Biostatistics in Public Health is a relatively new branch of Statistics that helps public health researchers, epidemiologists, and practitioners perform statistical analyses. Since the public health applications are often complex and multidimensional, the practical work associated with biostatistics in public health can be quite difficult. This guide aims to help laymen understand some of the topics that are highly important in the field.

Biostatistics is a branch of statistics that deals with the study of data and mathematical models for describing and analyzing biological, social and medical phenomena. It is an important part of public health practice because it enables us to make sense of complex data sets. This post explores all you need to know about Biostatistics In Public Health PPT, medical biostatistics ppt, limitations of biostatistics slideshare, application of biostatistics in public health and why do we need biostatistics in public health.

This course prepares recent graduate students to work in the field of public health. Students will receive an introduction to biostatistical methods, including basics of descriptive statistics, measures of center and spread, measures of association, analysis of variance techniques, linear regression analysis, and measures for comparing populations. The goal of this course is to develop quantitative skills that are applicable to public health practice.

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introduction to biostatistics pPT

what is biostatistics in public health

A biostatistician’s work is driven by questions relating to the health of people—as individuals or members of population. For example, how might the benefit of a treatment vary based on an individual’s characteristics, such as genotype or exposures? Every research question poses a unique challenge. Biostatisticians consider the nature of information available from sources such as genomic studies or large medical discharge records databases, and then how the information was collected and what populations it represents. They also must consider whether the question can be answered with currently available methods or if new analytical methods are required, for example, to account for missing data or a complex interaction among the genes being observed. In close consultation with subject experts such as cancer biologists and infectious disease specialists, as well as those responsible for collecting the data, biostatisticians develop a study designs, advise on study contact, and apply quantitative methods to analyze the resulting data. Not only will research results be reported to the scientific community, but also new methods or software may be developed for application to future studies.

Medical Biostatistics PPT

How is biostatistics used in public health?

Biostatistics can help identify the best way to deploy resources to treat populations. To control an epidemic, the goal is not only finding the best way to treat an infected person, but also to control spread in the population. In both infectious diseases and behavioral research, interventions provided to individuals may well impact others in the community. This requires research methods that permit investigation of the relationship between responses at individual patient and population levels.

Such research can now be done on a microbiological level. For example, it is possible to monitor not only the prevalence of HIV infection, but also to ascertain the strains of the virus that are infecting people and how they spread among various susceptible groups. Questions like this require new analytical tools because the data and processes involved are very complex.

The work by HSPH biostatisticians Marvin Zelen and Stephen Lagakos documenting a link between chemically tainted well water and cases of childhood leukemia in Woburn, Massachusetts in the 1970s was made famous by the book and movie A Civil Action. What are some other notable accomplishments by faculty members in the department?

Nan Laird, Harvey V. Fineberg Professor of Public Health and Professor of Biostatistics, and colleagues developed the EM algorithm, a technique that can be used to account for data that is missing by happenstance or by design. It uses the totality of the observed data in ways that take into account the fact that some data are missing. It is one of the most widely used advances in methods in the last 40 years and can be applied in a wide range of settings in biomedical research, as well as in genetics imaging reconstruction.

Professors Francesca Dominici and Brent Coull and Assistant Professor Corwin Zigler have developed new quantitative methods used in analyses that form the basis for air quality policy regulation for particulate matter and ozone. Their innovative methods account for the misaligned nature of the data (air pollution is measured at monitoring stations, whereas health data is often aggregated by zip codes) and also permit causal conclusions to be drawn from messy data. The impact of such work is very high. Since the estimated benefits of particulate matter reductions play such a central role in regulatory policy, these estimates must be based on the most rigorous possible science.

The Center for Biostatistics in AIDS Research (CBAR), headed by Professor Michael Hughes, developed study designs and statistical methods that proved vital in the development of highly effective strategies for the treatment of HIV infection and for the prevention of mother-to-child transmission of HIV. Contributions by CBAR statisticians were essential in helping change HIV infection from an almost certainly fatal condition to a manageable disease, leading to dramatic and permanent reductions in mortality associated with AIDS.

Biostatistics In Public Health PPT

We begin with Biostatistics In Public Health PPT, then medical biostatistics ppt, limitations of biostatistics slideshare, application of biostatistics in public health and why do we need biostatistics in public health.

Biostatistics is a branch of statistics that deals with the collection and analysis of biological data. Biostatistics can help identify the best way to deploy resources to treat populations. To control an epidemic, the goal is not only finding the best way to treat an infected person, but also to control spread in the population. In both infectious diseases and behavioral research, interventions provided to individuals may well impact others in the community. This requires research methods that permit investigation of the relationship between responses at individual patient and population levels.

