Understanding the level and characteristics of protection from past SARS-CoV-2 infection against subsequent re-infection, symptomatic COVID-19 disease, and severe disease is essential for predicting ...future potential disease burden, for designing policies that restrict travel or access to venues where there is a high risk of transmission, and for informing choices about when to receive vaccine doses. We aimed to systematically synthesise studies to estimate protection from past infection by variant, and where data allow, by time since infection.
In this systematic review and meta-analysis, we identified, reviewed, and extracted from the scientific literature retrospective and prospective cohort studies and test-negative case-control studies published from inception up to Sept 31, 2022, that estimated the reduction in risk of COVID-19 among individuals with a past SARS-CoV-2 infection in comparison to those without a previous infection. We meta-analysed the effectiveness of past infection by outcome (infection, symptomatic disease, and severe disease), variant, and time since infection. We ran a Bayesian meta-regression to estimate the pooled estimates of protection. Risk-of-bias assessment was evaluated using the National Institutes of Health quality-assessment tools. The systematic review was PRISMA compliant and was registered with PROSPERO (number CRD42022303850).
We identified a total of 65 studies from 19 different countries. Our meta-analyses showed that protection from past infection and any symptomatic disease was high for ancestral, alpha, beta, and delta variants, but was substantially lower for the omicron BA.1 variant. Pooled effectiveness against re-infection by the omicron BA.1 variant was 45·3% (95% uncertainty interval UI 17·3–76·1) and 44·0% (26·5–65·0) against omicron BA.1 symptomatic disease. Mean pooled effectiveness was greater than 78% against severe disease (hospitalisation and death) for all variants, including omicron BA.1. Protection from re-infection from ancestral, alpha, and delta variants declined over time but remained at 78·6% (49·8–93·6) at 40 weeks. Protection against re-infection by the omicron BA.1 variant declined more rapidly and was estimated at 36·1% (24·4–51·3) at 40 weeks. On the other hand, protection against severe disease remained high for all variants, with 90·2% (69·7–97·5) for ancestral, alpha, and delta variants, and 88·9% (84·7–90·9) for omicron BA.1 at 40 weeks.
Protection from past infection against re-infection from pre-omicron variants was very high and remained high even after 40 weeks. Protection was substantially lower for the omicron BA.1 variant and declined more rapidly over time than protection against previous variants. Protection from severe disease was high for all variants. The immunity conferred by past infection should be weighed alongside protection from vaccination when assessing future disease burden from COVID-19, providing guidance on when individuals should be vaccinated, and designing policies that mandate vaccination for workers or restrict access, on the basis of immune status, to settings where the risk of transmission is high, such as travel and high-occupancy indoor settings.
Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.
Nutrition surveys in many refugee settings routinely estimate anemia prevalence in high-risk population groups. Given the lack of information on anemia design effects (DEFF) observed in surveys in ...these settings, the goal of this paper is to better understand the magnitude and distribution of DEFFs and intracluster correlation coefficients (ICCs) in order to inform future survey design. Two-stage cluster surveys conducted during 2013-2016 were included if they measured hemoglobin in refugee children aged 6-59 months and/or non-pregnant women aged 15-49 years. Prevalence of anemia, anemia DEFFs and ICCs, mean cluster size, number of clusters, and total sample size were calculated per-survey for non-pregnant women and children. Non-parametric tests were used to assess differences and correlations of ICC and DEFF between women and children and inter-regional differences. Eighty-seven unique cluster surveys from nine countries were included in this analysis. More than 90% of all surveys had ICC values for anemia below 0.10. Median ICC for children was 0.032 (IQR: 0.015-0.048), not significantly different from that observed for non-pregnant women for whom the median was 0.024 (IQR: -0.002-0.055). DEFFs were significantly higher for children 1.54 (IQR: 1.21-1.82) versus women 1.20 (IQR: 0.99-1.46). Regional differences in DEFFs and ICCs were observed. Both ICCs and DEFF were relatively small for both non-pregnant women and preschool children and fall in a narrow range. Differences in ICCs between women and children were non-significant, suggesting similar inter-cluster distributions of anemia; significant differences in DEFF were likely attributable to differing cluster sizes. Given regional differences in both ICCs and DEFFs, location-specific values are preferred. However, in the absence of other context-specific information, we suggest using DEFFs of 1.4-1.8 if mean cluster size is around 20, and DEFFs of 1.2-1.4 if mean cluster size is around 10.
