Public Health Authorities Research Articles

A pragmatic randomized trial to examine the effect of combining healthy diet with mindfulness cognitive therapy to reduce depressive symptoms among university students in a low-resource setting: protocol for the NutriMind Project

BackgroundMental health disorders still rank as leading causes of morbidity worldwide despite increasing awareness and improvements in treatment. Notably, low- and middle-income countries like Uganda, are disproportionately affected by such disorders. The burden of depressive symptoms in these countries is particularly high among students, aggravated by poverty, malnutrition, and inadequate public health governance, yet it is clearly under-researched, making it hard to achieve several of UN Sustainability Development Goals. Current treatment options are insufficient to tackle the increased burden of depressive disease. This is more challenging for low-resource regions especially in Sub-Saharan Africa, suggesting the need for alternative treatments that can swiftly be applied if proven effective. The main aim of this randomized controlled trial (RCT) is therefore to examine if a low-cost healthy diet (based on local Ugandan foods) combined with easy-to access mindfulness cognitive therapy can reduce depressive symptoms among university students in Uganda.MethodsWe will recruit female and male students at Makerere University, the largest public university in Uganda, to an open, intention-to-treat, two-armed RCT. Those who score above a predefined threshold on a self-reported assessment of depressive symptoms, measured by the Center for Epidemiological Studies – Depression score (CES-D), are eligible for study inclusion and will be randomized to either an intervention (n = 125) or a control (n = 125) group. The intervention group will receive educational group-based sessions on how to prepare a Mediterranean-type of healthy diet and how to adhere to the principles of mindfulness-based cognitive therapy. Outcome measures include self-reported depression symptoms, quality of life, and dietary intakes. In addition we will perform a cost-effectiveness analysis. The RCT intervention will last 9 months, followed by additional 15 months with regular data collections.DiscussionWe here describe a novel approach to treat depressive symptoms among university students living in resource constraint settings, by combining a healthy diet with low threshold psychotherapy. If this intervention succeeds, our project can be viewed as a step towards evidence-based behavior practices for young adults with a common mental disorder (depression) that are beneficial to public mental health initiatives and management.Trial registrationThe RCT is registered in ClinicalTrials.gov (ID: NCT05848973). The date of registration was August 14, 2023.

Adherence to non-pharmaceutical interventions following COVID-19 vaccination: a federated cohort study

In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the “Google Health Studies” mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency.

Development and application of a WebGIS-based prediction system for multi-criteria decision analysis of porcine pasteurellosis

Porcine pasteurellosis is an infectious disease caused by Pasteurella multocida (P. multocida), which seriously endangers the healthy development of pig breeding industry. Early detection of disease transmission in animals is a crucial early warning for humans. Therefore, predicting risk areas for disease is essential for public health authorities to adopt preventive measures and control strategies against diseases. In this study, we developed a predictive model based on multi-criteria decision analysis (MCDA) and assessed risk areas for porcine pasteurellosis in the Chinese mainland. By using principal component analysis, the weights of seven spatial risk factors were determined. Fuzzy membership function was used to standardize all risk factors, and weight linear combination was used to create a risk map. The sensitivity of the risk map was analyzed by calculating the mean of absolute change rates of risk factors, as well as calculating an uncertainty map. The results showed that risk areas for porcine pasteurellosis were predicted to be locate in the south-central of the Chinese mainland, including Sichuan, Chongqing, Guangdong, and Guangxi. The maximum standard deviation of the uncertain map was less than 0.01and the ROC results showed that the prediction model has moderate predictive performance with the area under the curve (AUC) value of 0.80 (95% CI 0.75–0.84). Based on the above process, MCDA was combined with WebGIS technology to construct a system for predicting risk areas of porcine pasteurellosis. Risk factor data was directly linked to the developed model, providing decision support for disease prevention and control through monthly updates.

