Annotated Bibliography
Alex Coombs
Word Count: 2455
1) Dotse-Gborgbortsi, W.; Dwomoh, D.; Alegana, V.; Hill, A.; Tatem, AJ.; Wright, J.: 2020. The influence of distance and quality on utilization of birthing services at health facilities in Eastern Region, Ghana. BMJ Global Health, 2024.4: article e002020. 10.1136/bmjgh-2019-002020
The maternal mortality rate in sub-Saharan Africa is high in comparison to the rest of the world, at 546 deaths per 100,000 live births, and this number has declined at half the rate of the rest of the world. The key factor relating to this problem has shown itself to be skilled birth attendance during childbirth. Good health facilities and health care are core components of social freedom, so mitigating maternal mortality rate is in the interest of increasing real freedoms, and therefore development. This study aimed to use routinely collected childbirth data from HMIS (health management information services) for hospitals in Ghana to determine the effect of distance and quality of health care facilities on birthing services. To do this, the researchers used HMIS data derived from the DHIMS2(Ghana District Health Information Systems 2) to collect data on individual patients. DHIMS2 data recorded mothers’ places of residence along with other information such as occupation, birth outcome, and health insurance. Birthing quality was determined by whether a skilled birth attendant was present at the time of birth, and since less than 1% of hospitals did not have a skilled attendant, hospitals were used as a proxy for skilled birth attendance. Spatial distributions of potential demand for obstetric care were determined by a gridded map(100x100m) of estimated pregnancy in 2015. Mothers’ places of residence were overlaid onto this map, and straight lines from the mothers’ places of residence were used to judge the distance to the nearest health center. 3 maps were developed: 2 displaying expected movement and one displaying observed movement. Results showed that women traveled an average of 5.73 km to give birth, but women living in rural areas traveled significantly farther than those in urban areas at 7.53km. Also, most women bypassed their nearest health facility, although at a lower rate for hospitals. On average, the quality of healthcare for the observed destination and bypassed destinations were similar and were thought to be bypassed for reasons relating to reputation or familiarity. Overall, this analysis suggests that there is a decrease in quality facility usage of 6.7% per kilometer, which is much lower than other regions such as Zambia, which is 36%. More importantly, this study showed the importance of keeping accurate and consistent health records and HMIS’s ability to be used to assist development in the domain of public health. I chose this source since it describes a facet of healthcare in developing nations, which is one of the aspects of development I am planning to consider for my topic. More specifically, I am interested in the idea of efficiently spacing hospitals and other medical centers, which would inherently lead to better and lower-cost healthcare if the medical professionals in those hospitals are well trained. Since this is one of the ideas I may focus on, the method of creating maps using spatial distributions of mothers’ places of residence seems like a useful model for identifying optimal locations for a hospital.
2) Flowminder, “COVID-19 | Ghana: Report #1: Initial insights into the effect of mobility restrictions in Ghana, using anonymized and aggregated mobile phone data”, April 03, 2020https://statsghana.gov.gh/COVID-19%20press%20release%20report%20-%20analysis%20overview%20-%20final1.pdf.
This source analyzes the use of aggregated and anonymous data from MNO’s (Mobile Network Operators), which can be used to understand mobility patterns of populations to improve planning and decision making during the COVID-19 pandemic. The researchers in this study used the MNO data to track the mobility of Ghana’s population between and within the Greater Accra and Ashanti regions, using average active mobile phone subscribers in a region as a proxy for the number of people in it. COVID-19 is a prevalent public health concern, and this article aims to judge the effectiveness of Ghana’s lockdown. As a result, this article it is contributing to the development of Ghana by showing the efficacy in MNO data, reducing the unfreedoms associated with disease by increasing the efficiency of social freedoms such as good public health. The article describes the mobility pattern of Ghana’s population over time from February 17th – March 31st, a period of 6 weeks. The first 4 weeks were used as a baseline for population mobility, representing the time before the lockdown was announced. 3 points in the final 2 weeks were analyzed, occurring on March 16th, the date when initial restrictions were put into place, March 27th, when the lockdown was announced, and March 30th, when the lockdown was put into action. Data from the inter-district analysis, which measured the population changes in the Ayaso West district of the Accra region, showed that there was a small change in the overall number of phone subscribers after restrictions were announced, but a much larger decrease in subscribers after the lockdown was put into action. This trend was similar but slightly different than Awutu Senya East district, which had a peak the days following the announcement of the lockdown, even though average subscribers in the region dropped similarly to the Ayaso West District. This was likely due to the different characteristics of each region: such as the number of people who travel there to work or socialize. Overall, the inter-district data suggests that the lockdown was successful in reducing travel between districts in Ghana since average subscribers dropped significantly after the lockdown started. Data was also gathered on travel in between the Accra and Ashanti regions, which showed no significant decrease in travel after the lockdown was announced, but a significant decrease in inter-regional travel after the lockdown was put in place. This also reinforced the notion that the lockdown succeeded in mitigating travel to only essential trips. Further analysis aims to establish the degree at which the proportion of resident to non-resident subscribers influenced this decrease in travel. Healthcare is one of the development topics I am interested in looking at, and Covid-19 is an excellent model to understand how the modern world reacts to an epidemic. The reaction of Ghana to the pandemic shows the effectiveness of good legislation for limiting the spread of disease, which is a core principle of good public health. This study also presents a method of monitoring movement and the potential spread of disease, which is imperative for both creating and evaluating legislature meant to mitigate a disease’s impact.
