JYNNEOS™ effectiveness as post-exposure prophylaxis against mpox: Challenges using real-world outbreak data.

BACKGROUND: JYNNEOSTM vaccine has been used as post-exposure prophylaxis (PEP) during a mpox outbreak in New York City (NYC). Data on effectiveness are limited. METHODS: Effectiveness of a single dose of JYNNEOSTM vaccine administered subcutaneously ≤ 14 days as PEP for preventing mpox disease was assessed among individuals exposed to case-patients from May 22, 2022-August 24, 2022. Individuals were evaluated for mpox through 21 days post-exposure. An observational study was conducted emulating a sequence of nested "target" randomized trials starting each day after exposure. Results were adjusted for exposure risk and race/ethnicity. Analyses were conducted separately based on last (PEPL) and first (PEPF) exposure date. We evaluated the potential to overestimate PEP effectiveness when using conventional analytic methods due to exposed individuals developing illness before they can obtain PEP (immortal time bias) compared to the target trial. RESULTS: Median time from last exposure to symptom onset (incubation period) among cases that did not receive PEPL was 7 days (range 1-16). Time to PEPL receipt was 7 days (range 0-14). Among 549 individuals, adjusted PEPL and PEPF effectiveness was 19 % (95 % Confidence Interval [CI], -54 % to 57 %) and -7% (95 % CI, -144 % to 53 %) using the target trial emulation, respectively, and 78 % (95 % CI, 50 % to 91 %) and 73 % (95 % CI, 31 % to 91 %) using conventional analysis. CONCLUSIONS: Determining PEP effectiveness using real-world data during an outbreak is challenging. Time to PEP in NYC coupled with the observed incubation period resulted in overestimated PEP effectiveness using a conventional method. The target trial emulation, while yielding wide confidence intervals due to small sample size, avoided immortal time bias. While results from these evaluations cannot be used as reliable estimates of PEP effectiveness, we present important methodologic considerations for future evaluations.

Some principles for using epidemiologic study results to parameterize transmission models.

Background: Infectious disease models, including individual based models (IBMs), can be used to inform public health response. For these models to be effective, accurate estimates of key parameters describing the natural history of infection and disease are needed. However, obtaining these parameter estimates from epidemiological studies is not always straightforward. We aim to 1) outline challenges to parameter estimation that arise due to common biases found in epidemiologic studies and 2) describe the conditions under which careful consideration in the design and analysis of the study could allow us to obtain a causal estimate of the parameter of interest. In this discussion we do not focus on issues of generalizability and transportability. Methods: Using examples from the COVID-19 pandemic, we first identify different ways of parameterizing IBMs and describe ideal study designs to estimate these parameters. Given real-world limitations, we describe challenges in parameter estimation due to confounding and conditioning on a post-exposure observation. We then describe ideal study designs that can lead to unbiased parameter estimates. We finally discuss additional challenges in estimating progression probabilities and the consequences of these challenges. Results: Causal estimation can only occur if we are able to accurately measure and control for all confounding variables that create non-causal associations between the exposure and outcome of interest, which is sometimes challenging given the nature of the variables we need to measure. In the absence of perfect control, non-causal parameter estimates should still be used, as sometimes they are the best available information we have. Conclusions: Identifying which estimates from epidemiologic studies correspond to the quantities needed to parameterize disease models, and determining whether these parameters have causal interpretations, can inform future study designs and improve inferences from infectious disease models. Understanding the way in which biases can arise in parameter estimation can inform sensitivity analyses or help with interpretation of results if the magnitude and direction of the bias is understood.

Exploring COVID-19 vaccine hesitancy and uptake in Nairobi's urban informal settlements: an unsupervised machine learning analysis of a longitudinal prospective cohort study from 2021 to 2022.