Using System Dynamics for Local Authority Public Health practice in England

A key function of local authority public health teams in England is to undertake a regular health needs assessment of their population. This involves forecasting, among other things, prevalence and incidence of long term conditions and health care service demand. Over the last few years, Kent County Council Public Health has adopted the use of system dynamics modelling (SDM) to not only do this but generate scenarios to simulate the impact of different interventions across different cohorts defined by age groups and diagnosable conditions. These include “no conditions” as well as understand the combined effects of different interventions, demographic changes and wider health determinants. Risk factors, such as smoking, diet and physical activity, were calculated using local linked data for the incidence of single conditions, progression toward complex needs and levels of morbidity including frailty. These were used to create the dynamics of the model. Incidence, prevalence and mortality for each cohort were projected over 25 years with “do nothing” and “Better Health” scenarios. The size of the “no conditions” cohort increased, and the other cohorts decreased in size. The impact of the interventions on life expectancy at birth and healthy life expectancy was significant. SDM heavily influenced local authority commissioning decisions for health improvement services redesign such as smoking cessation, weight management as well as other secondary prevention measures such as hypertension management and cardiovascular disease screening delivered by General Practice and other health service partners.

The EU Artificial Intelligence Act (2024): Implications for healthcare

In August 2024, the EU Artificial Intelligence Act (AI Act) entered into force. This legally binding instrument sets rules for the development, the placing on the market, the putting into service, and the use of AI systems in the European Union. As the world's first extensive legal framework on AI, it aims to boost innovation while protecting individuals against the harms of AI. Since healthcare is one of the top sectors for AI deployment, the new rules will significantly reform national policies and practices on health technology. In this article, we highlight the implications of the AI Act for the healthcare sector. We give a comprehensive overview of the new legal obligations for various healthcare stakeholders (tech developers; healthcare professionals; public health authorities). We conclude that, due to its horizontal approach, it is necessary to adopt further guidelines to address the unique needs of the healthcare sector. To this end, we make recommendations for the upcoming implementation and standardization phase.

Experiences and Lessons Learned From Surging the Governmental Public Health Workforce During the COVID-19 Pandemic.

During the COVID-19 pandemic, the United States relied on the public health workforce to control the spread of COVID-19 while continuing to provide foundational public health services. Facing longstanding staffing shortages, state, tribal, local, and territorial (STLT) public health agencies (PHAs) used various strategies and supports to surge the workforce in response to the pandemic. The study explored (1) the types of strategies and supports STLT PHAs used to surge the public health workforce during the pandemic, (2) assessments of surge strategies and supports, and (3) approaches to using the range of surge strategies available. We conducted 27 semi-structured interviews in 2023 and performed thematic analysis. Interviewees included STLT public health officials, leaders of organizations that directly supported the COVID-19 workforce surge, and public health workforce experts indirectly involved in the workforce surge. To surge the workforce, interviewees relied on partnerships, staffing agencies, the National Guard, the Centers for Disease Control and Prevention Foundation's COVID-19 Corps, the Medical Reserve Corps, and other strategies. Interviewees valued strategies that rapidly engaged staff and volunteers at no cost to their PHA and flexible funding from the federal government to support surge efforts but noted shortcomings in the strategies and supports available. Interviewees described using multiple strategies simultaneously but noted challenges in implementing these strategies due to inadequate planning and insufficient staff and resources. The study underscores the need for STLT PHAs to establish mechanisms to surge the workforce as part of ongoing planning for emergency preparedness. Focus areas include building administrative and hiring capacity within STLT PHAs and sustaining partnerships and contractual agreements that helped fill staffing gaps during the pandemic. To support efforts to build workforce capacity to meet future surge management needs, STLT PHAs should consider creative solutions to attract and retain staff, as well as opportunities to engage students in public health work.

Food safety knowledge, attitudes and practices (KAP) of street vendors: a cross-sectional study in Jordan

