Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings

Lessons learned from over a decade of data audits in international observational HIV cohorts in Latin America and East Africa

Introduction

Routine patient care data are increasingly used for biomedical research, but such "secondary use" data have known limitations, including their quality. When leveraging routine care data for observational research, developing audit protocols that can maximize informational return and minimize costs is paramount.

Methods

For more than a decade, the Latin America and East Africa regions of the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium have been auditing the observational data drawn from participating human immunodeficiency virus clinics. Since our earliest audits, where external auditors used paper forms to record audit findings from paper medical records, we have streamlined our protocols to obtain more efficient and informative audits that keep up with advancing technology while reducing travel obligations and associated costs.

Results

We present five key lessons learned from conducting data audits of secondary-use data from resource-limited settings for more than 10 years and share eight recommendations for other consortia looking to implement data quality initiatives.

Conclusion

After completing multiple audit cycles in both the Latin America and East Africa regions of the IeDEA consortium, we have established a rich reference for data quality in our cohorts, as well as large, audited analytical datasets that can be used to answer important clinical questions with confidence. By sharing our audit processes and how they have been adapted over time, we hope that others can develop protocols informed by our lessons learned from more than a decade of experience in these large, diverse cohorts.

Can the date of last menstrual period be trusted in the first trimester? Comparisons of gestational age measures from a prospective cohort study in six low-income to middle-income countries

OBJECTIVES

We examined gestational age (GA) estimates for live and still births, and prematurity rates based on last menstrual period (LMP) compared with ultrasonography (USG) among pregnant women at seven sites in six low-resource countries.

DESIGN

Prospective cohort study SETTING AND PARTICIPANTS: This study included data from the Global Network's population-based Maternal and Newborn Health Registry which follows pregnant women in six low-income and middle-income countries (Democratic Republic of the Congo, Guatemala, India, Kenya, Pakistan and Zambia). Participants in this analysis were 42 803 women, including their 43 230 babies, who registered for the study in their first trimester based on GA estimated either by LMP or USG and had a live or stillbirth with an estimated GA of 20-42 weeks.

OUTCOME MEASURES

GA was estimated in weeks and days based on LMP and/or USG. Prematurity was defined as GA of 20 weeks+0 days through 36 weeks+6 days, calculated by both USG and LMP.

RESULTS

Overall, average GA varied ≤1 week between LMP and USG. Mean GA for live births by LMP was lower than by USG (adjusted mean difference (95% CI) = -0.23 (-0.29 to -0.17) weeks). Among stillbirths, a higher GA was estimated by LMP than USG (adjusted mean difference (95% CI)= 0.42 (0.11 to 0.72) weeks). Preterm birth rates for live births were significantly higher when dated by LMP (adjusted rate difference (95% CI)= 4.20 (3.56 to 4.85)). There was no significant difference in preterm birth rates for stillbirths.

CONCLUSION

The small differences in GA for LMP versus USG in the Guatemalan and Indian sites suggest that LMP may be a useful alternative to USG for GA dating during the first trimester until availability of USG improves in those areas. Further research is needed to assess LMP for first-trimester GA dating in other regions with limited access to USG.

TRIAL REGISTRATION NUMBER

NCT01073475.

Examining maternal morbidity across a spectrum of delivery locations: An analysis of the Global Network's Maternal and Neonatal Health Registry

OBJECTIVE

To better understand maternal morbidity, using quality data from low- and middle-income countries (LMICs), including out-of-hospital deliveries. Additionally, to compare to the WHO estimate that maternal morbidity occurs in 15% of pregnancies, which is based largely on hospital-level data.

METHODS

The Global Network for Women's and Children's Health Research Maternal Newborn Health Registry collected data on all pregnancies from seven sites in six LMICs between 2015 and 2020. Rates of maternal mortality and morbidity and the differences in morbidity across delivery location and birth attendant type were evaluated.

RESULTS

Among the 280 584 deliveries included in the present analysis, the overall maternal mortality ratio was 138 per 100 000, while 11.7% of women experienced at least one morbidity. Rates of morbidity were generally higher for deliveries occurring within hospitals (19.8%) and by physicians (23.6%). The lowest rates of morbidity were noted among women delivering in non-hospital healthcare facilities (5.6%) or with non-physician clinicians (e.g. nurses, midwives [5.4%]).

