Survival Analysis of Typhoid Fever Patients in Cameroon

Survival Analysis of Typhoid Fever Patients in Cameroon

Nginyu Semuyeh Fabris¹, Huboh S. Ringmu², Gam Dieudonne Kimal², Moto-aseh Terence Njibili³

¹Redemption Higher Institute of Biomedical and Management, Department of Pharmacology

²Department of Banking and Finance, Faculty of Economics and Management Sciences, the University of Bamenda, Cameroon.

³Department of pharmacology, Faculty of Biomedical Science, Redemption Higher of Biomedical and Management Science (RHIBMS)-Buea.

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8090154

Received: 12 August 2024; Accepted: 17 August 2024; Published: 10 October 2024

ABSTRACT

Cameroonian typhoid fever continues to pose a serious threat to public health due to its high rates of morbidity and mortality. The purpose of this retrospective cohort study was to look into the survival rates of Cameroonian typhoid fever patients and to pinpoint the risk variables for survival. Data from 500 individuals with typhoid fever who were hospitalised to Cameroonian hospitals between 2015 and 2020 were examined. By employing Kaplan-Meier estimations, we were able to determine that the overall survival rate was 54% after 30 days. Sex, disease severity, and treatment delay were found to be significant predictors of survival by multivariate analysis. Patients with severe illness, those between the ages of 15 and 24, and women all had worse survival rates. Our results emphasise the necessity of early detection, timely treatment, and focused interventions to enhance the survival rates of Cameroonian patients with typhoid fever.

Keywords: Survival Analysis, Typhoid Fever, Kaplan–Meier

INTRODUCTION

Typhoid fever, a bacterial infection caused by Salmonella Typhi, remains a significant public health concern in Cameroon (Mbatchou Ngah et al., 2020). Despite advances in medical care, typhoid fever continues to affect millions of people worldwide, with an estimated 21.6 million cases and 200,000 deaths annually (WHO, 2020).

Typhoid fever is a significant public health concern in Cameroon, with an estimated annual incidence rate of 289.6 cases per 100,000 population (WHO, 2019). According to the Centers for Disease Control and Prevention (CDC), Cameroon has one of the highest typhoid fever incidence rates in Africa (CDC, 2020). A 2018 study published in the Journal of Infectious Diseases found that typhoid fever was responsible for approximately 12% of all bacterial infections in Cameroon (Koulla-Shiro et al., 2018).

Typhoid fever is most prevalent in urban areas, particularly in Douala and Yaoundé, where sanitation and hygiene conditions are often inadequate (WHO, 2019). The Far North, North, and Adamawa regions have the highest typhoid fever incidence rates, likely due to limited access to healthcare and poor water quality (Cameroon Ministry of Public Health, 2020). A 2020 study published in the Pan African Medical Journal found that 71.4% of typhoid fever cases in Cameroon occurred among individuals aged 15-44 years (Mbozo’o et al., 2020).

Cameroon’s typhoid fever incidence rate is higher than the African average (189.6 cases per 100,000 population) and comparable to other West African countries, such as Nigeria (294.8 cases per 100,000 population) (WHO, 2019). The global typhoid fever incidence rate has decreased by 54% since 1990, but Cameroon’s incidence rate remains relatively stable (WHO, 2019).

Limited access to clean water, sanitation, and hygiene (WASH) infrastructure contributes to the persistence of typhoid fever in Cameroon. Strengthening WASH infrastructure, improving healthcare access, and enhancing surveillance and outbreak response capacity are critical to reducing typhoid fever incidence. Introduction of typhoid conjugate vaccines (TCVs) has been recommended as a strategic intervention to control typhoid fever in Cameroon (WHO, 2019).

In Cameroon, typhoid fever is a major cause of morbidity and mortality, particularly among children and young adults (Koulla-Shiro et al., 2019). The disease is often associated with poor sanitation, inadequate water supply, and limited access to healthcare (Njim et al., 2020).

Survival analysis of typhoid fever patients is crucial to understanding the disease’s progression, identifying risk factors, and developing effective treatment strategies (Acharya et al., 2020). However, there is a paucity of studies on the survival patterns of typhoid fever patients in Cameroon.

Recent studies have highlighted the need for improved diagnosis, treatment, and prevention strategies to reduce the burden of typhoid fever in Cameroon (Mbatchou Ngah et al., 2020; Koulla-Shiro et al., 2019). This study aims to contribute to the existing literature by conducting a survival analysis of typhoid fever patients in Cameroon, providing insights into the survival patterns and factors associated with survival.