PurposeThis study examined the knowledge, attitudes and practices (KAP) regarding food safety and hygiene among street food vendors (SFVs) in Jordan, along with associated factors.Design/methodology/approachThis study utilized a cross-sectional design and targeted a sample of 405 SFVs in Jordan's two most densely populated cities. Data were collected through in-person interviews using a validated and reliable structured questionnaire. Descriptive analysis and linear regression were conducted using SPSS v.25 software to examine associations and predict outcomes.FindingsThe findings reveal that SFVs possess a moderate level of knowledge but exhibit negative attitudes and inadequate practices regarding food safety. Significant associations were identified between age, education level, work experience, marital status, gender and the vendors' KAP. Older SFVs tend to exhibit lower knowledge and attitudes, whereas those with more experience and higher education levels demonstrate better KAP. Marital status and gender also influence knowledge and attitudes.Originality/valueThis study fills a critical gap in the research landscape by comprehensively examining the knowledge, attitudes and practices of street food vendors regarding food safety, with a focus on Jordan. Its findings shed light on the challenges facing the street food vending industry and offer actionable recommendations for enhancing food safety practices. As such, the study's originality and significance lie in its potential to drive positive change within this vital culinary tradition, safeguarding public health and economic livelihoods.Highlights -The study's novelty lies in its exploration of street food vendors' knowledge, attitudes, and practices (KAP) related to food safety, a facet critical to understanding and addressing the challenges facing this industry. It offers an in-depth examination of factors such as education, experience, age, and marital status that influence vendors' adherence to food safety measures. By focusing on the two most populous cities in Jordan, the study not only provides a comprehensive picture of the situation but also sets the groundwork for policy recommendations and interventions.-The research highlights a series of concerning findings. Street food vendors exhibit a moderate level of knowledge regarding food safety, with substantial gaps in understanding specific pathogens and transmission routes. Negative attitudes towards food safety are prevalent, translating into suboptimal hygiene practices. The study's results underscore the urgent need for tailored interventions to address these challenges and improve overall food safety practices within the street food vending sector.-The findings offer actionable insights for policymakers, public health authorities, and local governments. They suggest targeted educational initiatives to enhance vendors' understanding of food safety principles and their significance in preventing foodborne illnesses. Furthermore, the study emphasizes the need for improved infrastructure, access to clean water, and proper sanitation facilities to support vendors in implementing safer practices. By highlighting the associations between socio-demographic factors and food safety KAP, the study offers a blueprint for crafting interventions that address the unique needs of different subgroups of street food vendors.

Novel AI-driven Malaria Prediction for Optimizing Public Health Management

Malaria is an unmoving real common well-being apprehension in Asian nations like the Republic of India, where the state characterizes approximately 55% of the session events cutting-edge the infirmaries. They are usually distinguished by the absence of appropriate therapeutic care provision and the often late and error-prone diagnosis of the condition. In particular, commonly used devices such as the Rapid Diagnosis Test (RDT) are not completely dependable. They are primarily notable for their failure to provide adequate medical treatment and their tendency to diagnose the disease late and incorrectly. For example, widely used devices like the RDT are not consistent. To improve public health management actions, this work offers a unique augmented tree with penguin search optimization (AT+PSO) methodology for malaria forecasting. The suggested method combines the PSO algorithm with the augmented tree model, also known as random forest (RF). In the preprocessing stage, raw data samples are subjected to data normalization. Then, we applied the PSO to improve the characteristics of the RF model after successfully predicting malaria with the RF. The Python program is used to implement the suggested technique and analyze performance using a range of measures, including accuracy (0.988), sensitivity (0.987), specificity (0.991), F1-score (0.988), and MCC (0.975). In summary, our suggested approach produced the best results in terms of accuracy as opposed to other current strategies for predicting malaria to improve governance of public health.

Design of Epidemic Model For Covid-19 Disease Prediction Using Deep Learning

Promising technologies are available in the developing field of Public Health Surveillance (PHS) to help public health authorities make decisions more quickly by expediting the process of monitoring, analysing, and using unofficial sources. The cornerstone of public health practice is public health surveillance. Influencing policy decisions, spearheading new program initiatives, improving public relations, and helping organisations assess their research expenditures all depend on surveillance data. Public health experts may find that mathematical models are an effective tool in controlling epidemics, which might result in a significant drop in the number of cases and deaths. Moreover, decision-makers can optimise prospective control strategies, including as vaccination campaigns, lockdowns, and containment measures, by using mathematical models to produce long- and short-term forecasts. This work suggests the evolution of epidemics.