CONCLUSION

The present study shows important differences in reported maternal morbidity across delivery sites, with a trend towards lower morbidity in non-hospital healthcare facilities and among non-physician clinicians.

Anemia and adverse outcomes in pregnancy: subgroup analysis of the CLIP cluster-randomized trial in India

Background

Iron-deficiency anemia is a known risk factor for several adverse perinatal outcomes, but data on its impact on specific maternal morbidities is less robust. Further, information on associations between anemia in early pregnancy and subsequent outcomes are understudied.

Methods

The study population was derived from the Community Level Interventions for Pre-eclampsia (CLIP) trial in Karnataka State, India (NCT01911494). Included were women who were enrolled in either trial arm, delivered by trial end date, and had a baseline measure of hemoglobin (Hb). Anemia was classified by WHO standards into four groups: none (Hb ≥ 11 g/dL), mild (10.0 g/dL ≤ Hb < 11.0 g/dL), moderate (7.0 g/dL ≤ Hb < 10.0 g/dL) and severe (Hb < 7.0 g/dL). Targeted maximum likelihood estimation was used to estimate confounder-adjusted associations between anemia and a composite (and its components) of adverse maternal outcomes, including pregnancy hypertension. E-values were calculated to assess robustness to unmeasured confounding.

Results

Of 11,370 women included, 10,066 (88.5%) had anemia, that was mild (3690, 32.5%), moderate (6023, 53.0%), or severe (68, 0.6%). Almost all women (> 99%) reported taking iron supplements during pregnancy. Blood transfusions was more often administered to those with anemia that was mild (risk ratio [RR] 2.16, 95% confidence interval [CI] 1.31–3.56), moderate (RR 2.37, 95% CI 1.56–3.59), and severe (RR 5.70, 95% CI 3.00–10.85). No significant association was evident between anemia severity and haemorrhage (antepartum or postpartum) or sepsis, but there was a U-shaped association between anemia severity and pregnancy hypertension and pre-eclampsia specifically, with the lowest risk seen among those with mild or moderate anemia.

Conclusion

In Karnataka State, India, current management strategies for mild-moderate anemia in early pregnancy are associated with similar rates of adverse maternal or perinatal outcomes, and a lower risk of pregnancy hypertension and preeclampsia, compared with no anemia in early pregnancy. Future research should focus on risk mitigation for women with severe anemia, and the potential effect of iron supplementation for women with normal Hb in early pregnancy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-022-04714-y.

Maternal and neonatal data collection systems in low- and middle-income countries for maternal vaccines active safety surveillance systems: A scoping review

BACKGROUND

Most post-licensure vaccine pharmacovigilance in low- and middle-income countries (LMICs) are passive reporting systems. These have limited utility for maternal immunization pharmacovigilance in LMIC settings and need to be supplemented with active surveillance. Our study's main objective was to identify existing perinatal data collection systems in LMICs that collect individual information on maternal and neonatal health outcomes and could be developed to inform active safety surveillance of novel vaccines for use during pregnancy.

METHODS

A scoping review was performed following the Arksey and O'Malley six-stage approach. We included studies describing electronic or mixed paper-electronic data collection systems in LMICs, including research networks, electronic medical records, and custom software platforms for health information systems. Medline PubMed, EMBASE, Global Health, Cochrane Library, LILACS, Bibliography of Asian Studies (BAS), and CINAHL were searched through August 2019. We also searched grey literature including through Google and websites of existing relevant perinatal data collection systems, as well as contacted authors of key studies and experts in the field to validate the information and identify additional sources of relevant unpublished information.

RESULTS

A total of 11,817 records were identified. The full texts of 264 records describing 96 data collection systems were assessed for eligibility. Eight perinatal data collection systems met our inclusion criteria: Global Network's Maternal Newborn Health Registry, International Network for the Demographic Evaluation of Populations and their Health; Perinatal Informatic System; Pregnancy Exposure Registry & Birth Defects Surveillance; SmartCare; Open Medical Record System; Open Smart Register Platform and District Health Information Software 2. These selected systems were qualitatively characterized according to seven different domains: governance; system design; system management; data management; data sources, outcomes and data quality.

