Scholars have long posited that intimate partner violence (IPV) - physical violence, sexual violence, verbal abuse, and controlling behaviors between intimate partners - is impacted by masculinity norms of aggression and dominance (for example, see Haraway and O'Neil 1999; Moore and Stuart 2005). Despite this topic's immense potential for prevention and treatment policies, quantitative IPV research tends to eschew empirically informed measures of masculinities and femininities in favor of a simplistically binary male-female variable, likely in part because of methodological concerns over how to adequately measure the complexities of gender. This is unfortunate given that the representative nature of large-scale, quantitatively-analyzed research is of great value to policymakers. The present dissertation provides a thorough background on the gender and IPV literatures with a particular focus on the history and benefits of quantifying gender. Additionally, the Bem Sex-Role Inventory, the most widely used measure of gendered psychological traits in the literature, is used to predict IPV in an analysis of 4,027 nationally-representative adolescents in a relationship, data drawn from the National Longitudinal Study of Adolescent Health. This represents the first study using a gender trait measure to predict IPV utilizing a nationally-representative sample. Findings from negative binomial regression and multinomial logistic regression analyses reveal that a lack of femininity - rather than simply the presence of masculinity - predicts an increased risk of IPV perpetration and victimization for both men and women. Methodological and policy implications as well as future directions are discussed.

Exposure to fine particles (PM2.5) resulting from solid fuel use for household energy needs – including cooking, heating, and lighting – is one of the leading causes of ill-health globally and is responsible for approximately 4 million premature deaths and 84 million lost disability-adjusted life years globally. The well-established links between cooking and ill-health are modulated by complex social, behavioral, technological, and environmental issues that pose unique challenges to efforts that seek to reduce this large health burden. Despite growing interest in the field – and numerous technical solutions that, in the laboratory at least, reduce emissions of harmful air pollutants from solid fuel combustion – there exists a need for refined tools, models, and techniques (1) for measuring environmental pollution in households using solid fuel, (2) for tracking adoption of interventions, and (3) for estimating the potential health benefits attributable to an intervention.

Part of the need for higher spatial and temporal resolution data on particular concentrations and dynamics is being met by low-cost sensing platforms that provide large amounts of time-resolved data on critical parameters of interest, including PM2.5 concentrations and time-of-use metrics for heat-generating appliances, like stoves. Use of these sensors can result in non-trivial challenges, including those related to data management and analysis, and field logistics, but also enables novel lines of inquiry and insight. Chapter 2 presents a long-term deployment of real-time PM2.5 sensors in rural, solid-fuel-using kitchens, specifically seeking to evaluate how well commonly measured 24 or 48-hour samples represent long-term means. While short-term measures were poor predictors of long-term means, the dataset enabled evaluation of numerous sampling strategies – including sampling once per week, month, or season – that had much lower errors and higher probabilities of estimating the true mean.

Chapters 3 and 4 describe the selection and deployment of 200 advanced cookstoves to pregnant women in rural Palwal District, Haryana, India. Chapter 3 focuses on selection and evaluation of an intervention stove in the community, including preliminary measurement of exposure to PM2.5 and CO. These data suggest one method of piloting interventions and exposure assessment methods prior to larger rollouts to ensure community acceptability and feasibility. Chapter 4 specifically addresses adoption and use of the intervention stove over a period of approximately one year through the deployment of data-logging thermometers on 200 traditional and intervention stoves. Intervention stove use declined steadily over time and stabilized after approximately 200 days, while use of the traditional stove remained constant, emphasizing the need for monitoring both traditional and intervention stoves and for monitoring for periods of time beyond just the initial deployment to truly understand use. Chapter 4 additionally investigated intervention stove failures and how well short measures of stove use predict long- term trends (similar to the analysis performed in Chapter 2).

Chapter 5 focuses on utilizing the best available knowledge of exposure-response relationships to estimate the potential health impacts of an intervention at the national level in a software package called HAPIT, the Household Air Pollution Intervention Tool. HAPIT combines background disease data from the 2010 Global Burden of Disease with demographic and socioeconomic data and relative risk estimates from the integrated exposure-response curves to estimate disability-adjusted life years (DALYs) and deaths that could be averted by an exposure- reducing household air pollution intervention. Chapter 5 outlines the methodologies powering HAPIT and contains two example scenarios – one in which open fires are replaced by well- operating chimney stoves, and a second where they are replaced by LPG -- informed by data from the RESPIRE trial and ongoing work in Guatemala.

