Testing frameworks for early life effects: the developmental constraints and adaptive response hypotheses do not explain key fertility outcomes in wild female baboons.

In evolutionary ecology, two classes of explanations are frequently invoked to explain "early life effects" on adult outcomes. Developmental constraints (DC) explanations contend that costs of early adversity arise from limitations adversity places on optimal development. Adaptive response (AR) hypotheses propose that later life outcomes will be worse when early and adult environments are poorly "matched." Here, we use recently proposed mathematical definitions for these hypotheses and a quadratic-regression based approach to test the long-term consequences of variation in developmental environments on fertility in wild baboons. We evaluate whether low rainfall and/or dominance rank during development predict three female fertility measures in adulthood, and whether any observed relationships are consistent with DC and/or AR. Neither rainfall during development nor the difference between rainfall in development and adulthood predicted any fertility measures. Females who were low-ranking during development had an elevated risk of losing infants later in life, and greater change in rank between development and adulthood predicted greater risk of infant loss. However, both effects were statistically marginal and consistent with alternative explanations, including adult environmental quality effects. Consequently, our data do not provide compelling support for either of these common explanations for the evolution of early life effects.

Comparing population-level humoral and cellular immunity to SARS-Cov-2 in Bangalore, India.

Two types of immunity, humoral and cellular, offer protection against COVID. Humoral protection, contributed by circulating neutralizing antibodies, can provide immediate protection but decays more quickly than cellular immunity and can lose effectiveness in the face of mutation and drift in the SARS-CoV-2 spike protein. Therefore, population-level seroprevalence surveys used to estimate population-level immunity may underestimate the degree to which a population is protected against COVID. In early 2021, before India began its vaccination campaign, we tested for humoral and cellular immunity to SARS-Cov-2 in representative samples of slum and non-slum populations in Bangalore, India. We found that 29.7% of samples (unweighted) had IgG antibodies to the spike protein and 15.5% had neutralizing antibodies, but at up to 46% showed evidence of cellular immunity. We also find that prevalence of cellular immunity is significantly higher in slums than in non-slums. These findings suggest (1) that a significantly larger proportion of the population in Bangalore, India, had cellular immunity to SARS-CoV-2 than had humoral immunity, as measured by serological surveys, and (2) that low socio-economic status communities display higher frequency of cellular immunity, likely because of greater exposure to infection due to population density.

Contribution of infection and vaccination to population-level seroprevalence through two COVID waves in Tamil Nadu, India.

This study employs repeated, large panels of serological surveys to document rapid and substantial waning of SARS-CoV-2 antibodies at the population level and to calculate the extent to which infection and vaccination separately contribute to seroprevalence estimates. Four rounds of serological surveys were conducted, spanning two COVID waves (October 2020 and April-May 2021), in Tamil Nadu (population 72 million) state in India. Each round included representative populations in each district of the state, totaling ≥ 20,000 persons per round. State-level seroprevalence was 31.5% in round 1 (October-November 2020), after India's first COVID wave. Seroprevalence fell to 22.9% in round 2 (April 2021), a roughly one-third decline in 6 months, consistent with dramatic waning of SARS-Cov-2 antibodies from natural infection. Seroprevalence rose to 67.1% by round 3 (June-July 2021), with infections from the Delta-variant induced second COVID wave accounting for 74% of the increase. Seroprevalence rose to 93.1% by round 4 (December 2021-January 2022), with vaccinations accounting for 63% of the increase. Antibodies also appear to wane after vaccination. Seroprevalence in urban areas was higher than in rural areas, but the gap shrunk over time (35.7 v. 25.7% in round 1, 89.8% v. 91.4% in round 4) as the epidemic spread even in low-density rural areas.

Conceptual and analytical approaches for modelling the developmental origins of inequality.

In many species, individuals that experience harsh conditions during development have poor health and fitness outcomes in adulthood, compared with peers that do not. These early-life contributions to inequality are often attributed to two classes of evolutionary hypotheses: Developmental Constraints (DC) models, which focus on the deleterious effects of low-quality early-life environments, and Predictive Adaptive Response (PAR) hypotheses, which emphasize the costs individuals incur when they make incorrect predictions about conditions in adulthood. Testing these hypotheses empirically is difficult for conceptual and analytical reasons. Here, we help resolve some of these difficulties by providing mathematical definitions for DC, PAR (particularly focusing on 'external' PAR) and related concepts. We propose a novel, quadratic regression-based statistical test derived from these definitions. Our simulations show that this approach markedly improves the ability to discriminate between DC and PAR hypotheses relative to the status quo approach, which uses interaction effects. Simulated data indicate that the interaction effects approach often conflates PAR with DC, while the quadratic regression approach yields high sensitivity and specificity for detecting PAR. Our results highlight the value of linking verbal and visual models to a formal mathematical treatment for understanding the developmental origins of inequitable adult outcomes. This article is part of the theme issue 'Evolutionary ecology of inequality'.

