Prevalence of mental disorders and epidemiological associations in post-conflict primary care attendees: a cross-sectional study in the Northern Province of Sri Lanka

Background: Experiencing conflict and displacement can have a negative impact on an individual’s mental health. Currently, prevalence of mental health disorders (MHDs) at the primary care level in post-conflict areas within the Northern Province of Sri Lanka is unknown. We aimed to explore this prevalence in conflict-affected populations attending primary care, using a structured package of validated screening tools for MHDs. Methods: This cross-sectional study aimed to determine factors related to mental health disorders at the primary care level in Northern Province, Sri Lanka. A structured interview was conducted with internally displaced adults attending 25 randomly selected primary care facilities across all districts of Northern Sri Lanka (Jaffna, Mannar, Mullaitivu, Vavuniya). Participants were screened for depression, anxiety, psychosis, PTSD, and somatoform symptoms. Results: Among 533 female and 482 male participants (mean age 53.2 years), the prevalence rate for any MHD was 58.8% (95% CI, 53.8–61.4), with 42.4% screening positive for two or more disorders (95% CI, 38.6–46.1). Anxiety prevalence was reported at 46.7% (95% CI, 41.9–51.5), depression at 41.1% (95% CI, 38.7–44.5), PTSD at 13.7% (95% CI, 10.6–16.8), somatoform symptoms at 27.6% (95% CI, 23.6–31.5), and psychosis with hypomania at 17.6% (95% CI, 13.3–21.9). Conclusion: This is the first study at the primary care level to investigate prevalence of MHDs among conflict-affected populations in the Northern Province, Sri Lanka. Results highlight unmet mental health needs in the region. Training intervention to integrate mental health services into primary care is planned

Epidemiological data from the COVID-19 outbreak, real-time case information

Abstract: Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making

Epidemiological data from the COVID-19 outbreak, real-time case information

Abstract: Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making

Epidemiological data from the COVID-19 outbreak, real-time case information

Abstract: Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Pandemic Preparedness and COVID-19: lessons learned from national and subnational response, what we can learn from existing preparedness metrics, and how to prepare for novel threats