Most of the assessments have failed to find a clear association between COVID-19 mortality and infection rates and those pandemic preparedness metrics,1–8 including those studies that account for ...under-reporting and factors that influence COVID-19 fatality and infection rates such as age, sociodemographics, and key comorbidities.9 The recent article by Ledesma et al in this journal is the rare exception in finding several correlations between GHS indicators and COVID-19 excess mortality, but it has a critical error—it fails to account properly for gross domestic product (GDP) per capita.10 Doing so fundamentally changes the conclusions of the research as nearly all the paper’s significant findings are no longer statistically significant when this problem is addressed. ...a skewed variable can lead to heteroskedasticity, another violation of the standard linear regression model’s assumptions (figure 3A). Especially in smaller sample sizes (such as the one in Ledesma et al), skewed data can lead the estimator to be biased and inefficient, and the model results should not be trusted. ...it is the well-established standard to use a logarithmic transformation of GDP per capita.11 12 When GDP per capita is appropriately log-transformed, it leads to a distribution that is much closer to normal (figure 1B), has more of a linear relationship (figure 2B) with the dependent variable and has less heteroskedasticity (figure 3B). When using the natural log transforming only one estimate shows a statistically significant relationship between a GHS Index score and excess mortality Income-adjusted analysis (Ledesma et al) Log-transformed income-adjusted Pandemic preparedness capacity Coefficient (corrected 95% CI) P value Coefficient (corrected 95% CI) P value Overall score −0.21 (−0.41 to 0.02) 0.0004* −0.11 (−0.32 to 0.10) 0.0740 Prevention of the emergence or release of pathogens −0.11 (−0.26 to 0.03) 0.0086 −0.03 (−0.16 to 0.11) 0.5130 (1.1) Antimicrobial resistance −0.05 (−0.14 to 0.04) 0.0670 −0.01 (−0.10 to 0.08) 0.7153 (1.2) Zoonotic disease −0.09 (−0.23 to 0.04) 0.0200 −0.03 (−0.15 to 0.09) 0.3704 (1.3) Biosecurity −0.05 (−0.16 to 0.06) 0.1325 0.00 (−0.10 to 0.10) 0.9771 (1.4) Biosafety −0.03 (−0.11 to 0.04) 0.1577 0.01 (−0.06 to 0.08) 0.7273 (1.5) Dual-use research and culture of responsible science −0.04 (−0.17 to 0.09) 0.3027 −0.01 (−0.11 to 0.09) 0.7794 (1.6) Immunisation −0.08 (−0.17 to 0.01) 0.0047 −0.04 (−0.12 to 0.05) 0.1573 Early detection and reporting for epidemics of potential international concern −0.09 (−0.22 to 0.03) 0.0140 −0.05 (−0.15 to 0.06) 0.1402 (2.1) Laboratory systems strength and quality −0.05 (−0.13 to 0.03) 0.0540 −0.02 (−0.09 to 0.04) 0.2659 (2.1.1) Laboratory capacity for detecting priority diseases −0.06 (−0.14 to 0.02) 0.0166 −0.03 (−0.10 to 0.04) 0.1955 (2.1.2) Laboratory quality systems −0.02 (−0.09 to 0.05) 0.2774 −0.01 (−0.07 to 0.05) 0.5731 (2.2) Laboratory supply chains 0.02 (−0.1 to 0.14) 0.5140 0.05 (−0.07 to 0.17) 0.1687 (2.3) Real-time surveillance and reporting −0.02 (−0.10 to 0.05) 0.3634 −0.01 (−0.07 to 0.06) 0.7706 (2.4) Surveillance data accessibility and transparency −0.04 (−0.13 to 0.04) 0.1103 0.00 (−0.08 to 0.08) 0.9517 (2.5) Case-based investigation −0.09 (−0.19 to 0.01) 0.0042 −0.05 (−0.14 to 0.05) 0.0987 (2.6) Epidemiology workforce −0.07 (−0.17 to 0.04) 0.0332 −0.07 (−0.18 to 0.03) 0.0217 Rapid response to and mitigation of the spread of an epidemic −0.19 (−0.41 to 0.04) 0.0072* −0.08 (−0.33 to 0.17) 0.2662 (3.1) Emergency preparedness and response planning −0.07 (−0.19 to 0.04) 0.0270 −0.03 (−0.15 to 0.08) 0.3032 (3.1.1) National public health emergency preparedness and response plan −0.05 (−0.12 to 0.03) 0.0398 −0.02 (−0.08 to 0.05) 0.3565 (3.1.3) Non-pharmaceutical interventions planning −0.04 (−0.11 to 0.02) 0.0274 −0.02 (−0.09 to 0.05) 0.2821 (3.2) Exercising response plans −0.11 (−0.34 to 0.11) 0.1003 −0.12 (−0.34 to 0.10) 0.0633 (3.3) Emergency response operation −0.06 (−0.19 to 0.07) 0.1020 −0.02 (−0.14 to 0.10) 0.5684 (3.4) Linking public health and security authorities −0.02 (−0.07 to 0.04) 0.3364 0.00 (−0.05 to 0.05) 0.8315 (3.5) Risk communication −0.07 (−0.20 to 0.05) 0.0523 −0.04 (−0.16 to 0.08) 0.2612 (3.6) Access to communications infrastructure −0.17 (−0.33 to 0.01) 0.0007* −0.03 (−0.30 to 0.23) 0.6556 (3.7) Trade and travel restrictions 0.03 (−0.05 to 0.12) 0.1835 0.00 (−0.08 to 0.08) 0.9691 Sufficient and robust health sector to treat the sick and protect health workers −0.10 (−0.27 to 0.06) 0.0366 −0.03 (−0.20 to 0.14) 0.5480 (4.1) Health capacity in clinics, hospitals and community care centres −0.10 (−0.24 to 0.05) 0.0247 −0.01 (−0.18 to 0.15) 0.7643 (4.1.2) Facilities capacity −0.06 (−0.15 to 0.04) 0.0436 0.00 (−0.10 to 0.09) 0.8999 (4.2) Supply chain for health system and healthcare workers −0.06 (−0.17 to 0.05) 0.0878 −0.01 (−0.12 to 0.10) 0.8481 (4.3) Medical countermeasures and personnel deployment −0.02 (−0.10 to 0.06) 0.3205 0.01 (−0.06 to 0.08) 0.5644 (4.4) Healthcare access −0.05 (−0.42 to 0.32) 0.6395 0.01 (−0.32 to 0.34) 0.9189 (4.5) Communications with healthcare workers during a public health emergency −0.02 (−0.10 to 0.05) 0.3019 −0.01 (−0.09 to 0.06) 0.5135 (4.6) Infection control practices −0.03 (−0.08 to 0.03) 0.0738 −0.01 (−0.06 to 0.05) 0.7408 (4.7) Capacity to test and approve new medical countermeasures −0.06 (−0.18 to 0.06) 0.0991 −0.04 (−0.15 to 0.08) 0.2773 Commitments to improving national capacity, financing and adherence to norms −0.17 (−0.39 to 0.05) 0.0127 −0.12 (−0.33 to 0.09) 0.0513 (5.1) International Health Regulations reporting compliance and disaster risk reduction 0.01 (−0.09 to 0.11) 0.8552 0.02 (−0.07 to 0.11) 0.4440 (5.2) Cross-border agreements on public health and animal health emergency response −0.07 (−0.14 to 0.00) 0.0012 −0.05 (−0.12 to 0.03) 0.0336 (5.3) International commitments −0.06 (−0.16 to 0.03) 0.0261 −0.04 (−0.13 to 0.06) 0.1803 (5.4) Joint External Evaluation (JEE) and Performance of Veterinary Services (PVS) Pathway 0.03 (−0.10 to 0.16) 0.4266 −0.04 (−0.18 to 0.09) 0.2806 (5.5) Financing −0.07 (−0.19 to 0.05) 0.0432 −0.08 (−0.20 to 0.03) 0.0198 (5.6) Commitment to sharing of genetic and biological data and specimens −0.05 (−0.26 to 0.16) 0.4391 0.01 (−0.16 to 0.17) 0.9171 Overall risk environment and country vulnerability to biological threats −0.30 (−0.50 to 0.10) <0.0001* −0.25 (−0.50 to 0.01) 0.0016 (6.1) Political and security risk −0.15 (−0.30 to 0.00) 0.0014 −0.09 (−0.25 to 0.06) 0.0454 (6.1.1) Government effectiveness −0.21 (−0.35 to 0.06) <0.0001* −0.16 (−0.31 to 0.00) 0.0009* (6.2) Socio-economic resilience −0.23 (−0.39 to 0.07) <0.0001* −0.19 (−0.44 to 0.07) 0.0156 (6.2.3) Social inclusion −0.13 (−0.24 to 0.02) 0.0002* −0.08
•Database of Rift Valley Fever occurrences from 46 countries over 22 years.•Predictions of Rift Valley Fever suitability for every month over 1995–2016.•Identifies areas at-risk by synthesizing ...time-series of environmental predictions.•We use human and livestock data to identify possible hotspots of disease spillover.•We identify places where long-term and routine preparation efforts should be focused.