Chapter 6 synthesizes work from the proceeding chapters and offers suggestions for future lines of inquiry.

India found itself on the brink of economic collapse in early 1991. An emergency loan secured from the International Monetary Fund helped stave off national financial ruin. Terms of the loan agreement required India to liberalize its economy, opening it up for the first time since independence in 1947, to foreign investment. While nationalists were dismayed at the time, there is little dispute that those reforms in the early 1990s spurred India’s rapid economic growth, turning it in to one of the world’s largest economies today. As a result, more Indians than ever before are enjoying middle class lives; with increased access to world class educational opportunities and health services.

Yet this growth has in many ways widened the gap between India’s rich and poor. On the other side of this lacuna are hundreds of millions of Indians forced to endure inhumane conditions. For example, nearly 520 million Indians defecate in the open every single day, leading to the spread of fecal contamination. Epidemiological studies have drawn inextricable causal links between open defecation and disease. One gram of feces can contain viral pathogens, bacterial pathogens, protozoan cysts, and helminth eggs, thereby leading to diarrheal disease, the second leading cause of deaths in children under the age of five globally. Fecal contamination can also cause tropical diseases such as trachoma and schistosomiasis. This morbidity has been linked to a reduction in early-life growth, an important marker and predictor of human capital.

Similarly, maternal and child health outcomes remain poor. India still accounted for 15% of maternal deaths (45,000) worldwide in 2015. And while India’s under-5 mortality rate now matches the global average (39 deaths per 1,000 live births), its infant mortality rate, 32 deaths per 1,000 live births, is nearly three times the global average. Additionally, the Global Nutrition Report from 2018 highlights the burden of wasting and stunting in children under the age of five in India. According to the report, nearly a third of the world’s 150 million stunted children live in India, and over half of the world’s 50.5 million wasted children live in India. Furthermore, both stunting and wasting are associated with increased mortality, especially for those children who suffer from both. Finally, the National Family Health Survey conducted by the Ministry of Health and Family Welfare found that in 2015-2016, 53% of all Indian women were anemic. Anemia, combined with poor antenatal care, is particularly problematic as it is associated with a higher likelihood of a poor birth outcomes.

Paper one is an exploratory paper that qualitatively examines why people in rural Bihar, a state in north India, do not own or use toilets. This paper is rooted in the field of social epidemiology, and as such, seeks to elucidate the possible social determinants of latrine ownership and use in rural India. This paper is guided by the research question: what are people’s lived experiences in trying to build and use latrines in rural Bihar? Overall, we found that not owning a toilet cannot be conflated with a preference for open defecation, the government’s mode of subsidy payment for latrine construction matters, and urban bias – either real or perceived – could be a barrier to improved sanitation outcomes. These findings will help generate new hypotheses that should be tested in future research.

Paper two builds on this work by using the the 69th round of the National Sample Survey in India to examine the association between one specific social determinant – the amount of dwelling space owned by households – and the likelihood of latrine ownership. This paper also examines the variation in household latrine ownership that is attributable to village and state context. Findings from this paper suggest that the amount of dwelling space owned by households is significantly associated with their likelihood of latrine ownership. Furthermore, a significant amount of variation in household latrine ownership is attributable to village and state context. Further research is required to elucidate how village and state context is associated with household latrine ownership.

Paper three focuses on Anganwadi Workers (AWWs) in India. AWWs are one group of community health workers in India who are responsible for ensuring improved maternal and child health outcomes throughout India. The Ministry of Women and Child Development in India has issued guidelines on the amount of time AWWs are expected to spend on key activities. Given that time spent on activities is an indicator of AWW performance, this paper examines the association between various AWW characteristics and whether or not they spend the expected amount of time on certain activities. Our findings suggest that AWW characteristics such as caste, years of experience, and having a helper are significantly associated with whether or not AWWs spend the required amount of time on home visits, feeding children, preschool education, and filling out their paper registers.