Over the past 20 years, there has been a revolution in our ability to monitor infectious diseases. For example, it is now possible to monitor not only the prevalence of HIV infection but also to ascertain the strains of the virus that are infecting people and how they spread among various susceptible groups.

This kind of research can be done on a microbiological level. For example, it is possible to monitor not only the prevalence of HIV infection, but also to ascertain the strains of the virus that are infecting people and how they spread among various susceptible groups. Questions like this require new analytical tools because the data and processes involved are very complex.

medical biostatistics pPT

Next, we consider medical biostatistics ppt, limitations of biostatistics slideshare, application of biostatistics in public health and why do we need biostatistics in public health.

From the epitome of crunching numbers, statistical science has traveled a long distance. It is time that it is realized as a management science. This is especially true for medical biostatistics.

Conventionally, biostatistics deals with biological data including agriculture, veterinary science, and fisheries. However, most of us understand biostatistics as a science dealing with the data on life and health of human beings. When restricted to humans, it seems better to qualify this as “medical” biostatistics. This qualifier also makes it more medical than statistical with “medical” + “bio” component exceeding “statistics” component. Another rarely realized feature of this subject is its seminal role in managing data-based medical uncertainties. Accordingly, we propose to define medical biostatistics as the science of managing empirical uncertainties pertaining to human health. This definition provides an entirely new orientation to the subject, integrates it fully well with medical disciplines, and removes its alienation from medical professionals. It can also raise the bar and bring in a new mandate for this subject. This note describes the rationale for this proposal.

The common theme in these descriptions of the subject is the processing of biological data – their design, analysis, and interpretation. There is no mention of management or medical uncertainties anywhere. This conventional perception of biostatistics does not do justice to its functions and to the enormous contribution it makes to improve the quality of biological decisions. The perception can be changed by qualifying it by the term “medical” and restricting it to the issues having a direct impact on human health. With this change, the ultimate objective of medical biostatistics too would be to contribute to the efforts to improve people’s health like any other medical science. It already makes this contribution through the efficient management of data-based medical uncertainties, and this must reflect in the definition of the subject. The following details give the rationale of how and why medical biostatistics is the science of managing empirical medical uncertainties.

Medical Uncertainties

Medical uncertainties are well known, but they are easy to appreciate when their presence is realized at two levels. At the individual patient level, it is the potential fallibility of decisions regarding diagnosis, treatment, and prognosis of health conditions. At the group or community level, medical uncertainty comprises a lack of assurance regarding the role of primordial and proximal risk factors of various conditions of ill-health and regarding the exact effect of various promotive, preventive, and treatment interventions. In both these setups, a prominent component is the uncertainty regarding the present state and the future course with or without intervention.

Empirical uncertainties are data based. The handling of such uncertainties is easy when they are divided into aleatory and epistemic components.[1] These terms may sound new to medicine but are commonly used in seismic science and economics. Aleatory uncertainty arises from endogenous factors such as inherent biological variation, environmental factors, sociocultural and psychological factors, and random variation due to observers, instruments, and laboratories. Epistemic uncertainty arises from a lack of knowledge, conceptual errors, nonavailability of valid tools, and biases of various types. The sources of epistemic uncertainty are exogenous.

Management

Management essentially is a value addition process that tries to optimize the output by properly organizing the inputs. It involves elements such as goal setting; identifying quality and quantity of inputs such as men, machine, methods, material, and money in a production line, and their adequate and timely provision; minimizing risk opportunities and maximizing conducive environment for optimal functioning of the inputs; gauging performance; and taking rectifying and promoting steps – thus starting the cycle all over again.[2] Management is a flexible process and does not adhere to consistency and conformity. It is an art of accomplishing an assignment by translating complexity, specialization, and talents into performance.[2] In the following paragraphs, we examine the application of this management process to medical uncertainties and illustrate how medical biostatistics methods accomplish this.

In the case of management of empirical medical uncertainties, value addition is in terms of controlling these uncertainties so that their impact on decisions is minimal. The description and assessment of these uncertainties are an integral part of this process. Both these activities are done using medical biostatistics methods. The performance is assessed in terms of reaching valid and reliable results. This is the key output in this case also as is for management elsewhere.