Rift Valley Fever (RVF) poses a threat to human and animal health throughout much of Africa and the Middle East and has been recognized as a global health security priority and a key preparedness target.
We combined RVF occurrence data from a systematic literature review with animal notification data from an online database. Using boosted regression trees, we made monthly environmental suitability predictions from January 1995 to December 2016 at a 5 × 5-km resolution throughout regions of Africa, Europe, and the Middle East. We calculated the average number of months per year suitable for transmission, the mean suitability for each calendar month, and the “spillover potential,” a measure incorporating suitability with human and livestock populations.
Several countries where cases have not yet been reported are suitable for RVF. Areas across the region of interest are suitable for transmission at different times of the year, and some areas are suitable for multiple seasons each year. Spillover potential results show areas within countries where high populations of humans and livestock are at risk for much of the year.
The widespread environmental suitability of RVF highlights the need for increased preparedness, even in countries that have not previously experienced cases. These maps can aid in prioritizing long-term RVF preparedness activities and determining optimal times for recurring preparedness activities. Given an outbreak, our results can highlight areas often at risk for subsequent transmission that month, enabling decision-makers to target responses effectively.
Newborn mortality is increasingly concentrated in contexts of conflict and political instability. However, there are limited guidelines and data on the availability and quality of newborn care in ...conflict settings. In 2016, an interagency collaboration developed the
. In this study, we sought to understand the baseline availability and quality of essential newborn care in Bossaso, Somalia as part of an investigation to determine the feasibility and effectiveness of the
in improving newborn care in humanitarian settings.
A cross-sectional study was conducted at four purposely selected health facilities serving internally displaced persons affected by conflict in Bossaso. Essential newborn care practice and patient experience with childbirth care received at the facilities were assessed via observation of clinical practice during childbirth and the immediate postnatal period, and through postnatal interviews of mothers. Descriptive statistics and logistic regression were employed to summarize and examine variation by health facility.
Of the 332 pregnant women approached, 253 (76.2%) consented and were enrolled. 97.2% (95% CI: 94.4, 98.9) had livebirths and 2.8% (95% CI: 1.1, 5.6) had stillbirths. The early newborn mortality was 1.7% (95% CI: 0.3, 4.8). Nearly all 95.7%, (95% CI: 92.4, 97.8) births were attended by skilled health worker. Similarly, 98.0% (95% CI: 95.3, 99.3) of newborns received immediate drying, and 99.2% (95% CI: 97.1, 99.9) had delayed bathing. Few 8.6%, (95% CI: 5.4, 12.9) received immediate skin-to-skin contact and the practice varied significantly by facility (
< 0.001). One-third of newborns 30.1%, (95% CI: 24.4, 36.2) received early initiation of breastfeeding and there was significant variation by facility (
< 0.001). While almost all 99.2%, (95% CI: 97.2, 100) service providers wore gloves while attending births, handwashing was not as common 20.2%, (95% CI: 15.4, 25.6) and varied by facility (
< 0.001). Nearly all 92%, (95% CI: 86.9, 95.5) mothers were either very happy or happy with the childbirth care received at the facility.
