Estimating spatially disaggregated probability of severe COVID-19 and the impact of handwashing interventions: The case of Zimbabwe.

INTRODUCTION: The severity of COVID-19 disease varies substantially between individuals, with some infections being asymptomatic while others are fatal. Several risk factors have been identified that affect the progression of SARS-CoV-2 to severe COVID-19. They include age, smoking and presence of underlying comorbidities such as respiratory illness, HIV, anemia and obesity. Given that respiratory illness is one such comorbidity and is affected by hand hygiene, it is plausible that improving access to handwashing could lower the risk of severe COVID-19 among a population. In this paper, we estimate the potential impact of improved access to handwashing on the risk of respiratory illness and its knock-on impact on the risk of developing severe COVID-19 disease across Zimbabwe. METHODS: Spatial generalized additive models were applied to cluster level data from the 2015 Demographic and Health Survey. These models were used to generate continuous (1km resolution) estimates of risk factors for severe COVID-19, including prevalence of major comorbidities (respiratory illness, HIV without viral load suppression, anemia and obesity) and prevalence of smoking, which were aggregated to district level alongside estimates of the proportion of the population under 50 from Worldpop data. The risk of severe COVID-19 was then calculated for each district using published estimates of the relationship between comorbidities, smoking and age (under 50) and severe COVID-19. Two scenarios were then simulated to see how changing access to handwashing facilities could have knock on implications for the prevalence of severe COVID-19 in the population. RESULTS: This modeling conducted in this study shows that (1) current risk of severe disease is heterogeneous across the country, due to differences in individual characteristics and household conditions and (2) that if the quantifiable estimates on the importance of handwashing for transmission are sound, then improvements in handwashing access could lead to reductions in the risk of severe COVID-19 of up to 16% from the estimated current levels across all districts. CONCLUSIONS: Taken alongside the likely impact on transmission of SARS-CoV-2 itself, as well as countless other pathogens, this result adds further support for the expansion of access to handwashing across the country. It also highlights the spatial differences in risk of severe COVID-19, and thus the opportunity for better planning to focus limited resources in high-risk areas in order to potentially reduce the number of severe cases.

A stitch in time: The importance of water and sanitation services (WSS) infrastructure maintenance for cholera risk. A geospatial analysis in Harare, Zimbabwe.

Understanding the factors associated with cholera outbreaks is an integral part of designing better approaches to mitigate their impact. Using a rich set of georeferenced case data from the cholera epidemic that occurred in Harare from September 2018 to January 2019, we apply spatio-temporal modelling to better understand how the outbreak unfolded and the factors associated with higher risk of being a reported case. Using Call Detail Records (CDR) to estimate weekly population movement of the community throughout the city, results suggest that broader human movement (not limited to infected agents) helps to explain some of the spatio-temporal patterns of cases observed. In addition, results highlight a number of socio-demographic risk factors and suggest that there is a relationship between cholera risk and water infrastructure. The analysis shows that populations living close to the sewer network, with high access to piped water are associated with at higher risk. One possible explanation for this observation is that sewer bursts led to the contamination of the piped water network. This could have turned access to piped water, usually assumed to be associated with reduced cholera risk, into a risk factor itself. Such events highlight the importance of maintenance in the provision of SDG improved water and sanitation infrastructure.

Fall injuries in Sub-Saharan Africa: analysis of prospective injury registry from 23 health facilities in Malawi and Tanzania.

