Associations between gestational anthropometry, maternal HIV, and fetal and early infancy growth in a prospective rural/semi-rural Tanzanian cohort, 2012-13

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Study Justification:
This study aimed to evaluate the relationship between gestational anthropometry (measurements of the mother’s body during pregnancy) and birth and infant growth in a rural/semi-rural Tanzanian cohort. The study was conducted in a resource-restricted setting where identifying feasible screening tools is a priority. The findings of this study could provide valuable insights into the impact of gestational anthropometry on pregnancy outcomes and infant growth in similar settings.
Highlights:
– The study found that lower gestational mid-upper arm circumference (MUAC) and maternal HIV status were associated with lower infant weight-for-age and length-for-age z-scores from birth to 6 months.
– Even after adjusting for infant feeding practices, the association between gestational MUAC and infant anthropometry remained significant.
– Each 1cm increase in gestational MUAC was associated with a 0.11 increase in both weight-for-age and length-for-age z-scores.
– HIV-exposed infants had poorer anthropometry compared to HIV-unexposed infants throughout the first 6 months of life, despite maternal antiretroviral access.
– Routine assessment of gestational MUAC has the potential to identify at-risk women who may benefit from additional health interventions to optimize pregnancy outcomes and infant growth.
Recommendations:
– Further research is needed to establish gestational MUAC reference ranges specific to this population.
– Interventions should be developed and tested to improve gestational MUAC during pregnancy, with the goal of improving pregnancy outcomes and infant growth.
Key Role Players:
– Researchers and scientists specializing in maternal and child health in resource-restricted settings.
– Healthcare providers and policymakers involved in antenatal care and maternal and child health programs.
– Community health workers and volunteers who can assist in implementing interventions and monitoring gestational MUAC.
Cost Items for Planning Recommendations:
– Training programs for healthcare providers and community health workers on gestational MUAC assessment.
– Development and implementation of intervention programs to improve gestational MUAC.
– Monitoring and evaluation of the impact of interventions on pregnancy outcomes and infant growth.
– Communication and awareness campaigns to educate pregnant women and their families about the importance of gestational MUAC and the available interventions.
Please note that the cost items provided are general suggestions and may vary depending on the specific context and resources available.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study used a prospective cohort design and collected data from a relatively large sample size of 114 mothers. The study also adjusted for potential confounding variables and used statistical analysis to evaluate the associations between gestational anthropometry, maternal HIV, and fetal and early infancy growth. However, the study could be improved by providing more details on the methods used for data collection and analysis, as well as the limitations of the study. Additionally, the abstract could benefit from a clearer statement of the main findings and their implications.

Background: Healthcare access and resources differ considerably between urban and rural settings making cross-setting generalizations difficult. In resource-restricted rural/semi-rural environments, identification of feasible screening tools is a priority. The objective of this study was to evaluate gestational anthropometry in relation to birth and infant growth in a rural/semi-rural Tanzanian prospective cohort of mothers and their infants. Methods: Mothers (n = 114: 44 HIV-positive) attending antenatal clinic visits were recruited in their second or third trimester between March and November, 2012, and followed with their infants through 6-months post-partum. Demographic, clinical, and infant feeding data were obtained using questionnaires administered by a Swahili-speaking research nurse on demographic, socioeconomic, clinical, and infant feeding practices. Second or third trimester anthropometry (mid-upper arm circumference [MUAC], triceps skinfold thickness, weight, height), pregnancy outcomes, birth (weight, length, head circumference) and infant anthropometry (weight-for-age z-score [WAZ], length-for-age z-score [LAZ]) were obtained. Linear regression and mixed effect modeling were used to evaluate gestational factors in relation to pregnancy and infant outcomes. Results and discussion: Gestational MUAC and maternal HIV status (HIV-positive mothers = 39%) were associated with infant WAZ and LAZ from birth to 6-months in multivariate models, even after adjustment for infant feeding practices. The lowest gestational MUAC tertile was associated with lower WAZ throughout early infancy, as well as lower LAZ at 3 and 6-months. In linear mixed effects models through 6-months, each 1cm increase in gestational MUAC was associated with a 0.11 increase in both WAZ (P < 0.001) and LAZ (P = 0.001). Infant HIV-exposure was negatively associated with WAZ (β = -0.65, P < 0.001) and LAZ (β = -0.49, P < 0.012) from birth to 6-months. Conclusions: Lower gestational MUAC, evaluated using only a tape measure and minimal training that is feasible in non-urban clinic and community settings, was associated with lower infant anthropometric measurements. In this rural and semi-rural setting, HIV-exposure was associated with poorer anthropometry through 6-months despite maternal antiretroviral access. Routine assessment of MUAC has the potential to identify at-risk women in need of additional health interventions designed to optimize pregnancy outcomes and infant growth. Further research is needed to establish gestational MUAC reference ranges and to define interventions that successfully improve MUAC during pregnancy.