Essential newborn care interventions were not universally available. Quality of care varied by health facility and type of intervention. Training and supervision using the
could improve newborn outcomes.
The main objective of this study was to determine the frequency and patterns of HIV drug resistance-associated mutations among children under 18 months of age born to HIV-1-positive mothers enrolled ...in the prevention of mother-to-child transmission services in Haiti.
Between January 1, 2013 and December 31, 2014, HIV-positive remnant dried blood spots collected from children under 18 months of age for Early Infant Diagnosis at the National Public Health Laboratory were used for HIV-1 genotyping. HIV drug resistance mutations were analyzed using the Stanford Drug Resistance HIVdb program.
Of the 3555 dried blood spots collected for Early Infant Diagnosis, 360 (10.1%) were HIV-positive and 355 were available for genotyping. Of these, 304 (85.6%) were successfully genotyped and 217 (71.4%) had ≥1 drug resistance mutation. Mutations conferring resistance to nucleoside reverse transcriptase inhibitor (NRTIs) and non-NRTIs were present in 40.5% (123) and 69.1% (210), respectively. The most frequent mutations were K103N/S (48.0%), M184V (37.5%), G190A/S (15.1%), and Y181C/G/V (14.1%). Predicted drug resistance analysis revealed that 68.8% of the children had high-level resistance to non-NRTIs and 11.5% had intermediate to high-level resistance to abacavir.
This study showed high rates of resistance to NRTIs and non-NRTIs among newly HIV-diagnosed children in Haiti, suggesting that in the era of "Option B+" (initiation of lifelong combination antiretroviral therapy to pregnant women with HIV), the majority of children who acquire HIV infection through mother-to-child transmission of HIV have resistant HIV. These results have led the National HIV Program to revise the pediatric guidelines to include protease inhibitors in first-line regimens for all HIV-positive newborns.
Most studies of mental health in humanitarian aid workers have found low levels of posttraumatic stress disorder, making it hard to disaggregate and look at differences between subgroups. This study ...sought to identify the risk and protective factors associated with resistant, resilient, and nonresilient trajectories of stress response over time that could be used to inform more targeted training and organizational support programs for aid workers. Aid workers from 19 qualifying humanitarian organizations who aged ≥18 years and were to deploy for 3 to 12 months completed questionnaires at 3 time points (pre, post, and follow-up). We identified 3 unique groups (nonresilient, resistant, and resilient) using latent class growth analysis and identified predictors of subgroup classification using multivariate logistic regression. Single individuals were less likely to be in the resilient group than in the resistant group compared to coupled individuals. Individuals with one prior deployment were three times more likely to be nonresilient than resistant compared to individuals with no previous deployments. There was no significant difference in resistant, resilient, and nonresilient classification for individuals with >2 deployments. Findings suggest a need for supplemental training and psychosocial support post the first deployment as well as resources focused on potential this should be cumulative rather than accumulative effects of stress and trauma exposure for more seasoned deployers.
In Côte d’Ivoire, the Family Approach to Counseling and Testing (FACT) program began in 2015 and provides facility-based HIV testing to the sexual partners, children and other household family ...members of HIV-positive index cases. We evaluated whether the FACT program is an effective approach to HIV case finding. We reviewed 1762 index patient charts to evaluate outcomes of the FACT program, held across 36 facilities in Abidjan. Index cases enumerated a total of 644 partners, 2301 children and 508 other family members including parents and siblings. Among the partners tested for HIV, the positivity rate was 21%; for children the positivity rate was 5% and for all other family members the positivity rate was 11%. Offering HIV testing services to the family members of HIV positive index cases is an effective approach to case finding in Côte d’Ivoire. Particularly, offering HIV testing to the partners of positive women index cases can be key to identifying previously undiagnosed men and linking them to treatment.