BACKGROUND: Low-and middle-income countries account for over 80% of fall-related fatalities globally. However there is little emphasis on the issue and limited high quality data to understand the burden, and to inform preventive and management strategies. We characterise the burden of fall injuries in Malawi and Tanzania. METHODS: This multi-centre prospective descriptive study utilized trauma registry data from 10 hospitals in Malawi and 13 hospitals in Tanzania. The study included twelve months of data in Tanzania (October 2019 to September 2020), and eighteen months of data from Malawi (September 2018 to March 2020). We describe patient demographics, the causes, location, and nature of injuries, timing of arrival to hospital, and final disposition. Regression analyses were performed to determine risk factors for serious injuries. RESULTS: There were 93,178 trauma patients in the registries of both countries, of which 44,609 (47.9%) had fall related complaints. Fall injuries accounted for 55.3% and 17.4% of all trauma cases in Malawi and Tanzania respectively. Overall the median age was 16 years (Interquartile range (IQR) 8-31 years), and 62.8% were male. Most fall injuries (69.9%) occurred at home, were unintentional (98.1%), and were due to a ground level fall (74.9%). Nearly half of patients (47.9%) arrived at a facility using public transport, with median arrival time of 10 h (IQR 8-13 h) from initial injury. Extremities (87.0%) were the most commonly injured region, followed by head and neck (4.4%). Overall 3275 (7.4%) patients had potentially serious injuries. Age > 60 years was associated with two times odds of having serious injuries than those < 5 years, and those sustaining injury at work (adjusted Odds Ratio (aOR) 1.95 95% CI; 1.56-2.43) or recreational areas (aOR 3.47 95% CI; 2.93-4.10) had higher odds of serious injuries compared to those injured at home. CONCLUSIONS: In these facilities in Sub-Saharan Africa, fall injuries accounted for a substantial fraction of all injuries. While most common in younger males, those aged 5-13 and over 60 years were more likely to have serious injuries. Most falls occurred at home, but serious injuries were more likely to occur at recreational and work areas. Future efforts should focus on preventive strategies to mitigate these injuries.

Epidemiology of adult trauma injuries in Malawi: results from a multisite trauma registry.

BACKGROUND: Large-scale multisite trauma registries with broad geographic coverage in low-income countries are rare. This lack of systematic trauma data impedes effective policy responses. METHODS: All patients presenting with trauma at 10 hospitals in Malawi from September 2018 to March 2020 were enrolled in a prospective registry. Using data from 49,241 cases, we analyze prevalence, causes, and distribution of trauma in adult patients, and timeliness of transport to health facilities and treatment. RESULTS: Falls were the most common mechanism of injury overall, but road traffic crashes (RTCs) were the most common mechanism of serious injury, accounting for (48%) of trauma admissions. This pattern was consistent across all central and district hospitals, with only one hospital recording < 40% of admissions due to RTCs. 49% of RTC-linked trauma patients were not in motorized vehicles at the time of the crash. 84% of passengers in cars/trucks/buses and 48% of drivers of cars/trucks/buses from RTCs did not wear seatbelts, and 52% of motorcycle riders (driver and passenger) did not wear helmets. For all serious trauma cases (defined as requiring hospital admission), median time to hospital arrival was 5 h 20 min (IQR 1 h 20 min, 24 h). For serious trauma cases that presented on the same day that trauma occurred, median time to hospital arrival was 2 h (IQR 1 h, 11 h). Significant predictors of hospital admission include being involved in an RTC, age > 55, Glasgow Coma Score < 12, and presentation at hospital on a weekend. CONCLUSIONS: RTCs make up almost half of hospitalized trauma cases in this setting, are equally common in referral and district hospitals, and are an important predictor of injury severity. Pedestrians and cyclists are just as affected as those in vehicles. Many of those injured in vehicles do not take adequate safety precautions. Most trauma patients, including those with serious injuries, do not receive prompt medical attention. Greater attention to safety for both motorized and especially non-motorized road users, and more timely, higher quality emergency medical services, are important policy priorities for Malawi and other developing countries with high burdens of RTC trauma.

Pediatric trauma burden in Tanzania: analysis of prospective registry data from thirteen health facilities.

