Data:
The study utilizes data from Longitudinal ageing study of India (LASI), first wave: 2017-18, conducted by the collaboration of International Institute for Population Sciences (IIPS), Harvard T.H. Chan School of Public Health (HSPH), and University of Southern California (USC), and several other national and international institutions. [12]. The survey has been funded by the Ministry of Health and Family Welfare (MoHFW), the Government of India, the National Institute on Aging (NIA), and the United Nations Population Fund, India (UNFPA). The survey included the older adults (men and women) age 45 years and above across all the states (exclude Sikkim) and union territories in India. The LASI wave-I covers comprehensive aspects of chronic health conditions, functional and mental health, healthcare utilization, family and social networks, work and employment and life expectations.
The LASI has utilized a multistage stratified area probability sampling design to reach out a representative sample. In addition, the three stage sampling design used for rural areas while four-stage sampling design used for urban areas. Further, the first stage engaged to selection of primary sampling units (PSUs) i.e. Tehsils and Talukas. The second stage considered the selection of villages in rural areas and wards in urban areas. In the third stage, household were selected from pre-selected villages in rural areas where census enumeration blocks (CEBs) were selected in urban areas. In additional and final stage of sampling in urban areas, the process of selection of household was made through selected CEBs.
The LASI featured with 72, 250 individuals, including 31, 434 age 60 years and above and 6,749 individuals age 75 years and above. However, this study taken up 60 years and above population.
The Double Burden Of Communicable And Non-communicable Diseases
Double burden of disease refers to the situation where an individual suffers from both non-communicable and infectious diseases. A study classified the burden of diseases in three broad clusters: communicable diseases, non-communicable diseases, and injuries [7]. Our study examines the responses of communicable and non-communicable diseases only. Following diseases were included as communicable disease: Jaundice/ Hepatitis, Tuberculosis (TB), Malaria, Diarrhoea/gastroenteritis, Typhoid, Urinary Tract Infection, Chikungunya and Dengue. Within non-communicable diseases, following conditions were included: Hypertension or high blood pressure, diabetes or high blood sugar, Cancer or a malignant tumour, Chronic lung diseases such as asthma, chronic obstructive pulmonary disease/Chronic bronchitis or other chronic lung problems, Chronic heart diseases such as Coronary heart disease (heart attack or Myocardial Infarction), congestive heart failure, or other chronic heart problems, Stroke, Arthritis or rheumatism, Osteoporosis or other bone/joint diseases, Any neurological, or psychiatric problems such as depression, Alzheimer’s/Dementia, unipolar/bipolar disorders, convulsions, Parkinson’s, etc. and High cholesterol. All communicable and non-communicable diseases are diagnosed by the health professional.
Study Variables
Response variable
The response variables for this study are communicable diseases and non-communicable diseases. Communicable diseases are diagnosed by health professionals and asked as “In the past 2 years, have you had any of the following diseases?” and responses have been recorded in ‘yes’ and ‘no.’ Similarly, non-communicable diseases are also diagnosed by health professionals and asked in the form of ‘yes’ and ‘no.’
Predictors
The predictors for this study are considered as sex (male and female); age (60–69 and 70 years and above); marital status (currently married, never married, Divorced/Separated/Deserted/Widowhood), education (No education, below primary, primary, secondary, and higher); living arrangements (living alone, with spouse and with others); place of residence (rural and urban); currently working (yes and no); wealth index (poorest, poorer, middle, richer and richest); self-rated health (poor and good; physical activity (yes and no); tobacco use (no and yes); alcohol use (yes and no); ADL disability (severe, moderate and no disability), and IADL disability (severe, moderate and no disability). Furthermore, ADL and IADL disability constructed from five (bathing, dressing, mobility, feeding, and toileting) and seven (preparing a hot meal (cooking and serving), shopping for groceries, making telephone calls, taking medications, doing work around the house or garden, managing money, such as paying bills and keeping track of expenses and getting around or finding an address in an unfamiliar place) activities. Both the ADL and IADL disability was categorized into the three categories as “severe,” “moderate,” and “no disability” based on the scale given in previous studies [13, 14].
Statistical Measures
The analyses were carried out with statistical software STATA version 16th. The bi-variate technique was used to understand the prevalence of communicable diseases and non-communicable diseases by socio-demographic and health parameters and across the states in India. Further, binary logistic regression was used to predict the association between communicable and non-communicable diseases and socio-demographic and health parameters. The equation for binary logistic regression is given below,
$$\text{log}\left(\frac{{p}_{i}}{1-{p}_{i}}\right)=logit\left({p}_{i}\right)={\beta }_{0}+{\beta }_{1}{x}_{1}+{\beta }_{2}{x}_{2}+\dots +{\beta }_{n}{x}_{n}$$
In the above regression equation, \({p}_{i}\) is the probability of being perceived as communicable or non-communicable diseases, \({x}_{1}\), \({x}_{2}\)…\({x}_{2}\) are the predictors, \({\beta }_{0}\) is the intercept and \({\beta }_{1}\), \({\beta }_{2}\)…\({\beta }_{n}\) are the coefficients.
Furthermore, to understand the inequalities of communicable and non-communicable diseases in urban and rural areas, the Fairlie decomposition technique was used to predict the contribution toward rural-urban inequalities in CDs and NCDs. The Fairlie technique was first initiated by Fairlie in 1999 which used to estimate from a logit or probit model. The equation for Fairlie decomposition can be written as,
$${\text{Y}̄}^{U}-{\stackrel{̄}{\text{Y}}}^{R}=\left[\sum _{i=1}^{{N}^{U}}\frac{F\left({X}_{i}^{U}{\widehat{\beta }}^{U}\right)}{{N}^{U}}-\sum _{i=1}^{{N}^{R}}\frac{F({X}_{i}^{R}{\widehat{\beta }}^{R}}{{N}^{R}}\right]+\left[\sum _{i=1}^{{N}^{R}}\frac{F\left({X}_{i}^{R}{\widehat{\beta }}^{U}\right)}{{N}^{R}}-\sum _{i=1}^{{N}^{R}}\frac{F({X}_{i}^{R}{\widehat{\beta }}^{R}}{{N}^{R}}\right]$$
Where NU and NR is the sample size for urban and rural respectively,\({\text{Y}̄}^{U}\) and \({\stackrel{̄}{\text{Y}}}^{R}\) are the average probability of a binary outcome of interest for group urban and rural, F is the cumulative distribution function from the logistic distribution, distribution, \({X}_{i}^{R}\) and \({X}_{i}^{U}\) are the set of the average value of the independent variable and \({\widehat{\beta }}^{U}\) and \({\widehat{\beta }}^{R}\) are the coefficient estimates for the urban and rural, respectively.