Background

Objectives

Discussion

Conclusions

Health effects attributed to outdoor fine particulate matter (PM_2.5) rank it among the risk factors with the highest health burdens in the world, annually accounting for over 3.2 million premature deaths and over 76 million lost disability-adjusted life years. Existing PM_2.5 monitoring infrastructure cannot, however, be used to resolve variations in ambient PM_2.5 concentrations with adequate spatial and temporal density, or with adequate coverage of human time-activity patterns, such that the needs of modern exposure science and control can be met. Small, inexpensive, and portable devices, relying on newly available off-the-shelf sensors, may facilitate the creation of PM_2.5 datasets with improved resolution and coverage, especially if many such devices can be deployed concurrently with low system cost.

Datasets generated with such technology could be used to overcome many important problems associated with exposure misclassification in air pollution epidemiology. Chapter 2 presents an epidemiological study of PM_2.5 that used data from ambient monitoring stations in the Los Angeles basin to observe a decrease of 6.1 g (95% CI: 3.5, 8.7) in population mean birthweight following in utero exposure to the Southern California wildfires of 2003, but was otherwise limited by the sparsity of the empirical basis for exposure assessment. Chapter 3 demonstrates technical potential for remedying PM_2.5 monitoring deficiencies, beginning with the generation of low-cost yet useful estimates of hourly and daily PM_2.5 concentrations at a regulatory monitoring site. The context (an urban neighborhood proximate to a major goods-movement corridor) and the method (an off-the-shelf sensor costing approximately USD $10, combined with other low-cost, open-source, readily available hardware) were selected to have special significance among researchers and practitioners affiliated with contemporary communities of practice in public health and citizen science. As operationalized by correlation with 1h data from a Federal Equivalent Method (FEM) β-attenuation data, prototype instruments performed as well as commercially available equipment costing considerably more, and as well as another reference instrument under similar conditions at the same timescale (R² = 0.6). Correlations were stronger when 24 h integrating times were used instead (R² = 0.72).

Chapter 4 replicates and extends the results of Chapter 3, showing that similar calibrations may be reasonably exchangeable between near-roadway and background monitoring sites. Chapter 4 also employs triplicate sensors to obtain data consistent with near-field (< 50 m) observations of plumes from a major highway (I-880). At 1 minute timescales, maximum PM_2.5 concentrations on the order of 100 μg m^-3 to 200 μg m^-3 were observed, commensurate with the magnitude of plumes from wildfires on longer timescales, as well as the magnitude of plumes that might be expected near other major highways on the same timescale. Finally, Chapter 4 quantifies variance among calibration parameters for a large sample of the sensors, as well as the error associated with the remote transfer of calibrations between two sufficiently large sets (± 10 % for n = 12). These findings suggest that datasets generated with similar sensors could also improve upstream scientific understandings of fluxes resulting from indoor and outdoor emissions, atmospheric transformations, and transport, and may also facilitate timely and empirical verification of interventions to reduce emissions and exposures, in many important contexts (e.g., the provision of improved cookstoves; congestion pricing; mitigation policies attached to infill development; etc.). They also demonstrate that calibrations against continuous reference monitoring equipment could be remotely transferred, within practical tolerances, to reasonably sized and adequately resourced participatory monitoring campaigns, with minimal risk of disruption to existing monitoring infrastructure (i.e., established monitoring sites). Given a collaborator with a short window of access to a reference monitoring site, this would overcome a nominally important barrier associated with non-gravimetric, in-situ calibration of continuous PM_2.5 monitors. Progressive and disruptive prospects linked to a proliferation of comparable sensing technologies based on commodity hardware are discussed in Chapter 5.

Worldwide, nearly 3 billion people—40% of the global population—burn wood, coal, and other solid fuels every day to cook their food; this number is even larger when including those who heat their homes with solid fuels as well. Exposure to pollution from heating and cooking fires causes about 3 million deaths each year, making it one of the biggest environmental health problems the world faces. The harm from this smoke is not restricted to those who breathe it, however: it contains gases and particles that contribute to global climate change as well.

Chapter 2 shows that household cooking with solid fuels caused an estimated 12% of population-weighted ambient PM2.5 worldwide in 2010. Exposure to this air pollution caused the loss of 370,000 lives and 9.9 million disability-adjusted life years (DALYs) globally in the same year.