Management of Medical Uncertainties

The basic inputs for the management of empirical medical uncertainties are the data. These are invariably inflicted with aleatory variations and epistemic bottlenecks and require expert handling. The study design is a tool that helps to organize these inputs. An immaculately executed perfect design would substantially minimize the risk of reaching an invalid or unreliable conclusion and maximize the power of the study for fixed inputs. Considerations such as the definition of the study units and the variables under consideration, sample size, method of selection, the role of confounders, potential sources of bias including reliability and validity of medical assessments, and the method of analysis of data, are the elements that provide definite help in enhancing the chance of reaching a valid and reliable result. With tools such as probability and its derivatives that include frequency distribution, sensitivity, specificity, relative risk, and odds ratio; estimation methods in terms of effect size, its confidence interval, and meta-analysis; the test of hypothesis for assessing the absence of medically significant effect; and trend analysis that sieves clear signals from noise; medical biostatistics serves the purpose of managing uncertainties quite admirably. Biostatistics models provide the road map to optimize the output in terms of improved results for given inputs. Consideration of various probabilities awards it flexibility instead of consistency and conformity and makes the process of management of uncertainties more efficient and realistic. Decision analysis that combines value judgments regarding the utility of various possible outcomes with the evidence-based risk assessments at the stage of diagnosis and treatment is also an important function of the methods of medical biostatistics. All these help to effectively manage data-based medical uncertainties.

Aleatory uncertainties are the basic ingredients of all statistical methods and can be adequately managed by these methods because these uncertainties arise from variations, and handling variation is the core of these methods. The same cannot be stated about epistemic uncertainties. Sensitivity analysis[3] can be effectively used to delineate the impact of some epistemic uncertainties, although not all. However, epistemic uncertainties can be rarely minimized because they belong to the unknown domain. There is no solution for some epistemic gaps except further research because most epistemic gaps are rooted in the lack of knowledge. When the underlying process of emergence and progression of a health condition is unclear, modeling can help understand this process in some cases, although that may have to be based on conjectures in this case. These models may or may not stand the test of the time, but they add to the knowledge base. No science is available that can adequately deal with the unknown except, to some extent, statistics that pools all the unknowns together under the “error term,” provides methods to examine them, and helps to draw a valid inference. Medical biostatistics does all this for human health. The following example illustrates the role of medical biostatistics.

limitations of biostatistics slideshare

Now we focus on limitations of biostatistics slideshare, application of biostatistics in public health and why do we need biostatistics in public health.

(1) Statistics laws are true on average. Statistics are aggregates of facts, so a single observation is not a statistic. Statistics deal with groups and aggregates only.

(2) Statistical methods are best applicable to quantitative data.

(3) Statistics cannot be applied to heterogeneous data.

(4) If sufficient care is not exercised in collecting, analyzing and interpreting the data, statistical results might be misleading.

(5) Only a person who has an expert knowledge of statistics can handle statistical data efficiently.

(6) Some errors are possible in statistical decisions. In particular, inferential statistics involves certain errors. We do not know whether an error has been committed or not.

application of biostatistics in public health

Biostatistics can be used to do a lot more than personally identify your music. The subject of biostatistics is the application of statistical methods to biological and medical data, which helps find information that could otherwise be difficult to measure. Common uses for biostatistics are in predicting demand for resources such as natural gas and blood, determining health care budgets at state or national levels, and helping public health officials establish strategies to control an epidemic.

It can also be used to explore and quantify associations between different traits, behaviors, or diseases among individuals and populations. Biostatistics is used for medical research as another tool to measure and identify the risk factors for diseases or disorders. Despite the demonstrated success of biostatistics in healthcare, it has rarely been used or perceived as a useful tool to combat this global health challenge. This is because biostatistics tools have not been defined based on optimal scientific criteria but instead by decision-making processes that may be plagued by biases, unintended consequences, and corruption where accuracy has taken a back seat to expediency.

why do we need biostatistics in public health

How Biostatistics In Public Health Inform the Guidance of Experts During Disease Outbreaks

In essence, the statistical method is used to explain and predict some of the health outcomes and the direction of epidemics and pandemics, and it definitely influences decision-makers in public health. Those who are working on, proposing and advising the mitigation strategies in response to contagions use biostatistics data and results to guide public health and other healthcare practitioners on how to go about controlling these diseases. Two good examples would be studies that were designed to determine the effectiveness of mask-wearing as a measure to prevent the spread of certain viruses, as well as clinical trials designed to test the effectiveness and efficacy of vaccines, including those developed for the prevention of SARS-CoV-2.

Data Collection and Interpretation

When we talk about biostatistics in public health or any other application, the most important thing to keep in mind is that they are good only if the study or the design for data collection is appropriate and carefully planned. Then, we can extrapolate results and further interpret them.

Most research studies are based on samples, which represent populations. Selecting that sample to be as representative as possible of the population and the condition that we intend to study is very important for accurate generalization of the results and for the design of appropriate interventions. On the other hand, epidemiologic measures, such as morbidity, mortality, or incidence and prevalence rates, are based on every case and are used to assess and inform us about the health status of a population. An example of a reliable and sustainable source of local data about health and its determinants is the County Health Rankings & Roadmaps program opens in new window, developed through a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute.

