Population Preferences for Primary Care Models for Hypertension in Karnataka, India.

Importance: Hypertension contributes to more than 1.6 million deaths annually in India, with many individuals being unaware they have the condition or receiving inadequate treatment. Policy initiatives to strengthen disease detection and management through primary care services in India are not currently informed by population preferences. Objective: To quantify population preferences for attributes of public primary care services for hypertension. Design, Setting, and Participants: This cross-sectional study involved administration of a household survey to a population-based sample of adults with hypertension in the Bengaluru Nagara district (Bengaluru City; urban setting) and the Kolar district (rural setting) in the state of Karnataka, India, from June 22 to July 27, 2021. A discrete choice experiment was designed in which participants selected preferred primary care clinic attributes from hypothetical alternatives. Eligible participants were 30 years or older with a previous diagnosis of hypertension or with measured diastolic blood pressure of 90 mm Hg or higher or systolic blood pressure of 140 mm Hg or higher. A total of 1422 of 1927 individuals (73.8%) consented to receive initial screening, and 1150 (80.9%) were eligible for participation, with 1085 (94.3%) of those eligible completing the survey. Main Outcomes and Measures: Relative preference for health care service attributes and preference class derived from respondents selecting a preferred clinic scenario from 8 sets of hypothetical comparisons based on wait time, staff courtesy, clinician type, carefulness of clinical assessment, and availability of free medication. Results: Among 1085 adult respondents with hypertension, the mean (SD) age was 54.4 (11.2) years; 573 participants (52.8%) identified as female, and 918 (84.6%) had a previous diagnosis of hypertension. Overall preferences were for careful clinical assessment and consistent availability of free medication; 3 of 5 latent classes prioritized 1 or both of these attributes, accounting for 85.1% of all respondents. However, the largest class (52.4% of respondents) had weak preferences distributed across all attributes (largest relative utility for careful clinical assessment: ß = 0.13; 95% CI, 0.06-0.20; 36.4% preference share). Two small classes had strong preferences; 1 class (5.4% of respondents) prioritized shorter wait time (85.1% preference share; utility, ß = -3.04; 95% CI, -4.94 to -1.14); the posterior probability of membership in this class was higher among urban vs rural respondents (mean [SD], 0.09 [0.26] vs 0.02 [0.13]). The other class (9.5% of respondents) prioritized seeing a physician (the term doctor was used in the survey) rather than a nurse (66.2% preference share; utility, ß = 4.01; 95% CI, 2.76-5.25); the posterior probability of membership in this class was greater among rural vs urban respondents (mean [SD], 0.17 [0.35] vs 0.02 [0.10]). Conclusions and Relevance: In this study, stated population preferences suggested that consistent medication availability and quality of clinical assessment should be prioritized in primary care services in Karnataka, India. The heterogeneity observed in population preferences supports considering additional models of care, such as fast-track medication dispensing to reduce wait times in urban settings and physician-led services in rural areas.

Sociodemographic and Medical Risk Factors Associated With Antepartum Depression.

BACKGROUND: The increasing recognition of antenatal depression is an emerging area of concern in developing countries. We conducted a study to estimate the prevalence of antenatal mental distress and its relation with sociodemographic factors, obstetric factors, and physiological wellbeing in pregnant women attending public health facilities in Bengaluru, South India. METHODS: Nested within a cohort study, we assessed the mental status in 823 pregnant women in two public referral hospitals. Kessler Psychological Distress Scale (K-10 scale) was used to assess maternal depression. We collected information related to social-demographic characteristics and recent medical complaints. Descriptive statistics and odds ratios were calculated using SPSS version 20. RESULTS: Results show that 8.7% of the women exhibited symptoms of antenatal depression. Sociodemographic characteristics, such as respondent occupation, husband education, husband's occupation, total family income showed significance. First time pregnancy, anemia, and high blood pressure were also associated with mental distress. CONCLUSION: Our study has demonstrated feasibility of screening for mental health problems in public hospitals. Early detection of mental distress during pregnancy is crucial as it has a direct impact on the fetus. The public health facilities in low- and middle-income countries such as India should consider piloting and scaling up screening services for mental health conditions for pregnant women.