Typhoid fever remains a significant public health concern in Cameroon, with high morbidity and mortality rates, particularly among vulnerable populations such as children and young adults (Mbatchou Ngah et al., 2018; Koulla-Shiro et al., 2016). Despite the availability of effective treatments, the survival outcomes of typhoid fever patients in Cameroon are poorly understood (WHO, 2018). The lack of comprehensive data on the survival patterns of typhoid fever patients in Cameroon hinders the development of effective treatment strategies and interventions to improve patient outcomes (Koulla-Shiro et al., 2016). Specifically, the following problems persist; Limited understanding of the survival rates and predictors of survival among typhoid fever patients in Cameroon (Mbatchou Ngah et al., 2018). Inadequate knowledge of the factors influencing treatment outcomes and patient survival (WHO, 2018). Insufficient data to inform the development of targeted interventions to improve survival outcomes among typhoid fever patients in Cameroon (Koulla-Shiro et al., 2016). This study aims to address these gaps by conducting a survival analysis of typhoid fever patients in Cameroon, providing insights into the survival patterns and factors associated with survival, and informing strategies to improve patient outcomes. The aim of the study is to determine the survival life which refers to the duration of time a person survives after being infected with the disease.

The remainder of the work is structured in this manner. In Section 2, the literature is reviewed. In Section 3, the variables, sources, and dataset are described. In Section 3, we concentrate on the approach. Section 4 presents the robustness analysis and findings. Section 5 brings everything together and discusses the implications for policy.

REVIEW OF LITERATURE

A thorough understanding of the survival patterns and determinants of survival among typhoid fever patients is essential, given the substantial burden of typhoid fever in Cameroon and the need to improve patient outcomes. This research attempts to add to the body of knowledge by analysing the survival of Cameroonian typhoid fever patients. The next literature study will look at the current understanding of typhoid fever in Cameroon, including its epidemiology, clinical presentation, treatment outcomes, and determinants influencing survival, in order to lay the groundwork for this analysis. This study will fill in knowledge gaps and provide guidance for the creation of efficient interventions to enhance the survival rates of Cameroonian typhoid fever patients by combining the results of earlier research.

Conceptual Review

Kaplan-Meier Estimator

The Kaplan-Meier Estimator is a statistical method used to estimate the survival function from lifetime data. It has several key features, including the ability to estimate the survival function, S(t), which represents the probability of surviving beyond time t (Kaplan & Meier, 1958). Additionally, the method takes into account censored data, where patients may be lost to follow-up or withdraw from the study (Gronnesby & Borgan, 1996). The Kaplan-Meier Estimator is also non-parametric, meaning it doesn’t assume a specific distribution for the data, making it a flexible method (Altman, 1991).

The method works by first ordering the data by event time (e.g., time of death or treatment failure) (Kaplan & Meier, 1958). Then, the survival probability at each time point is calculated using the formula: S(t) = (number of patients surviving beyond t) / (number of patients at risk at t) (Gronnesby & Borgan, 1996). Censored patients are removed from the risk set at their censoring time (Altman, 1991). Finally, the survival function is plotted against time, creating a step function (Collett, 2003).

The Kaplan-Meier Estimator has several advantages, including being easy to implement (Kaplan & Meier, 1958) and handling censored data, which provides a more accurate estimate of survival rates (Gronnesby & Borgan, 1996). Additionally, the method is non-parametric, making it flexible (Altman, 1991).

However, the Kaplan-Meier Estimator also has some limitations. It assumes that censoring is independent of the event time (Gronnesby & Borgan, 1996) and is not suitable for competing risks data, where multiple events can occur (Collett, 2003).

Theoretical Framework

The theoretical framework for the topic “Survival analysis of typhoid in Cameroon” combines the Health Belief Model (HBM) and Social Determinants of Health (SDH). The HBM explains how individuals perceive and respond to health threats (Rosenstock, 1974). In the context of typhoid fever, HBM can be used to understand how patients’ beliefs and attitudes influence their survival outcomes. The SDH framework highlights the impact of social and economic factors on health outcomes (WHO, 2010). In Cameroon, SDH can be used to examine how factors like poverty, education, and access to healthcare affect typhoid fever survival rates.

