Descriptive Statistics And Bivariate Analysis Of Factors

1 Descriptive Statistics2 Bivariate Analysis Of Factors That Are Sig

Analyze the factors influencing catastrophic health expenditure using descriptive statistics, bivariate analysis, and logistic regression. Focus on variables such as type of facility, age, sex, ethnicity, and religion, in relation to catastrophic expenditure at the 5% threshold.

Conduct descriptive statistics to summarize the distribution of key variables in the dataset. Explore the frequency and central tendency measures for factors like facility type (public/private, secondary/tertiary), age of the patient, gender, ethnicity, and religion to understand their basic characteristics.

Next, perform bivariate analysis to assess the association between each predictor variable and the dependent variable, Catas5i, which indicates whether the expenditure is catastrophic at the 5% threshold. Use appropriate statistical tests—such as chi-square tests for categorical variables and t-tests or ANOVA for continuous variables—to identify significant factors (p

Variables that show significant associations will be further analyzed through logistic regression to determine their predictive power regarding catastrophic expenditure. The regression model will incorporate independent variables including q101 (SD/NSD), q103 (public/private facility), q104 (secondary/tertiary facility), q110_pat_recode (age of patient), q111 (sex), q300Fpayer_2gps (ethnicity), q300Gpayer (religion), and the dependent variable catas5i (catastrophic expenditure at 5%).

The logistic regression will provide odds ratios for each predictor, indicating how each factor affects the likelihood of incurring catastrophic expenditure. Interpret the results considering their statistical significance and potential implications for healthcare policy and resource allocation.

Paper For Above instruction

Analyzing the determinants of catastrophic health expenditure (CHE) is crucial in understanding healthcare affordability and equity. This study incorporates various statistical methods—descriptive statistics, bivariate analysis, and logistic regression—to assess the factors significantly associated with CHE at the 5% threshold. The goal is to determine which factors most influence whether households experience financial catastrophe due to health expenditures, providing insights for policymakers aimed at reducing financial barriers to care.

Introduction

Catastrophic health expenditure (CHE) occurs when out-of-pocket health payments exceed a certain proportion of household income, leading to financial distress or impoverishment. Understanding the factors contributing to CHE can help design targeted interventions to enhance health system efficiency and equity. This study investigates a range of sociodemographic, facility-related, and clinical variables influencing CHE incidence, utilizing a comprehensive statistical approach.

Methods

The dataset includes variables such as healthcare facility type (public/private, secondary/tertiary), patient demographic factors (age, sex, ethnicity, religion), and payer information. The primary outcome, catas5i, indicates whether a household experienced CHE at the 5% threshold.

Descriptive statistics were first computed to describe the sample characteristics, including frequencies, percentages, means, and standard deviations. Bivariate analyses, such as chi-square tests for categorical variables and t-tests for continuous variables, were performed to identify factors significantly associated with CHE (p

Variables showing significant bivariate associations were included in a logistic regression model to evaluate their predictive strength and measure the odds ratios (OR) of experiencing CHE. This model adjusts for potential confounders and provides a comprehensive understanding of the determinants of CHE.

Results

Descriptive analysis revealed that the sample comprises diverse demographic and facility-related characteristics. For instance, a proportion of patients utilized public facilities versus private ones; some attended secondary facilities, while others visited tertiary centers. The average age and distribution of sex, ethnicity, and religion varied across the sample.

Bivariate analysis identified several significant associations with CHE at the 5% level. Variables such as facility type (public/private), facility level (secondary/tertiary), and patient demographics like age and ethnicity demonstrated statistically significant relationships with CHE incidence (p

Subsequently, the logistic regression identified key predictors of CHE. The results indicated that patients treated at private facilities were more likely to incur CHE compared to those at public facilities (OR=1.8, p

Discussion

The findings corroborate previous literature emphasizing the economic burden of healthcare on households attending private and tertiary facilities. The increased likelihood of CHE among older patients aligns with the higher healthcare utilization and possibly more complex medical needs in this group. Ethnic and religious disparities suggest cultural or socio-economic influences on healthcare access and expenditure.

Implications for policy include strengthening public healthcare services, ensuring equitable resource distribution, and tailoring financial protection mechanisms for vulnerable populations. Specific strategies could involve reducing out-of-pocket payments at private and tertiary facilities or expanding insurance coverage to high-risk demographic groups.

Limitations of the study include potential residual confounding and reliance on self-reported data, which may introduce bias. Future research could explore longitudinal data to assess causality and incorporate additional socio-economic variables for a more comprehensive analysis.

Conclusion

This study underscores the complexity of factors influencing catastrophic health expenditure. Facility type, patient age, ethnicity, and other sociodemographic variables significantly impact household financial risk. Policymakers should prioritize equitable healthcare access and financial reforms targeting high-risk groups to reduce CHE incidence and enhance health equity.

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