from all participants aged 12 years and older.

AI Digest

Study Justification:

This study aimed to investigate the impact of neighborhood-based socioeconomic factors on cognitive impairment in children and adolescents with HIV in Lusaka, Zambia. The study aimed to address the gap in knowledge regarding the role of place-based inequalities, such as exposure to violence and access to nutritious food and clean water, in contributing to HIV-associated cognitive impairment. By understanding the neighborhood effects on cognition, the study aimed to provide insights into potential modifiable factors that could be targeted to improve cognitive outcomes in this population.

Study Highlights:

1. The study included 208 children with perinatally acquired HIV (ages 8-17) and 208 HIV-exposed uninfected controls.
2. Quantitative Geographic Information Systems (QGIS) and SaTScan were used to identify geographic regions with clusters of participants with HIV and cognitive impairment.
3. Residence in Chawama, one of the poorest neighborhoods in Lusaka, was significantly associated with cognitive impairment in participants with HIV.
4. Mediation analysis found that 46% of the cognitive effects of residence in Chawama were explained by higher rates of malnutrition, lower school attendance, and poorer self-reported health.
5. The study concluded that place-based socioeconomic inequality contributes to cognitive impairment in Zambian children and adolescents with HIV.

Recommendations for Lay Reader:

1. Addressing socioeconomic inequalities in neighborhoods is crucial for improving cognitive outcomes in children and adolescents with HIV.
2. Efforts should be made to reduce malnutrition, improve access to education, and enhance healthcare services in disadvantaged neighborhoods.
3. Policies and interventions should focus on creating supportive environments that promote healthy development and cognitive well-being for children and adolescents with HIV.

Recommendations for Policy Maker:

1. Develop and implement policies that target neighborhood-based socioeconomic determinants of cognitive impairment in children and adolescents with HIV.
2. Allocate resources to improve access to nutritious food, education, and healthcare services in disadvantaged neighborhoods.
3. Collaborate with community organizations and stakeholders to create comprehensive interventions that address the multifaceted needs of children and adolescents with HIV in disadvantaged neighborhoods.

Key Role Players:

1. Government agencies responsible for health, education, and social welfare.
2. Non-governmental organizations (NGOs) working in the field of HIV/AIDS and child development.
3. Community leaders and local representatives.
4. Healthcare providers and educators.
5. Researchers and experts in the field of HIV/AIDS and child development.

Cost Items for Planning Recommendations:

1. Funding for nutrition programs to address malnutrition in disadvantaged neighborhoods.
2. Investment in educational infrastructure and resources to improve access to quality education.
3. Allocation of resources for healthcare services, including HIV treatment and support.
4. Training and capacity-building programs for healthcare providers and educators.
5. Research funding for further studies and evaluations of interventions targeting neighborhood-based socioeconomic determinants of cognitive impairment.

