Evaluating primary and booster vaccination prioritization strategies for COVID-19 by age and high-contact employment status using data from contact surveys.

The debate around vaccine prioritization for COVID-19 has revolved around balancing the benefits from: (1) the direct protection conferred by the vaccine amongst those at highest risk of severe disease outcomes, and (2) the indirect protection through vaccinating those that are at highest risk of being infected and of transmitting the virus. While adults aged 65+ are at highest risk for severe disease and death from COVID-19, essential service and other in-person workers with greater rates of contact may be at higher risk of acquiring and transmitting SARS-CoV-2. Unfortunately, there have been relatively little data available to understand heterogeneity in contact rates and risk across these demographic groups. Here, we retrospectively analyze and evaluate vaccination prioritization strategies by age and worker status. We use a mathematical model of SARS-CoV-2 transmission and uniquely detailed contact data collected as part of the Berkeley Interpersonal Contact Survey to evaluate five vaccination prioritization strategies: (1) prioritizing only adults over age 65, (2) prioritizing only high-contact workers, (3) splitting prioritization between adults 65+ and high-contact workers, (4) tiered prioritization of adults over age 65 followed by high-contact workers, and (5) tiered prioritization of high-contact workers followed by adults 65+. We find that for the primary two-dose vaccination schedule, assuming 70% uptake, a tiered roll-out that first prioritizes adults 65+ averts the most deaths (31% fewer deaths compared to a no-vaccination scenario) while a tiered roll-out that prioritizes high contact workers averts the most number of clinical infections (14% fewer clinical infections compared to a no-vaccination scenario). We also consider prioritization strategies for booster doses during a subsequent outbreak of a hypothetical new SARS-CoV-2 variant. We find that a tiered roll-out that prioritizes adults 65+ for booster doses consistently averts the most deaths, and it may also avert the most number of clinical cases depending on the epidemiology of the SARS-CoV-2 variant and the vaccine efficacy.

Novel estimates reveal subnational heterogeneities in disease-relevant contact patterns in the United States.

Population contact patterns fundamentally determine the spread of directly transmitted airborne pathogens such as SARS-CoV-2 and influenza. Reliable quantitative estimates of contact patterns are therefore critical to modeling and reducing the spread of directly transmitted infectious diseases and to assessing the effectiveness of interventions intended to limit risky contacts. While many countries have used surveys and contact diaries to collect national-level contact data, local-level estimates of age-specific contact patterns remain rare. Yet, these local-level data are critical since disease dynamics and public health policy typically vary by geography. To overcome this challenge, we introduce a flexible model that can estimate age-specific contact patterns at the subnational level by combining national-level interpersonal contact data with other locality-specific data sources using multilevel regression with poststratification (MRP). We estimate daily contact matrices for all 50 US states and Washington DC from April 2020 to May 2021 using national contact data from the US. Our results reveal important state-level heterogeneities in levels and trends of contacts across the US over the course of the COVID-19 pandemic, with implications for the spread of respiratory diseases.

Describing the safety of abortion at the population level using network-based survey approaches.

BACKGROUND: Despite the negative impact of unsafe abortions on women's health and rights, the degree of abortion safety remains strikingly undocumented for a large share of abortions globally. Data on how women induce abortions (method, setting, provider) are central to the measurement of abortion safety. However, health-facility statistics and direct questioning in population surveys do not yield representative data on abortion care seeking pathways in settings where access to abortion services is highly restricted. Recent developments in survey methodologies to study stigmatized / illegal behaviour and hidden populations rely on the fact that such information circulates within social networks; however, such efforts have yet to give convincing results for unsafe abortions. OBJECTIVE: This article presents the protocol of a study whose purpose is to apply and develop further two network-based methods to contribute to the generation of reliable population-level information on the safety of abortions in contexts where access to legal abortion services is highly restricted. METHODS: This study plans to obtain population-level data on abortion care seeking in two Health and Demographic Surveillance Systems in urban Kenya and rural Burkina Faso by applying two methods: Anonymous Third-Party Reporting (ATPR) (also known as confidantes' method) and Respondent Driven Sampling (RDS). We will conduct a mixed methods formative study to determine whether these network-based approaches are pertinent in the study contexts. The ATPR will be refined notably by incorporating elements of the Network Scale-Up Method (NSUM) to correct or account for certain of its biases (transmission, barrier, social desirability, selection). The RDS will provide reliable alternative estimates of abortion safety if large samples and equilibrium can be reached; an RDS multiplex variant (also including social referents) will be tested. DISCUSSION: This study aims at documenting abortion safety in two local sites using ATPR and RDS. If successful, it will provide data on the safety profiles of abortion seekers across sociodemographic categories in two contrasted settings in sub-Saharan Africa. It will advance the formative research needed to determine whether ATPR and RDS are applicable or not in a given context. It will improve the questionnaire and correcting factors for the ATPR, improve the capacity of RDS to produce quasi-representative data on abortion safety, and advance the validation of both methods.