A prospective cohort of 44 HIV-positive and 70 HIV-negative pregnant women living in Magu District, Tanzania, was established between March to November 2012, with cohort observation continuing through July 2013. Participants were recruited from women seeking antenatal services at rurally-located dispensaries, representing the first level of public health care in Tanzania, and from women attending antenatal visits at Kisesa Health Centre, a publically accessible government-administrated healthcare facility located in the semi-rural region around Kisesa Village. Kisesa Health Centre offers basic antenatal and obstetrical services, outpatient child and adult healthcare, and provides HIV counselling, testing, and treatment. The clinic serves a large catchment area in the vicinity of Kisesa Village, located approximately 20 kilometers from the city of Mwanza. Participants in this observational study that were identified as malnourished or requiring additional clinical follow-up for any condition were referred via the study physician liaison to the main clinical services of Kisesa Health Centre. Mothers provided written informed consent for themselves and on behalf of their infants. Ethical approval for this study was granted by the Tanzanian National Health Research Ethics Review Committee and the Cornell University Institutional Review Board. Study eligibility included stated intention to live within the antenatal clinic catchment area until 6-months postpartum, confirmed maternal HIV serostatus [screening by Determine™ HIV-1/2 (Inverness Medical), confirmation by Uni-Gold™ HIV-1/2 (Trinity Biotech)] at enrollment, and singleton pregnancy (any eligible pregnant woman was enrolled, but they were subsequently withdrawn if a multiple delivery was confirmed). All HIV-positive women who met the eligibility criteria were invited to participate and HIV-negative women were invited to participate in the study at approximately double the rate as HIV-negative women joined, although there was no specific matching strategy employed. The required sample sizes for statistical analyses assuming α = 0.05 (two-sided), power = 80 % and when applicable, unequal group size in the ratio = 2:1, varied according to the statistical procedure (e.g. Student’s t-test, Chi-squared test, adjusted and unadjusted linear regression) and cohort participants included (e.g. maternal analyses only, infant only, both). The sample size recruited exceeded that required to detect mean differences in birth weight (mean/standard deviation = 300/425 g), birth length (1.25/2.00 cm), head circumference (1.0 /1.5 cm), birth MUAC (0.75/2.00 cm), gestational age at birth (1.5/2.5 weeks). Detectable linear regression effect sizes were targeted between small to medium effects for unadjusted (by convention small to medium effect size set at 0.02 < f < 0.15) to medium to large (by convention medium to large effect size set at 0.15 < f < 0.35) for adjusted (one predictor, six covariates) regression models. Women were enrolled in the cohort after completing their first trimester. A follow-up visit was scheduled during pregnancy if the original enrollment date preceded the anticipated delivery date by ≥3 weeks. All women were followed-up at delivery if they delivered at Kisesa Health Centre or within 72 h if they delivered elsewhere. Thereafter, women were followed with their infants at 1, 2, 3, and 6-months postpartum. Mother-infant follow-up visits corresponded to Tanzanian infant vaccination visits, with the exception of a study-specific visit at 6-months. If a mother-infant pair failed to return for a follow-up visit, a field worker traveled to their last known address to invite them to return to the clinic for a rescheduled appointment. Recruitment and retention strategies included compensating participants for transportation expenses as region-specific surveys indicated transportation expenses were a significant barrier to accessing clinic-based services in this region [11]. Maternal HIV testing is offered as part of routine antenatal care in Tanzania and many women are first diagnosed with HIV infection during pregnancy. At the time of this study, Tanzania followed Option A of the WHO 2010 prevention of mother-to-child transmission (PMTCT) guidelines [12]. Women with absolute CD4 cell counts ≤350 cells/μL or WHO clinical stage 3 or 4 irrespective of CD4 cell count were eligible for a first-line combination antiretroviral regimen, consisting of zidovudine (AZT) + lamivudine (3TC) + efavirenz (EFV). All other HIV-positive women received AZT for PMTCT starting as early as 14 weeks gestation. HIV-exposed infants were prescribed daily nevirapine for six weeks, followed by HIV testing at 3-months of age using dried blood spot HIV DNA-PCR that was analyzed at Bugando Medical Centre in Mwanza City, the closest regional hospital laboratory. Out of the 38 HIV-exposed infants enrolled in the cohort at delivery, 32 (84 %) tested negative for HIV at 3-months and 6 (16 %) exited the study prior to HIV testing (Fig. 1). Maternal and infant cohort follow-up Demographic, medical history, and infant feeding data were obtained through questionnaires administered by a Swahili-speaking research nurse. In the absence of ultrasound technology, participants were counseled to estimate their