Strength of Evidence

N/A

Abstract

HANDZ is a prospective cohort study that explores cognitive and psychiatric outcomes among children and adolescents living with HIV and HIV-exposed, uninfected (HEU) controls in Lusaka Province, Zambia [20]. HANDZ study is based in the Lusaka Province that contains the capital city and is 1 of the 10 Zambian provinces. The Lusaka Province contains 13 socioeconomically diverse constituencies, which are large neighborhoods with single-member representation in the National Assembly of Zambia [21]. Briefly, children and adolescents with perinatally acquired HIV (ages 8–17) were recruited from the Pediatric Center of Excellence (PCOE) in Lusaka, Zambia, a major outpatient pediatric HIV care referral center. Participants with HIV were included if treated with ART for longer than 1 year and excluded if they had a known history of CNS infection [20]. HEU controls were recruited from Lusaka neighborhoods by a community health worker using a stratified sampling method to ensure approximately equal age and sex distribution [20]. The HEU group provided a local normative sample for cognitive tests and served as a comparison group for rates of cognitive impairment. Each participant completed a demographic questionnaire, standardized interviews, and comprehensive neuropsychological testing. Participants were seen at baseline and subsequently every 3 months, with a median of 2 years of follow-up completed at the time of this analysis. Comprehensive neuropsychological testing was performed using a combination of the National Institutes of Health Toolbox—Cognition Battery and standard pencil-and-paper neuropsychological tests on a quarterly and annual basis, respectively [20]. Cognitive impairment was defined using a Global Deficit Score (GDS) approach [20]. Domain-specific deficit scores were calculated based on standard deviations below the mean performance of the control population, then domain-specific deficit scores were averaged to create the GDS. By convention, cognitive impairment was defined as a GDS score of greater than or equal to 0.5 [22]. SES was measured using an adaptation of the UNICEF Multiple Indicator Cluster Survey (MICS4) [23]. Individual SES variables of prespecified importance (maternal education, electricity, access to running water, presence of a flush toilet, food security, income, and possession index) were combined to form an SES index (SESI) ranging from 0 to 12. Negative life events (eg, hospitalization, exposure to violence or abuse, and illness or death of a family member) were measured using an instrument designed for the HANDZ study, the Negative Life Event Questionnaire (NLEQ), and summed into a Negative Life Event Index [20]. The components of the SES Index and Negative Life Event Index are listed in Supplementary Table 1. Each participant’s location of residence was approximated using Google Maps and OpenStreetMap. Estimated latitude/longitude coordinates and shapefiles of the Lusaka constituencies were overlayed in maps generated by quantitative GIS (QGIS) software (version 3.2.0) [24]. The geospatial relationship of prespecified socioeconomic factors was visualized. Distance between PCOE and the participants’ residence was calculated using the HubDistance tool in QGIS. To ensure the confidentiality and privacy of participants in the HANDZ study, participant points were enlarged, and constructed maps were zoomed out to view the entire city of Lusaka without specific landmarks. Geographic clustering analysis was performed with a spatial statistics software, SaTScan (version 9.6; https://www.satscan.org) using a Bernoulli model [25]. Maximum spatial cluster sizes were set at less than 50% of the population at risk within a circular window, default SaTScan parameters. Likelihood ratios were calculated for each cluster. Cluster analysis was not performed on the HEU sample, as these participants were recruited from specific constituencies, thus clustering detected in HEU participants could be an artifact of the location of recruitment. Additional statistical analyses were conducted using Stata 16.1 (College Station, TX). Chi-squared tests evaluated differences in dichotomous variables, t-test statistics for normally distributed continuous variables, and Kruskal-Wallis ranks for non-normally distributed continuous or ordinal variables. Constituencies identified as having clusters of participants with cognitive impairment using SaTScan with a significance of <=0.2 were evaluated with bivariable and multivariable logistic regression models. Two separate logistic regression models were fit; in the first, we adjusted for other SES variables and parental education in order to estimate the total causal effect of neighborhood of residence. In the second, we adjusted for confounding variables as well as all measured potential mediating variables in order to estimate the “direct” effect of neighborhood of residence. Using Dagitty (V. 3.0, http://www.dagitty.net), directed acyclic graphs (DAGs) were used to generate a causal model and select which variables to include in multivariable models (see Figure 1; Supplementary Table 2) [26, 27]. Mediation analysis using the “ldecomp” package in Stata was used to evaluate direct and indirect effects of neighborhood of residence on cognitive impairment [28]. Neighborhood of residence was used as the primary exposure variable, with each potential mediating variable chosen based on the DAG. P-values of <= .05 were considered significant in regression models. A directed acyclic graph (DAG) model of how socioeconomic status (SES) and neighborhood of residence influence cognitive impairment in Zambian children with HIV. This model implies that total effect of neighborhood of residence on cognition may be estimated by controlling for other SES variables and parental education. Testable implications of this model are that effects may be mediated through malnutrition, access to education, and exposure to violence and other negative life events. This study was approved by the institutional review boards of the University of Zambia (reference #004-08-17), the University of Rochester (protocol #00068985), and the National Health Research Authority of Zambia. Verbal and written parental permission were obtained from the parents of all participants who participated. Verbal and written assent was obtained from all participants aged 12 years and older.

Materials

Innovations

Based on the information provided, here are some potential innovations that could be used to improve access to maternal health:

1. Mobile Clinics: Implementing mobile clinics that can travel to different neighborhoods, including those with limited access to healthcare facilities, to provide maternal health services such as prenatal care, vaccinations, and health education.

2. Telemedicine: Utilizing telemedicine technology to connect pregnant women in remote or underserved areas with healthcare professionals who can provide virtual consultations, monitor their health remotely, and offer guidance and support throughout their pregnancy.

3. Community Health Workers: Training and deploying community health workers who are familiar with the local neighborhoods to provide maternal health services, conduct health screenings, offer health education, and refer women to appropriate healthcare facilities when needed.

4. Maternal Health Vouchers: Introducing a voucher system that provides pregnant women with access to essential maternal health services, including prenatal care, delivery, and postnatal care, at designated healthcare facilities. This can help overcome financial barriers and ensure that women receive the care they need.

5. Public-Private Partnerships: Collaborating with private healthcare providers to expand access to maternal health services in underserved areas. This can involve establishing partnerships to set up clinics or hospitals, providing training and resources to private providers, and ensuring that services are affordable and of high quality.

6. Health Education Programs: Implementing comprehensive health education programs that target pregnant women and their families, focusing on topics such as nutrition, hygiene, prenatal care, breastfeeding, and newborn care. These programs can be delivered through community workshops, mobile apps, or interactive online platforms.

7. Transportation Support: Addressing transportation barriers by providing transportation services or subsidies for pregnant women to access healthcare facilities for prenatal visits, delivery, and postnatal care. This can include partnering with local transportation providers or establishing community-based transportation systems.

8. Maternal Health Hotlines: Establishing toll-free hotlines staffed by trained healthcare professionals who can provide information, support, and guidance to pregnant women, answer their questions, and address their concerns regarding maternal health.

