Head of Household

The data used in this paper is drawn from household surveys conducted between March and May 2010 in Kanpur Dehat and Pratapgarh districts in Uttar Pradesh and in Vaishali district in Bihar.6 As mentioned above, these baseline surveys preceded the implementation of three CBHI schemes which offered insurance to targeted households.7 The target group consisted of 3686 SHG households (1284 in Pratapgarh, 1039 in Kanpur Dehat and 1363 in Vaishali) representing 21,366 individuals. All targeted households were surveyed. The primary respondents were the SHG members themselves or the head of the household, if the member was unavailable. Information on other household members was collected from the primary respondents.8While the survey gathered information on a wide range of socio-demographic and economic characteristics, of particular interest is the detailed information collected on health status, self-reported symptoms experienced during the four weeks preceding the survey for outpatient care and one year for inpatient care, and choice of provider. Respondents who reported an illness were asked whether they sought care, and if so, from which type of provider. Data pertaining to the following pre-selected providers were collected: traditional healers, priests, pharmacists, NDAPs, nurses, qualified private doctors, qualified public doctors, specialist public doctors, specialist private doctors and ‘others’.9Outpatient episodes were separated into acute or chronic.10 For chronic illnesses, information was gathered on the most recent visit; for acute illnesses, information was gathered for up to three illnesses and three visits per illness in the four weeks preceding the survey. While we have data on multiple illnesses and multiple visits, the analysis deals mainly with choice of healthcare provider for the first illness and the first visit, as most individuals (98 %) experienced only a single illness during the four-week period. While there are repeat-visits for the same illness, the number of cases is not as large as the first visit and perhaps more importantly, as will be discussed later, the choice of provider does not vary substantially in subsequent visits. In the case of inpatient care the survey enquired whether any household member had been hospitalized in the 12 months preceding the survey.
Consistent with the existing literature, the probability of healthcare use and the choice of provider are modelled as functions of individual and household level covariates [19 (link), 20 , 24 (link)]. The individual characteristics include the respondent’s demographics, educational attainment, occupational status and self-reported health status. For models related to acute illnesses, we use the socioeconomic characteristics of the household head, since a substantial proportion of the sample consists of children (41 %). We control for the nature of the respondent’s illness by including a set of self-reported symptom variables and health status is measured by the generic quality of life variable (EQ5D) which contains information on five dimensions of health: mobility, self-care, pain, ability to perform usual activities and mental health status. The scores from each question are converted into an index that is increasing in health and ranges between −1 to +1 using the procedure suggested by Dolan [25 (link)]. As these questions were administered only to individuals older than 12 years, the EQ5D measure is only used while modelling the probability of obtaining care for chronic conditions which is estimated only for respondents older than 12. Household level covariates include household size and gender of the household head, whether a household belongs to a scheduled tribe or caste and household socioeconomic status as captured by (the log of) per capita consumption.11

We adapted demographic and SES questions from the most recent DHS questionnaires [13 ]. Improved water and sanitation were based on World Health Organization definitions [14 ]. Site investigators reviewed questionnaires and identified items that were problematic in their sites. Each site approved a final list of questions and response categories and the associated data collection procedures. Final demographic questions focused on age and education of the head of household and child’s mother, as well as mother’s fertility history. The SES section assessed household assets, housing materials, water source and sanitation facilities, and ownership of land or livestock. The survey also included a question on monthly household income in local currency. The questionnaire was developed in English and translated into local languages as appropriate and back-translated for quality assurance.

Face-to-face and door-to-door interviews were conducted with household heads after completing informed consent forms. Some general information within each household was collected, such as the head of the household’s age, education level (illiterate, primary, secondary, or high school, diploma, and college), employment status (unemployed, employed, self-employed, pensioner), the number of family members. Their socioeconomic status was calculated considering possession of 9 specific items, including home, personal vehicle, washing machine, LCD TV, dishwasher, refrigerator, handmade rug, laptop, and microwave. Based on the number of items possessed by households, the socioeconomic status was categorized into three groups, low (3 items or less), moderate (4 to 6 items), and high (more than 7 items) [25 ]. In addition, they were asked whether they had chronic diseases (at least one of the non-communicable diseases, such as diabetes, cardiovascular disease, kidney disease, and cancer), a vulnerable group member in the household (child under 6, adolescent, disabled member, pregnant, handicapped, and elderly), receive financial help from the charity, the portion of income allocated to food purchase, covid-19-induced poverty (including job loss, reduced income, and reduced food purchase), and marital status. The heads of households completed the validated HFIAS (Household Food Insecurity Access Scale) questionnaire to assess food insecurity [26 (link)]. The FAO Indicator Guide was used to score a nine-item HFIAS questionnaire [27 ]. The results were categorized into mild/moderate and severe to make the results more understandable and more appropriate for interventions for policymakers.

Percentages were used to report the distribution of categorized variables. The chi-square test was used to measure the association between the grouped variables in this study. In addition, multiple logistic regression was used to examine the adjusted relationships (odds ratios and 95% CI) between the study variables (household heads, parents’ education, parents’ job, household size, having a chronic disease, having a member of vulnerable groups, consumption of vegetables, fruits, meat, nuts and legumes, socioeconomic status, cost, food cost to income ratio, government financial assistance, covid-19-induced poverty) and food insecurity.
Variables were included in the model (p-value under 0.2 in the univariable analysis) if they significantly contributed to the model’s fitness using the stepwise selection method, and the final model was reported. P-value < 0.05 was considered significant. STATA14.0 software (Stata, College Station, TX, USA) was used for all the statistical analyses.

The outcome variable of this study was vitamin A supplementation among children aged 6–35 in the last 6 months (yes/no). The independent variables were individual-level variables such as the age of the child, age of the mother, sex of the child, sex of household head, religion, marital status, mother's education, husband's education, wealth status, working status, birth order, parity, possession of radio, possession of a television, number of under-five children in the household, place of delivery of the child, mothers having ANC, and mothers having postnatal care. The community-level variables were residence and region. A region in this study was classified into five Oromia, Amhara, Southern Nation Nationalities and Peoples Region (SNNPR), Tigray, and others. The ‘others' region contains Addis Ababa, Somalia, Afar, Dire Dawa, Benishangul, and Gambella due to their low number of eligible participants for this study. The questionnaire of the survey was pretested and 2 days of training were given to the data collectors and supervisors before the onset of actual data collection.