Cost effectiveness of fractional doses of COVID-19 vaccine boosters in India.

BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to be a major global public health crisis that exacts significant human and economic costs. Booster vaccination of individuals can improve waning immunity and reduce the impact of community epidemics. METHODS: Using an epidemiological model that incorporates population-level severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and waning of vaccine-derived immunity, we identify the hypothetical potential of mass vaccination with fractionated vaccine doses specific to ChAdOx1 nCoV-19 (AZD1222 [Covishield]; AstraZeneca) as an optimal and cost-effective strategy in India's Omicron outbreak. FINDINGS: We find that the optimal strategy is 1/8 fractional dosing under mild (Re ∼ 1.2) and rapid (Re ∼ 5) transmission scenarios, leading to an estimated $6 (95% confidence interval [CI]: -13, 26) billion and $2 (95% CI: -26, 30) billion in health-related net monetary benefit over 200 days, respectively. Rapid and broad use of fractional dosing for boosters, together with delivery costs divided by fractionation, could substantially gain more net monetary benefit by $11 (95% CI: -10, 33) and $2 (95% CI: -23, 28) billion, respectively, under the mild and rapid transmission scenarios. CONCLUSIONS: Mass vaccination with fractional doses of COVID-19 vaccines to boost immunity in a vaccinated population could be a cost-effective strategy for mitigating the public health costs of resurgences caused by vaccine-evasive variants, and fractional dosing deserves further clinical and regulatory evaluation. FUNDING: Financial support was provided by the AIR@InnoHK Program from Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region.

Statistical inference and power analysis for direct and spillover effects in two-stage randomized experiments.

Two-stage randomized experiments become an increasingly popular experimental design for causal inference when the outcome of one unit may be affected by the treatment assignments of other units in the same cluster. In this paper, we provide a methodological framework for general tools of statistical inference and power analysis for two-stage randomized experiments. Under the randomization-based framework, we consider the estimation of a new direct effect of interest as well as the average direct and spillover effects studied in the literature. We provide unbiased estimators of these causal quantities and their conservative variance estimators in a general setting. Using these results, we then develop hypothesis testing procedures and derive sample size formulas. We theoretically compare the two-stage randomized design with the completely randomized and cluster randomized designs, which represent two limiting designs. Finally, we conduct simulation studies to evaluate the empirical performance of our sample size formulas. For empirical illustration, the proposed methodology is applied to the randomized evaluation of the Indian National Health Insurance Program. An open-source software package is available for implementing the proposed methodology.

Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications.

INTRODUCTION: The infection fatality rate (IFR) of COVID-19 has been carefully measured and analysed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries. METHODS: We systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using representative samples collected by February 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analysed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible. RESULTS: In most locations in developing countries, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups.Age-specific IFRs were roughly 2 times higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure. CONCLUSION: The burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to ensure medical equity to populations in developing countries through provision of vaccine doses and effective medications.

JUE insights: Does mobility explain why slums were hit harder by COVID-19 in Mumbai, India?

SARS-CoV-2 has had a greater burden, as measured by rate of infection, in poorer communities within cities. For example, 55% of Mumbai slums residents had antibodies to COVID-19, 3.2 times the seroprevalence in non-slum areas of the city according to a sero-survey done in July 2020. One explanation is that government suppression was less severe in poorer communities, either because the poor were more likely to be exempt or unable to comply. Another explanation is that effective suppression itself accelerated the epidemic in poor neighborhoods because households are more crowded and residents share toilet and water facilities. We show there is little evidence for the first hypothesis in the context of Mumbai. Using location data from smart phones, we find that slum residents had nominally but not significantly (economically or statistically) higher mobility than non-slums prior to the sero-survey. We also find little evidence that mobility in non-slums was lower than in slums during lockdown, a subset of the period before the survey.

Modeling comparative cost-effectiveness of SARS-CoV-2 vaccine dose fractionation in India.

