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Up-to-date information on the causes of child deaths is crucial to guide global efforts to improve child survival. We report new estimates for 2008 of the major causes of death in children younger than 5 years. We used multicause proportionate mortality models to estimate deaths in neonates aged 0–27 days and children aged 1–59 months, and selected single-cause disease models and analysis of vital registration data when available to estimate causes of child deaths. New data from China and India permitted national data to be used for these countries instead of predictions based on global statistical models, as was done previously. We estimated proportional causes of death for 193 countries, and by application of these proportions to the country-specific mortality rates in children younger than 5 years and birth rates, the numbers of deaths by cause were calculated for countries, regions, and the world. Of the estimated 8·795 million deaths in children younger than 5 years worldwide in 2008, infectious diseases caused 68% (5·970 million), with the largest percentages due to pneumonia (18%, 1·575 million, uncertainty range [UR] 1·046 million–1·874 million), diarrhoea (15%, 1·336 million, 0·822 million–2·004 million), and malaria (8%, 0·732 million, 0·601 million–0·851 million). 41% (3·575 million) of deaths occurred in neonates, and the most important single causes were preterm birth complications (12%, 1·033 million, UR 0·717 million–1·216 million), birth asphyxia (9%, 0·814 million, 0·563 million–0·997 million), sepsis (6%, 0·521 million, 0·356 million–0·735 million), and pneumonia (4%, 0·386 million, 0·264 million–0·545 million). 49% (4·294 million) of child deaths occurred in five countries: India, Nigeria, Democratic Republic of the Congo, Pakistan, and China. These country-specific estimates of the major causes of child deaths should help to focus national programmes and donor assistance. Achievement of Millennium Development Goal 4, to reduce child mortality by two-thirds, is only possible if the high numbers of deaths are addressed by maternal, newborn, and child health interventions. WHO, UNICEF, and Bill & Melinda Gates Foundation.

India's 2011 census revealed a growing imbalance between the numbers of girls and boys aged 0–6 years, which we postulate is due to increased prenatal sex determination with subsequent selective abortion of female fetuses. We aimed to establish the trends in sex ratio by birth order from 1990 to 2005 with three nationally representative surveys and to quantify the totals of selective abortions of girls with census cohort data. We assessed sex ratios by birth order in 0·25 million births in three rounds of the nationally representative National Family Health Survey covering the period from 1990 to 2005. We estimated totals of selective abortion of girls by assessing the birth cohorts of children aged 0–6 years in the 1991, 2001, and 2011 censuses. Our main statistic was the conditional sex ratio of second-order births after a firstborn girl and we used 3-year rolling weighted averages to test for trends, with differences between trends compared by linear regression. The conditional sex ratio for second-order births when the firstborn was a girl fell from 906 per 1000 boys (99% CI 798–1013) in 1990 to 836 (733–939) in 2005; an annual decline of 0·52% (p for trend=0·002). Declines were much greater in mothers with 10 or more years of education than in mothers with no education, and in wealthier households compared with poorer households. By contrast, we did not detect any significant declines in the sex ratio for second-order births if the firstborn was a boy, or for firstborns. Between the 2001 and 2011 censuses, more than twice the number of Indian districts (local administrative areas) showed declines in the child sex ratio as districts with no change or increases. After adjusting for excess mortality rates in girls, our estimates of number of selective abortions of girls rose from 0–2·0 million in the 1980s, to 1·2–4·1 million in the 1990s, and to 3·1–6·0 million in the 2000s. Each 1% decline in child sex ratio at ages 0–6 years implied 1·2–3·6 million more selective abortions of girls. Selective abortions of girls totalled about 4·2–12·1 million from 1980–2010, with a greater rate of increase in the 1990s than in the 2000s. Selective abortion of girls, especially for pregnancies after a firstborn girl, has increased substantially in India. Most of India's population now live in states where selective abortion of girls is common. US National Institutes of Health, Canadian Institute of Health Research, International Development Research Centre, and Li Ka Shing Knowledge Institute.