3) Lai, S.; Farnham, A.; Ruktanonchai, N.W.; Tatem, A.J.: 2019. Measuring mobility, disease connectivity and individual risk: A review of using phone data and mHealth for travel medicine. Journal of Travel Medicine, 2019.26(3): article taz019. DOI: 10.1093/jtm/taz19
The world has become more interconnected than ever before since the mobility of human populations has increased. However, the spread of diseases had also increased in conjunction with our increased mobility, prompting a global health concern. Accurate data on people’s movement can help mitigate the spread of disease since locational data can help experts simulate the progression of epidemics and identify high-risk areas. The use of mobile phones is providing us with more accurate real-time data than ever before due to the high penetration of mobile phone users around the world, including lower-income regions such as sub-Saharan Africa. This article aims to highlight some of the advantages and collection methods of mobile data, specifically CDR data, localized GPS data from social media and web browsers, and mHealth applications. Developing models using this mobile data would contribute to the world’s development by decreasing the unfreedoms associated with disease and increasing social freedoms such as efficient healthcare. CDR data is routinely collected by phone operators and contains subscriber identification, date and time of communications, and the location of the cell tower used to make a call or send a message. Since mobile phone penetration is high, anonymized CDR data can simulate the movement of a population by identifying an individual’s location by what cell tower they use. Models that have used CDR data, such as ones used to try and eliminate malaria in sub-Saharan Africa, have been successful in identifying transmission routes and local foci. The quickly updated nature of CDR data helps make efficient responses easier and planning more effective. Even though this data is widespread, its measurements can only be as precise as the distance between cell towers. Using GPS data from social media, such as twitter geotags and location data from web browsers, is more accurate then CDR data. However, since smartphone penetration is not nearly as high in low-income regions as developed ones, GPS data cannot be used as widely as CDR data. mHealth (mobile health) applications also present an unprecedented opportunity to improve travel health. They can passively track GPS data, and record responses from daily health questionnaires from its users to generate accurate and reliable data on both health and location. This would create better data than previous methodologies since it eliminates recall bias. Overall, mobile data and mHealth applications would be effective in improving global public health by increasing the efficiency of responses from officials due to their accuracy, breadth, and low cost. This study is useful since it provides 2 different and useful methods for tracking the movement of a population, which adds more nuance into data collection. Population tracking is imperative to understanding the spread of a virus, and how to mitigate it, so these methods are incredibly useful in the domain of public health. Also, the use of mHealth apps presents an interesting way to increase access and speed of healthcare to many people.
4) Sorichetta, A.; Nghiem, S.V.; Masetti, M.; Linard, C.; Richter, A. Transformative Urban Changes of Beijing in the Decade of the 2000s. Remote Sens. 2020, 12, 652.