Public Health Policy and Advocacy: A Management Perspective

The division of authority and accountability between the federal government and the states is one of the public health governance system's most significant advantages and concurrent problems. The policy management suggests that the best course of action is to develop institutional frameworks for consultative decision-making, allocate resources in an equitable manner, and implement policies in a coordinated manner. The proposed methods include improved fiduciary risk management, capacity building, technical support for state-specific strategic plans, active local self-government engagement, and community-based health output monitoring. The idea put forth in this paper is that HR rules, which are centred around the requirement that individuals with a background in public health management hold high positions in the field, should be tightened.

The Role of High-Level Isolation Units in Response to High-Consequence and Emerging Infectious Diseases: Experience of the German STAKOB Network.

This case study describes the experience of the German Permanent Working Group of Competence and Treatment Centers for High Consequence Infectious Diseases, known as STAKOB (Ständiger Arbeitskreis der Kompetenz- und Behandlungszentren für Krankheiten durch hochpathogene Erreger). STAKOB brings together public health authorities (competence centers) and high-level isolation units (treatment centers) to collaborate on the clinical management of high-consequence infectious diseases (HCIDs) and emerging infectious diseases. The network is coordinated by the Robert Koch Institute, Germany's federal public health institute. The main tasks of STAKOB are to strengthen HCID clinical and public health management and increase expert knowledge on HCID and non-HCID emerging infectious diseases in Germany. STAKOB enables the exchange of knowledge and experiences; development of guidelines on infection prevention and control measures, clinical management, and therapy; and support for the World Health Organization and other outbreak responses internationally. The past years have shown how important the STAKOB network is for Germany-not only in providing critical care for HCID cases but also increasing capacity to support public health and clinical management of emerging infectious disease cases. However, maintaining several high-level isolation units in Germany requires a high commitment of financial, material, and human resources. Due to the rarity of HCID and emerging infectious disease events, maintaining the appropriate level of preparedness and ensuring sufficient investments is an ongoing struggle. Nevertheless, it is essential to have a network ready to react to HCID and non-HCID emerging infectious diseases in times of a changing biosecurity and infectious landscape.

The devastating dance between opioid and housing crises: Evidence from OxyContin reformulation

Housing instability and drug misuse are two of the United States' most pressing challenges, each bearing profound health and societal consequences. A crucial yet largely underexplored question is the extent to which the opioid crisis has intensified housing instability. Our study ventures into this relatively uncharted nexus, investigating how the OxyContin reformulation, a pivotal moment in the U.S. opioid epidemic, impacted eviction rates. Employing a dose-response Difference-in-Differences model and analyzing eviction data from 2004 to 2016, we demonstrate that the OxyContin reformulation precipitated a significant increase in evictions, especially in areas with weak eviction protections or limited access to psychiatric treatment resources. Channel analyses reveal increased marijuana initiation and heightened mental and physical health issues following the reformulation. Moreover, the OxyContin reformulation leads to greater reliance on the Supplemental Nutrition Assistance Program, signaling an escalated financial strain on governmental resources. Finally, we find evidence of increased marital disruption post-reformulation. Our findings underscore the urgent need for collaborative efforts between public health and housing authorities to address both the opioid and housing crises.

Did COVID-19 risk perceptions influence the handwashing behavior of the Filipino population?

The COVID-19 pandemic reminded people of the importance of proper handwashing. The degree of health risk perceptions and handwashing behavior during the COVID-19 pandemic opens essential lessons for delivering water, sanitation, and hygiene (WASH) programs across the globe. We measured the relationship between COVID-19 risk perception and the handwashing behavior of 386 Filipinos in a highly urbanized city in the Philippines. Self-made COVID-19 risk perception scale (CRPS) (Cronbach's α 0.93) and handwashing behavior scale (HBS) (Cronbach's α 0.84) were administered face-to-face. The results show that the overall handwashing behavior has a median of 4.1 (frequently), while the interquartile range (IQR) is 0.5. Regression analysis revealed that COVID-19 risk perception has a significant positive association with overall handwashing behavior (B=0.369, p-value <0.05). The COVID-19 risk perception accounts for 36.9% of the variance in handwashing behavior, F (1, 384) =11.6360, p-value=0.0007. This study proposes that public health authorities target people's experiential learning style in designing and delivering intervention programs against non-compliance with proper handwashing.

Strategies to overcome mental health stigma: Insights and recommendations from young people with major depressive disorder (MDD).