last menstrual period from which gestational age was approximated, or for the 6 % (n = 7/114) of women unable to estimate their last menstrual period, gestational age was estimated from the first available fundal height according to standard clinic procedures. All maternal and infant anthropometric measurements were collected by trained research nurses. Gestational anthropometric measurements were collected upon enrollment that corresponded to the second or third trimester of pregnancy, and included weight, height, MUAC, and TSF. Maternal weight and height were measured using a standard balance beam scale with a height rod (Health O Meter, Inc., Bridgeview, IL) to the precision of 0.2 kg and 0.1 cm, respectively. MUAC was measured by tape measure to the nearest 0.1 cm and TSF was measured by Lange Skin Calipers (Cambridge Scientific Industries, Cambridge, MD) to the nearest 0.1 mm. Maternal blood was drawn for hemoglobin quantification and anemia was defined as hemoglobin (Hb) concentration <11 g/dL and severe anemia as Hb <8.5 g/dL according to reference values used for clinical referral in Tanzania [13]. All women identified as anemic were referred for clinical follow-up at Kisesa Health Centre. Infant birth anthropometric measurements (weight, height, head circumference, MUAC) were included in the birth anthropometry analyses if collected within 72 h of delivery (Fig. 1). Infant anthropometry was measured at 1, 2, 3 and 6-months. Infant weight and length were measured using a digital infant scale (Seca 334 Digital Baby Scale) to the nearest 0.01 kg and 0.1 cm, respectively, and infant tape measures were used to measure both infant head circumference and MUAC to the nearest 0.1 cm. Exclusive breastfeeding was defined according to the WHO definition, which allows for the infant to receive only breast milk and medications, oral rehydration solutions, vitamins and minerals [14]. Exclusive breastfeeding duration was defined as the period between birth and the age at which the infant first received food or non-breast milk liquids, other than medications. A study-specific breastfeeding score was evaluated at each visit, and the individual visit scores were summed to provide an overall breastfeeding score. Infant feeding practices were scored as: 0 = no breastfeeding; 1 = partial breastfeeding (defined as breast milk plus other foods and/or liquids); 2 = predominant breastfeeding (defined as breast milk plus locally-prepared non-prescription anti-colic/anti-gripe water); 3 = exclusive breastfeeding. The overall breastfeeding score summarized infant feeding practices over the one to 6-month period, with scores ranging from 0 to 12. An example score for an infant exclusively breastfed for 6-months would include a visit-specific breastfeeding score of 3 at each of 4 follow-up visits = 12. An example score for an infant who was exclusively breastfeeding at month-1, transitioned to predominant breastfeeding at month-2, transitioned to partial breastfeeding at month-3 and stopped all breastfeeding by the month-6 visit would be 3 + 2 + 1 + 0 = 6. If a mother missed a follow-up visit, information regarding infant feeding practices for the relevant month was ascertained at the subsequent study visit. Data were analyzed in STATA12 (STATA Corporation, Texas, USA). Means of normally-distributed continuous variables were compared using Student’s t-test and one-way analysis of variance (ANOVA) was used for comparisons of means across categorical variables. Proportions of categorical variables were compared using Pearson chi-square tests. Relationships between infant anthropometrics at birth and gestational clinical characteristics were analyzed using both unadjusted linear regression models, and linear regression models adjusted for covariates chosen a priori (maternal HIV status, age, parity, education, gestational age at the time of anthropometric measurement, and infant sex). Gestational MUAC was examined as both a continuous and categorical variable classified according to study-specific data-derived tertiles because there are no established gestational MUAC reference values. Infant weight-for-age (WAZ) and length-for-age (LAZ) z-scores were calculated using the WHO Child Growth Standards with the igrowup macro package (freely available at: www.who.int/childgrowth/software/en). Underweight and stunting were defined as having a z-score < -2 standard deviations below the WHO reference standards for WAZ and LAZ, respectively. Mixed effect models were used to compare infant growth trajectories between groups (HIV-exposed vs. HIV-unexposed and low vs. high gestational MUAC). For these analyses, MUAC was dichotomized into “low” and “high”, with low MUAC defined as MUAC is the lowest tertile of the cohort distribution and high MUAC defined as MUAC in the middle and highest tertiles combined. Posthoc pairwise comparisons were made at each age corresponding to study visit (birth, 1, 2, 3, and 6-months) and were based on estimated margins means. To account for multiple comparisons, the Bonferroni correction procedure was used in these analyses. Mixed effect models were also used to examine the relationships between infant anthropometry and covariates of interest (HIV and gestational MUAC), with adjustment for potential confounding variables.