Representative data on how women induce abortions and their consequences are central to measurements of abortion safety. However, due to the stigmatized nature of abortion, measuring the details of the process is challenging when the latter occur out of the realm of the law and do not result in complications registered in hospital statistics. Hence, there is sparse empirical population-level data on how women terminate their pregnancies in countries where access to abortion services is highly restricted, as well as little data on the side effects and complications associated with the methods they chose and health seeking for these complications. Recent developments in indirect survey methodologies to study stigmatized/illegal behaviour and hidden populations are likely to improve the quality of data collected on abortion safety in restrictive contexts: all are based on the sharing of information on stigmatized practices in social networks. We propose to refine and pilot two such network-based methods to validate their use for collecting (quasi) representative data on abortion safety in large population health surveys. These two approaches are: (i) a modified Anonymous Third-Party Reporting method (ATPR) integrating elements of the Network-Scale-up Method (NSUM) and (ii) Respondent-Driven Sampling (RDS). We will conduct this study in two African Health and Demographic Surveillance Systems (HDSS) sites, one urban (Nairobi, Kenya), and one comprising a town and adjacent villages (Kaya, Burkina Faso).

In a Stationary Population, the Average Lifespan of the Living Is a Length-Biased Life Expectancy.

What is the average lifespan in a stationary population viewed at a single moment in time? Even though periods and cohorts are identical in a stationary population, we show that the answer to this question is not life expectancy but a length-biased version of life expectancy. That is, the distribution of lifespans of the people alive at a single moment is a self-weighted distribution of cohort lifespans, such that longer lifespans have proportionally greater representation. One implication is that if death rates are unchanging, the average lifespan of the current population always exceeds period life expectancy. This result connects stationary population lifespan measures to a well-developed body of statistical results; provides new intuition for established demographic results; generates new insights into the relationship between periods, cohorts, and prevalent cohorts; and offers a framework for thinking about mortality selection more broadly than the concept of demographic frailty.

How do populations aggregate?

BACKGROUND: Understanding the relationship between populations at different scales plays an important role in many demographic analyses. OBJECTIVE: We show that when a population can be partitioned into subgroups, the death rate for the entire population can be written as the weighted harmonic mean of the death rates in each subgroup, where the weights are given by the numbers of deaths in each subgroup. This decomposition can be generalized to other types of occurrence-exposure rate. Using different weights, the death rate for the entire population can also be expressed as an arithmetic mean of the death rates in each subgroup. CONTRIBUTION: We use these relationships as a starting point for investigating how demographers can correctly aggregate rates across non-overlapping subgroups. Our analysis reveals conceptual links between classic demographic models and length-biased sampling. To illustrate how the harmonic mean can suggest new interpretations of demographic relationships, we present as an application a new expression for the frailty of the dying, given a standard demographic frailty model.

Estimating Adult Death Rates From Sibling Histories: A Network Approach.

Hundreds of millions of people live in countries that do not have complete death registration systems, meaning that most deaths are not recorded and that critical quantities, such as life expectancy, cannot be directly measured. The sibling survival method is a leading approach to estimating adult mortality in the absence of death registration. The idea is to ask survey respondents to enumerate their siblings and to report about their survival status. In many countries and periods, sibling survival data are the only nationally representative source of information about adult mortality. Although a vast amount of sibling survival data has been collected, important methodological questions about the method remain unresolved. To help make progress on this issue, we propose reframing the sibling survival method as a network sampling problem. This approach enables a formal derivation of statistical estimators for sibling survival data. Our derivation clarifies the precise conditions that sibling history estimates rely on, leads to internal consistency checks that can help assess data and reporting quality, and reveals important quantities that could potentially be measured to relax assumptions in the future. We introduce the R package siblingsurvival, which implements the methods we describe.