Young people with depression are met with stigma related to their mental health, which exacerbates loneliness, social isolation, and depression symptoms. While disclosing depression could improve one's mental health, stigma can also make social interactions more challenging and reduce the likelihood of receiving treatment. This research explored young people's experiences with stigma and recommendations for addressing it. Semi-structured interviews conducted with N=35 young people aged 18-25 years (Mage =20.09) were analyzed with thematic analysis. Participants met the criteria for clinical depression using the Mood and Feelings Questionnaire (score >27) or had recently obtained a medical diagnosis (N=18) of depression by a medical professional. Participants faced stigma when deciding to disclose their depression, which fed into a vicious cycle influencing feelings of loneliness, social isolation, and withdrawal. Their recommendations for others to avoid this cycle can be summarized under three main themes: (1) Social affirmation: identify allies and build meaningful connections; (2) Self-affirmation: build a constructive relationship with the self; and (3) Societal affirmation: structural changes are needed. The current research indicates that social, self-, and societal affirmation are considered important for reducing the detrimental impacts of stigma. Policies and programs are needed that provide mental health support to young people, and public awareness campaigns that guide young people to appropriate resources (i.e., support and intervention) via governmental public health bodies.

A Comparative Analysis of Drinking Water Provision and Hepatitis A Incidence in Uzbekistan in 2010-2023.

Objective This study aims to analyze the relationship between access to safe drinking water and the incidence of hepatitis A in Uzbekistan from 2010 to 2023to inform public health strategies for disease prevention. Methods We utilized hepatitis A incidence data from the Sanitary and Epidemiological Well-Being and Public Health Authority and drinking water provision data from the Government Statistics Agency of Uzbekistan. A linear regression analysis was performed using R 4.3.2 to investigate the correlation between these variables. The study examined hepatitis A cases per 100,000 population and the percentage of households with access to safe drinking water. Results Hepatitis A incidence fluctuated significantly over the study period, with a notable spike to 162 cases per 100,000 population in 2023, despite relatively stable access to safe drinking water (ranging from 67.4% to 77% of households). The analysis revealed a complex relationship between water access and hepatitis A incidence. The linear regression coefficient was 3.89 (adjusted R-squared: 0.3021, P-value: 0.02), indicating that each growing percent of water supply is raising the incidence of hepatitis 3.89 cases of hepatitis infection. Conclusion The reverse effect of water supply percentage and the incidence of hepatitis A incidence in Uzbekistan suggests that other factors play significant roles in disease transmission. These may include sanitation practices, hygiene behaviors, and vaccination coverage. The findings emphasize the need for a multifaceted approach to hepatitis A prevention, incorporating improved water infrastructure, enhanced sanitation, public education, and comprehensive vaccination programs. Further research is needed to identify specific determinants of hepatitis A transmission in Uzbekistan to guide targeted interventions and public health policies.

The role of civil society organizations (CSOs) as community-based knowledge brokers: A qualitative study with CSOs during the first year of the COVID-19 pandemic in Canada

Background: This study explored how civil society organizations in British Columbia, Canada, obtained, shared, and communicated multilingual COVID-19 information with people whose first language is not English. Aim: The aim was to examine civil society organizations’ role as community-based knowledge brokers during the first year of the COVID-19 pandemic. Methods: Commencing in December 2022, virtual semi-structured interviews were conducted with employees from civil society organizations in British Columbia (N=15). Results: Civil society organizations played a crucial role in sharing multilingual information with people whose first language is not English. They amplified public health messages, addressed confusion concerning public health orders, and engaged with community members to better understand and address local needs. Discussion: Civil society organizations contributed to health communication efforts and succeeded in reaching populations overlooked by mainstream communication channels. Conclusions: The COVID-19 pandemic provides an opportunity to reflect on the role of civil society organizations as community-based knowledge brokers that acted as intermediaries to support information-sharing from government public health communications to priority populations. Based on this study’s findings, we propose several recommendations to enhance equity-based preparedness, responses, and re­covery for health emergencies.

Validation of an acute respiratory infection phenotyping algorithm to support robust computerised medical record-based respiratory sentinel surveillance, England, 2023.