OBJECTIVES: To illustrate the utility of unsupervised machine learning compared with traditional methods of analysis by identifying archetypes within the population that may be more or less likely to get the COVID-19 vaccine. DESIGN: A longitudinal prospective cohort study (n=2009 households) with recurring phone surveys from 2020 to 2022 to assess COVID-19 knowledge, attitudes and practices. Vaccine questions were added in 2021 (n=1117) and 2022 (n=1121) rounds. SETTING: Five informal settlements in Nairobi, Kenya. PARTICIPANTS: Individuals from 2009 households included. OUTCOME MEASURES AND ANALYSIS: Respondents were asked about COVID-19 vaccine acceptance (February 2021) and vaccine uptake (March 2022). Three distinct clusters were estimated using K-Means clustering and analysed against vaccine acceptance and vaccine uptake outcomes using regression forest analysis. RESULTS: Despite higher educational attainment and fewer concerns regarding the pandemic, young adults (cluster 3) were less likely to intend to get the vaccine compared with cluster 1 (41.5% vs 55.3%, respectively; p<0.01). Despite believing certain COVID-19 myths, older adults with larger households and more fears regarding economic impacts of the pandemic (cluster 1) were more likely to ultimately to get vaccinated than cluster 3 (78% vs 66.4%; p<0.01), potentially due to employment requirements. Middle-aged women who are married or divorced and reported higher risk of gender-based violence in the home (cluster 2) were more likely than young adults (cluster 3) to report wanting to get the vaccine (50.5% vs 41.5%; p=0.014) but not more likely to have gotten it (69.3% vs 66.4%; p=0.41), indicating potential gaps in access and broader need for social support for this group. CONCLUSIONS: Findings suggest this methodology can be a useful tool to characterise populations, with utility for improving targeted policy, programmes and behavioural messaging to promote uptake of healthy behaviours and ensure equitable distribution of prevention measures.

Optimizing the Construction of Outcome Measures for Impact Evaluations of Intimate Partner Violence Prevention Interventions.

Most impact evaluations of intimate partner violence (IPV) prevention interventions use binary measures of "any" versus "no" physical and/or sexual IPV as their primary outcome measure, missing opportunities to capture nuance. In this study, we reanalyzed secondary data from six randomized controlled trials conducted in low- and middle-income countries-Bandebereho (Rwanda), Becoming One (Uganda), Indashyikirwa (Rwanda), MAISHA CRT01, MAISHA CRT02 (Tanzania), Stepping Stones Creating Futures (South Africa), and Unite for a Better Life (Ethiopia), to assess how different conceptualizations and coding of IPV variables can influence interpretations of the impact of an intervention. We compared the standard outcome measures to new measures that reflect the severity and intensity of violence and whether interventions prevent new cases of IPV or reduce or stop ongoing violence. Results indicate that traditional binary indicators masked some of the more subtle intervention effects, and the use of the new indicators allowed for a better understanding of the impacts of the interventions. Conclusions on whether a program is perceived "to work" are highly influenced by the IPV outcomes that the investigators choose to report, and how they are measured and coded. Lack of attention to outcome choice and measurement could lead to prematurely abandoning strategies useful for violence reduction or missing essential insights into how programs may or may not affect IPV. While these results must be interpreted cautiously, given differences in intervention types, the underlying prevalence of violence, sociodemographic factors, sample sizes, and other contextual differences across the trial sites, they can help us move toward a new approach to reporting multiple outcomes that allow us to unpack the "impact" of an intervention by assessing intervention effect by the severity of violence and type of prevention, whether primary and secondary.

Infectious disease dynamics and restrictions on social gathering size.

Social gatherings can be an important locus of transmission for many pathogens including SARS-CoV-2. During an outbreak, restricting the size of these gatherings is one of several non-pharmaceutical interventions available to policy-makers to reduce transmission. Often these restrictions take the form of prohibitions on gatherings above a certain size. While it is generally agreed that such restrictions reduce contacts, the specific size threshold separating "allowed" from "prohibited" gatherings often does not have a clear scientific basis, which leads to dramatic differences in guidance across location and time. Building on the observation that gathering size distributions are often heavy-tailed, we develop a theoretical model of transmission during gatherings and their contribution to general disease dynamics. We find that a key, but often overlooked, determinant of the optimal threshold is the distribution of gathering sizes. Using data on pre-pandemic contact patterns from several sources as well as empirical estimates of transmission parameters for SARS-CoV-2, we apply our model to better understand the relationship between restriction threshold and reduction in cases. We find that, under reasonable transmission parameter ranges, restrictions may have to be set quite low to have any demonstrable effect on cases due to relative frequency of smaller gatherings. We compare our conceptual model with observed changes in reported contacts during lockdown in March of 2020.