All research in public health and health sciences is and should be based on scientific methodology and planning. How do we plan and structure the data collection, data analysis, and interpretation of the findings, as well as the application of those findings? Biostats are truly valuable in emergencies, when connecting the reality of panic and uncertainty with the strength of the scientific method to provide solutions.

The National Healthcare Problem

The US public health and healthcare system, as we call it, is not functioning as one. A system consists of elements that are connected and working in synergy. The situation in this country at the beginning of the pandemic was not illustrative of that. It was so unfortunate that there was no effective connection between federal, state and local agencies, as that would have made this whole process of controlling the pandemic much smoother and produced a better outcome. A lot of responsibilities relative to public health were delegated to the states from the federal government without adequate support, creating a division between the national and state emergency responses, which became a huge problem in the initial weeks and months of this health crisis.

After the SARS pandemic, many of those in government had good intentions. A plan was developed in 2005, and the funding was provided to establish those links and that communication between the national, state and municipal governments; but sadly, the funding was eliminated without proper justification. When there’s no funding, usually there is no adequate provision of services, so the plan and strategies will not be implemented. There was and still is a disconnect, but the solution — as I see it, and as I teach my students in our Foundations and Issues of Public Health course — is to build those missing links and connect all levels of government, while establishing partnerships between public and private institutions, including civil society, non-profit and academic organizations.

One specific problem caused by this disconnect is that, at the beginning of the COVID-19 pandemic, private pharmaceutical companies started producing test kits, but they were not made available to governmental agencies to distribute to people. Developing strong public-private partnerships would solve this issue and make our system much more of a system for all people, which is something that government guarantees: care for all citizens.

A broader example is that we don’t often think about how much social and economic conditions, urban design and other “non-health” determinants are closely related to health outcomes, creating barriers for access to services in certain populations, especially those that are vulnerable and need them the most.

Biostatistical Lessons From the COVID-19 Pandemic

What We Can Learn From Surveillance, Data Collection and Vaccine Development

Biostatistics provide us with scientific, historical data and results; they give us direction for the future. If we examine certain diseases or trends in diseases, biostatistics should be guiding us on the right path. But the data have to be carefully collected and accurately interpreted to give biostatistics the power to support the symbiotic work of public health and healthcare professionals; otherwise, the biostats may be misleading, not useful and, in certain cases, even harmful.

To be effective, public health and healthcare systems need to work cooperatively, while cautiously recognizing that there may be some limitations when relying solely on surveillance data. The surveillance must accurately reflect the situation in a community in order to allow for appropriate comparisons between different communities, so that conclusions can be drawn and recommendations made. The reason for some lack of coordination in public health messaging at the national level last year could have been an uneven availability or utilization of testing in different localities, which may have caused inappropriate comparisons of data in those areas.

Another challenge that COVID-19 presented to scientists and practitioners was the wide variety of signs and symptoms that the SARS-CoV-2 virus causes in people. The timing of testing and onset of symptoms in patients is a very important factor in collecting accurate data to provide meaningful mitigation guidelines and care, which proved to be very challenging initially. As the pandemic progressed, this was better understood, and more accurate information was generated to guide public health guidelines and healthcare services.

Another positive note, which demonstrates the true value of biostatistics in this battle with COVID-19, is the success in developing new types of vaccines. This breakthrough is saving lives and providing us with facts about the effectiveness and efficacy of those vaccines in humans, while continuing to inform us about the progression of the virus in vaccinated vs. unvaccinated groups. It is encouraging information for public health and healthcare professionals and a source of comfort to the general public. It also gives us all a glimpse into potential future threats and our ability to quickly respond to them, both locally and globally.

Biostatistics is a branch of statistics used to predict an epidemic. Simply put, it helps identify the best way to deploy resources to treat populations. Let’s say a community has been infected by the Zika virus. In order to stop its spread, you’ll need to account for approximately how many individuals are already infected, as well as their average rate of infection (it’s not equal across different groups). If we assume that transmission only occurs through mosquito bites and that there are intact human-to-human chains of transmission, each infected individual has a constant risk of transmitting Zika. Regardless of when they were bitten or how long they have been ill, the risk of transmitting Zika is constant. We can model this by assuming that the number of new infections follows a Poisson distribution with parameter λ=μ/μ0, where μ0 is the average number of new infections per day (Mosquera-Machado et al. 2016). When faced with such a scenario and using this model, biostatistics helps us see how changing things like treatment amount (e.g., personnel for surveillance) will affect the spread of communicable disease in a population (more on this later).

public health biostatistics salary

According to the BLS, the average MPH in biostatistics salary totaled $84,440. Those who worked in the pharmaceutical industry earned more — $94,740. More recently, pay stood at entry-level biostatistics jobs probably pay a bit less than average since people employed in those positions are just starting out.