BACKGROUND: Trauma is among the leading causes of morbidity and mortality among pediatric and adolescent populations worldwide, with over ninety percent of childhood injuries occurring in low-income and middle-income countries. Lack of region-specific data on pediatric injuries is among the major challenges limiting the ability of health systems to implement interventions to prevent injuries and improve outcomes. We aim to characterize the burden of pediatric health injuries, initial healthcare interventions and outcomes seen in thirteen diverse healthcare facilities in Tanzania. METHODS: This was a prospective cohort study of children aged up to 18 years presenting to emergency units (EUs) of thirteen multi-level health facilities in Tanzania from 1st October 2019 to 30th September 2020. We describe injury patterns, mechanisms and early interventions performed at the emergency units of these health facilities. RESULTS: Among 18,553 trauma patients seen in all thirteen-health facilities, 4368 (23.5%) were children, of whom 2894 (66.7%) were male. The overall median age was 8 years (Interquartile range 4-12 years). Fall 1592 (36.5%) and road traffic crash (RTC) 840 (19.2%) were the top mechanisms of injury. Most patients 3748 (85.8%) arrived at EU directly from the injury site, using motorized (two or three) wheeled vehicles 2401 (55%). At EU, 651 (14.9%) were triaged as an emergency category. Multiple superficial injuries (14.4%), fracture of forearm (11.7%) and open wounds (11.1%) were the top EU diagnoses, while 223 (5.2%) had intracranial injuries. Children aged 0-4 years had the highest proportion (16.3%) of burn injuries. Being referred and being triaged as an emergency category were associated with high likelihood of serious injuries with adjusted odds ratio (AOR) 4.18 (95%CI 3.07-5.68) and 2.11 (95%CI 1.75-2.56), respectively. 1095 (25.1%) of patients were admitted to inpatient care, 14 (0.3%) taken to operation theatre, and 25 (0.6%) died in the EU. CONCLUSIONS: In these multilevel health facilities in Tanzania, pediatric injuries accounted for nearly one-quarter of all injuries. Over half of injuries occurred at home. Fall from height was the leading mechanism of injury, followed by RTC. Most patients sustained fractures of extremities. Future studies of pediatric injuries should focus on evaluating various preventive strategies that can be instituted at home to reduce the incidence and associated impact of such injuries.

Burden of Road Traffic Injuries in Tanzania: One-Year Prospective Study of Consecutive Patients in 13 Multilevel Health Facilities.

BACKGROUND: Road traffic injuries (RTIs) pose a severe public health crisis in Sub-Saharan Africa (SSA) and specifically in Tanzania, where the mortality due to RTIs is nearly double the global rate. There is a paucity of RTI data in Tanzania to inform evidence-based interventions to reduce the incidence and improve care outcomes. A trauma registry was implemented at 13 health facilities of diverse administrative levels in Tanzania. In this study, we characterize the burden of RTIs seen at these health facilities. METHODS: This was a one-year prospective descriptive study utilizing trauma registry data from 13 multilevel health facilities in Tanzania from 1 October 2019 to 30 September 2020. We provide descriptive statistics on patient demographics; location; share of injury; nature, type, and circumstances of RTI; injury severity; disposition; and outcomes. RESULTS: Among 18,553 trauma patients seen in 13 health facilities, 7,416 (40%) had RTIs. The overall median age was 28 years (IQR 22-38 years), and 79.3% were male. Most road traffic crashes (RTC) occurred in urban settings (68.7%), involving motorcycles (68.3%). Motorcyclists (32.9%) were the most affected road users; only 37% of motorcyclists wore helmets at the time of the crash. The majority (88.2%) of patients arrived directly from the site, and 49.0% used motorized (two- or three-) wheelers to travel to the health facility. Patients were more likely to be admitted to the ward, taken to operating theatre, died at emergency unit (EU), or referred versus being discharged if they had intracranial injuries (27.8% vs. 3.7%; p < 0.0001), fracture of the lower leg (18.9% vs. 6.4%; p < 0.0001), or femur fracture (12.9% vs. 0.4%; p < 0.0001). Overall, 36.1% of patients were admitted, 10.6% transferred to other facilities, and mortality was 2%. CONCLUSIONS: RTCs are the main cause of trauma in this setting, affecting mostly working-age males. These RTCs result in severe injuries requiring hospital admission or referral for almost half of the victims. Motorcyclists are the most affected group, in alignment with prior studies. These findings demonstrate the burden of RTCs as a public health concern in Tanzania and the need for targeted interventions with a focus on motorcyclists.