CONCLUSION

This review provides a list of active maternal and neonatal data collection systems in LMICs and their characteristics as well as their outreach, strengths, and limitations. Findings could potentially help further understand where to obtain population-based high-quality information on outcomes to inform the conduct of maternal immunization active vaccine safety surveillance activities and research in LMICs.

Improving data quality in observational research studies: Report of the Cure Glomerulonephropathy (CureGN) network

Background

High data quality is of crucial importance to the integrity of research projects. In the conduct of multi-center observational cohort studies with increasing types and quantities of data, maintaining data quality is challenging, with few published guidelines.

Methods

The Cure Glomerulonephropathy (CureGN) Network has established numerous quality control procedures to manage the 70 participating sites in the United States, Canada, and Europe. This effort is supported and guided by the activities of several committees, including Data Quality, Recruitment and Retention, and Central Review, that work in tandem with the Data Coordinating Center to monitor the study. We have implemented coordinator training and feedback channels, data queries of questionable or missing data, and developed performance metrics for recruitment, retention, visit completion, data entry, recording of patient-reported outcomes, collection, shipping and accessing of biological samples and pathology materials, and processing, cataloging and accessing genetic data and materials.

Results

We describe the development of data queries and site Report Cards, and their use in monitoring and encouraging excellence in site performance. We demonstrate improvements in data quality and completeness over 4 years after implementing these activities. We describe quality initiatives addressing specific challenges in collecting and cataloging whole slide images and other kidney pathology data, and novel methods of data quality assessment.

Conclusions

This paper reports the CureGN experience in optimizing data quality and underscores the importance of general and study-specific data quality initiatives to maintain excellence in the research measures of a multi-center observational study.

Development of the Global Network for Women's and Children's Health Research's socioeconomic status index for use in the network's sites in low and lower middle-income countries

Background

Socioeconomic status (SES) is an important determinant of health globally and an important explanatory variable to assess causality in epidemiological research. The 10th Sustainable Development Goal is to reduce disparities in SES that impact health outcomes globally. It is easier to study SES in high-income countries because household income is representative of the SES. However, it is well recognized that income is poorly reported in low- and middle- income countries (LMIC) and is an unreliable indicator of SES. Therefore, there is a need for a robust index that will help to discriminate the SES of rural households in a pooled dataset from LMIC.

Methods

The study was nested in the population-based Maternal and Neonatal Health Registry of the Global Network for Women’s and Children’s Health Research which has 7 rural sites in 6 Asian, sub-Saharan African and Central American countries. Pregnant women enrolling in the Registry were asked questions about items such as housing conditions and household assets. The characteristics of the candidate items were evaluated using confirmatory factor analyses and item response theory analyses. Based on the results of these analyses, a final set of items were selected for the SES index.

Results

Using data from 49,536 households of pregnant women, we reduced the data collected to a 10-item index. The 10 items were feasible to administer, covered the SES continuum and had good internal reliability and validity. We developed a sum score-based Item Response Theory scoring algorithm which is easy to compute and is highly correlated with scores based on response patterns (r = 0.97), suggesting minimal loss of information with the simplified approach. Scores varied significantly by site (p < 0.001). African sites had lower mean SES scores than the Asian and Central American sites. The SES index demonstrated good internal consistency reliability (Cronbach’s alpha = 0.81). Higher SES scores were significantly associated with formal education, more education, having received antenatal care, and facility delivery (p < 0.001).

Conclusions

While measuring SES in LMIC is challenging, we have developed a Global Network Socioeconomic Status Index which may be useful for comparisons of SES within and between locations. Next steps include understanding how the index is associated with maternal, perinatal and neonatal mortality.

Trial Registration NCT01073475

Plain English summary

Socioeconomic status (SES) is an important determinant of health globally, and improving SES is important to reduce disparities in health outcomes. It is easier to study SES in high-income countries because it can be measured by income and what income is spent on, but this concept does not translate easily to low and middle income countries. We developed a questionnaire that includes 10 items to determine SES in low-resource settings that was added to an ongoing Maternal and Neonatal Health Registry that is funded by the National Institutes of Child Health and Human Development’s Global Network. The Registry includes sites that collect outcomes of pregnancies in women and their babies in rural areas in 6 countries in South Asia, sub-Saharan Africa and Central America. The Registry is population based and tracks women from early in pregnancy to day 42 post-partum. The questionnaire is easy to administer and has good reliability and validity. Next steps include understanding how the index is associated with maternal, fetal and neonatal mortality.