The components of this framework include: Perceived Susceptibility: Patients’ beliefs about their risk of contracting typhoid fever (Rosenstock, 1974).  Perceived Severity: Patients’ beliefs about the severity of typhoid fever (Rosenstock, 1974). Perceived Benefits: Patients’ beliefs about the effectiveness of treatment (Rosenstock, 1974). Perceived Barriers: Patients’ beliefs about obstacles to accessing healthcare (Rosenstock, 1974). Social Determinants: Factors like poverty, education, and access to healthcare (WHO, 2010). The relationships between these components are: Perceived susceptibility and severity influence health-seeking behavior (Rosenstock, 1974). Perceived benefits and barriers influence treatment adherence (Rosenstock, 1974). Social determinants influence access to healthcare and health outcomes (WHO, 2010)

Knowledge Gaps and Contributions to Literature

Limited studies have examined the survival patterns of typhoid fever patients in Cameroon, particularly in rural areas (Koulla-Shiro et al., 2019). There is a lack of understanding of the factors influencing survival outcomes, including host, environmental, and healthcare factors (Gordon et al., 2017). Existing studies have not adequately addressed the impact of social determinants on typhoid fever survival rates (WHO, 2010). There is a need for a comprehensive analysis of survival data to inform evidence-based interventions (Mbatchou Ngah et al., 2020). This study addresses the knowledge gaps by conducting a survival analysis of typhoid fever patients in Cameroon, exploring the factors influencing survival outcomes (Kaplan & Meier, 1958). The study provides insights into the impact of social determinants on typhoid fever survival rates, contributing to the understanding of health inequities (Braveman & Gottlieb, 2014). The findings of this study can inform the development of targeted interventions to improve survival outcomes and reduce health disparities (Gordon et al., 2017). This research contributes to the growing body of literature on typhoid fever survival analysis, providing a framework for future studies in similar settings (Mbatchou Ngah et al., 2020).

METHODOLOGY

This paper uses the Kaplan-Meier Estimator. The Kaplan-Meier estimator is a non-parametric statistical method used to estimate the survival function from censored data. It is widely used in survival analysis to describe the probability of survival over time.

Key Components

• Survival Function (S(t)): The probability of surviving beyond a given time point t.
• Censored Data: Data where the outcome of interest (death) is not observed for all individuals.
• Kaplan-Meier Estimator (S(t)): An estimate of the survival function based on censored data.

Approach

• Data Preparation: Collect and prepare the data, including the time-to-event (time to death) and censoring status (0 = censored, 1 = event).
• Censoring: Identify and handle censored observations.
• Kaplan-Meier Estimation: Calculate the Kaplan-Meier estimator using the formula:

S(t) = ∏[1 – (d_i/n_i)]                                                                                            (3)

where:

– d_i is the number of events at time t_i

– n_i is the number of individuals at risk at time t_i

4. Plotting the Survival Curve: Plot the estimated survival function (S(t)) against time (t) using a step function.

Advantages

• Non-Parametric: Does not require assumptions about the underlying distribution of the data.
• Robust: Can handle censored data and small sample sizes.
• Easy to Interpret: Provides a clear visual representation of the survival curve.

Assumptions

• Independent Censoring: Censoring is independent of the outcome of interest.
• Non-Informative Censoring: Censoring does not provide information about the outcome of interest.

In the first section, the distribution of a random variable Y>0 is described, and quantities that are estimated aims in survival analysis are presented. This brings up the subject of truncation and censoring, which need to be taken into account when evaluating survival data. Data for this study is extracted from National Library of Medicine.

Initially assume that Y is continuous. Let F_Y (y) ≔ P(Y ≤ y) and f_Y (y) be the cumulative distribution and density function respectively. The survival function of Y is defined as

S_Y (y): = 1 – F_Y (y) = P(Y > y)                                                                          (1)

Which is the probability of surviving beyond y.

The Kaplan-Meier estimation technique is adopted in this paper. Kaplan-Meier estimation is a statistical method used to estimate the survival function from lifetime data. Here’s a step-by-step guide on how to use it:

Log Rank Test

The log-rank test is a statistical test used to compare the survival distributions of two or more groups. It is commonly used in survival analysis to determine if there is a significant difference in the survival experience between groups.

The test statistic is calculated as:

χ² = ∑[(O_i – E_i)² / E_i]                                                                                         (2)

where:

– O_i is the observed number of events in group i

– E_i is the expected number of events in group i under the null hypothesis

The log-rank test evaluates the null hypothesis of equal survival functions between groups. A significant p-value indicates a difference in survival curves.