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012-2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network (<1%) where 50% of the crashes identified occurred. Even with limitations in the representativeness of the data, the results can provide urban planners with useful information that can be used to target road safety improvements where resources are limited. The work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments.

Managing the spread of disease with mobile phone data.

While human mobility has important benefits for economic growth, it can generate negative externalities. This paper studies the effect of mobility on the spread of disease in a low-incidence setting when people do not internalize their risks to others. Using malaria as a case study and 15 billion mobile phone records across nine million SIM cards, this paper quantifies the relationship between travel and the spread of disease. The estimates indicate that an infected traveler contributes to 1.66 additional cases reported in the health facility at the traveler's destination. This paper develops a simulation-based policy tool that uses mobile phone data to inform strategic targeting of travelers based on their origins and destinations. The simulations suggest that targeting informed by mobile phone data could reduce the caseload by 50 percent more than current strategies that rely only on previous incidence.

Implementation of a multi-center digital trauma registry: Experience in district and central hospitals in Malawi.

BACKGROUND: Trauma is a rapidly growing component of the burden of disease in developing countries; yet systematic data collection about trauma in such contexts is relatively rare. METHODS: This paper describes the implementation of a trauma registry in 10 government-run hospitals in Malawi, with a focus on implementation logistics, stakeholder engagement strategies, and data quality procedures. RESULTS: 51 337 trauma cases were recorded over the first 14 months of registry operations. The number of cases per month, data accuracy, and the geographic coverage of the registry improved over time as data quality measures were implemented. CONCLUSIONS: Multi-center digital trauma registries are feasible in low-resource settings. Stakeholder engagement, periodic in-person and frequent digital follow up with data teams, and regular channeling of findings back to data collection teams help to improve data quality and completeness over a 14 month period. Financial and staffing constraints remain challenges for sustainability over time, but this experience demonstrates the feasibility of large-scale registry operations.

Predicting Dynamic Patterns of Short-Term Movement.

Short-term human mobility has important health consequences, but measuring short-term movement using survey data is difficult and costly, and use of mobile phone data to study short-term movement is only possible in locations that can access the data. Combining several accessible data sources, Senegal is used as a case study to predict short-term movement within the country. The focus is on two main drivers of movement-economic and social-which explain almost 70 percent of the variation in short-term movement. Comparing real and predicted short-term movement to measure the impact of population movement on the spread of malaria in Senegal, the predictions generated by the model provide estimates for the effect that are not significantly different from the estimates using the real data. Given that the data used in this paper are often accessible in other country settings, this paper demonstrates how predictive modeling can be used by policy makers to estimate short-term mobility.

Effects of employer-sponsored health insurance costs on Social Security taxable wages.

The increasing cost of employer contributions for employee health insurance reduces the share of compensation subject to the Social Security payroll tax. Rising insurance contributions can also have a more subtle effect on the Social Security tax base because they influence the distribution of money wages above and below the taxable maximum amount. This article uses the Medical Expenditure Panel Survey to analyze trends in employer health insurance contributions and the distribution of those costs up and down the wage distribution. Our analysis shows that employer health insurance contributions increased faster than overall compensation during 1996-2008, but such contributions grew only slightly faster among workers earning less than the taxable maximum than they did among those earning more. Because employer health insurance contributions represent a much higher percentage of compensation below the taxable maximum, health insurance cost trends exerted a disproportionate downward pressure on money wages below the taxable maximum.