Religious leaders can motivate men to cede power and reduce intimate partner violence: Experimental evidence from Uganda.

Violence committed by men against women in intimate relationships is a pervasive problem around the world. Patriarchal norms that place men as the head of household are often to blame. Previous research suggests that trusted authorities can shift perceptions of norms and create behavior change. In many settings, a compelling authority on behavior in relationships is religious leaders, who are influential sources of information about proper conduct in relationships and gatekeepers of marriage, but may also uphold traditional gender roles. One way leaders exert their influence is through premarital or couples counseling courses. In this study, we test whether, if given an opportunity to offer a more progressive religious interpretation of gender roles during these courses, religious leaders could motivate men to share power and thereby reduce violence. Building on existing faith networks of Christian religious leaders in western Uganda, we conducted a large pair-matched, randomized controlled trial among 1,680 heterosexual couples in which participants were randomized to attend a 12-session group counseling course or wait-listed. We find that the program shifted power from men to women and reduced intimate partner violence by five percentage points, comparable with more intensive secular programs. These improvements were largest among couples counseled by religious leaders who held the most progressive views at baseline and who critically engaged with the material. Our findings suggest that religious leaders can be effective agents of change for reducing violence.

Infectious disease dynamics and restrictions on social gathering size.

Social gatherings can be an important locus of transmission for many pathogens including SARS-CoV-2. During an outbreak, restricting the size of these gatherings is one of several non-pharmaceutical interventions available to policy-makers to reduce transmission. Often these restrictions take the form of prohibitions on gatherings above a certain size. While it is generally agreed that such restrictions reduce contacts, the specific size threshold separating "allowed" from "prohibited" gatherings often does not have a clear scientific basis, which leads to dramatic differences in guidance across location and time. Building on the observation that gathering size distributions are often heavy-tailed, we develop a theoretical model of transmission during gatherings and their contribution to general disease dynamics. We find that a key, but often overlooked, determinant of the optimal threshold is the distribution of gathering sizes. Using data on pre-pandemic contact patterns from several sources as well as empirical estimates of transmission parameters for SARS-CoV-2, we apply our model to better understand the relationship between restriction threshold and reduction in cases. We find that, under reasonable transmission parameter ranges, restrictions may have to be set quite low to have any demonstrable effect on cases due to relative frequency of smaller gatherings. We compare our conceptual model with observed changes in reported contacts during lockdown in March of 2020.

All MCIDs Are Wrong, But Some May be Useful.

OBJECTIVE: To demonstrate how to apply a baseline-adjusted receiver operator characteristic curve (AROC) analysis for minimum clinically important differences (MCIDs) in an empirical data set and discuss new insights relating to MCIDs. DESIGN: Retrospective study. METHODS: This study includes data from 999 active-duty military service patients enrolled in the United States Military Health System's Military Orthopedics Tracking Injuries and Outcomes Network. Anchored MCIDs were calculated using the standard receiver operator characteristic analysis and the AROC analysis for the Patient-Reported Outcome Measure Information System (PROMIS) Pain Interference and Defense and Veterans Pain Rating Scale (DVPRS). Point estimates where confidence intervals (CIs) crossed the 0.5 identity line on the area-under-the-curve (AUC) analysis were considered statistically invalid. MCID estimates where CIs crossed 0 were considered theoretically invalid. RESULTS: In applying an AROC analysis, the region of AUC and MCID validity for the PROMIS Pain Interference score exists when the baseline score is greater than 61.0 but less than 72.3. For DVPRS, the region of MCID validity is when the baseline score is greater than 5.9 but less than 7.9. CONCLUSION: Baseline values influence not only the MCID but also the accuracy of the MCID. MCIDs are statistically and theoretically valid for only a discrete range of baseline scores. Our findings suggest that the MCID may be too flawed a construct to accurately benchmark treatment outcomes. J Orthop Sports Phys Ther 2022;52(6):401-407. doi:10.2519/jospt.2022.11193.

Engaging Education About Risks of Opioid Use With Patients Before Elective Surgery of the Lower Extremity Did Not Reduce Postoperative Opioid Utilization: A Randomized Controlled Trial.

