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Application of Markov process modelling to health status switching behaviour of infants
1Department of Community Health, Ghana Medical School, Accra.
Insights
Infant health transitions from well to sick states can be modeled using Markov chains. After initial age-dependent changes, health status patterns become stable, reaching equilibrium around 12 months.
Area of Science:
- Pediatric Health Surveillance
- Biostatistics
- Epidemiological Modeling
Background:
- Investigates infant health status transitions using Markov process modeling.
- Utilizes monthly health records of 1152 children from birth to 18 months in Uganda.
Purpose of the Study:
- To apply Markov process modeling to infant health status switching behavior.
- To analyze month-to-month transitions between 'Well' and 'Sick' states.
Main Methods:
- Defined two health states: 'Well' (W) and 'Sick' (S).
- Employed a Markov model to analyze transitions (W-->W, W-->S, S-->W, S-->S) based on monthly health records.
- Calculated age-specific transition probabilities from birth to 18 months.
Main Results:
- Health state transition probabilities showed age-dependence in the first 5 months.
- From the sixth month onwards, transition probabilities stabilized, indicating a time-homogeneous Markov Chain.
- A steady-state distribution was achieved around 12 months of age.
Conclusions:
- Infant health transitions can be modeled by age-dependent Markov Chains that become time-homogeneous.
- The model demonstrates a stabilization of health status patterns after the initial months of life.
- Transition probabilities offer insights into disease prevalence in infants.
Background:
This study is an attempt to apply Markov process modelling to health status switching behaviour of infants. The data for the study consist of monthly records of diagnosed illnesses for 1152 children, each observed from the month of first contact with Kasangati Health Centre, Kampala, Uganda, until age 18 months.
Methods:
Only two states of health are considered in the study, a 'Health' state, denoted by W: (for Well), and an 'Illness' state denoted by S: (for Sick). The data are thus reduced to monthly records (W or S) of the states of health of the study sample. The simplest model of dependence of current health state on the past is one that links the current state to the immediately preceding month only; that is a Markov model. The starting point of this study was therefore to determine the proportions of children making the transitions W-->W, W-->S, S-->W, S-->S, from one month to the next, for each month from birth (month 0) to 18 months of age (month 18). These were used as estimates of the probabilities of making these transitions for each month from birth. This paper discusses the main features emerging from the study of these transition probabilities.
Results:
In the first 5 months after birth, the probabilities of making the transitions W-->W, W-->S, S-->W, S-->S from one month to the next, showed some dependence on the age of the child. From the sixth month on, however, the dependence on age seemed to wear off. The transition probabilities remained the same from then on, suggesting that the switching pattern between health states behaves, eventually, like a time-homogeneous Markov Chain. This time-homogeneous chain attained a steady state distribution at about 12 months from birth.
Conclusions:
The study has shown that the transitions between Health and Illness for infants, from month to month, can be modelled by a Markov Chain for which the (single-step) transition probabilities are generally time-dependent or age-dependent. After the first few months of life the dependence on age may wear off, as in this study, leading to a time-homogeneous Markov Chain, which eventually attains a steady state distribution in about 12 months. Interpretations of the transition probabilities as measures of disease prevalence are discussed.