Access to public healthcare in Nairobi County is unequal among social classes. Lower social classes have worse healthcare than either the upper or the middle classes. These health inequalities are correlated with socio-economic inequalities. The higher socio-economic classes have better access to healthcare than the lower socio-economic classes. Higher incomes, education, employment and wealth result in better health of the households in the County. Unequal access to healthcare contributes to disparities in health status, increases costs for both the insured and the uninsured. Lack of access to healthcare reduces disposable incomes, particularly burdening the lower income households. These households cannot afford the care they need. This has forced them to forego such care altogether. The objectives of the study were three, namely: to evaluate the influence of demographic variables in access to public healthcare, to evaluate the influence of socio-cultural factors in access to public health care, and to evaluate the influence of institutional factors in access to public healthcare. The study used descriptive design, specifically, cross- sectional design for collection, measurements and analysis of data. The study took place in Nairobi County. The target population was households living in Nairobi County, where the sample was drawn from. The sampling techniques included multi-stage random sampling, random sampling, stratifies random sampling, cluster random sampling, convenient sampling and purposive sampling. The sample size was obtained using Chadha’s formula (2006) to arrive at 1066 sample size. Data collection instruments included observations, face- to- face interviews, questionnaires, in-depth interviews and focus group discussions. Qualitative data was analyzed thematically but quantitative data was analyzed using descriptive statistics. Data were analyzed using SPSS version 23. The results show that there were positive correlations between independent and dependent variables. The P-value was statistically significant. The results were not due to random chance and that P- 0.01< 0.05 and this confirms a positive relations ships between the variables. The relationships were mutually inclusive and highly correlated. On that basis, the null hypotheses were rejected and the alternate hypotheses accepted. The results show that demographic (disposing of), socio-cultural (need) factors influence access to healthcare. Socio-economic factors should be addressed to benefit all the households. Socio-cultural factors should be distributed fairly among the households. Health systems should be improved and adequately financed to provide the requisite resources to all the households.