National malaria death rates are difficult to assess because reliably diagnosed malaria is likely to be cured, and deaths in the community from undiagnosed malaria could be misattributed in retrospective enquiries to other febrile causes of death, or vice-versa. We aimed to estimate plausible ranges of malaria mortality in India, the most populous country where the disease remains common. Full-time non-medical field workers interviewed families or other respondents about each of 122 000 deaths during 2001–03 in 6671 randomly selected areas of India, obtaining a half-page narrative plus answers to specific questions about the severity and course of any fevers. Each field report was sent to two of 130 trained physicians, who independently coded underlying causes, with discrepancies resolved either via anonymous reconciliation or adjudication. Of all coded deaths at ages 1 month to 70 years, 2681 (3·6%) of 75 342 were attributed to malaria. Of these, 2419 (90%) were in rural areas and 2311 (86%) were not in any health-care facility. Death rates attributed to malaria correlated geographically with local malaria transmission ratesderived independently from the Indian malaria control programme. The adjudicated results show 205 000 malaria deaths per year in India before age 70 years (55 000 in early childhood, 30 000 at ages 5–14 years, 120 000 at ages 15–69 years); 1·8% cumulative probability of death from malaria before age 70 years. Plausible lower and upper bounds (on the basis of only the initial coding) were 125 000–277 000. Malaria accounted for a substantial minority of about 1·3 million unattended rural fever deaths attributed to infectious diseases in people younger than 70 years. Despite uncertainty as to which unattended febrile deaths are from malaria, even the lower bound greatly exceeds the WHO estimate of only 15 000 malaria deaths per year in India (5000 early childhood, 10 000 thereafter). This low estimate should be reconsidered, as should the low WHO estimate of adult malaria deaths worldwide. US National Institutes of Health, Canadian Institute of Health Research, Li Ka Shing Knowledge Institute.

ObjectivesLength-for-age z-scores (LAZ) and stunting prevalence (%LAZ <–2) are commonly used to quantify linear growth faltering in young children in low- and middle-income countries (LMICs). The Healthy Birth, Growth and Development knowledge integration (HBGDki) consortium described postnatal linear growth faltering using LAZ-by-age trajectory modelling and child-level LAZ threshold-crossing events, including incident stunting onset (first occurrence of LAZ <–2) and stunting reversal (LAZ rising from <–2 to ≥–2). Using simulations, we assessed the suitability of these LAZ threshold-crossing metrics for characterising linear growth faltering in LMICs.MethodsWe simulated a synthetic cohort with a harmonically downward-shifting LAZ trajectory from birth to 24 months of age, with mean LAZs similar to the HBGDki pooled South Asian cohorts, and without any input parameters intended to differentially affect individuals’ growth across the height distribution or at different ages. We compared HBGDki empirical estimates of age interval-specific frequencies of incident stunting onset and stunting reversal with those from the synthetic cohort. Using synthetic cohorts, we examined how estimates of incident onset and reversal were affected by missing data, magnitude of the whole-population shift in the LAZ distribution and strength of the between-time-point correlation. We also compared the 3–24 month pattern of linear growth faltering expressed as age-related trajectories of average growth delay (chronological age minus height–age), mean LAZ or stunting prevalence.ResultsEmpirical estimates of age interval-specific incident stunting onset and stunting reversal in the HBGDki cohorts were similar to those observed in a synthetic cohort. Variability in LAZ threshold-crossing event rates is explained by starting LAZ, between-time-point correlation and the magnitude of the whole-population shift in the LAZ distribution. Incident stunting onset is also affected by missing data in preceding intervals. Stunting reversal occurs due to within-child variability (ie, imperfect between-time-point correlation) in the absence of any other phenomena that cause stunted children to become non-stunted at a later age. The linear growth faltering pattern based on growth delay differed from corresponding age-related trajectories of mean LAZ or stunting prevalence.ConclusionsIn longitudinal studies of linear growth faltering in LMICs, LAZ threshold-crossing indicators are byproducts of whole-population shifts in LAZ and within-child variability and should be interpreted accordingly. Reporting incident stunting onset and reversal rates, or analyses in which children are grouped by the timing of LAZ threshold-crossing events, may detract from efforts to understand when and why nearly all children in LMICs grow more slowly than expected for their age. Since mean LAZ and stunting prevalence are unsuitable for quantifying the rate and timing of population-average postnatal linear growth faltering, growth delay is recommended for consideration as a preferred metric.