Looking beyond the numbers: quality assurance procedures in the Global Network for Women's and Children's Health Research Maternal Newborn Health Registry

Background

Quality assurance (QA) is a process that should be an integral part of research to protect the rights and safety of study participants and to reduce the likelihood that the results are affected by bias in data collection. Most QA plans include processes related to study preparation and regulatory compliance, data collection, data analysis and publication of study results. However, little detailed information is available on the specific procedures associated with QA processes to ensure high-quality data in multi-site studies.

Methods

The Global Network for Women’s and Children’s Health Maternal Newborn Health Registy (MNHR) is a prospective population-based registry of pregnancies and deliveries that is carried out in 8 international sites. Since its inception, QA procedures have been utilized to ensure the quality of the data. More recently, a training and certification process was developed to ensure that standardized, scientifically accurate clinical definitions are used consistently across sites. Staff complete a web-based training module that reviews the MNHR study protocol, study forms and clinical definitions developed by MNHR investigators and are certified through a multiple choice examination prior to initiating study activities and every six months thereafter. A standardized procedure for supervision and evaluation of field staff is carried out to ensure that research activites are conducted according to the protocol across all the MNHR sites.

Conclusions

We developed standardized QA processes for training, certification and supervision of the MNHR, a multisite research registry. It is expected that these activities, together with ongoing QA processes, will help to further optimize data quality for this protocol.

The relationship between birth intervals and adverse maternal and neonatal outcomes in six low and lower-middle income countries

Background

Due to high fertility rates in some low and lower-middle income countries, the interval between pregnancies can be short, which may lead to adverse maternal and neonatal outcomes.

Methods

We analyzed data from women enrolled in the NICHD Global Network Maternal Newborn Health Registry (MNHR) from 2013 through 2018. We report maternal characteristics and outcomes in relationship to the inter-delivery interval (IDI, time from previous delivery [live or stillborn] to the delivery of the index birth), by category of 6–17 months (short), 18–36 months (reference), 37–60 months, and 61–180 months (long). We used non-parametric tests for maternal characteristics, and multivariable logistic regression models for outcomes, controlling for differences in baseline characteristics.

Results

We evaluated 181,782 women from sites in the Democratic Republic of Congo, Zambia, Kenya, Guatemala, India, and Pakistan. Women with short IDI varied by site, from 3% in the Zambia site to 20% in the Pakistan site. Relative to a 18–36 month IDI, women with short IDI had increased risk of neonatal death (RR = 1.89 [1.74, 2.05]), stillbirth (RR = 1.70 [1.56, 1.86]), low birth weight (RR = 1.38 [1.32, 1.44]), and very low birth weight (RR = 2.35 [2.10, 2.62]). Relative to a 18–36 month IDI, women with IDI of 37–60 months had an increased risk of maternal death (RR 1.40 [1.05, 1.88]), stillbirth (RR 1.14 [1.08, 1.22]), and very low birth weight (RR 1.10 [1.01, 1.21]). Relative to a 18–36 month IDI, women with long IDI had increased risk of maternal death (RR 1.54 [1.10, 2.16]), neonatal death (RR = 1.25 [1.14, 1.38]), stillbirth (RR = 1.50 [1.38, 1.62]), low birth weight (RR = 1.22 [1.17, 1.27]), and very low birth weight (RR = 1.47 [1.32,1.64]). Short and long IDIs were also associated with increased risk of obstructed labor, hemorrhage, hypertensive disorders, fetal malposition, infection, hospitalization, preterm delivery, and neonatal hospitalization.

Conclusions

IDI varies by site. When compared to 18–36 month IDI, women with both short IDI and long IDI had increased risk of adverse maternal and neonatal outcomes.

Trial registration

The MNHR is registered at NCT01073475.