IntroductionRespiratory sentinel surveillance systems leveraging computerised medical records (CMR) use phenotyping algorithms to identify cases of interest, such as acute respiratory infection (ARI). The Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) is the English primary care-based sentinel surveillance network.AimThis study describes and validates the RSC's new ARI phenotyping algorithm.MethodsWe developed the phenotyping algorithm using a framework aligned with international interoperability standards. We validated our algorithm by comparing ARI events identified during the 2022/23 influenza season in England through use of both old and new algorithms. We compared clinical codes commonly used for recording ARI.ResultsThe new algorithm identified an additional 860,039 cases and excluded 52,258, resulting in a net increase of 807,781 cases (33.84%) of ARI compared to the old algorithm, with totals of 3,194,224 cases versus 2,386,443 cases. Of the 860,039 newly identified cases, the majority (63.7%) were due to identification of symptom codes suggestive of an ARI diagnosis not detected by the old algorithm. The 52,258 cases incorrectly identified by the old algorithm were due to inadvertent identification of chronic, recurrent, non-infectious and other non-ARI disease.ConclusionWe developed a new ARI phenotyping algorithm that more accurately identifies cases of ARI from the CMR. This will benefit public health by providing more accurate surveillance reports to public health authorities. This new algorithm can serve as a blueprint for other CMR-based surveillance systems wishing to develop similar phenotyping algorithms.

Spatiotemporal Modeling of Aedes aegypti Risk: Enhancing Dengue Virus Control through Meteorological and Remote Sensing Data in French Guiana.

French Guiana lacks a dedicated model for developing an early warning system tailored to its entomological contexts. We employed a spatiotemporal modeling approach to predict the risk of Aedes aegypti larvae presence in local households in French Guiana. The model integrated field data on larvae, environmental data obtained from very high-spatial-resolution Pleiades imagery, and meteorological data collected from September 2011 to February 2013 in an urban area of French Guiana. The identified environmental and meteorological factors were used to generate dynamic maps with high spatial and temporal resolution. The study collected larval data from 261 different surveyed houses, with each house being surveyed between one and three times. Of the observations, 41% were positive for the presence of Aedes aegypti larvae. We modeled the Aedes larvae risk within a radius of 50 to 200 m around houses using six explanatory variables and extrapolated the findings to other urban municipalities during the 2020 dengue epidemic in French Guiana. This study highlights the potential of spatiotemporal modeling approaches to predict and monitor the evolution of vector-borne disease transmission risk, representing a major opportunity to monitor the evolution of vector risk and provide valuable information for public health authorities.

Applying up: How ethnographers powered public health changes in the United States during the COVID‐19 pandemic

Abstract“Studying up” was Laura Nader's provocation to anthropologists to scrutinize the actions of the powerful few in relation to the ordeals of the powerless many. Engaging this lineage, this article describes CommuniVax, a rapid ethnographic research coalition supporting an equitable COVID‐19 vaccine rollout in the United States. By tapping hyperlocal knowledge as well as that held by public health and governmental authorities at higher reaches, the coalition opened the possibility for a more just pandemic response and stronger democratic institutions. The project also broke with prevailing features in the studying up tradition: It operated as a multidisciplinary collective rather than a lone ethnographer, partnered with elites rather than a priori holding them at arm's length, and emphasized pragmatic solutions over scholastic critiques. These departures embody an “applying up” modality, supporting the idea ethnographers have plural strategies for studying actors who exert strong influence over others’ lives.

Linking Maternal and Child Health Data to Enhance Public Health Surveillance and Implement Sustainable Interventions.

Our initiative aims to enhance the public health informatics infrastructure for surveillance of maternal and child health (MCH) using data captured from electronic health records (EHRs), public health information systems, and administrative health data. Our work includes development, validation, and application of linkage algorithms across records for mothers and children; integration of data across myriad sources; design of routine surveillance reports; and design of longitudinal studies to examine determinants and outcomes in MCH populations. Our work is conducted in partnership with governmental public health agencies, health care providers, academic institutions, and community-based organizations. Future work will build on the enhanced informatics infrastructure to draw from additional public health data sources and/or expand surveillance efforts to include prioritized MCH outcomes. We will further translate knowledge gained from surveillance into action, working with our partners to improve and sustain better MCH equitably in our population.