Climate change has accelerated dramatically in the modern era as our energy needs have increased. This is in no small part due to urbanization and industrialization. The country that has had the largest urbanization effort in the last century is China, and it is only continuing its efforts to build up its infrastructure. As a result of its urbanization and use of non-renewable energy sources, China is the largest contributor of greenhouse gases in the world. This study used data science methods, specifically analyzing data from QSCAT(Quick SCAT satellites), to determine the urbanization of Beijing from 2000-2010. Urbanization was compared to the density of NO2 in the atmosphere to look for a link between the two. Since climate change is a matter of public and environmental health, limiting it is falls within the elimination of unfreedoms that Amartya Sen specifies. Now more than ever, mitigating the effects of climate change paramount to the development of the entire world. To understand the extent of Beijing’s development the researchers used radar backscatter data of Beijing to analyze the amount of urbanization that occurred in that region. The radar backscatter data was able to achieve this since the backscatter reflected off tall buildings and more modern industrial materials such as metal and glass is more intense that reflected of materials such as wood. To create the models of Beijing’s urban development over time, radar backscatter data collected from QSCAT satellites and modified using a DSM (Density sampling mean) method to format it into a grid. The DSM method was used to overcome the weakness of QSCAT data, which is its low spatial resolution. To achieve this, the DSM method took the average radar backscatter intensity of a particular area over a year and created an average intensity measure for it. After generating the images showcasing the intensity of radar backscatter in Beijing, the researchers isolated the urban regions from the rural and natural areas by comparing their diagrams with images of nightlight emitted from Beijing. The results showed that the Urbanization of Beijing quadrupled over the decade they studied. Using a regression model, the study showed that the increase in tropospheric NO2 density had a correlation coefficient of 0.9063 with Beijing’s urbanization efforts, suggesting that Beijing’s urbanization and climate negative climate impacts are strongly correlated. This study demonstrates that this method can be used to model the development and environmental impact of that urbanization, which can be applied to monitor and make judgments based on our effects on the environment. This study aims to show the relation of urbanization to climate change, but the part that interests me the most is the specific method of visualizing the infrastructure development. This pertains to my interest in improving medical infrastructure since it can possibly be used in conjunction with other methods such as Bayesian modeling to help estimate population densities. Also, knowing rates of development in urban areas is important for deciding the location of medical infrastructure when optimizing its immediate and future effectiveness.
5) Douglas R. Leasure, Warren C. Jochem, Eric M. Weber, Vincent Seaman, Andrew J. Tatem 2020. Nation population mapping from sparse survey data: A hierarchal Bayesian modeling framework to account for uncertainty Proceedings of the National Academy of Sciences Sep 202, 201913050 DOI: 10.1073/pnas.1913050117
Accurate and available census data is integral for understanding the size of densities of a country’s population, which is used to inform administrative bodies what legislation or other actions are beneficial for a country. Also, the portion of a population that would benefit the most from good census data are those most at risk for disease and poverty, but accurate census data is lacking in many developing countries. Good public services fall under the domain of eliminating unfreedoms associated with disease and poverty, among other aspects of development, meaning that improving population data is relevant to development. Methods such as satellite imagery and micro censuses have helped in the past but are not nearly as effective as true population census data. Bayesian modeling frameworks allow for the estimation of a more complete data set from sparse data points. In the case of this study, a Bayesian modeling framework was used to estimate the population and population densities of Nigeria using micro censuses. To achieve this, the researchers in this study used a Bayesian model implementing a hierarchal framework that aided it in predicting population densities across settled areas and administrative units. Furthermore, they used covariates such as high-resolution satellite data to identify settled areas and unsettled areas. Using the data collected from the Bayesian modeling framework, the researchers generated population data containing the overall population and population density for 100m x 100m squares of settled areas. The predicted population estimates for these areas, where micro census data was not collected, was fairly accurate, and overall population density across regions was similar to the national average. There were some exceptions, however, such as how the rural Ebonyi state and urban Kano state had a larger population densities than expected. Also, the model overestimated the population of smaller spatial areas since there were many large clusters of small populations in the model. Other limitations of this method were that it did not account for uncertainty in any of the micro censuses. Also, its most recent data was from 2014, so the population estimates from the micro surveys were most likely underestimated. Finally, the model assumes that no one lives in unsettled areas, which is likely false. This method of estimation has shown itself to be effective but is still less accurate and contains more uncertainty than a population census, meaning it cannot serve as a replacement for true census data. Also, this model itself does rely on census data to some degree since micro censuses were used as individual data points. Overall, this source provides an incredibly useful insight into the capabilities of Bayesian modeling. As stated above, accurate population data is imperative for effective management of public health and effective healthcare, which is probably the aspect of human development I will be focusing on.