Given global Coronavirus Disease 2019 (COVID-19) vaccine shortages and inequity of vaccine distributions, fractionation of vaccine doses might be an effective strategy for reducing public health and economic burden, notwithstanding the emergence of new variants of concern. In this study, we developed a multi-scale model incorporating population-level transmission and individual-level vaccination to estimate the costs of hospitalization and vaccination and the economic benefits of reducing COVID-19 deaths due to dose-fractionation strategies in India. We used large-scale survey data of the willingness to pay together with data of vaccine and hospital admission costs to build the model. We found that fractional doses of vaccines could be an economically viable vaccination strategy compared to alternatives of either full-dose vaccination or no vaccination. Dose-sparing strategies could save a large number of lives, even with the emergence of new variants with higher transmissibility.

The Potential Beneficial Effects of Vaccination on Antigenically Evolving Pathogens.

AbstractAlthough vaccines against antigenically evolving pathogens such as seasonal influenza ; and are designed to protect against circulating strains by affecting the emergence and transmission of antigenically divergent strains, they might in theory also be able to change the rate of antigenic evolution. Vaccination might slow antigenic evolution by increasing immunity, reducing the overall prevalence or population size of the pathogen. This reduction could decrease the supply and growth rates of mutants and might thereby slow adaptation. But vaccination might accelerate antigenic evolution by increasing the transmission advantage of more antigenically diverged strains relative to less diverged strains (i.e., by positive selection). Such evolutionary effects could affect vaccination's direct benefits to individuals and indirect benefits to the host population (i.e., the private and social benefits). To investigate these potential impacts, we simulated vaccination against a continuously circulating influenza-like pathogen in a simple population. On average, more vaccination decreased the incidence of infection. Notably, this decrease was driven partly by a vaccine-induced decline in the rate of antigenic evolution. To understand how the evolutionary effects of vaccines might affect their social and private benefits, we fitted linear panel models to simulated data. By slowing evolution, vaccination increased the social benefit and decreased the private benefit. Thus, vaccination's potential social and private benefits may differ from current theory, which omits evolutionary effects. These results suggest that conventional vaccines against influenza and other antigenically evolving pathogens, if protective against transmission and given to the appropriate populations, could further reduce disease burden by slowing antigenic evolution.

Representative estimates of COVID-19 infection fatality rates from four locations in India: cross-sectional study.

OBJECTIVES: To estimate age-specific and sex-specific mortality risk among all SARS-CoV-2 infections in four settings in India, a major lower-middle-income country and to compare age trends in mortality with similar estimates in high-income countries. DESIGN: Cross-sectional study. SETTING: India, multiple regions representing combined population >150 million. PARTICIPANTS: Aggregate infection counts were drawn from four large population-representative prevalence/seroprevalence surveys. Data on corresponding number of deaths were drawn from official government reports of confirmed SARS-CoV-2 deaths. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was age-specific and sex-specific infection fatality rate (IFR), estimated as the number of confirmed deaths per infection. The secondary outcome was the slope of the IFR-by-age function, representing increased risk associated with age. RESULTS: Among males aged 50-89, measured IFR was 0.12% in Karnataka (95% CI 0.09% to 0.15%), 0.42% in Tamil Nadu (95% CI 0.39% to 0.45%), 0.53% in Mumbai (95% CI 0.52% to 0.54%) and an imprecise 5.64% (95% CI 0% to 11.16%) among migrants returning to Bihar. Estimated IFR was approximately twice as high for males as for females, heterogeneous across contexts and rose less dramatically at older ages compared with similar studies in high-income countries. CONCLUSIONS: Estimated age-specific IFRs during the first wave varied substantially across India. While estimated IFRs in Mumbai, Karnataka and Tamil Nadu were considerably lower than comparable estimates from high-income countries, adjustment for under-reporting based on crude estimates of excess mortality puts them almost exactly equal with higher-income country benchmarks. In a marginalised migrant population, estimated IFRs were much higher than in other contexts around the world. Estimated IFRs suggest that the elderly in India are at an advantage relative to peers in high-income countries. Our findings suggest that the standard estimation approach may substantially underestimate IFR in low-income settings due to under-reporting of COVID-19 deaths, and that COVID-19 IFRs may be similar in low-income and high-income settings.

Challenges To Reducing Discrimination And Health Inequity Through Existing Civil Rights Laws.