9. Maternal Health Awareness Campaigns: Conducting targeted awareness campaigns to educate communities about the importance of maternal health, dispel myths and misconceptions, and encourage early and regular prenatal care-seeking behavior.

10. Strengthening Healthcare Infrastructure: Investing in the improvement and expansion of healthcare facilities, particularly in underserved areas, to ensure that they have the necessary equipment, supplies, and skilled healthcare professionals to provide quality maternal health services.

It’s important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of the community in question.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health based on the study findings would be to implement neighborhood-based interventions that address the socioeconomic determinants of cognitive impairment in Zambian children with HIV. These interventions should focus on reducing concentrated poverty, improving access to nutritious food and clean water, increasing school attendance, and enhancing access to education and healthcare.

Specific recommendations could include:

1. Targeted interventions in neighborhoods with high rates of cognitive impairment: Identify neighborhoods with high rates of cognitive impairment among children with HIV and prioritize resources and interventions in these areas. This could involve establishing community health centers or mobile clinics that provide maternal health services, including prenatal care, postnatal care, and family planning.

2. Addressing malnutrition: Implement programs that address malnutrition in these neighborhoods, such as providing nutritional supplements, promoting breastfeeding, and educating caregivers on proper nutrition for pregnant women and young children.

3. Improving access to education: Enhance access to education by establishing or improving schools in these neighborhoods, providing scholarships or financial assistance for children from low-income families, and implementing programs to encourage school attendance.

4. Enhancing healthcare access: Increase access to healthcare services by establishing or improving healthcare facilities in these neighborhoods, training healthcare providers on maternal health issues, and implementing outreach programs to educate and raise awareness about maternal health.

5. Addressing environmental factors: Identify and address other environmental factors that may contribute to cognitive impairment, such as exposure to violence or abuse. This could involve implementing community-based programs that promote safety, provide counseling services, and support victims of violence.

6. Collaboration and partnerships: Foster collaboration and partnerships between government agencies, non-governmental organizations, healthcare providers, and community leaders to ensure the successful implementation of these interventions. This could involve coordinating efforts, sharing resources, and leveraging expertise to maximize impact.

It is important to note that these recommendations should be tailored to the specific context and needs of the neighborhoods in question. Regular monitoring and evaluation should also be conducted to assess the effectiveness of these interventions and make necessary adjustments.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthening healthcare infrastructure: Invest in improving healthcare facilities, including maternal health clinics and hospitals, in areas with limited access to quality healthcare services. This could involve building new facilities, upgrading existing ones, and ensuring they have the necessary equipment and trained healthcare professionals.

2. Mobile health clinics: Implement mobile health clinics that can reach remote and underserved areas, providing essential maternal health services such as prenatal care, vaccinations, and postnatal care. These clinics can be equipped with medical professionals, diagnostic tools, and medications to provide comprehensive care.

3. Community health workers: Train and deploy community health workers who can provide basic maternal health services, education, and support in their communities. These workers can conduct health screenings, provide health education, assist with prenatal and postnatal care, and refer women to appropriate healthcare facilities when needed.

4. Telemedicine: Utilize telemedicine technologies to provide remote consultations and support for maternal health. This can help overcome geographical barriers and allow women in remote areas to access healthcare professionals and receive guidance and support during pregnancy and postpartum.

5. Health education and awareness campaigns: Implement targeted health education programs to raise awareness about maternal health, pregnancy care, and the importance of seeking timely medical assistance. These campaigns can be conducted through various channels, including community meetings, radio, television, and social media.

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

1. Data collection: Gather data on the current state of maternal health access, including information on healthcare facilities, population distribution, transportation infrastructure, and socio-economic factors. This data can be obtained through surveys, interviews, existing databases, and government reports.

2. Geographic mapping: Use Geographic Information Systems (GIS) software, such as QGIS, to map the distribution of healthcare facilities, population density, and areas with limited access to maternal health services. This will help identify areas that require intervention.

3. Modeling access: Develop a model that takes into account various factors influencing access to maternal health, such as distance to healthcare facilities, transportation options, socio-economic status, and availability of healthcare professionals. This model can be based on existing research and data on healthcare utilization patterns.

4. Scenario analysis: Simulate different scenarios by incorporating the recommended innovations, such as the establishment of mobile health clinics or the deployment of community health workers. Assess how these interventions would impact access to maternal health services in different areas.

5. Evaluation and refinement: Evaluate the simulated impact of the recommendations and refine the model based on the results. This may involve adjusting parameters, incorporating additional data, or considering alternative interventions. Continuously monitor and evaluate the effectiveness of the implemented innovations to inform future improvements.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different innovations on improving access to maternal health and make informed decisions on resource allocation and implementation strategies.

Author

Dima Health

Statistics:

Citations: 2

Identifiers:

DOI: 10.1093/jpids/piab076

ISSN: 20487193

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Mental Health, Social Determinants

Study Countries:

Zambia

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study, Quasi Experimental Study

Study Approach:

Quantitative