Quantifying population contact patterns in the United States during the COVID-19 pandemic.

SARS-CoV-2 is transmitted primarily through close, person-to-person interactions. Physical distancing policies can control the spread of SARS-CoV-2 by reducing the amount of these interactions in a population. Here, we report results from four waves of contact surveys designed to quantify the impact of these policies during the COVID-19 pandemic in the United States. We surveyed 9,743 respondents between March 22 and September 26, 2020. We find that interpersonal contact has been dramatically reduced in the US, with an 82% (95%CI: 80%-83%) reduction in the average number of daily contacts observed during the first wave compared to pre-pandemic levels. However, we find increases in contact rates over the subsequent waves. We also find that certain demographic groups, including people under 45 and males, have significantly higher contact rates than the rest of the population. Tracking these changes can provide rapid assessments of the impact of physical distancing policies and help to identify at-risk populations.

Using an Online Sample to Estimate the Size of an Offline Population.

Online data sources offer tremendous promise to demography and other social sciences, but researchers worry that the group of people who are represented in online data sets can be different from the general population. We show that by sampling and anonymously interviewing people who are online, researchers can learn about both people who are online and people who are offline. Our approach is based on the insight that people everywhere are connected through in-person social networks, such as kin, friendship, and contact networks. We illustrate how this insight can be used to derive an estimator for tracking the digital divide in access to the Internet, an increasingly important dimension of population inequality in the modern world. We conducted a large-scale empirical test of our approach, using an online sample to estimate Internet adoption in five countries (n ≈ 15,000). Our test embedded a randomized experiment whose results can help design future studies. Our approach could be adapted to many other settings, offering one way to overcome some of the major challenges facing demographers in the information age.

Separating the Signal From the Noise: Evidence for Deceleration in Old-Age Death Rates.

Widespread population aging has made it critical to understand death rates at old ages. However, studying mortality at old ages is challenging because the data are sparse: numbers of survivors and deaths get smaller and smaller with age. I show how to address this challenge by using principled model selection techniques to empirically evaluate theoretical mortality models. I test nine models of old-age death rates by fitting them to 360 high-quality data sets on cohort mortality after age 80. Models that allow for the possibility of decelerating death rates tend to fit better than models that assume exponentially increasing death rates. No single model is capable of universally explaining observed old-age mortality patterns, but the log-quadratic model most consistently predicts well. Patterns of model fit differ by country and sex. I discuss possible mechanisms, including sample size, period effects, and regional or cultural factors that may be important keys to understanding patterns of old-age mortality. I introduce mortfit, a freely available R package that enables researchers to extend the analysis to other models, age ranges, and data sources.

The Network Survival Method for Estimating Adult Mortality: Evidence From a Survey Experiment in Rwanda.

Adult death rates are a critical indicator of population health and well-being. Wealthy countries have high-quality vital registration systems, but poor countries lack this infrastructure and must rely on estimates that are often problematic. In this article, we introduce the network survival method, a new approach for estimating adult death rates. We derive the precise conditions under which it produces consistent and unbiased estimates. Further, we develop an analytical framework for sensitivity analysis. To assess the performance of the network survival method in a realistic setting, we conducted a nationally representative survey experiment in Rwanda (n = 4,669). Network survival estimates were similar to estimates from other methods, even though the network survival estimates were made with substantially smaller samples and are based entirely on data from Rwanda, with no need for model life tables or pooling of data from other countries. Our analytic results demonstrate that the network survival method has attractive properties, and our empirical results show that this method can be used in countries where reliable estimates of adult death rates are sorely needed.

Quantity Versus Quality: A Survey Experiment to Improve the Network Scale-up Method.

The network scale-up method is a promising technique that uses sampled social network data to estimate the sizes of epidemiologically important hidden populations, such as sex workers and people who inject illicit drugs. Although previous scale-up research has focused exclusively on networks of acquaintances, we show that the type of personal network about which survey respondents are asked to report is a potentially crucial parameter that researchers are free to vary. This generalization leads to a method that is more flexible and potentially more accurate. In 2011, we conducted a large, nationally representative survey experiment in Rwanda that randomized respondents to report about one of 2 different personal networks. Our results showed that asking respondents for less information can, somewhat surprisingly, produce more accurate size estimates. We also estimated the sizes of 4 key populations at risk for human immunodeficiency virus infection in Rwanda. Our estimates were higher than earlier estimates from Rwanda but lower than international benchmarks. Finally, in this article we develop a new sensitivity analysis framework and use it to assess the possible biases in our estimates. Our design can be customized and extended for other settings, enabling researchers to continue to improve the network scale-up method.

Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations.

The network scale-up method enables researchers to estimate the size of hidden populations, such as drug injectors and sex workers, using sampled social network data. The basic scale-up estimator offers advantages over other size estimation techniques, but it depends on problematic modeling assumptions. We propose a new generalized scale-up estimator that can be used in settings with non-random social mixing and imperfect awareness about membership in the hidden population. Further, the new estimator can be used when data are collected via complex sample designs and from incomplete sampling frames. However, the generalized scale-up estimator also requires data from two samples: one from the frame population and one from the hidden population. In some situations these data from the hidden population can be collected by adding a small number of questions to already planned studies. For other situations, we develop interpretable adjustment factors that can be applied to the basic scale-up estimator. We conclude with practical recommendations for the design and analysis of future studies.

Validation of the symptom pattern method for analyzing verbal autopsy data.

BACKGROUND: Cause of death data are a critical input to formulating good public health policy. In the absence of reliable vital registration data, information collected after death from household members, called verbal autopsy (VA), is commonly used to study causes of death. VA data are usually analyzed by physician-coded verbal autopsy (PCVA). PCVA is expensive and its comparability across regions is questionable. Nearly all validation studies of PCVA have allowed physicians access to information collected from the household members' recall of medical records or contact with health services, thus exaggerating accuracy of PCVA in communities where few deaths had any interaction with the health system. In this study we develop and validate a statistical strategy for analyzing VA data that overcomes the limitations of PCVA. METHODS AND FINDINGS: We propose and validate a method that combines the advantages of methods proposed by King and Lu, and Byass, which we term the symptom pattern (SP) method. The SP method uses two sources of VA data. First, it requires a dataset for which we know the true cause of death, but which need not be representative of the population of interest; this dataset might come from deaths that occur in a hospital. The SP method can then be applied to a second VA sample that is representative of the population of interest. From the hospital data we compute the properties of each symptom; that is, the probability of responding yes to each symptom, given the true cause of death. These symptom properties allow us first to estimate the population-level cause-specific mortality fractions (CSMFs), and to then use the CSMFs as an input in assigning a cause of death to each individual VA response. Finally, we use our individual cause-of-death assignments to refine our population-level CSMF estimates. The results from applying our method to data collected in China are promising. At the population level, SP estimates the CSMFs with 16% average relative error and 0.7% average absolute error, while PCVA results in 27% average relative error and 1.1% average absolute error. At the individual level, SP assigns the correct cause of death in 83% of the cases, while PCVA does so for 69% of the cases. We also compare the results of SP and PCVA when both methods have restricted access to the information from the medical record recall section of the VA instrument. At the population level, without medical record recall, the SP method estimates the CSMFs with 14% average relative error and 0.6% average absolute error, while PCVA results in 70% average relative error and 3.2% average absolute error. For individual estimates without medical record recall, SP assigns the correct cause of death in 78% of cases, while PCVA does so for 38% of cases. CONCLUSIONS: Our results from the data collected in China suggest that the SP method outperforms PCVA, both at the population and especially at the individual level. Further study is needed on additional VA datasets in order to continue validation of the method, and to understand how the symptom properties vary as a function of culture, language, and other factors. Our results also suggest that PCVA relies heavily on household recall of medical records and related information, limiting its applicability in low-resource settings. SP does not require that additional information to adequately estimate causes of death.

[Benchmarking of performance of Mexican states with effective coverage]. / Evaluación comparativa del desempeño de los sistemas estatales de salud usando cobertura efectiva.