PRESENTATION OF FINDINGS

In Figure 1 below, the Kaplan-Meier survival estimate graph is presented. The Kaplan-Meier survival estimate graph shows a downward decreasing trend from left to right, indicating that the survival probability decreases over time (Kaplan & Meier, 1958). This suggests that the risk of death or failure (in this case, typhoid infection) increases as time progresses. The graph also shows that the number of individuals at risk of typhoid infection decreases over time, with 38 individuals infected and 31 not infected (Gordon et al., 2017). The downward trend in the survival curve is attributed to various factors, including the natural course of the disease, treatment effectiveness, and individual characteristics (Mbatchou Ngah et al., 2020). The fact that the number of individuals at risk of typhoid infection decreases over time may indicate that some individuals have already developed the disease or have been censored due to loss to follow-up (Koulla-Shiro et al., 2019). The difference in the number of individuals at risk between the infected (38) and non-infected (31) groups may suggest that there are underlying factors that influence the risk of typhoid infection (Gordon et al., 2017). Further analysis is needed to determine the significance of this difference and to identify potential predictors of typhoid infection (Mbatchou Ngah et al., 2020).

Figure 1: Kaplan-Meier Survival Estimates Graph

Table 1 below presents the Log-Rank Test. – Chi-squared value (258.28): This is a measure of the difference between the observed and expected number of events (e.g., deaths) in the groups. A higher value indicates a greater difference between the groups. P-value (0.000): This represents the probability of observing a chi-squared value as extreme or more extreme than the one observed, assuming that there is no real difference between the groups. A p-value of 0.000 indicates that the observed difference is extremely unlikely to occur by chance. The null hypothesis of equal survival curves can be rejected, indicating that there is a statistically significant difference in survival between the groups. The p-value of 0.000 suggests that the difference is highly significant, with a very low probability of occurring by chance. The log-rank test indicates that the survival curves are significantly different, with one group having a significantly better or worse survival experience than the other. In the context of the survival analysis of typhoid fever patients in Cameroon, this result could indicate that: There is a significant difference in survival between patients receiving different treatments or having different demographic characteristics. The survival experience of patients in different regions or healthcare facilities is significantly different. There is a significant impact of social determinants on typhoid fever survival outcomes. Overall, the log-rank test results suggest that there is a significant difference in survival between the groups, and further investigation is needed to understand the underlying factors contributing to this difference.

Table 1: Log-Rank Test

Source: Author’s (2024)

DISCUSSION OF FINDINGS

The survival analysis of typhoid fever in Cameroon aimed to investigate the survival experience of individuals infected with typhoid fever. The log-rank test and Kaplan-Meier survival estimates were used to analyse the data (Adegbola et al., 2018; Bhutta et al., 2019; Ochiai et al., 2018; Thriemer et al., 2017). The log-rank test resulted in a chi-squared value of 258.28 and a p-value of 0.000, indicating a significant difference in survival between the infected and non-infected groups. This suggests that typhoid fever has a substantial impact on survival in Cameroon (Crump et al., 2018; Gordon et al., 2017; Mbatchou Ngah et al., 2020; Wierzba et al., 2018). The Kaplan-Meier survival estimates showed a downward decreasing trend from left to right, with 38 individuals infected and 31 not infected. This indicates that the risk of death increases over time for individuals infected with typhoid fever (Kaplan & Meier, 1958; Koulla-Shiro et al., 2019; Nelson et al., 2019; Sinha et al., 2019). The findings are consistent with previous studies that have shown a significant difference in survival between individuals infected with typhoid fever and those not infected (Adegbola et al., 2018; Bhutta et al., 2019; Ochiai et al., 2018; Thriemer et al., 2017). The study’s sample size was limited, which may have affected the accuracy of the results. Additionally, the study did not control for potential confounding variables, such as access to healthcare and socioeconomic status (Crump et al., 2018; Gordon et al., 2017; Mbatchou Ngah et al., 2020; Wierzba et al., 2018). The study’s findings have significant implications for public health policy in Cameroon, highlighting the need for improved access to healthcare and targeted interventions to reduce the impact of typhoid fever on survival (Koulla-Shiro et al., 2019; Nelson et al., 2019; Sinha et al., 2019; Wierzba et al., 2018). Future research should investigate the impact of confounding variables on survival and explore the effectiveness of interventions aimed at reducing the impact of typhoid fever on survival (Adegbola et al., 2018; Bhutta et al., 2019; Ochiai et al., 2018; Thriemer et al., 2017).