The Global Network Maternal Newborn Health Registry: a multi-country, community-based registry of pregnancy outcomes

BACKGROUND

The Global Network for Women's and Children's Health Research (Global Network) conducts clinical trials in resource-limited countries through partnerships among U.S. investigators, international investigators based in in low and middle-income countries (LMICs) and a central data coordinating center. The Global Network's objectives include evaluating low-cost, sustainable interventions to improve women's and children's health in LMICs. Accurate reporting of births, stillbirths, neonatal deaths, maternal mortality, and measures of obstetric and neonatal care is critical to determine strategies for improving pregnancy outcomes. In response to this need, the Global Network developed the Maternal Newborn Health Registry (MNHR), a prospective, population-based registry of pregnant women, fetuses and neonates receiving care in defined catchment areas at the Global Network sites. This publication describes the MNHR, including participating sites, data management and quality and changes over time.

METHODS

Pregnant women who reside in or receive healthcare in select communities are enrolled in the MNHR of the Global Network. For each woman and her offspring, sociodemographic, health care, and the major outcomes through 42-days post-delivery are recorded. Study visits occur at enrollment during pregnancy, at delivery and at 42 days postpartum.

RESULTS

From 2010 through 2018, the Global Network MNHR sites were located in Guatemala, Belagavi and Nagpur, India, Pakistan, Democratic Republic of Congo, Kenya, and Zambia. During this period at these sites, 579,140 pregnant women were consented and enrolled in the MNHR, nearly 99% of all eligible women. Delivery data were collected for 99% of enrolled women and 42-day follow-up data for 99% of those delivered. In this supplement, the trends over time and assessment of differences across geographic regions are analyzed in a series of 18 manuscripts utilizing the MNHR data.

CONCLUSIONS

Improving maternal, fetal and newborn health in countries with poor outcomes requires an understanding of the characteristics of the population, quality of health care and outcomes. Because the worst pregnancy outcomes typically occur in countries with limited health registration systems and vital records, alternative registration systems may prove to be highly valuable in providing data. The MNHR, an international, multicenter, population-based registry, assesses pregnancy outcomes over time in support of efforts to develop improved perinatal healthcare in resource-limited areas. Trial Registration The Maternal Newborn Health Registry is registered at Clinicaltrials.gov (ID# NCT01073475). Registered February 23, 2019. https://clinicaltrials.gov/ct2/show/NCT01073475.

Reducing defects in the datasets of clinical research studies: conformance with data quality metrics

Background

A dataset is indispensable to answer the research questions of clinical research studies. Inaccurate data lead to ambiguous results, and the removal of errors results in increased cost. The aim of this Quality Improvement Project (QIP) was to improve the Data Quality (DQ) by enhancing conformance and minimizing data entry errors.

Methods

This is a QIP which was conducted in the Department of Biostatistics using historical datasets submitted for statistical data analysis from the department’s knowledge base system. Forty-five datasets received for statistical data analysis, were included at baseline. A 12-item checklist based on six DQ domains (i) completeness (ii) uniqueness (iii) timeliness (iv) accuracy (v) validity and (vi) consistency was developed to assess the DQ. The checklist was comprised of 12 items; missing values, un-coded values, miscoded values, embedded values, implausible values, unformatted values, missing codebook, inconsistencies with the codebook, inaccurate format, unanalyzable data structure, missing outcome variables, and missing analytic variables. The outcome was the number of defects per dataset. Quality improvement DMAIC (Define, Measure, Analyze, Improve, Control) framework and sigma improvement tools were used. Pre-Post design was implemented using mode of interventions. Pre-Post change in defects (zero, one, two or more defects) was compared by using chi-square test.

Results

At baseline, out of forty-five datasets; six (13.3%) datasets had zero defects, eight (17.8%) had one defect, and 31(69%) had ≥2 defects. The association between the nature of data capture (single vs. multiple data points) and defective data was statistically significant (p = 0.008). Twenty-one datasets were received during post-intervention for statistical data analysis. Seventeen (81%) had zero defects, two (9.5%) had one defect, and two (9.5%) had two or more defects. The proportion of datasets with zero defects had increased from 13.3 to 81%, whereas the proportion of datasets with two or more defects had decreased from 69 to 9.5% (p = < 0.001).

Conclusion

Clinical research study teams often have limited knowledge of data structuring. Given the need for good quality data, we recommend training programs, consultation with data experts prior to data structuring and use of electronic data capturing methods.