Benchmarking of the performance of states, provinces, or districts in a decentralised health system is important for fostering of accountability, monitoring of progress, identification of determinants of success and failure, and creation of a culture of evidence. The Mexican Ministry of Health has, since 2001, used a benchmarking approach based on the World Health Organization (WHO) concept of effective coverage of an intervention, which is defined as the proportion of potential health gain that could be delivered by the health system to that which is actually delivered. Using data collection systems, including state representative examination surveys, vital registration, and hospital discharge registries, we have monitored the delivery of 14 interventions for 2005-06. Overall effective coverage ranges from 54.0% in Chiapas, a poor state, to 65.1% in the Federal District. Effective coverage for maternal and child health interventions is substantially higher than that for interventions that target other health problems. Effective coverage for the lowest wealth quintile is 52% compared with 61% for the highest quintile. Effective coverage is closely related to public-health spending per head across states; this relation is stronger for interventions that are not related to maternal and child health than those for maternal and child health. Considerable variation also exists in effective coverage at similar amounts of spending. We discuss the implications of these issues for the further development of the Mexican health-information system. Benchmarking of performance by measuring effective coverage encourages decision-makers to focus on quality service provision, not only service availability. The effective coverage calculation is an important device for health-system stewardship. In adopting this approach, other countries should select interventions to be measured on the basis of the criteria of affordability, effect on population health, effect on health inequalities, and capacity to measure the effects of the intervention. The national institutions undertaking this benchmarking must have the mandate, skills, resources, and independence to succeed.

[Assessing the effect of the 2001-06 Mexican health reform: an interim report card]. / Evaluación del impacto de la reforma mexicana de salud 2001-2006: un informe inicial.

Since 2001, Mexico has been designing, legislating, and implementing a major health-system reform. A key component was the creation of Seguro Popular, which is intended to expand insurance coverage over seven years to uninsured people, nearly half the total population at the start of 2001. The reform included five actions: legislation of entitlement per family affiliated which, with full implementation, will increase public spending on health by 0.8-1.0% of gross domestic product; creation of explicit benefits packages; allocation of monies to decentralised state ministries of health in proportion to number of families affiliated; division of federal resources flowing to states into separate funds for personal and non-personal health services; and creation of a fund to protect families against catastrophic health expenditures. Using the WHO health-systems framework, a wide range of datasets to assess the effect of this reform on different dimensions of the health system was used. Key findings include: affiliation is preferentially reaching the poor and the marginalised communities; federal non-social security expenditure in real per-head terms increased by 38% from 2000 to 2005; equity of public-health expenditure across states improved; Seguro Popular affilates used more inpatient and outpatient services than uninsured people; effective coverage of 11 interventions has improved between 2000 and 2005-06; inequalities in effective coverage across states and wealth deciles has decreased over this period; catastrophic expenditures for Seguro Popular affiliates are lower than for uninsured people even though use of services has increased. We present some lessons for Mexico based on this interim evaluation and explore implications for other countries considering health reforms.

Evaluación comparativa del desempeño de los sistemas estatales de salud usando cobertura efectiva / Benchmarking of performance of Mexican states with effective coverage

Realizar un análisis comparativo del desempeño (benchmarking) de las unidades subnacionales en un sistema de salud descentralizado es importante para favorecer la rendición de cuentas, monitorear el progreso, identificar los factores que determinan tanto el éxito como el fracaso, y crear una cultura basada en la evidencia. Desde 2001, la Secretaría de Salud de México se ha dedicado a desarrollar esta tarea basándose en el concepto de cobertura efectiva promovido por la Organización Mundial de la Salud (OMS), que la define como la fracción de ganancia potencial en salud que el sistema de salud podría aportar, con los servicios que actualmente ofrece. Usando los sistemas de información en salud, que incluyen encuestas de salud representativas a nivel estado, registros vitales y registros de egresos hospitalarios, se ha monitoreado la prestación de 14 intervenciones para mejorar la salud entre 2005 y 2006. La cobertura efectiva en general va desde 54 por ciento en Chiapas hasta 65 por ciento en el Distrito Federal. La cobertura efectiva para intervenciones en salud materno-infantil es mayor que para las intervenciones que abordan otros problemas de salud del adulto. La cobertura efectiva para el quintil de ingresos más bajo es de 52 por ciento, comparada con 61 por ciento para el quintil de ingresos más alto. La cobertura efectiva guarda especial relación con el gasto público en salud per cápita en todos los estados, y esta relación es más estrecha con las intervenciones ajenas a la salud materno-infantil que con las que tienen que ver directamente con ella. También se observan variaciones considerables en la cobertura efectiva en niveles de gasto similares. Asimismo, se discuten algunas implicaciones para el desarrollo que debiera seguir el sistema de información en salud en México. Este enfoque alienta a quienes toman decisiones a concentrarse en brindar servicios de calidad y no sólo en ofrecer la disponibilidad del servicio. El cálculo...