Height-age is the age at which growth-faltered children’s average observed height or length equals the median height or length of a child growth standard, corresponding to a length-for-age z-score (LAZ) of 0. In randomized controlled trials (RCTs) in low- and middle-income countries (LMICs), expression of linear growth outcomes using height-age may enhance the interpretability of intervention effects compared to conventional use of LAZ. Height-age can be used to derive the proportion of maximal benefit (PMB), whereby PMB = 0% indicates no effect and PMB = 100% indicates the intervention promoted growth at the rate expected for healthy children with the same starting height-age. In this proof-of-concept study, height-age and PMB were compared to LAZ in a meta-analysis of RCTs of small-quantity lipid-based nutrient supplements (SQ-LNS). Pooling across 15 trials in 10 LMICs, mean differences (MD; SQ-LNS minus control) in LAZ and height-age were 0.15 (95%CI: 0.12, 0.17) and 12 days (95%CI: 9, 14), respectively (N = 36,970). LAZ MD and height-age MD were highly correlated (rho = 0.74 overall and 0.94 upon exclusion of an outlier). The pooled PMB indicated that SQ-LNS achieves 11% of optimal growth potential (95% CI: [9.4, 12]; N = 19,768; 12 comparisons), but there was a substantial impact of between-trial heterogeneity (I 2  = 90%). In conclusion, the effect of SQ-LNS on linear growth can be alternatively expressed in terms of height-age instead of LAZ. The PMB may enhance the interpretability of effect estimates by quantifying the extent to which an intervention improves growth in relation to a biological threshold, but further research is required to establish its validity and usefulness for assessing and comparing intervention effectiveness.

Background Ongoing high neonatal mortality rates (NMRs) represent a global challenge. In 2021, of the 5 million deaths reported worldwide for children under five years of age, 47% were newborns. Pakistan has one of the five highest national NMRs in the world, with an estimated 39 neonatal deaths per 1,000 live births. Reducing newborn deaths requires sustainable, evidence-based, and cost-effective interventions that can be integrated within existing community healthcare infrastructure across regions with high NMR. Methods This pragmatic, community-based, parallel-arm, open-label, cluster randomized controlled trial aims to estimate the effect of Lady Health Workers (LHWs) providing an integrated newborn care kit (iNCK) with educational instructions to pregnant women in their third trimester, compared to the local standard of care in Gilgit-Baltistan, Pakistan, on neonatal mortality and other newborn and maternal health outcomes. The iNCK contains a clean birth kit, 4% chlorhexidine topical gel, sunflower oil emollient, a ThermoSpot™ temperature monitoring sticker, a fleece blanket, a click-to-heat reusable warmer, three 200 μg misoprostol tablets, and a pictorial instruction guide and diary. LHWs are also provided with a handheld scale to weigh the newborn. The primary study outcome is neonatal mortality, defined as a newborn death in the first 28 days of life. Discussion This study will generate policy-relevant knowledge on the effectiveness of integrating evidence-based maternal and newborn interventions and delivering them directly to pregnant women via existing community health infrastructure, for reducing neonatal mortality and morbidity, in a remote, mountainous area with a high NMR. Trial registration NCT04798833, March 15, 2021.

Background The health management information system (HMIS) is an integral component of a strong health care system. Despite its importance for decision-making, the quality of HMIS data remains of concern in low- and middle-income countries. To address challenges with the quality of maternal and child health (MCH) data gathered within Malawi’s HMIS, we conducted a pilot study evaluating different support modalities to district-level HMIS offices. We hypothesized that providing regular, direct financial assistance to HMIS offices would enable staff to establish strategies and priorities based on local context, resulting in more accurate, timely, and complete MCH data. Methods The pilot intervention was implemented in Mwanza district, while Chikwawa, Neno, and Ntchisi districts served as control sites given support received from other institutions. The intervention consisted of providing direct financial assistance to Mwanza’s HMIS office following the submission of detailed budgets and lists of planned activities. In the control districts, we performed interviews with the HMIS officers to track the HMIS-related activities. We evaluated the intervention by comparing data quality between the post- and pre-intervention periods in the intervention and control districts. Additionally, we conducted interviews with Mwanza’s HMIS office staff to determine the acceptability and appropriateness of the intervention. Results Following the 10-month intervention period, we observed improvements in MCH data quality in Mwanza. The availability and completeness of MCH data collected in the registers increased by 22 and 18 percentage points, respectively. The consistency of MCH data between summary reports and electronic HMIS also improved. In contrast, 2/3 control districts noted minimal changes or reductions in data quality after 10 months. The qualitative interviews confirmed that, despite some challenges, the intervention was well received by the participating HMIS office. HMIS staff preferred our strategy to other conventional strategies that fail to give them the independence to make decisions. Conclusions This pilot intervention demonstrated an alternative approach to support HMIS offices in their daily efforts to improve data quality. Given the Ministry of Health’s (MoH) interest in strengthening its HMIS, our intervention provides a strategy that the MoH and local and international partners could consider to rapidly improve HMIS data with minimal oversight.