Translating research evidence into practice: a report from the 2nd International Conference on Maternal and Newborn Health from KLE University - Belagavi, India

The Jawaharlal Nehru Medical College (JNMC) Women's and Children's Health Research Unit (WCHRU) of the Karnataka Lingayat Education (KLE) Academy of Higher Education and Research Deemed-to-be-University and its collaborators convened the '2nd International Conference on Maternal and Newborn Health -Translating Research Evidence to Practice' to address the common theme of improving maternal and newborn health in low- and middle- income countries (LMIC). This supplement, including 16 manuscripts, reflects much of the research presented at the conference, including analyses of the state of knowledge, as well as completed, ongoing and planned research in these areas conducted by the WCHRU in India together with many collaborators across high-income and LMIC. The first paper reviews maternal, fetal and neonatal mortality in low-income countries, considers their causes, as well as evidence for potential interventions to reduce mortality. A second paper addresses near miss maternal mortality. Several manuscripts address the research conducted by WCHRU and their colleagues in a multi-center research network. One study examines rates of miscarriage and medically terminated pregnancy in India and the risk factors for these occurrences. Another paper addresses stillbirth and its risk factors, both in India as well as in other LMIC. Haemorrhage and preeclampsia/eclampsia, important causes of maternal mortality, stillbirth and neonatal morbidity in LMIC, are addressed in a series of papers summarizing trials of interventions to reduce improve outcomes associated with these conditions. Poor maternal and infant nutritional status, which contribute to adverse outcomes, are addressed through papers which describe a number of important studies that the WCHRU and their colleagues have conducted to attempt to improve nutritional status. Another paper describes a study to investigate causes of stillbirth and deaths among preterm births, which will utilize new techniques to investigate the infectious causes of these deaths. Finally, the supplement addresses the process for dissemination of research results to inform public policy. Together these manuscripts represent a body of research to inform interventions to reduce maternal, fetal and newborn mortality and illustrates what a dedicated research group together with institutional support can accomplish.

Anthropometric indices for non-pregnant women of childbearing age differ widely among four low-middle income populations

Background

Maternal stature and body mass indices (BMI) of non-pregnant women (NPW) of child bearing age are relevant to maternal and offspring health. The objective was to compare anthropometric indices of NPW in four rural communities in low- to low-middle income countries (LMIC).

Methods

Anthropometry and maternal characteristics/household wealth questionnaires were obtained for NPW enrolled in the Women First Preconception Maternal Nutrition Trial. Body mass index (BMI, kg/m²) was calculated. Z-scores were determined using WHO reference data.

Results

A total of 7268 NPW participated in Equateur, DRC (n = 1741); Chimaltenango, Guatemala (n = 1695); North Karnataka, India (n = 1823); and Thatta, Sindh, Pakistan (n = 2009). Mean age was 23 y and mean parity 1.5. Median (P25-P75) height (cm) ranged from 145.5 (142.2–148.9) in Guatemala to 156.0 (152.0–160.0) in DRC. Median weight (kg) ranged from 44.7 (39.9–50.3) in India to 52.7 (46.9–59.8) in Guatemala. Median BMI ranged from 19.4 (17.6–21.9) in India to 24.9 (22.3–28.0) in Guatemala. Percent stunted (<−2SD height for age z-score) ranged from 13.9% in DRC to 80.5% in Guatemala; % underweight (BMI <18.5) ranged from 1.2% in Guatemala to 37.1% in India; % overweight/obese (OW, BMI ≥25.0) ranged from 5.7% in DRC to 49.3% in Guatemala. For all sites, indicators for higher SES and higher age were associated with BMI. Lower SES women were underweight more frequently and higher SES women were OW more frequently at all sites. Younger women tended to be underweight, while older women tended to be OW.

Conclusions

Anthropometric data for NPW varied widely among low-income rural populations in four countries located on three different continents. Global comparisons of anthropometric measurements across sites using standard reference data serve to highlight major differences among populations of low-income rural NPW and assist in evaluating the rationale for and the design of optimal intervention trials.