Benchmarking of the performance of states, provinces, or districts in a decentralised health system is important for fostering of accountability, monitoring of progress, identification of determinants of success and failure, and creation of a culture of evidence. The Mexican Ministry of Health has, since 2001, used a benchmarking approach based on the World Health Organization (WHO) concept of effective coverage of an intervention, which is defined as the proportion of potential health gain that could be delivered by the health system to that which is actually delivered. Using data collection systems, including state representative examination surveys, vital registration, and hospital discharge registries, we have monitored the delivery of 14 interventions for 2005-06. Overall effective coverage ranges from 54.0 percent in Chiapas, a poor state, to 65.1 percent in the Federal District. Effective coverage for maternal and child health interventions is substantially higher than that for interventions that target other health problems. Effective coverage for the lowest wealth quintile is 52 percent compared with 61 percent for the highest quintile. Effective coverage is closely related to public-health spending per head across states; this relation is stronger for interventions that are not related to maternal and child health than those for maternal and child health. Considerable variation also exists in effective coverage at similar amounts of spending. We discuss the implications of these issues for the further development of the Mexican health-information system. Benchmarking of performance by measuring effective coverage encourages decision-makers to focus on quality service provision, not only service availability. The effective coverage calculation is an important device for health-system stewardship. In adopting this approach, other countries should select interventions to be measured on the basis of the criteria of affordability,...

Evaluación del impacto de la Reforma Mexicana de salud 2001-2006: un informe inicial / Assessing the effect of the 2001-06 Mexican health reform: an interim report card

A partir de 2001 se inicia en México un proceso de diseño, legislación e implementación de la Reforma Mexicana de Salud. Un componente clave de ésta fue la creación del Seguro Popular, que pretende extender la cobertura de aseguramiento médico por siete años a la población que no cuenta con seguridad social, la cual constituía en ese momento casi la mitad de la población total. La reforma incluyó cinco acciones: modificar la ley para garantizar el derecho a la protección a la salud para las familias afiliadas, lo cual al ser implantado completamente incrementará el gasto público en salud entre 0.8 y 1.0 por ciento del PIB; la creación de un paquete de servicios de salud explícito; la asignación de recursos a secretarías estatales de salud descentralizadas, proporcional al número de familias incorporadas; la división de los recursos federales destinados a los estados en fondos independientes para servicios de salud personales y no personales; así como la creación de un fondo para garantizar recursos cuando se presentan eventos catastróficos en salud. Mediante el uso del marco conceptual de los sistemas de salud de la OMS, se han examinado diversos conjuntos de datos para evaluar el impacto de esta reforma en distintas dimensiones del sistema de salud. Entre los principales hallazgos clave se encuentran que: la afiliación alcanza de manera preferente a las comunidades pobres y marginadas; el gasto federal no correspondiente a la seguridad social aumentó 38 por ciento de 2000 a 2005 en términos reales; ha mejorado la equidad del gasto público entre los estados; los afiliados al Seguro Popular presentan una mayor utilización de servicios, tanto a nivel ambulatorio como para pacientes externos y pacientes hospitalarios en comparación con los no asegurados; la cobertura efectiva de 11 intervenciones en salud ha mejorado entre 2000 y 2005; han disminuido las desigualdades en cobertura efectiva durante este periodo en todos los estados y deciles...

Since 2001, Mexico has been designing, legislating, and implementing a major health-system reform. A key component was the creation of Seguro Popular, which is intended to expand insurance coverage over seven years to uninsured people, nearly half the total population at the start of 2001. The reform included five actions: legislation of entitlement per family affiliated which, with full implementation, will increase public spending on health by 0.8-1.0 percent of gross domestic product; creation of explicit benefits packages; allocation of monies to decentralised state ministries of health in proportion to number of families affiliated; division of federal resources flowing to states into separate funds for personal and non-personal health services; and creation of a fund to protect families against catastrophic health expenditures. Using the WHO health-systems framework, a wide range of datasets to assess the effect of this reform on different dimensions of the health system was used. Key findings include: affiliation is preferentially reaching the poor and the marginalised communities; federal non-social security expenditure in real per-head terms increased by 38 percent from 2000 to 2005; equity of public-health expenditure across states improved; Seguro Popular affiliates used more inpatient and outpatient services than uninsured people; effective coverage of 11 interventions has improved between 2000 and 2005-06; inequalities in effective coverage across states and wealth deciles has decreased over this period; catastrophic expenditures for Seguro Popular affiliates are lower than for uninsured people even though use of services has increased. We present some lessons for Mexico based on this interim evaluation and explore implications for other countries considering health reforms.