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or text messaging services that provide pregnant women with information and reminders about prenatal care, nutrition, and appointments. This can help overcome barriers to accessing healthcare in rural areas by providing information directly to women’s phones.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in remote areas to consult with healthcare providers through video calls or phone consultations. This can help address the shortage of healthcare providers in rural areas and provide access to specialized care.

3. Community Health Workers: Train and deploy community health workers in rural areas to provide basic prenatal care, education, and support to pregnant women. These workers can help bridge the gap between healthcare facilities and the community, ensuring that women receive the care they need.

4. Mobile Clinics: Establish mobile clinics that travel to remote areas to provide prenatal care and screenings. This can bring healthcare services closer to women in underserved areas and reduce the need for long-distance travel.

5. Improving Transportation: Address transportation barriers by providing transportation vouchers or arranging transportation services for pregnant women to access healthcare facilities. This can help overcome the challenge of long distances and lack of transportation options in rural areas.

6. Community-Based Health Education: Conduct community-based health education programs to raise awareness about the importance of prenatal care and maternal health. This can help empower women and their families to prioritize their health and seek appropriate care.

7. Strengthening Health Systems: Invest in improving healthcare infrastructure, staffing, and resources in rural areas to ensure that healthcare facilities are equipped to provide quality maternal health services. This can include training healthcare providers, improving facilities, and ensuring the availability of essential supplies and medications.

These innovations can help improve access to maternal health by addressing the unique challenges faced by pregnant women in rural and semi-rural settings.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health would be to implement routine assessment of mid-upper arm circumference (MUAC) during pregnancy in rural and semi-rural settings. This recommendation is based on the findings that lower gestational MUAC was associated with lower infant anthropometric measurements, even after adjusting for infant feeding practices and maternal HIV status.

Implementing routine assessment of MUAC, using only a tape measure and minimal training, can help identify at-risk women who may need additional health interventions to optimize pregnancy outcomes and infant growth. This screening tool is feasible in non-urban clinic and community settings, where healthcare access and resources may be limited. Further research is needed to establish gestational MUAC reference ranges and define interventions that successfully improve MUAC during pregnancy.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations for improving access to maternal health:

1. Implement mobile health (mHealth) solutions: Utilize mobile phones and other digital technologies to provide maternal health information, reminders for antenatal visits, and access to telemedicine services for remote consultations.

2. Strengthen community-based healthcare services: Establish and support community health workers who can provide basic antenatal care, education, and referrals for pregnant women in rural and semi-rural areas.

3. Improve transportation infrastructure: Enhance transportation networks and services to ensure that pregnant women can easily access healthcare facilities for antenatal care, delivery, and postnatal care.

4. Increase availability of maternal health services: Expand the number of healthcare facilities in rural and semi-rural areas, ensuring that they are adequately staffed and equipped to provide comprehensive maternal health services.

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

1. Define the target population: Identify the specific population that will benefit from the recommendations, such as pregnant women in rural and semi-rural areas of Tanzania.

2. Collect baseline data: Gather data on the current state of access to maternal health services in the target population, including factors such as distance to healthcare facilities, availability of transportation, and utilization of antenatal care.

3. Develop a simulation model: Create a mathematical model that represents the target population and simulates the impact of the recommendations on access to maternal health services. The model should consider factors such as population size, geographical distribution, healthcare infrastructure, and the proposed interventions.

4. Input data and parameters: Input the baseline data and parameters into the simulation model, including information on the current state of access to maternal health services and the potential impact of the recommendations.

5. Run simulations: Run multiple simulations using different scenarios, such as varying levels of implementation of the recommendations or different population characteristics. This will allow for the evaluation of the potential impact of the recommendations on access to maternal health services.

6. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health services. This may include evaluating changes in the number of women accessing antenatal care, the distance traveled to healthcare facilities, and the overall utilization of maternal health services.

7. Validate and refine the model: Validate the simulation model by comparing the simulated results with real-world data, if available. Refine the model based on feedback and additional data to improve its accuracy and reliability.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community members. Use the results to advocate for the implementation of the recommended interventions and to inform decision-making processes related to improving access to maternal health services.

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