More than fifty years after the passage of the Civil Rights Act of 1964, health care for racial and ethnic minorities remains in many ways separate and unequal in the United States. Moreover, efforts to improve minority health care face challenges that differ from those confronted during de jure segregation. We review these challenges and examine whether stronger enforcement of existing civil rights legislation could help overcome them. We conclude that stronger enforcement of existing laws-for example, through executive orders to strengthen enforcement of the laws and congressional action to allow private individuals to bring lawsuits against providers who might have engaged in discrimination-would improve minority health care, but this approach is limited in what it can achieve. Complementary approaches outside the legal arena, such as quality improvement efforts and direct transfers of money to minority-serving providers-those seeing a disproportionate number of minority patients relative to their share of the population-might prove to be more effective.

The need for better evidence to evaluate the health & economic benefits of India's Rashtriya Swasthya Bima Yojana.

In this review the existing evidence on the impact of Rashtriya Swasthya Bima Yojana (RSBY) is discussed in the context of international literature available on health insurance. We describe potential pathways through which health insurance can affect health and economic outcomes, discuss evidence from other developing countries, and identify potential biases and inconsistencies in existing studies on RSBY impact. Given the relatively recent introduction of RSBY, lack of quality, verifiable data on utilization patterns, and the absence of reliable evaluation studies, there is a need to exercise caution while assessing the merits of the programme. Considering the enormous potential and cost of the programme, we emphasize the need for a rigorous impact evaluation of RSBY. It will not only help capture the real impact of the scheme, but may also be able to estimate the extent of systemic inefficiencies at the level of the consumer.

Learning during a crisis: The SARS epidemic in Taiwan.

SARS struck Taiwan in 2003, causing a national crisis. Many people feared that SARS would spread through the health care system, and outpatient visits fell by more than 30% in the course of a few weeks. We examine how both public information and the behavior and opinions of peers contributed to this reaction. We identify a peer effect through a difference-in-difference comparison of longtime residents and recent arrivals, who are less socially connected. Although several forms of social interaction may contribute to this pattern, social learning is a plausible explanation for our finding. We find that people respond to both public information and to their peers. In a dynamic simulation based on the regressions, social interactions substantially magnify the response to SARS.

Incentives for reporting disease outbreaks.

BACKGROUND: Countries face conflicting incentives to report infectious disease outbreaks. Reports of outbreaks can prompt other countries to impose trade and travel restrictions, which has the potential to discourage reporting. However, reports can also bring medical assistance to contain the outbreak, including access to vaccines. METHODS: We compiled data on reports of meningococcal meningitis to the World Health Organization (WHO) from 54 African countries between 1966 and 2002, a period is marked by two events: first, a large outbreak reported from many countries in 1987 associated with the Hajj that resulted in more stringent requirements for meningitis vaccination among pilgrims; and second, another large outbreak in Sub-Saharan Africa in 1996 that led to a new international mechanism to supply vaccines to countries reporting a meningitis outbreak. We used fixed-effects regression modeling to statistically estimate the effect of external forcing events on the number of countries reporting cases of meningitis to WHO. FINDINGS: We find that the Hajj vaccination requirements started in 1988 were associated with reduced reporting, especially among countries with relatively fewer cases reported between 1966 and 1979. After the vaccine provision mechanism was in place in 1996, reporting among countries that had previously not reported meningitis outbreaks increased. INTERPRETATION: These results indicate that countries may respond to changing incentives to report outbreaks when they can do so. In the long term, these incentives are likely to be more important than surveillance assistance in prompt reporting of outbreaks.

Economic epidemiology of avian influenza on smallholder poultry farms.

Highly pathogenic avian influenza (HPAI) is often controlled through culling of poultry. Compensating farmers for culled chickens or ducks facilitates effective culling and control of HPAI. However, ensuing price shifts can create incentives that alter the disease dynamics of HPAI. Farmers control certain aspects of the dynamics by setting a farm size, implementing infection control measures, and determining the age at which poultry are sent to market. Their decisions can be influenced by the market price of poultry which can, in turn, be set by policy makers during an HPAI outbreak. Here, we integrate these economic considerations into an epidemiological model in which epidemiological parameters are determined by an outside agent (the farmer) to maximize profit from poultry sales. Our model exhibits a diversity of behaviors which are sensitive to (i) the ability to identify infected poultry, (ii) the average price of infected poultry, (iii) the basic reproductive number of avian influenza, (iv) the effect of culling on the market price of poultry, (v) the effect of market price on farm size, and (vi) the effect of poultry density on disease transmission. We find that under certain market and epidemiological conditions, culling can increase farm size and the total number of HPAI infections. Our model helps to inform the optimization of public health outcomes that best weigh the balance between public health risk and beneficial economic outcomes for farmers.