In Chapter 3 I demonstrate that household heating with solid fuels caused an estimated 21% of population-weighted ambient PM2.5 in 2010 in Central Europe, 13% in Eastern Europe, 12% in Western Europe, and 8% in North America. Exposure to this air pollution results caused approximately 60,000 premature deaths in Europe, and nearly 10,000 deaths in North America, as well as an estimated 1.0 million disability-adjusted life years (DALYs) in Europe and 160,000 DALYs in North America.

Chapter 4 addresses drivers of household wood combustion pollution in the San Francisco Bay Area, where the sector is the largest source of PM2.5 and regulators recently introduced amendments to wood burning rules for the airshed. Fireplaces are the source of the vast majority (84%) of PM2.5 from residential wood combustion in the San Francisco Bay Area, despite their use primarily as an aesthetic or recreational combustion activity. By evaluating hypothetical fuel and combustion device changeouts, I find that replacing fireplaces with gas would yield significant health and economic benefits. Specifically, retrofitting frequently used fireplaces (300,000 units) to gas inserts in the Bay Area’s nine counties would reduce sector emissions by about 90%, avoiding approximately 140-310 premature deaths and 19,000 lost days of work each year, and creating upwards of $1 billion in annual financial benefits from improved public health.

Chapter 5 explains methodological overlaps and differences between the previous chapters. In Chapter 6, I explore the current regulatory and policy mechanisms specific to household heating with solid fuels, and relate these to the climate change implications associated with the sector. In Chapter 7, I highlight the relative dearth of data on household heating with biomass and its nuanced climate implications. This leads to a series of recommendations for future research, including collection of better household heating data in China and further work to understand how household combustion of biomass interfaces with both local air quality policy and climate change mitigation, outlining areas where this topic is currently visible in California.

Airborne particulate matter smaller than 2.5 µm in diameter (PM2.5) is among the biggest determinants of disease world-wide. In 2013, exposure to PM2.5 caused an estimated 7 million deaths and 189 million Disability-Adjusted Life Years (DALYs). About half of this calculated burden arises from the indoor residential use of solid fuels – like wood, coal, dung, and crop waste – for cooking, a practice common to about 2.8 billion people. The rest is attributable to ambient concentrations produced by combustion sources like power plants, heating utilities and appliances, and motor vehicles. Despite the ubiquity of the problem, many monitoring, research, and policy efforts employ proxies of total exposure like outdoor ambient concentrations. These proxies are inadequate for the quantification of actual exposures and can prove misleading when used to estimate health effects. More work is needed to progress the use of total exposure – which is often a complex function of a person’s interaction with numerous environments with varying PM2.5 concentrations – as a proper PM2.5 risk metric.

The first research chapter of this dissertation, Chapter 2, estimates changes in total PM2.5 exposure from indoor concentrations (including contributions from second hand smoke), outdoor concentrations, and time-activity in 2014 and in 2024 under alternative emissions policy pathways in one of the most polluted capital cities on Earth: Ulaanbaatar, Mongolia. Ulaanbaatar’s air pollution crisis is seasonal; heavy use of residential coal heating during its harsh and lengthy winters produces some of the worst air pollution in the world, but many cities are more polluted on an annual basis. With this in mind, seasonal exposure patterns are considered separately to produce estimates of annual exposures. These values are combined with projected background disease and population levels and some of the latest available exposure-response functions to project PM2.5-related health impacts. Policy pathways are estimated for business as usual; moderate reductions in heating, power plant, and motor vehicle emissions; and major reductions in the same sectors. The analysis estimates a 2014 population-wide annual average exposure of 59 µg/m3, which increases to 60 µg/m3 in 2024 under business as usual but falls to 32 µg/m3 and 12 µg/m3 under moderate and major emissions control policies, respectively. Annual PM2.5-related deaths and DALYs are estimated at about 1,400 and 40,000, respectively, in 2014. Under business as usual, about 18,000 deaths and 530,000 DALYs are accrued through 2024. Exposure reductions resulting from the moderate control policy pathway avert an estimated 110,000 DALYs and 4,000 deaths from the business as usual pathway between 2014-2024. An estimated 240,000 DALYs and 8,000 deaths are averted under major reduction policies. In all, Chapter 2 highlights the need for aggressive action, especially related to residential heating and tobacco smoking, to avert a growing pollution crisis in Ulaanbaatar.