Metrics to quantify child growth vary across studies of the developmental origins of health and disease. We conducted a scoping review of child growth studies in which length/height, weight or body mass index (BMI) was measured at ≥ 2 time points. From a 10% random sample of eligible studies published between Jan 2010-Jun 2016, and all eligible studies from Oct 2015-June 2016, we classified growth metrics based on author-assigned labels (e.g., 'weight gain') and a 'content signature', a numeric code that summarized the metric's conceptual and statistical properties. Heterogeneity was assessed by the number of unique content signatures, and label-to-content concordance. In 122 studies, we found 40 unique metrics of childhood growth. The most common approach to quantifying growth in length, weight or BMI was the calculation of each child's change in z-score. Label-to-content discordance was common due to distinct content signatures carrying the same label, and because of instances in which the same content signature was assigned multiple different labels. In conclusion, the numerous distinct growth metrics and the lack of specificity in the application of metric labels challenge the integration of data and inferences from studies investigating the determinants or consequences of variations in childhood growth.

Child growth patterns assessment is critical to design public health interventions. However, current analytical approaches may overlook population heterogeneity. To overcome this limitation, we developed a growth trajectories clustering pipeline that incorporates a shape-respecting distance, baseline centering (i.e., birth-size normalized trajectories) and Gestational Age (GA)-correction to characterize shape-based child growth patterns. We used data from 3945 children (461 preterm) in the 2004 Pelotas Birth Cohort with at least 3 measurements between birth (included) and 11 years of age. Sex-adjusted weight-, length/height- and body mass index-for-age z-scores were derived at birth, 3 months, and at 1, 2, 4, 6 and 11 years of age (INTERGROWTH-21st and WHO growth standards). Growth trajectories clustering was conducted for each anthropometric index using k-means and a shape-respecting distance, accounting or not for birth size and/or GA-correction. We identified 3 trajectory patterns for each anthropometric index: increasing ( High ), stable ( Middle ) and decreasing ( Low ). Baseline centering resulted in pattern classification that considered early life growth traits. GA-correction increased the intercepts of preterm-born children trajectories, impacting their pattern classification. Incorporating shape-based clustering, baseline centering and GA-correction in growth patterns analysis improves the identification of subgroups meaningful for public health interventions.

Background Most households in low and middle income countries, including in India, use solid fuels (coal/coke/lignite, firewood, dung, and crop residue) for cooking and heating. Such fuels increase child mortality, chiefly from acute respiratory infection. There are, however, few direct estimates of the impact of solid fuel on child mortality in India. Methods We compared household solid fuel use in 1998 between 6790 child deaths, from all causes, in the previous year and 609 601 living children living in 1.1 million nationally-representative homes in India. Analyses were stratified by child's gender, age (neonatal, post-neonatal, 1-4 years) and colder versus warmer states. We also examined the association of solid fuel to non-fatal pneumonias. Results Solid fuel use was very common (87% in households with child deaths and 77% in households with living children). After adjustment for demographic factors and living conditions, solid-fuel use significantly increase child deaths at ages 1-4 (prevalence ratio (PR) boys: 1.30, 95%CI 1.08-1.56; girls: 1.33, 95%CI 1.12-1.58). More girls than boys died from exposure to solid fuels. Solid fuel use was also associated with non-fatal pneumonia (boys: PR 1.54 95%CI 1.01-2.35; girls: PR 1.94 95%CI 1.13-3.33). Conclusions Child mortality risks, from all causes, due to solid fuel exposure were lower than previously, but as exposure was common solid, fuel caused 6% of all deaths at ages 0-4, 20% of deaths at ages 1-4 or 128 000 child deaths in India in 2004. Solid fuel use has declined only modestly in the last decade. Aside from reducing exposure, complementary strategies such as immunization and treatment could also reduce child mortality from acute respiratory infections.