Thesis (Ph.D.)--University of Washington, 2023The COVID-19 pandemic has been one of the most catastrophic health emergencies of all time, leading to millions of deaths and hundreds of millions of infections worldwide. Yet, a global pandemic impacting all of humanity was not unforeseen, with countless studies articulating the spillover potential of various pathogens into humans and the ability for such viruses to replicate. Similarly, pandemic preparedness frameworks and metrics existed pre-COVID to quantify pandemic risks and vulnerabilities for a given country to emphasize both strong existing capacities as well as areas for improvement for emerging outbreaks and pandemics of international concern. The unprecedented scale of COVID-19 has renewed focus on pandemic preparedness and response. This research aims to understand the drivers of differential COVID-19 outcomes across various countries and within key countries and to quantify how current preparedness indices of immunization measure up against retrospective COVID-19 vaccination uptake. In the first aim, we consider whether existing pandemic preparedness indicators nationally were informative of better COVID-19 outcomes, and investigate what other political, social, health or demographic covariates influenced heterogeneities in COVID-19 across the globe. In the second aim, we focus our analyses subnationally to investigate drivers of within-country heterogeneities, and again investigate whether pre-pandemic preparedness was informative of COVID-19 outcomes, and whether national patterns were persistent subnationally. In the third aim, we build on our findings from the first and second aim that pandemic preparedness composite measures were not predictive of COVID-19 successes, and decompose such metrics to investigate one specific indicator of routine immunization in order to understand whether our measurement of vaccine readiness was truly informative of pandemic vaccine delivery. In the first chapter Pandemic preparedness and COVID-19: an exploratory analysis of infection and fatality rates, and contextual factors associated with preparedness in 177 countries, from Jan 1, 2020, to Sept 30, 2021, we used multi-stage log-log regression models to understand drivers of cross-country differences in COVID-19 infections and mortality from January 2020 through September 2021, expanding on previous research looking at COVID-19 outcomes in relation to pandemic preparedness scores. We first controlled for immutable factors including daily seasonality and age profile, and secondly controlled for baseline risk factors like age profile, seasonality, density, and BMI. Following these adjustments, we modeled our standardized COVID-19 infections and infection fatality rates against policy-amenable factors, including pandemic preparedness indicators, health care readiness, and social and political characteristics. We found that the largest drivers of reduced COVID-19 infections were not associated with existing pandemic preparedness metrics, but instead to higher trust in the government and in other people. As trust is an essential driver of effective risk communication and behavioral modification such as vaccine uptake or social distancing, improving trust prior to the next pandemic is essential. In the second chapter An exploratory analysis of improved COVID-19 outcomes in subnational locations across two countries: the United States and Brazil, January 2020 through May 2022, we again used multi-stage log-log regression models to understand within-country drivers of COVID-19 outcomes in Brazil and the United States. These two countries were chosen for their high overall COVID-19 burdens, but also for heterogeneous COVID-19 burdens, responses, and high political polarization. This time, our analysis ran from January 2020 through May 2022, and we controlled for daily seasonality and variant prevalence in the first stage, followed by a standardization for baseline risk factors. We again modeled these standardized estimates versus policy-amenable factors, including pandemic preparedness indicators, health care readiness, and social and political characteristics, though many of the covariates from the first chapter were not available at the state-level and either had to be modeled or omitted. Although there were observable differences within these countries, we identified no significant policy-amenable drivers of COVID-19 differences within countries following baseline standardization, though hospital beds per capita were found to be significantly related to higher infections. Trust was not a key driver of COVID-19 outcomes in Brazil and the United States, though the sample sizes of our trust variables were small and had wide confidence intervals. Similarly, modeled pandemic preparedness indicators were not predictive of improved COVID-19 outcomes subnationally. Our research additionally suggests that access to high quality health care is a potential avenue to explore to reduce the burden of disease in future pandemics. Within-country efforts to prepare for the next pandemic may be best focused on improving access to care and reducing existing burdens of comorbidities such as obesity and cancer which drive not only high mortality in general but are exacerbated in pandemics like COVID-19 where undue morbidity and mortality were observed among the chronically ill and elderly, and to improve estimates of trust and pandemic preparedness at a local level to better understand true disparities nationally. In the final chapter Considering measles containing vaccine as a proxy for pandemic preparedness in the context of COVID-19: are we truly measuring what matters?, we look at a country’s routine measles containing vaccine (MCV) coverage – a proxy for immunization readiness and vaccine delivery in preparedness metrics like the Joint External Evaluation (JEE) – and model it against at least one dose COVID-19 vaccination between December 1, 2020 and December 1, 2022 for national and subnational locations separately. Vaccination has been an incredible tool throughout the COVID-19 pandemic in reducing morbidity and mortality, but has been highly inequitable in its distribution, so we wanted to understand how closely heterogeneities in MCV as a routine measure mapped to those seen in COVID-19 vaccine uptake. Moreover, composite scores of pandemic preparedness proved non-informative of COVID-19 outcomes, and so we sought to understand whether specific indices were beneficial in understanding specific aspects of the COVID-19 pandemic, such as immunization. In each location, we consider the time to various thresholds of coverage (1%, 5%, and 10%) to understand the relationship between pre-pandemic immunization and speed of novel vaccine roll out, controlling for pre-pandemic vaccine hesitancy and percentage of the population over 65 years of age. We consider the maximum number of persons vaccinated in a single day (smoothed and averaged over a one-month period to adjust for noisy data) as a measure of speed of scale-up and separately estimate the maximum level of coverage achieved for at least one dose COVID-19 vaccination coverage. We model these additional COVID-19 vaccine outcomes against MCV as well, again controlling for vaccine hesitancy and percentage of the population over 65 years of age. Our research suggests that the level of pre-pandemic one-dose measles vaccine coverage across 134 countries was successful in predicting the time to vaccine roll out at varying thresholds and the overall vaccination level achieved. In the subnational model, we found no significant relationships between routine immunization coverage and one-dose COVID-19 vaccine delivery, a relationship that persisted across all data subsets and additional indicators of routine immunization. While composite metrics of pandemic preparedness are not effective at predicting pandemic COVID-19 outcomes, specific, targeted indicators have stronger predictive validity than the composites. Specifically, this analysis demonstrates that measles vaccine coverage is an effective metric for quantifying immunization readiness at the national level, and can provide utility for considering equitable delivery of vaccines and therapies for future threats. This dissertation takes a comprehensive look at pandemic preparedness measures and assess their validity with a COVID-19 lens. We find that pre-pandemic composite scores held little validity for predicting better COVID-19 outcomes across the globe, nor within two key countries. However, when drilling down to individual components of these composite scores, we found a correlation between pre-pandemic immunization readiness and pandemic vaccine delivery at a national level, suggesting the possibility of wider validity of individual indicators for various facets of pandemic preparedness and response. Trust in other people and in the government was identified as a key driver of lower COVID-19 infections in the national analysis, though lack of focused, high-quality subnational data limited the extension of these findings in the second, subnational aim. Considering trust as an essential tool to build prior to subsequent pandemics and monitor on an ongoing basis, as well as focusing dedicated resources and efforts to better quantify and understand trust nationally and subnationally will have extensive payoffs in better understanding current patterns of trust, as well as improving messaging and adherence for novel threats. COVID-19 was the first of what is likely to be many outbreaks and pandemics in our lifetimes given a growing number of interactions between humans and wildlife due to climate change and urbanization, allowing for spillover of novel and re-emerging pathogens into human populations. Lessons learned from this dissertation would be useful for guiding pandemic preparedness plans to consider how to reformat existing metrics to be most suited for planning for a multitude of different diseases in the future, and to ensure that subnational capacities and vulnerabilities are thoroughly addressed