Chapter 3 presents some of the first personal PM2.5 exposure measurements conducted in rural Lao women cooking primarily with wood. Measurements were taken during a stove intervention program in which traditional open fire and bucket stoves were ostensibly replaced with an ACE-1 fan stove. Average 48-hour concentrations before and after the intervention are reported at 123 µg/m3 and 81 µg/m3, respectively. Measurements of kitchen concentrations, ambient concentrations, and other environmental data are combined with an extensive set of survey responses to reliably model mean 48-hour average PM2.5 exposures before and after the intervention using machine learning, ensemble, and cross-validation techniques (for the full model: r2 = 0.26, predicted mean before intervention = 120 µmg/m3, predicted mean after intervention = 88 µg/m3).

Chapter 4 proposes the use of a household appliance, the smart smoke detector, as a tool for cost-effectively monitoring indoor PM2.5 concentrations, which are often overlooked by regulatory monitoring networks and health effects research. A particularly popular smart smoke detector, the Nest Protect, is reverse engineered. Its onboard optical sensor is co-opted and characterized for the real-time measurement of PM2.5 mass concentrations. Very good agreement is observed between processed Nest Protect signal and output from a co-located research grade monitor, the DustTrak II (r2 > 0.99).

The final chapter, Chapter 5, reiterates the thread common among Chapters 3-5 – advancing PM2.5 risk science through better estimation of total exposures – and discusses key areas for future research.

Household energy transitions in low and middle-income countries can play an important and often immediate role in improving human welfare in various dimensions. Residential use of kerosene, particularly as a lighting fuel in inefficient lamps, is one such energy use with the potential for significant improvements and benefits. I present measurements and analyses investigating characteristics of kerosene use, pollutant emissions from its use for household lighting and how distal drivers affecting price and access to electricity influence household consumption. Towards informing household air pollution assessment methodologies, a comparison of markers of complex pollutant mixtures is performed in the context of residential sources of combustion in Nepal.

Laboratory and field measurement showed that 7-9% of kerosene consumed by widely used simple wick lamps is converted to carbonaceous particulate matter that is nearly pure black carbon. Combined with estimates of bottom-up fuel consumption from survey-based estimates of user prevalence, these high emission factors increase previous black carbon emission estimates from kerosene by 20-fold, to 270 Gg/year (90% uncertainty bounds: 110, 590 Gg/year). Applying consumption and lighting device stock estimates from a recent UN assessment approximately doubles the central emissions estimate. Estimated global BC emissions from kerosene lighting sources are approximately one eighth of residential biomass BC emissions and one sixth of those from diesel. As a source, the net effect of pollutant emissions from kerosene lamps on climate would be positive (warming) given the relatively small cooling effect of co-emitted pollutants.

Indoor area measurements during operation of cooking and lighting appliances indicated that trends in polycyclic aromatic hydrocarbons and elemental carbon did not always reflect trends in standard markers of pollution, suggesting that their use as supplemental markers could provide added information. The most pronounced difference was observed from area concentrations during use of the sawdust stove, where total PAH concentrations were measured to be three times greater than from the second highest PAH source, the traditional biomass-burning chulo stove, but sawdust stove PM2.5 concentrations were three times lower than from the chulo. Kerosene stove measurements showed average retene concentrations to be ten times higher than from the highest biomass source, adding to existing skepticism over the use of retene as a source marker of softwood combustion.

From an analysis of residential kerosene use in India, lighting was found to account for over 60% of residential kerosene consumption. The electrified household population constituted an approximately equal share of kerosene demand as the non-electrified household population. Impacts resulting from kerosene lighting activities were also substantial, providing a potential opportunity to improve population welfare while also alleviating economic burdens associated with government subsidies. Reductions in ambient primary PM2.5 resulting from the abatement of kerosene as a lighting source were estimated to avert between 0.27-1.6 million years of life lost in 2030. Early PM2.5 control costs to replace kerosene with pico-solar lighting devices were estimated to yield a net savings of $3.5 billion in 2030, principally due to reductions in kerosene purchases. Kerosene demand for lighting was found to be highly price sensitive, so that in a scenario in which current subsidies are phased out by 2030, kerosene demand drops by 97% compared to the Baseline. The economic inefficiency implied results in an estimated deadweight loss from the kerosene subsidy of $950 million in 2005, with over three quarters attributed to its use as a secondary lighting source. The most effective measures for reducing kerosene burden would be those that address use as a secondary lighting fuel.