INTRODUCTION: After elective orthopaedic surgery, many individuals go on to become long-term opioid users. Mitigating this risk has become a priority for surgeons, other members of the medical care team, and healthcare systems. The purpose of this study was to compare opioid utilization after lower extremity orthopaedic surgery between patients who received an interactive video education session highlighting the risks of opioid use and those who did not. METHODS: Patients undergoing elective surgery of the lower extremity in the orthopaedic clinic at the Brooke Army Medical Center between July 2015 and February 2017 were recruited at their preoperative appointment and randomized in a 1:1 ratio to receive a one-time interactive opioid education session or usual care education. Unique days' supply of opioids and unique prescriptions were compared using a generalized linear model. Individuals were also grouped by whether they had become long-term opioid users after surgery, and frequencies within each intervention group were compared. RESULTS: There were 120 patients, 60 randomized to each group and followed for 1 year. There were no significant differences between opioid days' supply (mean diff = 8.33, 95% confidence interval -4.21 to 20.87) and unique prescriptions after surgery (mean diff = 0.45, 95% confidence interval -0.25 to 1.15). Most participants did not have any opioids past the initial 30 days after surgery, regardless of intervention (n = 77), and only three became long-term opioid users (one in usual care and two in interactive education). Sixteen in usual education and 18 in enhanced education filled at least one prescription in 6 months or later after the surgical procedure. CONCLUSION: Opioid use beyond 30 days of surgery was no different for participants who received enhanced education compared with usual education. Few became long-term opioid users after surgery (2.5%), although 28.3% were still filling opioid prescriptions 6 months after surgery.

Drowning risk and climate change: a state-of-the-art review.

Drowning and climate change are both significant global health threats, yet little research links climate change to drowning risk. Research into the epidemiology, risk factors and preventive strategies for unintentional drowning in high-income and in low-income and middle-income countries has expanded understanding, but understanding of disaster and extreme weather-related drowning needs research focus. As nation states and researchers call for action on climate change, its impact on drowning has been largely ignored. This state-of-the-art review considers existing literature on climate change as a contributor to changes in drowning risks globally. Using selected climate change-related risks identified by the World Meteorological Organization and key risks to the Sustainable Development Goals as a framework, we consider the drowning risks associated with heat waves, hydrometeorological hazards, drought and water scarcity, damaged infrastructure, marine ecosystem collapse, displacement, and rising poverty and inequality. Although the degree of atmospheric warming remains uncertain, the impact of climate change on drowning risk is already taking place and can no longer be ignored. Greater evidence characterising the links between drowning and climate change across both high-income and low-income and middle-income contexts is required, and the implementation and evaluation of drowning interventions must reflect climate change risks at a local level, accounting for both geographical variation and the consequences of inequality. Furthermore, collaboration between the injury prevention, disaster risk reduction and climate change mitigation sectors is crucial to both prevent climate change from stalling progress on preventing drowning and further advocate for climate change mitigation as a drowning risk reduction mechanism.

Problems with evidence assessment in COVID-19 health policy impact evaluation: a systematic review of study design and evidence strength.

INTRODUCTION: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. METHODS: We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on 26 November 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation. RESULTS: After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-sectional), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes. DISCUSSION: The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigour to be actionable by policy-makers. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.

Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation: A systematic review of study design and evidence strength.

INTRODUCTION: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature. METHODS: We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on November 26, 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation, assessing what impact evaluation method was used, graphical display of outcomes data, functional form for the outcomes, timing between policy and impact, concurrent changes to the outcomes, and an overall rating. RESULTS: After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. The majority (n=23/36) of studies in our sample examined the impact of stay-at-home requirements. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-section), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 1/27 studies passed all of the above checks, and 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes. DISCUSSION: The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigor to be actionable by policymakers. This was largely driven by the circumstances under which policies were passed making it difficult to attribute changes in COVID-19 outcomes to particular policies. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.

Using Detection And Attribution To Quantify How Climate Change Is Affecting Health.

The question of whether, how, and to what extent climate change is affecting health is central to many climate and health studies. We describe a set of formal methods, termed detection and attribution, used by climatologists to determine whether a climate trend or extreme event has changed and to estimate the extent to which climate change influenced that change. We discuss events where changing weather patterns were attributed to climate change and extend these analyses to include health impacts from heat waves in 2018 and 2019 in Europe and Japan, and we show how such impact attribution could be applied to melting ice roads in the Arctic. Documenting the causal chain from emissions of greenhouse gases to observed human health outcomes is important input into risk assessments that prioritize health system preparedness and response interventions and into financial investments and communication about potential risk to policy makers and to the public.

