MétaCan
Menu
Back to cohort
Record W7126299417

Infrastructure and occupational factors associated with infectious diseases in Massachusetts, USA

2024· other· en· W7126299417 on OpenAlexaboutno aff
Bethany Marino Haley

Bibliographic record

VenueOpenBU (Boston University) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsInfectious disease (medical specialty)DiseaseCombined sewerSnowmeltWaterborne diseasesPublic healthPopulation
DOInot available

Abstract

fetched live from OpenAlex

Patterns of infectious disease in human populations result from complex interactions between the infectious agent, the disease host, and the environment in which host and agent interact. Many environmental factors influence infectious disease dynamics including characteristics of the natural and built environments and social systems. This dissertation focuses on key environmental factors associated with two timely infectious diseases in the United States (US): acute gastrointestinal illness (AGI) following overflow discharge from combined sewer systems (CSS) in Massachusetts (MA), and patterns of COVID-19 outcomes in relation to the spatial distribution of essential workers in MA. CSS exist in over 700 municipalities in the US as well as cities in Canada, Europe, Asia, and Australasia. When heavy rainfall or snowmelt occurs, CSS discharge untreated or undertreated wastewater into nearby waterways in combined sewer overflow (CSO) events. The association between CSO events and health has been understudied, and critical gaps in knowledge remain, including which exposure pathway(s) may be most relevant in communities downstream of CSO releases, whether CSO events and precipitation are independently associated with AGI, and which subpopulations may be at greatest risk of developing AGI following CSO events. The rapid spread and severity of COVID-19 disease led to widespread stay-at-home orders in the US beginning in March of 2020. In MA, only a designated set of essential businesses remained open during the stay-at-home orders (March through May 2020). During this period, essential workers who performed their duties in person were at higher risk of contracting COVID-19 than people who worked from home, but among essential workers, risk varied by industry- and worksite-specific factors. While differential risk of COVID-19 mortality and outbreaks are documented among essential workers, risk of COVID-19 infection among essential workers has been difficult to ascertain because occupation is frequently missing from state and federal COVID-19 surveillance efforts. The overall objective of this dissertation is to assess the associations between these two infectious diseases in MA and key environmental factors that influence how pathogens and susceptible hosts interact. The relationship between CSO events and AGI was investigated in a geographic region of MA where sewage discharges impact a river that is both a drinking water source and recreational destination for hundreds of thousands of people. Statewide patterns of COVID-19 cases and deaths were evaluated in relation to relative representation of categories of essential workers in MA census tracts. This dissertation relies on health outcome data from administrative and surveillance datasets, all analyses employ ecologic study designs with individual-level data aggregated to small areas, and methods incorporating geospatial and temporal data are included in each study. In Chapter 2, the association between extreme CSO events and AGI was assessed in MA municipalities bordering the CSO-impaired Merrimack River with a secondary analysis evaluating differences in the associations between CSO and AGI based on municipal drinking water source. In the 4-days following 95th percentile upstream CSO discharge events, the cumulative risk ratio (CRR) of AGI increased by 17% and CRR increased by 62% after 99th percentile CSO events. Stratification by drinking water source suggests that the association between CSO events and AGI is most pronounced among municipalities that do not have river-sourced drinking water, but there is elevated risk of AGI among all municipalities regardless of drinking water source following the largest CSO events. These findings suggest that CSO discharge volume is a critical factor in the association between CSO events and AGI, and that exposure to CSO discharge may occur through multiple pathways. Chapter 3 extends the work of Chapter 2 to an evaluation of the strength of the association between CSO events and AGI across subpopulations defined by age, sex, healthcare payer type, area-level social vulnerability, and drinking water source. The CRR of AGI was most pronounced among young people ages 5–19 and people living in areas of low social vulnerability relative to cumulative risk for the population as a whole. The results of this study suggest that the association between CSO events and AGI differs among subpopulations characterized by both physiological and social characteristics. In Chapter 4, categories of essential workers were defined from the broad set of essential occupations defined in the MA emergency response to COVID-19. The association between census-tract-resolution populations of essential worker categories and COVID-19 cases and deaths was evaluated, adjusting for multiple sociodemographic risk factors for COVID-19. Elevated COVID-19 case incidence was observed among census tracts with the highest populations of workers in construction, building maintenance, transportation, production, and public-facing sales and service occupations. Reduced case incidence was observed in tracts with the highest populations of essential workers able to work from home. These findings suggest that occupational composition of census tracts in MA may have influenced community-level COVID-19 transmission, possibly through spread from essential workers to those in their households and communities. Overall, the findings of this dissertation provide insight into the environmental factors associated AGI and COVID-19 in MA. In both cases, consideration of environmental factors provides opportunities to inform public health intervention measures for infectious diseases that are 1) influenced by climate change, and 2) symptomatic of globalization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.615

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.216
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOpenBU (Boston University)French-language works237,207