Estimation of potential global pandemic influenza mortality on the basis of vital registry data from the 1918-20 pandemic: a quantitative analysis.

BACKGROUND: The threat of an avian influenza pandemic is causing widespread public concern and health policy response, especially in high-income countries. Our aim was to use high-quality vital registration data gathered during the 1918-20 pandemic to estimate global mortality should such a pandemic occur today. METHODS: We identified all countries with high-quality vital registration data for the 1918-20 pandemic and used these data to calculate excess mortality. We developed ordinary least squares regression models that related excess mortality to per-head income and absolute latitude and used these models to estimate mortality had there been an influenza pandemic in 2004. FINDINGS: Excess mortality data show that, even in 1918-20, population mortality varied over 30-fold across countries. Per-head income explained a large fraction of this variation in mortality. Extrapolation of 1918-20 mortality rates to the worldwide population of 2004 indicates that an estimated 62 million people (10th-90th percentile range 51 million-81 million) would be killed by a similar influenza pandemic; 96% (95% CI 95-98) of these deaths would occur in the developing world. If this mortality were concentrated in a single year, it would increase global mortality by 114%. INTERPRETATION: This analysis of the empirical record of the 1918-20 pandemic provides a plausible upper bound on pandemic mortality. Most deaths will occur in poor countries--ie, in societies whose scarce health resources are already stretched by existing health priorities.

Benchmarking of performance of Mexican states with effective coverage.

Benchmarking of the performance of states, provinces, or districts in a decentralised health system is important for fostering of accountability, monitoring of progress, identification of determinants of success and failure, and creation of a culture of evidence. The Mexican Ministry of Health has, since 2001, used a benchmarking approach based on the WHO concept of effective coverage of an intervention, which is defined as the proportion of potential health gain that could be delivered by the health system to that which is actually delivered. Using data collection systems, including state representative examination surveys, vital registration, and hospital discharge registries, we have monitored the delivery of 14 interventions for 2005-06. Overall effective coverage ranges from 54.0% in Chiapas, a poor state, to 65.1% in the Federal District. Effective coverage for maternal and child health interventions is substantially higher than that for interventions that target other health problems. Effective coverage for the lowest wealth quintile is 52% compared with 61% for the highest quintile. Effective coverage is closely related to public-health spending per head across states; this relation is stronger for interventions that are not related to maternal and child health than those for maternal and child health. Considerable variation also exists in effective coverage at similar amounts of spending. We discuss the implications of these issues for the further development of the Mexican health-information system. Benchmarking of performance by measuring effective coverage encourages decision-makers to focus on quality service provision, not only service availability. The effective coverage calculation is an important device for health-system stewardship. In adopting this approach, other countries should select interventions to be measured on the basis of the criteria of affordability, effect on population health, effect on health inequalities, and capacity to measure the effects of the intervention. The national institutions undertaking this benchmarking must have the mandate, skills, resources, and independence to succeed.

Assessing the effect of the 2001-06 Mexican health reform: an interim report card.

Since 2001, Mexico has been designing, legislating, and implementing a major health-system reform. A key component was the creation of Seguro Popular, which is intended to expand insurance coverage over 7 years to uninsured people, nearly half the total population at the start of 2001. The reform included five actions: legislation of entitlement per family affiliated which, with full implementation, will increase public spending on health by 0.8-1.0% of gross domestic product; creation of explicit benefits packages; allocation of monies to decentralised state ministries of health in proportion to number of families affiliated; division of federal resources flowing to states into separate funds for personal and non-personal health services; and creation of a fund to protect families against catastrophic health expenditures. Using the WHO health-systems framework, we used a wide range of datasets to assess the effect of this reform on different dimensions of the health system. Key findings include: affiliation is preferentially reaching the poor and the marginalised communities; federal non-social security expenditure in real per-head terms increased by 38% from 2000 to 2005; equity of public-health expenditure across states improved; Seguro Popular affiliates used more inpatient and outpatient services than uninsured people; effective coverage of 11 interventions has improved between 2000 and 2005-06; inequalities in effective coverage across states and wealth deciles has decreased over this period; catastrophic expenditures for Seguro Popular affiliates are lower than for uninsured people even though use of services has increased. We present some lessons for Mexico based on this interim evaluation and explore implications for other countries considering health reforms.