Using Implementation Science For Health Adaptation: Opportunities For Pacific Island Countries.

The health risks of a changing climate are immediate and multifaceted. Policies, plans, and programs to reduce climate-related health impacts exist, but multiple barriers hinder the uptake of these strategies, and information remains limited on the factors affecting implementation. Implementation science-a discipline focused on systematically examining the gap between knowledge and action-can address questions related to implementation and help the health sector scale up successful adaptation measures in response to climate change. Implementation science, in the context of a changing climate, can guide decision makers in introducing and prioritizing potential health adaptation and disaster risk management solutions, advancing sustainability initiatives, and evaluating and improving intervention strategies. In this article we highlight examples from Pacific Island countries and outline approaches based on implementation science to enhance the capacity of health systems to anticipate, prepare for, respond to, and recover from climate-related exposures.

The evaluation of the Woman's Condom marketing approach: What value did peer-led interpersonal communication add to the promotion of a new female condom in urban Lusaka?

During a mass media campaign accompanying the launch of the Maximum Diva Woman's Condom (WC) in Lusaka, Zambia, a cluster-randomized evaluation was implemented to measure the added impact of a peer-led interpersonal communication (IPC) intervention on the awareness and uptake of the new female condom (FC). The WC and mass media campaign were introduced simultaneously in 40 urban wards in April 2016; half of the wards were randomly assigned to the treatment (IPC intervention) with cross-sectional surveys conducted before (n = 2,364) and one year after (n = 2,430) the start of the intervention. A pre-specified intention-to-treat (ITT) analysis measured the impact of randomization to IPC at the community level. In adjusted ITT models, there were no statistically significant differences between intervention and control groups. Due to significant implementation challenges, we also conducted exploratory secondary analyses to estimate effects among those who attended an IPC event (n = 66) using instrumental variable and inverse probability weighting analyses. In addition to increases in FC identification (IPC attendees had higher reported use of any condom, improved perceptions of FC's, and were more likely to have discussed contraceptive use with their partner as compared to non-attendees). The introduction of a new FC product combined with an IPC intervention significantly increased general knowledge and awareness in the community as compared to media alone, but did not lead to detectable community level impacts on other primary outcomes of interest. Observational evidence from our study suggests that IPC attendance is associated with increased use and negotiation. Future studies should explore the intensity and duration of IPC programming necessary to achieve detectable community level impacts on behavior. Trial Registration: AEARCTR-0000899.

Investigating the Efficacy of a Hybrid EMT Course.

The Pennsylvania College of Technology implemented a region-wide hybrid emergency medical technician (EMT) training course in 2015, following the release of the National Emergency Medical Services Education Standards as well as the Commonwealth of Pennsylvania's transition away from a state-developed EMT exam and towards the EMT exam developed by the National Registry of EMTs. Previous classes were conducted by the training sites in the region utilizing a traditional, classroom-based model. 79.32% of students who completed training within the classes offered by the region between 2007 and 2013 successfully passed the state exam and became certified as EMT's (n=418), compared to 80.52% of the students who completed the current training program conducted by Penn College (n=186). This analysis finds the Penn College program's pass rates on the NREMT cognitive exam (both first attempt and within three attempts) exceed the previous pass rates on the state exam for the region and mirror the national pass rate metrics during the study period. This study concludes that the Penn College hybrid EMT course is an effective method of training new providers for the region.

Exploring gender and partner communication: Theory of planned behavior predictors for condom use among urban youth in Zambia.