Cluster surveys provide rapid but representative estimates of key nutrition indicators in humanitarian crises. For these surveys, an accurate estimate of the design effect is critical to calculate a ...sample size that achieves adequate precision with the minimum number of sampling units. This paper describes the variability in design effect for three key nutrition indicators measured in small-scale surveys and models the association of design effect with parameters hypothesized to explain this variability.
380 small-scale surveys from 28 countries conducted between 2006 and 2013 were analyzed. We calculated prevalence and design effect of wasting, underweight, and stunting for each survey as well as standard deviations of the underlying continuous Z-score distribution. Mean cluster size, survey location and year were recorded. To describe design effects, median and interquartile ranges were examined. Generalized linear regression models were run to identify potential predictors of design effect.
Median design effect was under 2.00 for all three indicators; for wasting, the median was 1.35, the lowest among the indicators. Multivariable linear regression models suggest significant, positive associations of design effect and mean cluster size for all three indicators, and with prevalence of wasting and underweight, but not stunting. Standard deviation was positively associated with design effect for wasting but negatively associated for stunting. Survey region was significant in all three models.
This study supports the current field survey guidance recommending the use of 1.5 as a benchmark for design effect of wasting, but suggests this value may not be large enough for surveys with a primary objective of measuring stunting or underweight. The strong relationship between design effect and region in the models underscores the continued need to consider country- and locality-specific estimates when designing surveys. These models also provide empirical evidence of a positive relationship between design effect and both mean cluster size and prevalence, and introduces standard deviation of the underlying continuous variable (Z-scores) as a previously unexplored factor significantly associated with design effect. The magnitude and directionality of this association differed by indicator, underscoring the need for further investigation into the relationship between standard deviation and design effect.
Accurate assessment of maternal deaths is difficult in countries lacking standardized data sources for their review. As a first step to investigate suspected maternal deaths, WHO suggests ...surveillance of "pregnancy-related deaths", defined as deaths of women while pregnant or within 42 days of termination of pregnancy, irrespective of cause. Rapid Ascertainment Process for Institutional Deaths (RAPID), a surveillance tool, retrospectively identifies pregnancy-related deaths occurring in health facilities that may be missed by routine surveillance to assess gaps in reporting these deaths.
We used RAPID to review pregnancy-related deaths in six tertiary obstetric care facilities in three departments in Haiti. We reviewed registers and medical dossiers of deaths among women of reproductive age occurring in 2014 and 2015 from all wards, along with any additional available dossiers of deaths not appearing in registers, to capture pregnancy status, suspected cause of death, and timing of death in relation to the pregnancy. We used capture-recapture analyses to estimate the true number of in-hospital pregnancy-related deaths in these facilities.
Among 373 deaths of women of reproductive age, we found 111 pregnancy-related deaths, 25.2% more than were reported through routine surveillance, and 22.5% of which were misclassified as non-pregnancy-related. Hemorrhage (27.0%) and hypertensive disorders (18.0%) were the most common categories of suspected causes of death, and deaths after termination of pregnancy were statistically significantly more common than deaths during pregnancy or delivery. Data were missing at multiple levels: 210 deaths had an undetermined pregnancy status, 48.7% of pregnancy-related deaths lacked specific information about timing of death in relation to the pregnancy, and capture-recapture analyses in three hospitals suggested that approximately one-quarter of pregnancy-related deaths were not captured by RAPID or routine surveillance.
Across six tertiary obstetric care facilities in Haiti, RAPID identified unreported pregnancy-related deaths, and showed that missing data was a widespread problem. RAPID is a useful tool to more completely identify facility-based pregnancy-related deaths, but its repeated use would require a concomitant effort to systematically improve documentation of clinical findings in medical records. Limitations of RAPID demonstrate the need to use it alongside other tools to more accurately measure and address maternal mortality.
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