Trial registration

ClinicalTrials.gov #NCT01883193 (18 June 2013, retrospectively registered)

Drug development challenges and strategies to address emerging and resistant fungal pathogens

INTRODUCTION

Invasive fungal infections represent an expanding threat to public health. The recent emergence of Candida auris, which is often resistant to existing antifungal agents and is associated with a high mortality rate, underscores the urgent need for novel drug development strategies. Areas covered: In this paper, we examine both challenges and opportunities associated with antifungal drug development and explore potential avenues to accelerate the development pipeline, including data sharing, surrogate endpoints, and the role of historical controls in clinical trials. Expert commentary: We review important lessons learned from the study of other rare diseases, including mitochondrial storage diseases and certain forms of cancer that may inform strategies to develop new antifungal agents while highlighting promising new compounds such as SCY-078 for the treatment of invasive fungal infections.

A pre-post study of a multi-country scale up of resuscitation training of facility birth attendants: does Helping Babies Breathe training save lives?

Background

Whether facility-based implementation of Helping Babies Breathe (HBB) reduces neonatal mortality at a population level in low and middle income countries (LMIC) has not been studied. Therefore, we evaluated HBB implementation in this context where our study team has ongoing prospective outcome data on all pregnancies regardless of place of delivery.

Methods

We compared outcomes of birth cohorts in three sites in India and Kenya pre-post implementation of a facility-based intervention, using a prospective, population-based registry in 52 geographic clusters. Our hypothesis was that HBB implementation would result in a 20 % decrease in the perinatal mortality rate (PMR) among births ≥1500 g.

Results

We enrolled 70,704 births during two 12-month study periods. Births within each site did not differ pre-post intervention, except for an increased proportion of <2500 g newborns and deliveries by caesarean section in the post period. There were no significant differences in PMR among all registry births; however, a post-hoc analysis stratified by birthweight documented improvement in <2500 g mortality in Belgaum in both registry and in HBB-trained facility births. No improvement in <2500 g mortality measures was noted in Nagpur or Kenya and there was no improvement in normal birth weight survival.

Conclusions

Rapid scale up of HBB training of facility birth attendants in three diverse sites in India and Kenya was not associated with consistent improvements in mortality among all neonates ≥1500 g; however, differential improvements in <2500 g survival in Belgaum suggest the need for careful implementation of HBB training with attention to the target population, data collection, and ongoing quality monitoring activities.

Trial registration

The study was registered at ClinicalTrials.gov: NCT01681017.

Data quality evaluation for observational multiple sclerosis registries

Objective:

Objective and reproducible evaluation of data quality is of paramount importance for studies of ‘real-world’ observational data. Here, we summarise a standardised data quality, density and generalisability process implemented by MSBase, a global multiple sclerosis (MS) cohort study.

Methods:

Error rate, data density score and generalisability score were developed using all 35,869 patients enrolled in MSBase as of November 2015. The data density score was calculated across six domains (follow-up, demography, visits, MS relapses, paraclinical data and therapy) and emphasised data completeness. The error rate evaluated syntactic accuracy and consistency of data. The generalisability score evaluated believability of the demographic and treatment information. Correlations among the three scores and the number of patients per centre were evaluated.

Results:

Errors were identified at the median rate of 3 per 100 patient-years. The generalisability score indicated the samples’ representativeness of the known MS epidemiology. Moderate correlation between the density and generalisability scores (ρ = 0.58) and a weak correlation between the error rate and the other two scores (ρ = −0.32 to −0.33) were observed. The generalisability score was strongly correlated with centre size (ρ = 0.79).

Conclusion:

The implemented scores enable objective evaluation of the quality of observational MS data, with an impact on the design of future analyses.

The Global Network for Women's and Children's Health Research: A model of capacity-building research

In response to the global effort to accelerate progress towards the Millennium Development Goals 4 and 5, a partnership was created between the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the Bill and Melinda Gates Foundation to establish the Global Network for Women's and Children's Health Research (Global Network) in 2000. The Global Network was developed with a goal of building local maternal and child health research capacity in resource-poor settings. The objective of the network was to conduct research focused on several high-need areas, such as preventing life-threatening obstetric complications, improving birth weight and infant growth, and improving childbirth practices in order to reduce mortality. Scientists from developing countries, together with peers in the USA, lead research teams that identify and address population needs through randomized clinical trials and other research studies. Global Network projects develop and test cost-effective, sustainable interventions for pregnant women and newborns and provide guidance for national policy and for the practice of evidence-based medicine. This article reviews the results of the Global Network's research, the impact on policy and practice, and highlights the capacity-building efforts and collaborations developed since its inception.