OBJECTIVE: To understand how knowledge and perceptions of condoms and partner communication influence use of condoms in a high HIV prevalence setting and gender-specific differences. METHODS: A cross-sectional study was conducted in Zambia from 2015 to 2016. The survey included questions on demographics, sexual behavior, contraceptive perceptions, and behaviors. We constructed multivariate regression models using the Theory of Planned Behavior to determine associations between knowledge, perceptions, and perceived control with intended, communicated, and reported use of condoms by gender. RESULTS: The participants were 2388 sexually active urban residents aged 18-24 years. In the sample, 1646 (69%) were female, 841 (35%) married, and 1894 (61%) unemployed. Partner communication was the predictor most associated with use of condoms. Among women, partner communication was associated with over three times higher odds of condom use (odds ratio [OR] 3.51, 95% confidence interval [CI] 2.65-4.65) but being married reduced the odds of condom use by 76% (OR 0.24, 95% CI 0.17-0.33). For men, a network of friends that was supportive of the use of contraception was associated with increased odds of 55% for use of condoms (OR 1.55, 95% CI 1.10-2.18). CONCLUSION: Public health programs aimed at increasing safer sexual behavior and use of condoms must consider improving gender equity and partner communication, as knowledge of contraceptives and positive perceptions are not enough to ensure their use.

The child survival impact of the Ghana Essential Health Interventions Program: A health systems strengthening plausibility trial in Northern Ghana.

BACKGROUND: The Ghana Health Service in collaboration with partner institutions implemented a five-year primary health systems strengthening program known as the Ghana Essential Health Intervention Program (GEHIP). GEHIP was a plausibility trial implemented in an impoverished region of northern Ghana around the World Health Organizations (WHO) six pillars combined with community engagement, leadership development and grassroots political support, the program organized a program of training and action focused on strategies for saving newborn lives and community-engaged emergency referral services. This paper analyzes the effect of the GEHIP program on child survival. METHODS: Birth history data assembled from baseline and endline surveys are used to assess the hazard of child mortality in GEHIP treatment and comparison areas prior to and after the start of treatment. Difference-in-differences (DiD) methods are used to compare mortality change over time among children exposed to GEHIP relative to children in the comparison area over the same time period. Models test the hypothesis that a package of systems strengthening activities improved childhood survival. Models adjusted for the potentially confounding effects of baseline differentials, secular mortality trends, household characteristics such as relative wealth and parental educational attainment, and geographic accessibility of clinical care. RESULTS: The GEHIP combination of health systems strengthening activities reduced neonatal mortality by approximately one half (HR = 0.52, 95% CI = 0.28,0.98, p = 0.045). There was a null incremental effect of GEHIP on mortality of post-neonate infants (from 1 to 12 months old) (HR = 0.72; 95% CI = 0.30,1.79; p = 0.480) and post-infants (from 1 year to 5 years old) -(HR = 1.02; 95% CI = 0.55-1.90; p = 0.940). Age-specific analyses show that impact was concentrated among neonates. However, effect ratios for post-infancy were inefficiently assessed owing to extensive survival history censoring for the later months of childhood. Children were observed only rarely for periods over 40 months of age. CONCLUSION: GEHIP results show that a comprehensive approach to newborn care is feasible, if care is augmented by community-based nurses. It supports the assertion that if appropriate mechanisms are put in place to enable the various pillars of the health system as espoused by WHO in rural impoverished settings where childhood mortality is high, it could lead to accelerated reductions in mortality thereby increasing survival of children. Policy implications of the pronounced neonatal effect of GEHIP merit national review for possible scale-up.

Unexplored Opportunities: Use of Climate- and Weather-Driven Early Warning Systems to Reduce the Burden of Infectious Diseases.

PURPOSE OF REVIEW: Weather and climate influence multiple aspects of infectious disease ecology. Creating and applying early warning systems based on temperature, precipitation, and other environmental data can identify where and when outbreaks of climate-sensitive infectious diseases could occur and can be used by decision makers to allocate resources. Whether an outbreak actually occurs depends heavily on other social, political, and institutional factors. RECENT FINDINGS: Improving the timing and confidence of seasonal climate forecasting, coupled with knowledge of exposure-response relationships, can identify prior conditions conducive to disease outbreaks weeks to months in advance of outbreaks. This information could then be used by public health professionals to improve surveillance in the most likely areas for threats. Early warning systems are well established for drought and famine. And while weather- and climate-driven early warning systems for certain diseases, such as dengue fever and cholera, are employed in some regions, this area of research is underdeveloped. Early warning systems based on temperature, precipitation, and other environmental data provide an opportunity for early detection leading to early action and response to potential pathogen threats, thereby reducing the burden of disease when compared with passive health indicator-based surveillance systems.