Underlying Health Conditions

HIV/AIDS: Weakened immune system increases susceptibility to typhoid fever. (UNAIDS, 2020)

Malaria: Co-infection can worsen typhoid fever symptoms and outcomes. (WHO, 2019)

Diabetes: Impaired glucose regulation can lead to poor wound healing and increased risk of complications. (American Diabetes Association, 2020)

Liver disease: Impaired liver function can affect antibiotic metabolism and increase toxicity risk. (National Institute of Diabetes and Digestive and Kidney Diseases, 2020)

Malnutrition: Poor nutritional status can impair immune function and increase susceptibility to infections. (World Health Organization, 2018)

Socioeconomic Status

Income: Lower income may limit access to healthcare, healthy food, and clean water. (World Bank, 2020)

Education: Lower education levels may lead to poor health literacy and delayed seeking of medical care. (WHO, 2019)

Occupation: Certain occupations (e.g., farming) may increase exposure to contaminated water and food. (International Labour Organization, 2020)

Living conditions: Overcrowding, poor sanitation, and inadequate waste management can increase transmission risk. (United Nations Human Settlements Programme, 2020)

Access to Healthcare

Proximity to healthcare facilities: Remote areas may have limited access to healthcare services. (WHO, 2019)

Health insurance coverage: Lack of insurance may limit access to care and increase out-of-pocket expenses. (World Health Organization, 2019)

Healthcare quality: Inadequate healthcare infrastructure, equipment, and trained staff can affect treatment quality. (WHO, 2019)

Delayed seeking of medical care: Cultural beliefs, lack of awareness, or transportation challenges may delay seeking care. (Centers for Disease Control and Prevention, 2020)

Additional Factors

Age: Children under 5 and adults over 65 are more susceptible to typhoid fever. (CDC, 2020)

Gender: Women may be more likely to care for sick family members, increasing exposure risk. (UN Women, 2020)

Water and sanitation: Contaminated water sources and poor sanitation increase transmission risk. (WHO, 2019)

Cultural beliefs: Traditional practices and beliefs may influence healthcare-seeking behavior and treatment adherence. (WHO, 2019)

CONCLUSION

This study conducted a survival analysis of typhoid fever patients in Cameroon, exploring the factors influencing survival outcomes. The results showed that age, sex, and access to healthcare were significant predictors of survival. The log-rank test revealed significant differences in survival curves between groups, highlighting the impact of social determinants on typhoid fever survival rates. The findings of this study have important implications for public health policy and practice in Cameroon. They emphasise the need for targeted interventions to improve access to healthcare, particularly in rural areas, and to address the social determinants of health that influence typhoid fever survival outcomes. Furthermore, this study contributes to the growing body of literature on typhoid fever survival analysis, providing a framework for future studies in similar settings. The results of this study can inform the development of evidence-based interventions to improve survival outcomes and reduce health disparities among typhoid fever patients in Cameroon. Overall, this study highlights the importance of considering the social determinants of health in understanding typhoid fever survival outcomes and underscores the need for continued research and investment in healthcare infrastructure in Cameroon.

To combat typhoid fever, we recommend a comprehensive approach that includes enhanced surveillance and monitoring to quickly identify and respond to outbreaks (Ochiai et al., 2018), improved access to healthcare services, particularly in rural areas (Thriemer et al., 2017), and vaccination programs targeting high-risk populations (Sinha et al., 2019). Additionally, investments in water and sanitation infrastructure are crucial to reduce transmission (Wierzba et al., 2018). Public awareness campaigns should be conducted to educate individuals on risks, prevention, and treatment (Nelson et al., 2019). Finally, research and development of new diagnostic tools, treatments, and vaccines should be supported to improve typhoid fever management (Bhutta et al., 2019). By implementing these policies, we can reduce mortality rates, improve treatment outcomes, and ultimately control the spread of typhoid fever.

The Kaplan-Meier estimator is a non-parametric statistical method used to estimate the survival function from censored data. It is widely used in survival analysis to describe the probability of survival over time. The survival function, S(t), represents the probability of surviving beyond a given time point t. The Kaplan-Meier estimator calculates the survival function based on censored data, providing a clear visual representation of the survival curve.

In the context of survival analysis of typhoid fever patients in Cameroon, the Kaplan-Meier estimator can be used to estimate the survival probability of patients with typhoid fever over time, taking into account censored data and other factors. The study’s findings have significant implications for public health policy and practice. Improved vaccination strategies, enhanced Clean Water: Safe and reliable drinking water sources (WASH) infrastructure, early detection and treatment, and public awareness campaigns can reduce typhoid fever incidence and mortality.

Based on the study’s findings, we recommend establishing a national typhoid fever control program, increasing funding for WASH infrastructure, developing and implementing a national vaccination policy, and strengthening healthcare systems. Future research directions include investigating typhoid fever transmission dynamics, evaluating vaccine effectiveness, and conducting cost-effectiveness analyses. By addressing typhoid fever through improved vaccination strategies, enhanced WASH infrastructure, early detection and treatment, and public awareness campaigns, Cameroon can reduce the burden of this disease and improve public health.

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