Parameters associated with design effect of child anthropometry indicators in small-scale field surveys

Abstract Background Cluster surveys provide rapid but representative estimates of key nutrition indicators in humanitarian crises. For these surveys, an accurate estimate of the design effect is critical to calculate a sample size that achieves adequate precision with the minimum number of sampling units. This paper describes the variability in design effect for three key nutrition indicators measured in small-scale surveys and models the association of design effect with parameters hypothesized to explain this variability. Methods 380 small-scale surveys from 28 countries conducted between 2006 and 2013 were analyzed. We calculated prevalence and design effect of wasting, underweight, and stunting for each survey as well as standard deviations of the underlying continuous Z-score distribution. Mean cluster size, survey location and year were recorded. To describe design effects, median and interquartile ranges were examined. Generalized linear regression models were run to identify potential predictors of design effect. Results Median design effect was under 2.00 for all three indicators; for wasting, the median was 1.35, the lowest among the indicators. Multivariable linear regression models suggest significant, positive associations of design effect and mean cluster size for all three indicators, and with prevalence of wasting and underweight, but not stunting. Standard deviation was positively associated with design effect for wasting but negatively associated for stunting. Survey region was significant in all three models. Conclusions This study supports the current field survey guidance recommending the use of 1.5 as a benchmark for design effect of wasting, but suggests this value may not be large enough for surveys with a primary objective of measuring stunting or underweight. The strong relationship between design effect and region in the models underscores the continued need to consider country- and locality-specific estimates when designing surveys. These models also provide empirical evidence of a positive relationship between design effect and both mean cluster size and prevalence, and introduces standard deviation of the underlying continuous variable (Z-scores) as a previously unexplored factor significantly associated with design effect. The magnitude and directionality of this association differed by indicator, underscoring the need for further investigation into the relationship between standard deviation and design effect